RevYOLOv5_2 のバックアップ(No.1)

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---

私的AI研究会 > RevYOLOv5_2

【復習】物体検出アルゴリズム「YOLO V5」２ == 編集中 == †

「PyTorch ではじめる AI開発」Chapter04 で使用する「YOLO V5」について復習する。
「YOLO V5」の学習を深堀する

Official YOLOv5 考察２ 学習編 †

学習プログラム「train.py」 †

• 実行ディレクトリは「workspace_pylearn/yolov5/」
  

• 「train.py」実行時のコマンドパラメータ
  

  コマンドオプション	引数	初期値	意味
  --weights	str	yolov7s.pt	学習済み重みモデルファイル
  --cfg	str		使用するYOLOのバージョン(cfgフォルダからモデルの種類に合わせて指定)
  --data	str	data/coco.yaml	学習データの情報を記載したyamlファイルのパス
  --hyp	str	data/hyp.scratch.p5.yaml	ハイパーパラメータのパス
  --epochs	int	300	学習回数指定
  --batch-size	int	16	バッチサイズ指定
  --img-size	list	[640, 640]	学習画像のサイズを指定(--img-size 640 640)
  --rect	なし	-	rectangular training
  --resume	なし	-	前回中断した学習(training)を途中から再開する
  --nosave	なし	-	最終 checkpoint のみ保存
  --notest	なし	-	only test final epoch
  --noautoanchor	なし	-	自動アンカーチェックの無効化
  --evolve	なし	-	evolve hyperparameters
  --bucket	str		gsutil bucket
  --cache-images	str	ram	cashの保存先指定 (ram or disk)
  --image-weights	なし	-	use weighted image selection for training
  --device	str		使用プロセッサの指定(0 or 0,1,2,3 or cpu) (指定なしの場合 cuda)
  --multi-scale	なし	-	画像サイズを±50%変化させる
  --single-cls	なし	-	train multi-class data as single-class
  --adam	なし	-	Adam optimizerを使用する (指定しなければ SGD が使用される)
  --sync-bn	なし	-	SyncBatchNormを使用する (DDPモードでのみ使用可能)
  --local_rank	int	-1	DDP parameter, do not modify
  --workers	int	8	データローダーワーカーの最大数
  --project	str	runs/train	学習結果の記録フォルダパス
  --name	str	project/name	学習結果の記録フォルダの下のフォルダ名(推論ごとにインクリメント)
  --entity	なし	None	W&B entity
  --exist-ok	なし	-	学習結果を上書き保存(指定すれば上書き)
  --quad	なし	-	quad dataloader
  --linear-lr	なし	-	linear LR
  --label-smoothing	0.0	Label smoothing epsilon
  --upload_dataset	なし	-	Upload dataset as W&B artifact table
  --bbox_interval	なし	-1	Set bounding-box image logging interval for W&B'
  --save_period	int	-1	指定 epoch 毎にチェックポイントを保存する (-1 = (無効))
  --artifact_alias	なし	latest	version of dataset artifact to be used
  --freeze	list	[0]	学習しない層を指定 backbone of yolov7=50, first3=0 1 2'
  --v5-metric	なし	-	assume maximum recall as 1.0 in AP calculation

オープンデータセットを用いた追加学習「交通標識の検出」 †

• 引用サイト → 金子邦彦研究室:物体検出，物体検出のための追加学習の実行（YOLOv5，PyTorch，Python を使用）（Windows 上）
  

• YOLO形式のオープンデータセット Traffic Signs Dataset を使用する
  

前準備 †

1. Traffic Signs Dataset in YOLO format のページを開く
   ・「Download」ボタンを押す
   　Kaggleへ登録するか，Google アカウントなどでサインインする
   ・ダウンロードした「archive.zip」を解凍する
   ・できた「archive/」フォルダを「workspace_pylearn/yolov5_test/」フォルダにコピー（移動）する
   
   
2. 「archive/ts/ts/」ディレクトリ内の 900 個のテキストファイル（"00000.txt"から"00899.txt"）を処理する
   　cocoデータセットに使用されているクラス番号にデータセットのクラス番号 80, 81, 82, 83 を追加する
   

   80, prohibitory
   81, danger
   82, mandatory
   83, other
3. クラス番号変換プログラム「data_select1.py」を実行
   ・実行ディレクトリは「workspace_pylearn/yolov5_test/」
   

   (py_learn) python data_select1.py

   ・「data_select1.py」実行ログ

   (py_learn) python data_select1.py
       :
   archive/ts/ts/00892.txt が見つかりませんでした
   filename: archive/ts/ts/00893.txt
      83 0.6139705882352942 0.62 0.020588235294117647 0.035
   
   filename: archive/ts/ts/00894.txt
      80 0.2963235294117647 0.67625 0.020588235294117647 0.035
   
      80 0.8180147058823529 0.685625 0.028676470588235293 0.04875
   
      83 0.8158088235294118 0.628125 0.03602941176470588 0.06125
   
   filename: archive/ts/ts/00895.txt
      80 0.15845588235294117 0.630625 0.03308823529411765 0.05625
   
      80 0.7279411764705882 0.5525 0.03529411764705882 0.06
   
   filename: archive/ts/ts/00896.txt
      80 0.6080882352941176 0.53875 0.027941176470588237 0.0475
   
   filename: archive/ts/ts/00897.txt
      83 0.6 0.6725 0.01764705882352941 0.03
   
   filename: archive/ts/ts/00898.txt
      80 0.24926470588235294 0.65 0.023529411764705882 0.04
   
      80 0.6125 0.6575 0.023529411764705882 0.04
   
   filename: archive/ts/ts/00899.txt
      81 0.65625 0.63625 0.04191176470588235 0.0625

   ・ソースファイル
   
   ▼「data_select1.py」
   ▲「data_select1.py」

   # -*- coding: utf-8 -*-
   ##------------------------------------------
   ## YOLOv5 追加学習のためのデータセット作成
   ##   Step 1 ダウンロードと ClassID 修正
   ##   Traffic Signs Dataset in YOLO format
   ##   https://www.kaggle.com/datasets/valentynsichkar/traffic-signs-dataset-in-yolo-format?resource=downloa
   ##
   ##               2024.04.09 Masahiro Izutsu
   ##------------------------------------------
   ## https://www.kkaneko.jp/ai/win/yolov5.html
   ## data_select1.py
   ##      2024/04/15  すべての行を処理するように変更
   
   '''
   "archive/ts/ts/" ディレクトリ内の900個のテキストファイル（"00000.txt"から"00899.txt"）を処理する。 
   各ファイルの行からクラス番号（0, 1, 2, 3）を取り出し，その値が80未満の場合，80を加算してファイルに書き戻す。
    ファイルが存在しない場合，エラーメッセージを表示する
   '''
   
   for i in range(0, 900):  # 00000.txt から 00899.txt までスキャンする（実際のファイル数は 741）
       filename = f"archive/ts/ts/{i:05}.txt"
       try:
           with open(filename, "r", encoding="utf-8") as file:
               # すべての行を処理する
               lines = file.readlines()
               for ln in range(len(lines)):
                   one_line = lines[ln].strip()
                   parts = one_line.split()
                   class_number = int(parts[0])
                   # もともとのクラス番号 (class_number) は 0, 1, 2, 3 である。80を加えて，元のファイルのクラス番号を更新する。
                   if class_number < 80:
                       updated_class_number = class_number + 80
                       x1, y1, x2, y2 = map(float, parts[1:])
                       updated_line = f"{updated_class_number} {x1} {y1} {x2} {y2}\n"
                       lines[ln] = updated_line
   
           with open(filename, "w", encoding="utf-8") as file:
               file.writelines(lines)
           # 確認表示
           with open(filename, "r", encoding="utf-8") as file:
               lines = file.readlines()
               print(f"filename: {filename}")
               for ln in range(len(lines)):
                   print("  ", lines[ln])
   
       except FileNotFoundError:
           print(f"{filename} が見つかりませんでした")
   
   exit()

   
   
4. 画像の幅を 640pixel に縮小する
   ・実行ディレクトリは「workspace_pylearn/yolov5_test/」
   ・画像縮小プログラム「data_select2.py」を実行
   

   (py_learn) python data_select2.py

   ・「data_select2.pu」実行ログ

   (py_learn) python data_select2.py
   ./archive/ts/ts/00000.jpg を変換
   ./archive/ts/ts/00001.jpg を変換
   ./archive/ts/ts/00002.jpg を変換
   ./archive/ts/ts/00003.jpg を変換
       :
   ./archive/ts/ts/00896.jpg を変換
   ./archive/ts/ts/00897.jpg を変換
   ./archive/ts/ts/00898.jpg を変換
   ./archive/ts/ts/00899.jpg を変換

   ・ソースファイル
   
   ▼「data_select2.py」
   ▲「data_select2.py」

   # -*- coding: utf-8 -*-
   ##------------------------------------------
   ## YOLOv5 追加学習のためのデータセット作成
   ##   Step 1 ダウンロードと ClassID 修正
   ##   Traffic Signs Dataset in YOLO format
   ##   https://www.kaggle.com/datasets/valentynsichkar/traffic-signs-dataset-in-yolo-format?resource=downloa
   ##
   ##               2024.04.09 Masahiro Izutsu
   ##------------------------------------------
   ## https://www.kkaneko.jp/ai/win/yolov5.html
   ## data_select2.py
   
   '''
   画像サイズを 640 ピクセルにする
   '''
   
   from PIL import Image
   import os
   
   # カレントディレクトリ
   data_dir = './archive/ts/ts'
   
   # 新しい幅
   new_width = 640
   
   # カレントディレクトリ内のすべてのファイル
   for f in os.listdir(data_dir):
       filename = data_dir + '/' + f
       # .jpgファイルのみを処理
       if filename.endswith('.jpg'):
           print(f"{filename} を変換")
           with Image.open(filename) as img:
               # アスペクト比を保持した高さを計算
               aspect_ratio = new_width / img.width
               new_height = int(img.height * aspect_ratio)
               # リサイズ
               resized_img = img.resize((new_width, new_height))
               # 元のファイルを上書き
               resized_img.save(filename)
   
   exit()

   
   
5. データセット名を「ts_dataset」として配置するディレクトリを作成する
   ・以下のコマンドを実行
   

   mkdir -p ts_dataset/images/train
   mkdir -p ts_dataset/images/val
   mkdir -p ts_dataset/labels/train
   mkdir -p ts_dataset/labels/val

   ・作成結果
   

   (py_learn) PS > tree
   yolov5_test/
   ├─archive
   │  └─ts
   │      └─ts
   └─ts_dataset
       ├─images
       │  ├─train
       │  └─val
       └─labels
           ├─train
           └─val

6. 「ts_dataset」ディレクトリに画像ファイルとラベルファイルを配置する
   検証用(val)：学習用(train) がおよそ 8:2 になるようにする
   ・検証用はファイル名の末尾が 1 (77個), 5 (73個) のもの計150個を移動
   

   move archive/ts/ts/*1.txt ts_dataset/labels/val
   move archive/ts/ts/*5.txt ts_dataset/labels/val
   move archive/ts/ts/*1.jpg ts_dataset/images/val
   move archive/ts/ts/*5.jpg ts_dataset/images/val

   ・学習用は残りのファイルを移動｛741-150 = 531個）
   

   move archive/ts/ts/*.txt ts_dataset/labels/train
   move archive/ts/ts/*.jpg ts_dataset/images/train

7. 「ts_dataset/」フォルダ内にファイル ts.yaml を作成
   ・データセットの配置場所は「yolov5/data/」フォルダとする
   

   train: data/ts_dataset/images/train
   val: data/ts_dataset/images/val
   nc: 84
   names:
     0: person
     1: bicycle
     2: car
       :
     79: toothbrush
     80: prohibitory
     81: danger
     82: mandatory
     83: other

   ▼「ts.yaml」詳細
   ▲「ts.yaml」詳細

   train: data/ts_dataset/images/train
   val: data/ts_dataset/images/val
   nc: 84
   names:
     0: person
     1: bicycle
     2: car
     3: motorcycle
     4: airplane
     5: bus
     6: train
     7: truck
     8: boat
     9: traffic light
     10: fire hydrant
     11: stop sign
     12: parking meter
     13: bench
     14: bird
     15: cat
     16: dog
     17: horse
     18: sheep
     19: cow
     20: elephant
     21: bear
     22: zebra
     23: giraffe
     24: backpack
     25: umbrella
     26: handbag
     27: tie
     28: suitcase
     29: frisbee
     30: skis
     31: snowboard
     32: sports ball
     33: kite
     34: baseball bat
     35: baseball glove
     36: skateboard
     37: surfboard
     38: tennis racket
     39: bottle
     40: wine glass
     41: cup
     42: fork
     43: knife
     44: spoon
     45: bowl
     46: banana
     47: apple
     48: sandwich
     49: orange
     50: broccoli
     51: carrot
     52: hot dog
     53: pizza
     54: donut
     55: cake
     56: chair
     57: couch
     58: potted plant
     59: bed
     60: dining table
     61: toilet
     62: tv
     63: laptop
     64: mouse
     65: remote
     66: keyboard
     67: cell phone
     68: microwave
     69: oven
     70: toaster
     71: sink
     72: refrigerator
     73: book
     74: clock
     75: vase
     76: scissors
     77: teddy bear
     78: hair drier
     79: toothbrush
     80: prohibitory
     81: danger
     82: mandatory
     83: other

   
   
8. 「ts_dataset/」フォルダを「yolov5/data/」内にコピー（移動）する
   

学習の実行 †

1. エポック数 30 で実行する
   ・実行ディレクトリは「workspace_pylearn/yolov5/」
   ・学習モデルはデフォールト設定「yolov7s」を使用する
   ・学習結果フォルダ名は「ts_yolov5s_ep30」とする

   (py_learn) python train.py --data data/ts_dataset/ts.yaml --weights yolov5s.pt --img 640 --epochs 30 --name ts_yolov5s_ep30

   ・GPU を使用しない場合は以下のコマンドを実行する
   

   (py_learn) python train.py --data data/ts_dataset/ts.yaml --weights yolov5s.pt --img 640 --epochs 30 --name ts_yolov5s_ep30 --device cpu

2. 学習の終了
   

   (py_learn) python train.py --data data/ts_dataset/ts.yaml --weights yolov5s.pt --img 640 --epochs 30 --name ts_yolov5s_ep30
   train: weights=yolov5s.pt, cfg=, data=data/ts_dataset/ts.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=30, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=ts_yolov5s_ep30, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
   remote: Enumerating objects: 24, done.
   remote: Counting objects: 100% (24/24), done.
   remote: Compressing objects: 100% (24/24), done.
   remote: Total 24 (delta 13), reused 1 (delta 0), pack-reused 0
   Unpacking objects: 100% (24/24), 9.93 KiB | 462.00 KiB/s, done.
   From https://github.com/ultralytics/yolov5
      d07d0cf6..cf8b67b7  master     -> origin/master
   github:  YOLOv5 is out of date by 9 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
   Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
   TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
   Overriding model.yaml nc=80 with nc=84
   
                    from  n    params  module                                  arguments
     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
     1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
     2                -1  1     18816  models.common.C3                        [64, 64, 1]
     3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
       :
   Starting training for 30 epochs...
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          0/29      3.42G     0.1287    0.02085     0.1002         46        640: 100%|██████████| 37/37 [00:04<00:00,  8.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230    0.00304     0.0611    0.00226   0.000532
       :
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         29/29      3.65G    0.02481   0.007517    0.01037         49        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.912      0.884      0.933      0.684
   
   30 epochs completed in 0.036 hours.
   Optimizer stripped from runs\train\ts_yolov5s_ep30\weights\last.pt, 14.8MB
   Optimizer stripped from runs\train\ts_yolov5s_ep30\weights\best.pt, 14.8MB
   
   Validating runs\train\ts_yolov5s_ep30\weights\best.pt...
   Fusing layers...
   Model summary: 157 layers, 7236673 parameters, 0 gradients, 16.5 GFLOPs
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:01<0
                      all        150        230      0.905       0.89      0.932      0.689
              prohibitory        150        108      0.954      0.953       0.98      0.763
                   danger        150         38      0.934      0.921      0.959      0.684
                mandatory        150         35      0.876       0.81        0.9      0.687
                    other        150         49      0.854      0.878       0.89      0.621
   Results saved to runs\train\ts_yolov5s_ep30

   ▼「train.py」実行ログ詳細
   ▲「train.py」実行ログ詳細

   (py_learn) PS C:\anaconda_win\workspace_pylearn\yolov5> python train.py --data data/ts_dataset/ts.yaml --weights yolov5s.pt --img 640 --epochs 30 --name ts_yolov5s_ep30
   train: weights=yolov5s.pt, cfg=, data=data/ts_dataset/ts.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=30, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=ts_yolov5s_ep30, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
   remote: Enumerating objects: 24, done.
   remote: Counting objects: 100% (24/24), done.
   remote: Compressing objects: 100% (24/24), done.
   remote: Total 24 (delta 13), reused 1 (delta 0), pack-reused 0
   Unpacking objects: 100% (24/24), 9.93 KiB | 462.00 KiB/s, done.
   From https://github.com/ultralytics/yolov5
      d07d0cf6..cf8b67b7  master     -> origin/master
   github:  YOLOv5 is out of date by 9 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
   Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
   TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
   Overriding model.yaml nc=80 with nc=84
   
                    from  n    params  module                                  arguments
     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
     1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
     2                -1  1     18816  models.common.C3                        [64, 64, 1]
     3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
     4                -1  2    115712  models.common.C3                        [128, 128, 2]
     5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]
     6                -1  3    625152  models.common.C3                        [256, 256, 3]
     7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]
     8                -1  1   1182720  models.common.C3                        [512, 512, 1]
     9                -1  1    656896  models.common.SPPF                      [512, 512, 5]
    10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]
    11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    12           [-1, 6]  1         0  models.common.Concat                    [1]
    13                -1  1    361984  models.common.C3                        [512, 256, 1, False]
    14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]
    15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    16           [-1, 4]  1         0  models.common.Concat                    [1]
    17                -1  1     90880  models.common.C3                        [256, 128, 1, False]
    18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]
    19          [-1, 14]  1         0  models.common.Concat                    [1]
    20                -1  1    296448  models.common.C3                        [256, 256, 1, False]
    21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]
    22          [-1, 10]  1         0  models.common.Concat                    [1]
    23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]
    24      [17, 20, 23]  1    240033  models.yolo.Detect                      [84, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
   Model summary: 214 layers, 7246177 parameters, 7246177 gradients, 16.7 GFLOPs
   
   Transferred 343/349 items from yolov5s.pt
   AMP: checks passed
   optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
   train: Scanning C:\anaconda_win\workspace_pylearn\yolov5\data\ts_dataset\labels\train... 591 images, 0 backgrounds, 0 c
   train: WARNING  C:\anaconda_win\workspace_pylearn\yolov5\data\ts_dataset\images\train\00340.jpg: 1 duplicate labels removed
   train: New cache created: C:\anaconda_win\workspace_pylearn\yolov5\data\ts_dataset\labels\train.cache
   val: Scanning C:\anaconda_win\workspace_pylearn\yolov5\data\ts_dataset\labels\val... 150 images, 0 backgrounds, 0 corru
   val: New cache created: C:\anaconda_win\workspace_pylearn\yolov5\data\ts_dataset\labels\val.cache
   
   AutoAnchor: 4.43 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
   Plotting labels to runs\train\ts_yolov5s_ep30\labels.jpg...
   Image sizes 640 train, 640 val
   Using 8 dataloader workers
   Logging results to runs\train\ts_yolov5s_ep30
   Starting training for 30 epochs...
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          0/29      3.42G     0.1287    0.02085     0.1002         46        640: 100%|██████████| 37/37 [00:04<00:00,  8.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230    0.00304     0.0611    0.00226   0.000532
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          1/29      3.64G     0.1039    0.01941    0.06545         53        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230    0.00397      0.594     0.0526     0.0118
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          2/29      3.65G    0.09454    0.01781    0.04951         32        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230     0.0226     0.0556     0.0172    0.00371
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          3/29      3.65G    0.08653    0.01659    0.04304         37        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230     0.0736      0.238      0.106      0.042
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          4/29      3.65G    0.07595    0.01581    0.03856         33        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.139       0.45      0.228     0.0895
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          5/29      3.65G    0.06804     0.0139    0.03444         44        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.749      0.314      0.425      0.206
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          6/29      3.65G    0.06295    0.01304    0.03212         48        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.703      0.361       0.44      0.234
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          7/29      3.65G    0.05831    0.01187    0.02852         74        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.435      0.659      0.538      0.258
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          8/29      3.65G    0.05609    0.01114    0.02617         42        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.591      0.684      0.682      0.339
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          9/29      3.65G    0.05391    0.01047    0.02247         50        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230       0.47      0.684      0.536      0.298
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         10/29      3.65G    0.05144   0.009963     0.0219         38        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.867      0.733      0.841      0.429
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         11/29      3.65G    0.04968   0.009795    0.01991         51        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.785      0.785      0.812      0.475
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         12/29      3.65G    0.04783    0.01023    0.01957         47        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.749      0.756      0.788      0.455
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         13/29      3.65G    0.04579   0.009305    0.01832         38        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.749      0.794      0.793      0.467
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         14/29      3.65G      0.044   0.009433    0.01659         39        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.843      0.824      0.884      0.527
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         15/29      3.65G    0.04319   0.009144    0.01619         62        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.899       0.84      0.902      0.554
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         16/29      3.65G     0.0407    0.00878    0.01458         37        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.836       0.82      0.872      0.501
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         17/29      3.65G    0.03939   0.008869    0.01431         43        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.918      0.848       0.92      0.544
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         18/29      3.65G    0.03764   0.008324    0.01392         39        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.952      0.812      0.915      0.534
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         19/29      3.65G    0.03544   0.008533    0.01277         37        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.939       0.86      0.926      0.569
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         20/29      3.65G    0.03505   0.008579    0.01321         48        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230       0.95       0.84      0.925      0.619
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         21/29      3.65G    0.03357     0.0083    0.01221         36        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.962      0.855      0.933      0.622
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         22/29      3.65G    0.03258   0.008084    0.01167         59        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.956      0.834      0.928      0.631
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         23/29      3.65G    0.03153   0.008387    0.01206         65        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.912      0.872      0.926      0.668
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         24/29      3.65G    0.02976   0.007869    0.01135         47        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.907      0.866      0.925      0.653
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         25/29      3.65G    0.02866   0.007615    0.01101         46        640: 100%|██████████| 37/37 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.903       0.87      0.926      0.653
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         26/29      3.65G    0.02773   0.007582    0.01107         42        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.905      0.891      0.932      0.687
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         27/29      3.65G    0.02651   0.007505    0.01082         42        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.921       0.86      0.932      0.682
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         28/29      3.65G    0.02511   0.007342    0.01027         33        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:01<0
                      all        150        230       0.91      0.872      0.933      0.683
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         29/29      3.65G    0.02481   0.007517    0.01037         49        640: 100%|██████████| 37/37 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:00<0
                      all        150        230      0.912      0.884      0.933      0.684
   
   30 epochs completed in 0.036 hours.
   Optimizer stripped from runs\train\ts_yolov5s_ep30\weights\last.pt, 14.8MB
   Optimizer stripped from runs\train\ts_yolov5s_ep30\weights\best.pt, 14.8MB
   
   Validating runs\train\ts_yolov5s_ep30\weights\best.pt...
   Fusing layers...
   Model summary: 157 layers, 7236673 parameters, 0 gradients, 16.5 GFLOPs
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:01<0
                      all        150        230      0.905       0.89      0.932      0.689
              prohibitory        150        108      0.954      0.953       0.98      0.763
                   danger        150         38      0.934      0.921      0.959      0.684
                mandatory        150         35      0.876       0.81        0.9      0.687
                    other        150         49      0.854      0.878       0.89      0.621
   Results saved to runs\train\ts_yolov5s_ep30

   
   
3. メッセージ「Results saved to runs\train\ts_yolov5s_ep30」のファイルを確認
   

   (py_learn) ls runs/train/ts_yolov5s_ep30
   Mode                 LastWriteTime         Length Name
   ----                 -------------         ------ ----
   d-----        2024/04/15      9:20                weights
   -a----        2024/04/15      9:23         558388 confusion_matrix.png
   -a----        2024/04/15      9:23         226814 F1_curve.png
   -a----        2024/04/15      9:19            401 hyp.yaml
   -a----        2024/04/15      9:20         126318 labels.jpg
   -a----        2024/04/15      9:20         223809 labels_correlogram.jpg
   -a----        2024/04/15      9:19           1208 opt.yaml
   -a----        2024/04/15      9:23         124885 PR_curve.png
   -a----        2024/04/15      9:23         172410 P_curve.png
   -a----        2024/04/15      9:23           9145 results.csv
   -a----        2024/04/15      9:23         289059 results.png
   -a----        2024/04/15      9:23         175122 R_curve.png
   -a----        2024/04/15      9:20         501238 train_batch0.jpg
   -a----        2024/04/15      9:20         483837 train_batch1.jpg
   -a----        2024/04/15      9:20         496065 train_batch2.jpg
   -a----        2024/04/15      9:23         448059 val_batch0_labels.jpg
   -a----        2024/04/15      9:23         451714 val_batch0_pred.jpg
   -a----        2024/04/15      9:23         447649 val_batch1_labels.jpg
   -a----        2024/04/15      9:23         453702 val_batch1_pred.jpg
   -a----        2024/04/15      9:23         389031 val_batch2_labels.jpg
   -a----        2024/04/15      9:23         395697 val_batch2_pred.jpg

   ・学習結果モデルは「runs/train/exp*/weights」評価指標は「runs/train/exp*/」
   

   F1 curve	P curve	PR curve	R curve

   
   
   
   
   
   
   
   
   
   
   
   
   

実行結果を使って推論 †

1. 「detect2.py」で推論実行（静止画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2.py --weights ./runs/train/ts_yolov5s_ep30/weights/best.pt --source data/ts_dataset/images/val/00001.jpg

   
   ・実行ログ（結果は「runs/detect/exp*」*は順次更新）
   

   (py_learn) python detect2.py --weights ./runs/train/ts_yolov5s_ep30/weights/best.pt --source data/ts_dataset/images/val/00001.jpg
   detect2: weights=['./runs/train/ts_yolov5s_ep30/weights/best.pt'], source=data/ts_dataset/images/val/00001.jpg, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7236673 parameters, 0 gradients, 16.5 GFLOPs
   Speed: 1.0ms pre-process, 52.0ms inference, 46.1ms NMS per image at shape (1, 3, 640, 640)
   Results saved to runs\detect\exp25

2. 「detect2.py」で推論実行（動画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2.py --weights ./runs/train/ts_yolov5s_ep30/weights/best.pt --source data/images/traffic-sign-to-test.mp4

   
   ・実行ログ（結果は「runs/detect/exp*」*は順次更新）
   

   (py_learn) python detect2.py --weights ./runs/train/ts_yolov5s_ep30/weights/best.pt --source data/images/traffic-sign-to-test.mp4
   detect2: weights=['./runs/train/ts_yolov5s_ep30/weights/best.pt'], source=data/images/traffic-sign-to-test.mp4, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7236673 parameters, 0 gradients, 16.5 GFLOPs
   Speed: 0.2ms pre-process, 5.1ms inference, 5.0ms NMS per image at shape (1, 3, 640, 640)
   Results saved to runs\detect\exp32

実行結果を使って推論（日本語表示） †

1. ラベルファイルを作成する
   ・「ts_names_jp」日本語（「coco.names_jp」をコピーし編集）
   

   人
   自転車
   車
       :
   ヘアドライヤー
   歯ブラシ
   標識-禁止
   標識-危険
   標識-必須
   標識-その他

   ・「ts_names」英語（「coco.names」をコピーし編集）
   

   person
   bicycle
   car
       :
   toothbrush
   prohibitory
   danger
   mandatory
   other

2. 「detect2_yolov5.py」で推論実行（静止画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/ts_yolov5s_ep30/weights/best.pt -i data/ts_dataset/images/val/00001.jpg -l ts_names_jp

   
   ・実行ログ
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/ts_yolov5s_ep30/weights/best.pt -i data/ts_dataset/images/val/00001.jpg -l ts_names_jp
   
   Object detection YoloV5 in PyTorch Ver. 0.05: Starting application...
      OpenCV virsion : 4.9.0
   
      - Image File   :  data/ts_dataset/images/val/00001.jpg
      - YOLO v5      :  ultralytics/yolov5
      - Pretrained   :  ./runs/train/ts_yolov5s_ep30/weights/best.pt
      - Confidence lv:  0.25
      - Label file   :  ts_names_jp
      - Program Title:  y
      - Speed flag   :  y
      - Processed out:  non
      - Use device   :  cuda:0
   
   Using cache found in C:\Users\izuts/.cache\torch\hub\ultralytics_yolov5_master
   YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7236673 parameters, 0 gradients, 16.5 GFLOPs
   Adding AutoShape...
   
   FPS average:       7.90
   
    Finished.

3. 「detect2_yolov5.py」で推論実行（動画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/ts_yolov5s_ep30/weights/best.pt -i data/images/traffic-sign-to-test.mp4 -l ts_names_jp

   
   ・実行ログ
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/ts_yolov5s_ep30/weights/best.pt -i data/images/traffic-sign-to-test.mp4 -l ts_names_jp
   
   Object detection YoloV5 in PyTorch Ver. 0.05: Starting application...
      OpenCV virsion : 4.9.0
   
      - Image File   :  data/images/traffic-sign-to-test.mp4
      - YOLO v5      :  ultralytics/yolov5
      - Pretrained   :  ./runs/train/ts_yolov5s_ep30/weights/best.pt
      - Confidence lv:  0.25
      - Label file   :  ts_names_jp
      - Program Title:  y
      - Speed flag   :  y
      - Processed out:  non
      - Use device   :  cuda:0
   
   Using cache found in C:\Users\izuts/.cache\torch\hub\ultralytics_yolov5_master
   YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7236673 parameters, 0 gradients, 16.5 GFLOPs
   Adding AutoShape...
   
   FPS average:      52.70
   
    Finished.

結果から見た疑問点 †

• 「元々の cocoデータセット 80クラスに追加学習して 84のクラスとなった学習済みモデル」で「新たに追加した4クラスしか識別できない」のはなぜか？
  
  
  ・追加学習モデルを使っての検出（84クラス）ログ
  

  (py_learn) python detect2.py --weights ./runs/train/ts_yolov5s_ep30/weights/best.pt --source data/images/bus.jpg
  detect2: weights=['./runs/train/ts_yolov5s_ep30/weights/best.pt'], source=data/images/bus.jpg, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
  YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
  
  Fusing layers...
  Model summary: 157 layers, 7236673 parameters, 0 gradients, 16.5 GFLOPs
  Speed: 1.0ms pre-process, 50.0ms inference, 45.1ms NMS per image at shape (1, 3, 640, 640)
  Results saved to runs\detect\exp8

  
  ・元々の yolov5s.pt モデルでの検出（80クラス）ログ
  

  (py_learn) python detect2.py --source data/images/bus.jpg
  detect2: weights=yolov5s.pt, source=data/images/bus.jpg, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
  YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
  
  Fusing layers...
  YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs
  Speed: 0.0ms pre-process, 51.9ms inference, 47.0ms NMS per image at shape (1, 3, 640, 640)
  Results saved to runs\detect\exp9

• 追加学習で元のクラスの識別機能を保持したまま、新しくクラスを追加することはできないか？
  ・『学習済みのディープニューラルネットワークの最終層を除去し、その上で新しい 4クラスを学習できるように調整を行っているため』らしい
  ・『学習済みのディープニューラルネットワークの最終層を保持したまま新しいクラスを追加するような学習方法はあるか？
  

疑問点についての考察 †

• 「追加学習」で解決すべき問題でないかもしれない
  ・元の学習済みのモデルで推論を行った後追加の学習させたモデルで推論を行うことで目的を達成できる →
  ・元の 80クラスの識別結果と左上に追加モデルでの『標識』が識別できているのがわかる
  
  
• この結果から見るに上記の学習で「Traffic Signs Dataset」データセットのクラス番号(ID) を「0, 1, 2, 3」→「80, 81, 82, 83」とシフトする意味はないと考える
  ・追加学習する「Traffic Signs Dataset」データセットには cocoデータセットに使用されているクラス番号に相当するアノテーションデータは含まれていない
  ・推論結果で必要に応じて対応すればよいことである
  

「Traffic Signs Dataset」を再学習 †

1. クラス番号(ID) を変更しないデータセット「ts0_dataset」を作成する
   
2. エポック数 30 で実行する
   ・実行ディレクトリは「workspace_pylearn/yolov5/」
   ・学習モデルはデフォールト設定「yolov7s」を使用する
   ・学習結果フォルダ名は「ts0_yolov5s_ep30」とする

   (py_learn) python train.py --data data/ts0_dataset/ts.yaml --weights yolov5s.pt --img 640 --epochs 30 --name ts0_yolov5s_ep30

   ・GPU を使用しない場合は以下のコマンドを実行する
   

   (py_learn) python train.py --data data/ts0_dataset/ts.yaml --weights yolov5s.pt --img 640 --epochs 30 --name ts0_yolov5s_ep30 --device cpu

3. 学習の終了
   

       :
   Validating runs\train\ts0_yolov5s_ep30\weights\best.pt...
   Fusing layers...
   Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 5/5 [00:01<0
                      all        150        230      0.924       0.89      0.929      0.683
              prohibitory        150        108       0.96      0.944      0.988      0.793
                   danger        150         38      0.973      0.945      0.951      0.691
                mandatory        150         35       0.85      0.812      0.887      0.621
                    other        150         49      0.911      0.857      0.889      0.627
   Results saved to runs\train\ts0_yolov5s_ep30

   ・学習結果モデルは「runs/train/ts0_dataset_ep30/weights」評価指標は「runs/train/ts0_dataset_ep30/」
   

   F1 curve	P curve	PR curve	R curve

   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
4. ドライブレコーダの画像で認識してみる
   
   
   cocoデータセットのオブジェクトと交通標識が検出できている。
   

2つまでの学習済みモデルを指定できる YOLO V5 物体検出プログラム「detect3_yolov5.py」 †

• 作成プログラムの仕様
  ・前述の「detect2_yolov5.py」改良版で以前のコマンドはそのまま動作する
  ・オンライン／オフライン（ローカル）どちらでも動作する
  ・検出された オブジェクトを領域と文字で表示する
  ・複数のモデルを指定した場合は最初のモデルの最終クラスID の次に自動的にシフトする
  　最初のモデル(0～79) 次のモデル(0～3) → (80～83)
  　最初のモデル(0～79) を次のモデルが包括する場合(0～83)はそのままのクラスID を使用する
  ・文字の表示は「日本語/英語」の表記が可能
  ・オブジェクトの種類によって色分け表示する（Ultralytics オリジナル配色の指定も可能）
  ・入力ソースは「WEBカメラ(0～9)/動画ファイル/静止画ファイル」に対応する。
  ・結果を画像出力できる
  
  
• 「yolov5」ディレクトリ直下に統合したラベルファイルを用意しておく
  ・「ts_names_jp」日本語
  

  人                  ← coco データ          ID: 0
  自転車
  車
      :
  ヘアドライヤー
  歯ブラシ            ← coco データ          ID:79
  標識-禁止           ← Traffic Signs データ ID:80 (0)
  標識-危険
  標識-必須
  標識-その他         ← Traffic Signs データ ID:83 (3)

  ・「ts_names」英語
  

  person              ← coco データ          ID: 0
  bicycle
  car
      :
  toothbrush          ← coco データ          ID:79
  prohibitory         ← Traffic Signs データ ID:80 (0)
  danger
  mandatory
  other               ← Traffic Signs データ ID:83 (3)

  
  
• コマンドパラメータ
  

  コマンドオプション	初期値	意味
  -i , --image	'../../Videos/car_m.mp4'	入力ソースのパス またはカメラ(cam/cam0～cam9)
  -y , --yolov5	'ultralytics/yolov5'	yolov5ディレクトリのパス（ローカルの場合は yolov5 のパス）
  -m , --models	'yolov5s'	モデル名（ローカルの場合は モデルファイルのパス）※1
  -ms , --models2	''	2番目に推論するモデル名（モデルファイルのパス）
  -l , --labels	'coco.names_jp'	ラベルファイルのパス(coco.name, coco_name_jp)
  -c , --conf	0.25	オブジェクト検出レベルの閾値
  -t , --title	'y'	タイトルの表示(y/n)
  -s , --speed	'y'	速度の表示(y/n)
  -o , --out	'non'	出力結果の保存パス <path/filename> ※2
  -cpu	-	CPUフラグ（指定すれば 常に CPU動作）
  --log	3	ログ出力レベル (0/1/2/3/4/5)
  --ucr	-	配色フラグ（指定すれば Ultralytics カラー）

  ※1 オンライン動作の場合の指定できるモデルの種類「yolov5n」「yolov5s」「yolov5m」「yolov5l」「yolov5x」
  ※2 出力ファイル名までのディレクトリ・パスは必ず存在すること（存在しない場合は保存しない）
  
  ・「-y , --yolov5」パラメータ指定の例
  

  -y ultralytics/yolov5                                       ← オンライン（TorchHub）<default>
  -y ./                                                       ← オフライン（ローカル）

  　※ 初回起動時にキャッシュにダウンロードされ以後はキャッシュで動作する
  
  ・「-m , --models」パラメータ指定の例
  

  -m yolov5s                                                  ← オンライン（TorchHub）<default>
  -m ./test/yolov5s.pt                                        ← オフライン（ローカル）

  　※ モデルが指定場所にない場合は、初回実行時に自動的にダウンロードされる
  
  
  ▼　コマンドパラメータ詳細
  ▲　コマンドパラメータ詳細

  (py_learn) python detect3_yolov5.py -h
  usage: detect3_yolov5.py [-h] [-i IMAGE_FILE] [-y YOLOV5] [-m MODELS] [-ms MODELS2] [-c CONFIDENCE] [-l LABELS]
                           [-t TITLE] [-s SPEED] [-o IMAGE_OUT] [-cpu] [--log LOG] [--ucr]
  
  options:
    -h, --help            show this help message and exit
    -i IMAGE_FILE, --image IMAGE_FILE
                          Absolute path to image file or cam/cam0/cam1 for camera stream.
    -y YOLOV5, --yolov5 YOLOV5
                          YOLO V5 directry absolute path.
    -m MODELS, --models MODELS
                          yolov5n/yolov5m/yolov5l/yolov5x or model file absolute path.
    -ms MODELS2, --models2 MODELS2
                          second model file absolute path.
    -c CONFIDENCE, --conf CONFIDENCE
                          confidences labels Default value is 0.25
    -l LABELS, --labels LABELS
                          Language.(jp/en) Default value is 'jp'
    -t TITLE, --title TITLE
                          Program title flag.(y/n) Default value is 'y'
    -s SPEED, --speed SPEED
                          Speed display flag.(y/n) Default calue is 'y'
    -o IMAGE_OUT, --out IMAGE_OUT
                          Processed image file path. Default value is 'non'
    -cpu                  Optional. CPU only!
    --log LOG             Log level(-1/0/1/2/3/4/5) Default value is '3'
    --ucr                 use Ultralytics color

  
  
• オンライン実行例
  

  (py_learn) python detect3_yolov5.py -m yolov5s.pt -ms runs/train/ts0_yolov5s_ep30/weights/best.pt -i data/images/drive003_s.mp4 -l ts_names_jp

  ・実行結果
  
  

  (py_learn) python detect3_yolov5.py -m yolov5s.pt -ms runs/train/ts0_yolov5s_ep30/weights/best.pt -i data/images/drive003_s.mp4 -l ts_names_jp
   Starting..
  
  Object detection YoloV5 in PyTorch Ver. 0.06: Starting application...
     OpenCV virsion : 4.9.0
  
     - Image File   :  data/images/drive003_s.mp4
     - YOLO v5      :  ultralytics/yolov5
     - Pretrained   :  yolov5s.pt
     - Pretrained 2 :  runs/train/ts0_yolov5s_ep30/weights/best.pt
     - Confidence lv:  0.25
     - Label file   :  ts_names_jp
     - Program Title:  y
     - Speed flag   :  y
     - Processed out:  non
     - Use device   :  cuda:0
     - Log Level    :  3
  
  Using cache found in C:\Users\<USER>/.cache\torch\hub\ultralytics_yolov5_master
  YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
  
  Fusing layers...
  YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs
  Adding AutoShape...
  Using cache found in C:\Users\<USER>/.cache\torch\hub\ultralytics_yolov5_master
  YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
  
  Fusing layers...
  Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
  Adding AutoShape...
  
  FPS average:      22.00
  
   Finished.
• オフライン実行例
  

  (py_learn) python detect3_yolov5.py -m yolov5s.pt -ms runs/train/ts0_yolov5s_ep30/weights/best.pt -i data/images/drive003_s.mp4 -l ts_names_jp -y ./

  ・実行結果
  

  (py_learn) python detect3_yolov5.py -m yolov5s.pt -ms runs/train/ts0_yolov5s_ep30/weights/best.pt -i data/images/drive003_s.mp4 -l ts_names_jp -y ./
   Starting..
  
  Object detection YoloV5 in PyTorch Ver. 0.06: Starting application...
     OpenCV virsion : 4.9.0
  
     - Image File   :  data/images/drive003_s.mp4
     - YOLO v5      :  ./
     - Pretrained   :  yolov5s.pt
     - Pretrained 2 :  runs/train/ts0_yolov5s_ep30/weights/best.pt
     - Confidence lv:  0.25
     - Label file   :  ts_names_jp
     - Program Title:  y
     - Speed flag   :  y
     - Processed out:  non
     - Use device   :  cuda:0
     - Log Level    :  3
  
  YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
  
  Fusing layers...
  YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs
  Adding AutoShape...
  YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
  
  Fusing layers...
  Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
  Adding AutoShape...
  
  FPS average:      22.60
  
   Finished.

• ソースコード
  
  ▼「detect3_yolov5.py」
  ▲「detect3_yolov5.py」

  # -*- coding: utf-8 -*-
  ##------------------------------------------
  ## 【復習】「PyTorch で始める AI開発」
  ##   Chapter 04 / Extra edition     Ver. 0.6
  ##       YoloV5 in PyTorch による物体検出
  ##
  ##               2024.09.13 Masahiro Izutsu
  ##------------------------------------------
  ## detect3_yolov5.py    (detect3_yolov5.py を改良)
  ##  Ver. 0.03   2024/04/09  classID=119 まで対応
  ##  Ver. 0.04   2024/04/13  クラウド/ローカル切り替え
  ##  Ver. 0.05   2024/04/15  confidence 閾値設定/カメラ入力(cam0-cam9)
  ##  Ver. 0.06   2024/04/15  プログラム名変更 2つ目のモデル追加/ログ出力
  
  # -y <YOLOv5>                                   -m <Pretrained model>
  #    'ultralytics/yolov5'                          'yolov5s' [yolov5n][yolov5m][yolov5l][yolov5x]      Torch Hub on line
  #    '/anaconda_win/workspace_pylearn/yolov5'      '/anaconda_win/workspace_pylearn/yolov5/yolov5s'              off line
  #
  # 例：Windows
  #       python detect2_yolov5.py                (Torch Hub on line )
  #       python detect2_yolov5.py -y '/anaconda_win/workspace_pylearn/yolov5' -m '/anaconda_win/workspace_pylearn/yolov5/yolov5s'
  #
  # 例：Linux
  #       python detect2_yolov5.py                (Torch Hub on line)
  #       python detect2_yolov5.py -y '~/workspace_pylearn/yolov5' -m '~/workspace_pylearn/yolov5/yolov5s'
  
  # Color Escape Code
  GREEN = '\033[1;32m'
  RED = '\033[1;31m'
  NOCOLOR = '\033[0m'
  YELLOW = '\033[1;33m'
  
  # 定数定義
  WINDOW_WIDTH = 640
  
  from os.path import expanduser
  INPUT_DEF = expanduser('../../Videos/car_m.mp4')
  LANG_DEF = 'coco.names_jp'                                    # 2024/04/09
  
  # import処理
  import sys
  import cv2
  import numpy as np
  import argparse
  import torch
  from torch import nn
  from torchvision import transforms, models
  from PIL import Image
  import platform
  
  import my_puttext                                           # my library 2024.03.13
  import my_fps                                               # my library 2024.03.13
  import my_color80                                           # my library 2024.03.13
  import my_logging
  
  from ultralytics.utils.plotting import colors
  
  TEXT_COLOR = my_color80.CR_white
  
  # タイトル
  title = 'Object detection YoloV5 in PyTorch Ver. 0.06'
  
  # Parses arguments for the application
  def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument('-i', '--image', metavar = 'IMAGE_FILE', type=str,
              default = INPUT_DEF,
              help = 'Absolute path to image file or cam/cam0/cam1 for camera stream.')
      parser.add_argument('-y', '--yolov5', metavar = 'YOLOV5', type=str,
              default = 'ultralytics/yolov5',
              help = 'YOLO V5 directry absolute path.')
      parser.add_argument('-m', '--models', metavar = 'MODELS', type=str,
              default = 'yolov5s',
              help = 'yolov5n/yolov5m/yolov5l/yolov5x or model file absolute path.')
      parser.add_argument('-ms', '--models2', metavar = 'MODELS2', type=str,
              default = '',
              help = 'second model file absolute path.')
      parser.add_argument('-c', '--conf', metavar = 'CONFIDENCE',
              default = 0.25,                                 # 2024/04/14
              help = 'confidences labels Default value is 0.25')
      parser.add_argument('-l', '--labels', metavar = 'LABELS',
              default = LANG_DEF,                             # 2024/04/09
              help = 'Language.(jp/en) Default value is \'jp\'')
      parser.add_argument('-t', '--title', metavar = 'TITLE',
              default = 'y',
              help = 'Program title flag.(y/n) Default value is \'y\'')
      parser.add_argument('-s', '--speed', metavar = 'SPEED',
              default = 'y',
              help = 'Speed display flag.(y/n) Default calue is \'y\'')
      parser.add_argument('-o', '--out', metavar = 'IMAGE_OUT',
              default = 'non',
              help = 'Processed image file path. Default value is \'non\'')
      parser.add_argument("-cpu", default = False, action = 'store_true',
              help="Optional. CPU only!")
      parser.add_argument('--log', metavar = 'LOG', default = '3',
              help = 'Log level(-1/0/1/2/3/4/5) Default value is \'3\'')
      parser.add_argument("--ucr", default=False, action="store_true",
              help="use Ultralytics color")
      return parser
  
  # モデル基本情報の表示
  def display_info(args, image, yolov5, models, models2, conf, labels, titleflg, speedflg, outpath, use_device, log):
      print('\n' + GREEN + title + ': Starting application...' + NOCOLOR)
      print('   OpenCV virsion :',cv2.__version__)
      print('\n   - ' + YELLOW + 'Image File   : ' + NOCOLOR, image)
      print('   - ' + YELLOW + 'YOLO v5      : ' + NOCOLOR, yolov5)
      print('   - ' + YELLOW + 'Pretrained   : ' + NOCOLOR, models)
      if models2 != '':
          print('   - ' + YELLOW + 'Pretrained 2 : ' + NOCOLOR, models2)
      print('   - ' + YELLOW + 'Confidence lv: ' + NOCOLOR, conf)
      print('   - ' + YELLOW + 'Label file   : ' + NOCOLOR, labels)
      print('   - ' + YELLOW + 'Program Title: ' + NOCOLOR, titleflg)
      print('   - ' + YELLOW + 'Speed flag   : ' + NOCOLOR, speedflg)
      print('   - ' + YELLOW + 'Processed out: ' + NOCOLOR, outpath)
      print('   - ' + YELLOW + 'Use device   : ' + NOCOLOR, use_device)
      if args.ucr:
          print('   - ' + YELLOW + 'Class color  : ' + NOCOLOR, 'Ultralytics')
      print('   - ' + YELLOW + 'Log Level    : ' + NOCOLOR, log, '\n')
  
  # 画像の種類を判別する
  #   戻り値: 'jeg''png'... 画像ファイル
  #           'None'        画像ファイル以外 (動画ファイル)
  #           'NotFound'    ファイルが存在しない
  import os
  def is_pict(filename):
      '''
      try:
          imgtype = imghdr.what(filename)
      except FileNotFoundError as e:
          imgtype = 'NotFound'
      return str(imgtype)
      '''
      if not os.path.isfile(filename):
          return 'NotFound'
  
      types = ['.bmp','.png','.jpg','.jpeg','.JPG','.tif']
      for ss in types:
          if filename.endswith(ss):
              return ss
      return 'None'
  
  # TorchHubからモデルを読み込む （クラウド/ローカル切り替え）    2024/04/15
  def load_model(yolov5, models):
      cust = 'custom' if 0 < models.find('yolo') else ''
      if yolov5 == 'ultralytics/yolov5':
          if cust == '':
              if -1 == models.find('.'):
                  model = torch.hub.load(yolov5, models)
              else:
                  model = torch.hub.load(yolov5, 'custom', models)
          else:
              model = torch.hub.load(yolov5, cust, models)
      else:
          if cust == '':
              if -1 == models.find('.'):
                  model = torch.hub.load(yolov5, models, source='local')
              else:
                  model = torch.hub.load(yolov5, 'custom', models, source='local')
          else:
              model = torch.hub.load(yolov5, cust, models, source='local')
  
      return model
  
  
  # ** main関数 **
  def main():
  
      # 日本語フォント指定
      fontPIL = my_puttext.get_font()                         # 2024.03.13
  
      # Argument parsing and parameter setting
      args = parse_args().parse_args()
      input_stream = args.image
      labels = args.labels                                    # 2024/04/09
      titleflg = args.title
      speedflg = args.speed
  
      # アプリケーション・ログ設定
      module = os.path.basename(__file__)
      module_name = os.path.splitext(module)[0]
      logger = my_logging.get_module_logger_sel(module_name, int(args.log))
      logger.info(' Starting..')
  
      # 入力 cam/cam0-cam9 対応                               # 2024/04/15
      if input_stream.find('cam') == 0 and len(input_stream) < 5:
          input_stream = 0 if input_stream == 'cam' else int(input_stream[3])
          isstream = True
      else:
          filetype = is_pict(input_stream)
          isstream = filetype == 'None'
          if (filetype == 'NotFound'):
              print(RED + "\ninput file Not found." + NOCOLOR)
              quit()
      outpath = args.out
      conf = args.conf
      yolov5 = args.yolov5 if platform.system()=='Windows' else expanduser(args.yolov5)
      models = args.models if platform.system()=='Windows' else expanduser(args.models)
      models2 = args.models2 if platform.system()=='Windows' else expanduser(args.models2)
      
      # 判定ラベル
      with open(labels, 'r', encoding="utf-8") as labels_file:
          label_list = labels_file.read().splitlines()
  
      # GPUが使用できるか調べる
      use_device = 'cuda:0' if not args.cpu and torch.cuda.is_available() else 'cpu'
  
      # 情報表示
      display_info(args, input_stream, yolov5, models, models2, conf, labels, titleflg, speedflg, outpath, use_device, args.log)
  
      # TorchHubからモデルを読み込む
      model = load_model(yolov5, models)
  
      # モデルを推論用に設定する
      model.eval()
      model.to(use_device)
  
      class_num = len(model.names)
      class_ofs = 0
  
      # ２つ目のモデル指定がある場合
      model2 = None
      if models2 != '':
          model2 = load_model(yolov5, models2)
          model2.eval()
          model2.to(use_device)
          class_ofs = class_num
  
          # 2番目のモデルのクラスID が１番目のクラスにすべて含まれているかチェック
          key = list(model.names)
          val = list(model.names.values())
          eq = True
          for i in range(len(key)):
              if (key[i], val[i]) not in model2.names.items():
                  eq = False
          if eq:
              class_ofs = 0               # ID はすべて含んでいる
  
      # 入力準備
      if (isstream):
          # カメラ 
          cap = cv2.VideoCapture(input_stream)
          ret, frame = cap.read()
          loopflg = cap.isOpened()
      else:
          # 画像ファイル読み込み
          frame = cv2.imread(input_stream)
          if frame is None:
              print(RED + "\nUnable to read the input." + NOCOLOR)
              quit()
  
          # アスペクト比を固定してリサイズ
          img_h, img_w = frame.shape[:2]
          if (img_w > WINDOW_WIDTH):
              height = round(img_h * (WINDOW_WIDTH / img_w))
              frame = cv2.resize(frame, dsize = (WINDOW_WIDTH, height))
          loopflg = True                                      # 1回ループ
  
      # 処理結果の記録 step1
      if (outpath != 'non'):
          if (isstream):
              fps = int(cap.get(cv2.CAP_PROP_FPS))
              out_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
              out_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
              fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
              outvideo = cv2.VideoWriter(outpath, fourcc, fps, (out_w, out_h))
  
      # 計測値初期化
      fpsWithTick = my_fps.fpsWithTick()
      fps_total = 0
      fpsWithTick.get()                                       # fps計測開始
  
      # メインループ 
      while (loopflg):
          if frame is None:
              print(RED + "\nUnable to read the input." + NOCOLOR)
              quit()
  
          # ニューラルネットワークを実行する
          results = model(frame, size=640)
          message = []                                        # 表示メッセージ
          bbox = results.xyxy[0].detach().cpu().numpy()
          if models2 != '':                                   # 2つ目のモデル指定がある場合
              results2 = model2(frame, size=640)
              bbox2 = results2.xyxy[0].detach().cpu().numpy()
              for i in range(len(bbox2)):
                  bbox2[i][5] += class_ofs                    # クラスID の調整
              bbox = np.append(bbox, bbox2, axis=0)           # 結果を追加する
  
          logger.debug(bbox)
          for preds in bbox:
              xmin = int(preds[0])
              ymin = int(preds[1])
              xmax = int(preds[2])
              ymax = int(preds[3])
              confidence  = preds[4]
              class_id  = int(preds[5])
              color_id = class_id if class_id < 80 else class_id - 40 # 2024/04/09
              
              if (confidence > conf):                         # 低い確率を除外
                  # オブジェクト別の色指定                    # 2024/04/16
                  if args.ucr:
                      BOX_COLOR = colors(color_id, True)
                      LABEL_BG_COLOR = BOX_COLOR
                  else:
                      BOX_COLOR = my_color80.get_boder_bgr80(color_id)
                      LABEL_BG_COLOR = my_color80.get_back_bgr80(color_id)
  
                  # ラベル描画領域を得る
                  x0,y0,x1,y1 = my_puttext.cv2_putText(img = frame,
                                         text = label_list[class_id] + ': %.2f' % confidence,
                                         org = (xmin+5, ymin+18), fontFace = fontPIL,
                                         fontScale = 14,
                                         color = TEXT_COLOR,
                                         mode = 0,
                                         areaf=True)
                  xx = xmax if xmax > x1 else x1              # 横が領域を超える場合は超えた値にする
                  cv2.rectangle(frame,(xmin, ymin), (xx, ymin+20), LABEL_BG_COLOR, -1)
                  my_puttext.cv2_putText(img = frame,
                                         text = label_list[class_id] + ': %.2f' % confidence,
                                         org = (xmin+5, ymin+18), fontFace = fontPIL,
                                         fontScale = 14,
                                         color = TEXT_COLOR,
                                         mode = 0)
                  # 画像に枠を描く
                  cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), BOX_COLOR, 2)
  
          # FPSを計算する
          fps = fpsWithTick.get()
          st_fps = 'fps: {:>6.2f}'.format(fps)
          if (speedflg == 'y'):
              cv2.rectangle(frame, (10, 38), (95, 55), (90, 90, 90), -1)
              cv2.putText(frame, st_fps, (15, 50), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.4, color=(255, 255, 255), lineType=cv2.LINE_AA)
  
          # タイトル描画
          if (titleflg == 'y'):
              cv2.putText(frame, title, (12, 32), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(0, 0, 0), lineType=cv2.LINE_AA)
              cv2.putText(frame, title, (10, 30), cv2.FONT_HERSHEY_DUPLEX, fontScale=0.8, color=(200, 200, 0), lineType=cv2.LINE_AA)
  
          # 画像表示 
          window_name = title + "  (hit 'q' or 'esc' key to exit)"
          cv2.namedWindow(window_name, flags=cv2.WINDOW_AUTOSIZE | cv2.WINDOW_GUI_NORMAL) 
          cv2.imshow(window_name, frame)
  
          # 処理結果の記録 step2
          if (outpath != 'non'):
              if (isstream):
                  outvideo.write(frame)
              else:
                  cv2.imwrite(outpath, frame)
  
          # 何らかのキーが押されたら終了 
          breakflg = False
          while(True):
              key = cv2.waitKey(1)
              prop_val = cv2.getWindowProperty(window_name, cv2.WND_PROP_ASPECT_RATIO)
              if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:        
                  print('\n Window close !!')
                  sys.exit(0)
              if key == 27 or key == 113 or (prop_val < 0.0):     # 'esc' or 'q'
                  breakflg = True
                  break
              if (isstream):
                  break
  
          if ((breakflg == False) and isstream):
              # 次のフレームを読み出す
              ret, frame = cap.read()
              if ret == False:
                  break
              loopflg = cap.isOpened()
          else:
              loopflg = False
  
      # 終了処理 
      if (isstream):
          cap.release()
  
          # 処理結果の記録 step3
          if (outpath != 'non'):
              if (isstream):
                  outvideo.release()
  
      cv2.destroyAllWindows()
  
      print('\nFPS average: {:>10.2f}'.format(fpsWithTick.get_average()))
      print('\n Finished.')
  
  # main関数エントリーポイント(実行開始)
  if __name__ == "__main__":
      sys.exit(main())

オープンデータセットによる学習「マスク着用の判定」 †

• 引用サイト → Face Mask Detection）
  

• PascalVOC形式のオープンデータセット Face Mask Detection Dataset を使用する
  Mask Wearing Dataset から YOLO形式のデータセットを入手可能だが、データ数が少ないのと、以前使用したので、今回は違うものを使ってみる
  

前準備 †

1. データセットと変換ツールのダウンロード
   (1) Face Mask Detection Dataset のページを開く
   (2)「Download」ボタンを押す
   　Kaggleへ登録するか，Google アカウントなどでサインインする
   (3) ダウンロードした「archive.zip」を解凍する
   
   (4)「workspace_pylearn」の下に「p2y_conv」フォルダを作成する
   

   (py_learn) PS > cd /anaconda_win/workspace_pylearn/
   (py_learn) PS > mkdir p2y_conv
   (py_learn) PS > cd p2y_conv

   (5) P2Y Converter サイトから「main.py」をダウンロードし、「p2y_conv」ディレクトリ内に配置する
   (6) (3)で解凍してできた「archive/」フォルダ内の「annotations」「images」を「p2y_conv」ディレクトリ内にコピー（移動）する
   
   
   
2. PascalVOC形式のアノテーションデータ(.xml)を YOLO形式(.txt)にフォーマット変換
   ・ 以下の作業は「p2y_conv/」ディレクトリ内でおこなう
   (1)「main.py」を「p2y.py」としてコピーし環境に対応した変更をる
   

   p2y.py(main.py)
       :
   PPATH = '/anaconda_win/workspace_pylearn/p2y_conv/'
   absolutepath_of_directory_with_xmlfiles = PPATH + 'annotations/'
   absolutepath_of_directory_with_imgfiles = PPATH + 'images/'
   absolutepath_of_directory_with_yolofiles = PPATH + 'format_yolo/'
   absolutepath_of_directory_with_classes_txt = PPATH
   absolutepath_of_directory_with_error_txt = PPATH + 'error/'
       :

   (2) 作業用のディレクトリを作成する
   

   mkdir format_yolo
   mkdir error

   p2y_conv/
   ├─annotations
   ├─error
   ├─format_yolo
   ├─images
   ├─main.py
   └─p2y.py

   (3)「lxml」ライブラリをインストールする
   

   (py_learn) PS > pip install lxml
   Collecting lxml
     Downloading lxml-5.2.1-cp311-cp311-win_amd64.whl.metadata (3.5 kB)
   Downloading lxml-5.2.1-cp311-cp311-win_amd64.whl (3.8 MB)
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.8/3.8 MB 11.1 MB/s eta 0:00:00
   Installing collected packages: lxml
   Successfully installed lxml-5.2.1

   (4) フォーマット変換を実行する
   

   (py_learn) python p2y.py
   libpng warning: iCCP: Not recognizing known sRGB profile that has been edited

3. データセットのまとめ
   (1) データセットのディレクトリを作成する
   ・以下のコマンドを実行
   

   mkdir -p mask_dataset/images/train
   mkdir -p mask_dataset/images/val
   mkdir -p mask_dataset/labels/train
   mkdir -p mask_dataset/labels/val

   ・作成結果
   

   (py_learn) PS > tree
   p2y_conv/
   ├─annotations
   ├─error
   ├─format_yolo
   ├─images
   └─mask_dataset
       ├─images
       │  ├─train
       │  └─val
       └─labels
           ├─train
           └─val

   (2)「mask_dataset」ディレクトリに画像ファイルとラベルファイルを配置する
   検証用(val)：学習用(train) がおよそ 8:2 になるようにする
   ・検証用はファイル名の末尾が 1 (86個), 5 (85個) のもの計171個を移動
   

   move format_yolo/*1.txt mask_dataset/labels/val
   move format_yolo/*5.txt mask_dataset/labels/val
   move images/*1.png mask_dataset/images/val
   move images/*5.png mask_dataset/images/val

   ・学習用は残りのファイルを移動｛853-171 = 682個）
   

   move format_yolo/*.txt mask_dataset/labels/train
   move images/*.png mask_dataset/images/train

   (3)「mask_dataset/」フォルダ内にファイル mask.yaml を作成
   ・データセットの配置場所は「yolov5/data/」フォルダとする
   

   train: data/mask_dataset/images/train
   val: data/mask_dataset/images/val
   nc: 3
   names:
     0: without_mask
     1: with_mask
     2: mask_weared_incorrect
     3: motorcycle

   (4)「mask_dataset/」フォルダを「yolov5/data/」内にコピー（移動）する
   

学習の実行 †

1. エポック数 100 で実行する
   ・実行ディレクトリは「workspace_pylearn/yolov5/」
   ・学習モデルはデフォールト設定「yolov7s」を使用する
   ・学習結果フォルダ名は「mask_yolov5s」とする
   

   (py_learn) python train.py --epochs 100 --data data/mask_dataset/mask.yaml --weights yolov5s.pt --name mask_yolov5s

   ・GPU を使用しない場合は以下のコマンドを実行する
   

   (py_learn) python train.py --epochs 100 --data data/mask_dataset/mask.yaml --weights yolov5s.pt --name mask_yolov5s --device cpu

2. 学習の終了
   

   (py_learn) python train.py --epochs 100 --data data/mask_dataset/mask.yaml --weights yolov5s.pt --name mask_yolov5s
   train: weights=yolov5s.pt, cfg=, data=data/mask_dataset/mask.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=mask_yolov5s, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
   github:  YOLOv5 is out of date by 3 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
   Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
   TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
   Overriding model.yaml nc=80 with nc=4
   
                    from  n    params  module                                  arguments
     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
     1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
     2                -1  1     18816  models.common.C3                        [64, 64, 1]
     3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
       :
   Starting training for 100 epochs...
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          0/99      3.35G     0.1051    0.06055    0.03577         79        640: 100%|██████████| 43/43 [00:04<00:00,  9.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.69     0.0704     0.0216    0.00536
       :
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         99/99      4.41G    0.01885    0.01993   0.001163         75        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.848      0.814       0.86       0.59
   
   100 epochs completed in 0.143 hours.
   Optimizer stripped from runs\train\mask_yolov5s\weights\last.pt, 14.4MB
   Optimizer stripped from runs\train\mask_yolov5s\weights\best.pt, 14.4MB
   
   Validating runs\train\mask_yolov5s\weights\best.pt...
   Fusing layers...
   Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.859      0.849      0.888      0.609
             without_mask        171        122      0.773      0.885      0.899       0.59
                with_mask        171        630      0.932      0.931      0.965      0.684
    mask_weared_incorrect        171         26      0.872      0.731      0.801      0.554
   Results saved to runs\train\mask_yolov5s

   ▼「train.py」実行ログ詳細
   ▲「train.py」実行ログ詳細

   (py_learn) python train.py --epochs 100 --data data/mask_dataset/mask.yaml --weights yolov5s.pt --name mask_yolov5s
   train: weights=yolov5s.pt, cfg=, data=data/mask_dataset/mask.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=mask_yolov5s, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
   github:  YOLOv5 is out of date by 3 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
   Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
   TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
   Overriding model.yaml nc=80 with nc=4
   
                    from  n    params  module                                  arguments
     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
     1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
     2                -1  1     18816  models.common.C3                        [64, 64, 1]
     3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
     4                -1  2    115712  models.common.C3                        [128, 128, 2]
     5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]
     6                -1  3    625152  models.common.C3                        [256, 256, 3]
     7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]
     8                -1  1   1182720  models.common.C3                        [512, 512, 1]
     9                -1  1    656896  models.common.SPPF                      [512, 512, 5]
    10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]
    11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    12           [-1, 6]  1         0  models.common.Concat                    [1]
    13                -1  1    361984  models.common.C3                        [512, 256, 1, False]
    14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]
    15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    16           [-1, 4]  1         0  models.common.Concat                    [1]
    17                -1  1     90880  models.common.C3                        [256, 128, 1, False]
    18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]
    19          [-1, 14]  1         0  models.common.Concat                    [1]
    20                -1  1    296448  models.common.C3                        [256, 256, 1, False]
    21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]
    22          [-1, 10]  1         0  models.common.Concat                    [1]
    23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]
    24      [17, 20, 23]  1     24273  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
   Model summary: 214 layers, 7030417 parameters, 7030417 gradients, 16.0 GFLOPs
   
   Transferred 343/349 items from yolov5s.pt
   AMP: checks passed
   optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
   train: Scanning C:\anaconda_win\workspace_pylearn\yolov5\data\mask_dataset\labels\train... 682 images, 0 backgrounds, 0
   train: WARNING  Cache directory C:\anaconda_win\workspace_pylearn\yolov5\data\mask_dataset\labels is not writeable: [WinError 183] : 'C:\\anaconda_win\\workspace_pylearn\\yolov5\\data\\mask_dataset\\labels\\train.cache.npy' -> 'C:\\anaconda_win\\workspace_pylearn\\yolov5\\data\\mask_dataset\\labels\\train.cache'
   val: Scanning C:\anaconda_win\workspace_pylearn\yolov5\data\mask_dataset\labels\val... 171 images, 0 backgrounds, 0 cor
   val: WARNING  Cache directory C:\anaconda_win\workspace_pylearn\yolov5\data\mask_dataset\labels is not writeable: [WinError 183] : 'C:\\anaconda_win\\workspace_pylearn\\yolov5\\data\\mask_dataset\\labels\\val.cache.npy' -> 'C:\\anaconda_win\\workspace_pylearn\\yolov5\\data\\mask_dataset\\labels\\val.cache'
   
   AutoAnchor: 5.55 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset
   Plotting labels to runs\train\mask_yolov5s\labels.jpg...
   Image sizes 640 train, 640 val
   Using 8 dataloader workers
   Logging results to runs\train\mask_yolov5s
   Starting training for 100 epochs...
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          0/99      3.35G     0.1051    0.06055    0.03577         79        640: 100%|██████████| 43/43 [00:04<00:00,  9.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.69     0.0704     0.0216    0.00536
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          1/99      4.41G    0.08192    0.04846    0.02121         67        640: 100%|██████████| 43/43 [00:03<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.769       0.19     0.0995      0.032
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          2/99      4.41G    0.07935    0.04207    0.01834         83        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.769      0.183      0.112     0.0377
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          3/99      4.41G    0.07218    0.03915    0.01724         60        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.544      0.416      0.283     0.0952
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          4/99      4.41G    0.06119    0.03696     0.0145         89        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.565      0.475      0.338      0.122
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          5/99      4.41G    0.05579    0.03693    0.01203         90        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.682      0.447      0.396      0.176
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          6/99      4.41G    0.05158    0.03469    0.01042         68        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.761      0.496       0.48      0.234
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          7/99      4.41G    0.04887    0.03386   0.009272         68        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.753      0.509      0.474      0.211
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          8/99      4.41G    0.04631    0.03263    0.00888        207        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.905      0.503      0.564      0.273
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          9/99      4.41G    0.04507    0.03335   0.008373         78        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.929      0.554      0.613      0.358
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         10/99      4.41G    0.04216     0.0299    0.00798        142        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.901      0.542      0.597      0.285
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         11/99      4.41G    0.04115     0.0307   0.008121         72        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.886      0.553      0.607      0.322
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         12/99      4.41G    0.04034    0.03206   0.007108        119        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.905      0.557      0.609      0.324
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         13/99      4.41G    0.03864     0.0303   0.007133         91        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.923      0.548       0.61      0.341
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         14/99      4.41G    0.03765    0.03079   0.006593         91        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:00<0
                      all        171        778      0.912       0.56      0.618      0.314
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         15/99      4.41G    0.03754    0.03185   0.006909        109        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.896      0.567      0.607       0.33
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         16/99      4.41G    0.03702    0.03072   0.006434        115        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.935      0.573      0.629      0.374
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         17/99      4.41G    0.03657    0.02973   0.006558         68        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.929      0.578      0.644      0.395
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         18/99      4.41G    0.03636    0.03259   0.006621        164        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.926      0.558      0.638      0.381
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         19/99      4.41G    0.03474    0.02924   0.006014         73        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.925      0.568      0.663      0.404
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         20/99      4.41G    0.03353    0.03008   0.005776         48        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.939      0.546      0.638        0.4
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         21/99      4.41G    0.03369    0.02975   0.006082        131        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.943      0.563      0.656        0.4
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         22/99      4.41G    0.03419    0.02884   0.005488         59        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.934      0.553      0.668      0.417
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         23/99      4.41G    0.03338    0.02761   0.005553         44        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.952      0.567      0.675      0.407
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         24/99      4.41G    0.03331    0.02906   0.005624        104        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.94      0.571      0.672      0.415
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         25/99      4.41G    0.03239    0.02725   0.005505         79        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.948      0.573      0.702       0.44
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         26/99      4.41G    0.03184    0.02699   0.005355         95        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.818      0.642      0.676      0.401
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         27/99      4.41G    0.03124    0.02666   0.005242         42        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.675      0.647      0.695      0.431
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         28/99      4.41G     0.0308    0.02896   0.005306         85        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.659      0.662      0.687      0.437
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         29/99      4.41G    0.03131    0.02665   0.005278         49        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.885      0.634      0.688       0.44
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         30/99      4.41G     0.0304    0.02694   0.004846         66        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.616      0.682      0.688      0.444
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         31/99      4.41G    0.02945    0.02757   0.004831         51        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.763      0.675      0.676      0.405
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         32/99      4.41G    0.02965    0.02752    0.00461        130        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.762      0.733      0.762      0.497
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         33/99      4.41G    0.02996    0.02561   0.004403         75        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.838      0.716      0.763      0.489
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         34/99      4.41G    0.02936    0.02624   0.003965        107        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.766      0.745      0.775      0.501
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         35/99      4.41G    0.02946    0.02683   0.004163         53        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.731      0.735      0.772      0.494
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         36/99      4.41G    0.02868    0.02562   0.003824         51        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.74      0.751      0.761      0.471
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         37/99      4.41G    0.02841    0.02709   0.003661         91        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.909      0.683      0.759      0.491
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         38/99      4.41G    0.02946    0.02841    0.00366         92        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.768      0.764      0.798      0.518
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         39/99      4.41G    0.02856    0.02682   0.003546         77        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.809      0.717      0.779      0.504
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         40/99      4.41G    0.02754     0.0259   0.003536         48        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.829      0.774      0.818      0.523
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         41/99      4.41G    0.02746    0.02545   0.003263         75        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.795      0.784      0.814      0.522
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         42/99      4.41G    0.02789    0.02589    0.00354         80        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.866      0.734      0.805      0.519
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         43/99      4.41G    0.02785    0.02654   0.003272         65        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.765       0.77      0.797      0.515
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         44/99      4.41G    0.02648    0.02617   0.003113         60        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.882      0.791       0.85       0.55
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         45/99      4.41G    0.02703    0.02565   0.003004         78        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.872      0.769      0.848       0.54
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         46/99      4.41G    0.02704    0.02547   0.002782         75        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.836       0.78      0.823      0.528
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         47/99      4.41G     0.0269    0.02641   0.002753         90        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.836      0.767      0.805      0.523
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         48/99      4.41G    0.02615    0.02451   0.002561         88        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.827       0.79      0.837      0.551
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         49/99      4.41G     0.0259    0.02599   0.002557         69        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.84      0.793       0.85      0.565
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         50/99      4.41G     0.0261    0.02376   0.002365        121        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.846      0.794       0.84      0.548
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         51/99      4.41G    0.02553     0.0248   0.002372         78        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.852      0.794      0.855       0.57
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         52/99      4.41G    0.02561    0.02417   0.002499         63        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.875      0.789      0.865      0.562
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         53/99      4.41G    0.02539      0.025   0.002371         74        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.872      0.771       0.84      0.564
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         54/99      4.41G    0.02514    0.02344   0.002231        136        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.87      0.754      0.844      0.559
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         55/99      4.41G    0.02519    0.02457   0.002228         63        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.884      0.781      0.843      0.564
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         56/99      4.41G     0.0242    0.02459    0.00215         67        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.852      0.796      0.854       0.56
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         57/99      4.41G    0.02431    0.02375   0.001989         98        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.84      0.802      0.854      0.581
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         58/99      4.41G    0.02462    0.02456   0.001973         74        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.876      0.809      0.871      0.583
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         59/99      4.41G    0.02505    0.02474   0.001895         92        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.911      0.789       0.87      0.576
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         60/99      4.41G    0.02398     0.0228   0.002016         48        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.842      0.788       0.85      0.581
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         61/99      4.41G    0.02351    0.02405    0.00177         54        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.938      0.803      0.871      0.592
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         62/99      4.41G    0.02383    0.02467    0.00173        112        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.911      0.773      0.851      0.581
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         63/99      4.41G    0.02386      0.024   0.001924         99        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.877      0.825      0.872      0.579
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         64/99      4.41G    0.02275    0.02339   0.001726         62        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.895      0.804      0.865      0.587
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         65/99      4.41G    0.02288    0.02347   0.001597        111        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.859      0.849      0.888      0.607
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         66/99      4.41G    0.02258     0.0216   0.001675         52        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.873      0.829      0.876      0.589
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         67/99      4.41G    0.02268     0.0237   0.001742         82        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.857      0.875      0.876      0.587
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         68/99      4.41G    0.02201    0.02348   0.001615        113        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.831       0.85      0.858      0.573
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         69/99      4.41G    0.02263    0.02294   0.001527         60        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.875       0.78      0.846      0.562
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         70/99      4.41G    0.02162     0.0219   0.001599         53        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.88      0.816      0.874      0.599
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         71/99      4.41G    0.02186    0.02351   0.001494         70        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.873      0.812      0.855      0.582
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         72/99      4.41G    0.02196    0.02211   0.001572         77        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.88      0.773       0.85       0.58
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         73/99      4.41G    0.02145    0.02341   0.001484         95        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.88      0.807      0.854      0.576
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         74/99      4.41G    0.02171    0.02345   0.001453         66        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.845      0.836      0.862      0.581
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         75/99      4.41G    0.02128    0.02272   0.001519         67        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.864      0.821      0.867      0.584
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         76/99      4.41G    0.02094    0.02281   0.001223         40        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.845      0.833      0.868      0.593
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         77/99      4.41G    0.02102    0.02174    0.00133        102        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.848      0.834       0.87       0.59
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         78/99      4.41G    0.02105    0.02172   0.001171         59        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.886      0.814      0.867      0.594
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         79/99      4.41G    0.02107    0.02259   0.001177        106        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.93      0.801      0.871      0.594
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         80/99      4.41G    0.02067    0.02213   0.001498        161        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.843      0.816      0.861      0.592
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         81/99      4.41G    0.02095    0.02207    0.00136         55        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.883      0.815      0.862      0.586
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         82/99      4.41G    0.02013    0.02178   0.001258         84        640: 100%|██████████| 43/43 [00:03<00:00, 10.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.866      0.834      0.861      0.595
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         83/99      4.41G    0.02068    0.02235   0.001233        103        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778       0.81      0.859      0.859       0.59
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         84/99      4.41G    0.01991    0.02144    0.00116         88        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.909      0.781       0.86      0.597
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         85/99      4.41G    0.01994    0.02198   0.001115         71        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.883      0.813      0.853      0.575
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         86/99      4.41G    0.01994    0.02229   0.001158         46        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.888      0.823      0.851      0.581
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         87/99      4.41G    0.02001    0.02167   0.001083         45        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.866      0.809      0.849      0.586
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         88/99      4.41G    0.01939    0.02168  0.0009812         77        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.855      0.813      0.856      0.585
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         89/99      4.41G    0.01946    0.02135   0.001199         51        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.859      0.825      0.861      0.589
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         90/99      4.41G    0.01931    0.02054   0.001064         85        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.885      0.803      0.869      0.585
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         91/99      4.41G    0.01965    0.02123  0.0009763        106        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.891      0.805      0.869      0.586
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         92/99      4.41G     0.0197    0.02175   0.001261         83        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.862      0.808      0.871      0.598
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         93/99      4.41G    0.01915    0.02116  0.0009928        105        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.906      0.765      0.861      0.592
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         94/99      4.41G    0.01909    0.02033  0.0009784        105        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.859      0.801      0.858       0.59
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         95/99      4.41G    0.01901    0.02131  0.0009813         70        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.831      0.831      0.859      0.586
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         96/99      4.41G    0.01892    0.02219  0.0009219        105        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.921      0.781      0.865      0.593
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         97/99      4.41G    0.01874    0.02093  0.0008694         54        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.855      0.812      0.862      0.592
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         98/99      4.41G    0.01946    0.02096   0.001002        101        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.872        0.8      0.864      0.592
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         99/99      4.41G    0.01885    0.01993   0.001163         75        640: 100%|██████████| 43/43 [00:03<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.848      0.814       0.86       0.59
   
   100 epochs completed in 0.143 hours.
   Optimizer stripped from runs\train\mask_yolov5s\weights\last.pt, 14.4MB
   Optimizer stripped from runs\train\mask_yolov5s\weights\best.pt, 14.4MB
   
   Validating runs\train\mask_yolov5s\weights\best.pt...
   Fusing layers...
   Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 6/6 [00:01<0
                      all        171        778      0.859      0.849      0.888      0.609
             without_mask        171        122      0.773      0.885      0.899       0.59
                with_mask        171        630      0.932      0.931      0.965      0.684
    mask_weared_incorrect        171         26      0.872      0.731      0.801      0.554
   Results saved to runs\train\mask_yolov5s

   
   
3. メッセージの最後「Results saved to runs\train\mask_yolov5s」を確認
   ・学習結果モデルは「runs/train/mask_yolov5s/weights」評価指標は「runs/train/mask_yolov5s/」
   

   F1 curve	P curve	PR curve	R curve

   
   
   
   
   
   
   
   
   
   
   
   
   

実行結果を使って推論 †

1. 「detect2.py」で推論実行（静止画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Images/mask-test.jpg --view-img

   
   ・実行ログ（結果は「runs/detect/exp*」*は順次更新）
   

   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Images/mask-test.jpg --view-img
   detect2: weights=['./runs/train/mask_yolov5s/weights/best.pt'], source=../../Images/mask-test.jpg, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=True, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
   Speed: 1.0ms pre-process, 49.2ms inference, 42.0ms NMS per image at shape (1, 3, 640, 640)
   Results saved to runs\detect\exp10

2. 「detect2.py」で推論実行（動画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Videos/mask.mov --view-img

   
   ・実行ログ（結果は「runs/detect/exp*」*は順次更新）
   

   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Videos/mask.mov --view-img
   detect2: weights=['./runs/train/mask_yolov5s/weights/best.pt'], source=../../Videos/mask.mov, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=True, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
   Speed: 0.6ms pre-process, 6.4ms inference, 1.0ms NMS per image at shape (1, 3, 640, 640)
   Results saved to runs\detect\exp12

3. 「detect2.py」その他の実行例
   

   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Images/mask.jpg --view-img
   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Videos/mask2.mp4 --view-img
   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Videos/mask-test.mp4 --view-img

   
   
   

実行結果を使って推論（日本語表示） †

1. ラベルファイルを作成する
   ・「ts_names_jp」日本語（「coco.names_jp」をコピーし編集）
   

   マスクなし
   マスクあり
   マスク着用 _不正確

   ・「ts_names」英語（「coco.names」をコピーし編集）
   

   without_mask
   with_mask
   mask_weared_incorrect

2. 「detect2_yolov5.py」で推論実行（静止画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/mask_yolov5s/weights/best.pt -i ../../Images/mask-test.jpg -l mask_names_jp

   
   ・実行ログ
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/mask_yolov5s/weights/best.pt -i ../../Images/mask-test.jpg -l mask_names_jp
   
   Object detection YoloV5 in PyTorch Ver. 0.02: Starting application...
      OpenCV virsion : 4.9.0
   
      - Image File   :  ../../Images/mask-test.jpg
      - YOLO v5      :  ultralytics/yolov5
      - Pretrained   :  ./runs/train/mask_yolov5s/weights/best.pt
      - Label file   :  mask_names_jp
      - Program Title:  y
      - Speed flag   :  y
      - Processed out:  non
      - Use device   :  cuda:0
   
   Using cache found in C:\Users\izuts/.cache\torch\hub\ultralytics_yolov5_master
   YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
   Adding AutoShape...
   
   FPS average:       9.00
   
    Finished.

3. 「detect2_yolov5.py」で推論実行（動画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/mask_yolov5s/weights/best.pt -i ../../Videos/mask.mov -l mask_names_jp

   
   ・実行ログ
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/mask_yolov5s/weights/best.pt -i ../../Videos/mask.mov -l mask_names_jp
   
   Object detection YoloV5 in PyTorch Ver. 0.02: Starting application...
      OpenCV virsion : 4.9.0
   
      - Image File   :  ../../Videos/mask.mov
      - YOLO v5      :  ultralytics/yolov5
      - Pretrained   :  ./runs/train/mask_yolov5s/weights/best.pt
      - Label file   :  mask_names_jp
      - Program Title:  y
      - Speed flag   :  y
      - Processed out:  non
      - Use device   :  cuda:0
   
   Using cache found in C:\Users\izuts/.cache\torch\hub\ultralytics_yolov5_master
   YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs
   Adding AutoShape...
   
   FPS average:      45.70
   
    Finished.

4. 「detect2_yolov5.py」その他の実行例
   

   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Images/mask.jpg --view-img
   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Videos/mask2.mp4 --view-img
   (py_learn) python detect2.py --weights ./runs/train/mask_yolov5s/weights/best.pt --source ../../Videos/mask-test.mp4 --view-img

   
   
   

カスタムセットによる学習「じゃんけんの判定」 †

• 以前に カスタムデータによる学習４「じゃんけんの判定（その３）」 で作成した自前のデータセットを使用する
  
• 「YOLOv7」用に作成したものはそのまま利用可能
  

前準備 †

1. データセットの配置
   「janken4_dataset/」フォルダを「yolov5/data/」内にコピー（移動）する
   

学習の実行 †

1. エポック数 100 で実行する
   ・実行ディレクトリは「workspace_pylearn/yolov5/」
   ・学習モデルはデフォールト設定「yolov7s」を使用する
   ・学習結果フォルダ名は「janken4_yolov5s」とする
   

   (py_learn) python train.py --epochs 100 --data data/janken4_dataset/janken4_dataset.yaml --weights yolov5s.pt --name janken4_yolov5s

   ・GPU を使用しない場合は以下のコマンドを実行する
   

   (py_learn) python train.py --epochs 100 --data data/janken4_dataset/janken4_dataset.yaml --weights yolov5s.pt --name janken4_yolov5s --device cpu

2. 学習の終了
   

   (py_learn) python train.py --epochs 100 --data data/mask_dataset/mask.yaml --weights yolov5s.pt --name mask_yolov5s --device cpu

3. 学習の終了
   

   (py_learn) python train.py --epochs 100 --data data/janken4_dataset/janken4_dataset.yaml --weights yolov5s.pt --name janken4_yolov5s
   train: weights=yolov5s.pt, cfg=, data=data/janken4_dataset/janken4_dataset.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=janken4_yolov5s, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
   github:  YOLOv5 is out of date by 3 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
   Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
   TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
   Overriding model.yaml nc=80 with nc=3
   
                    from  n    params  module                                  arguments
     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
     1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
     2                -1  1     18816  models.common.C3                        [64, 64, 1]
     3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
       :
   Starting training for 100 epochs...
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          0/99      3.35G    0.08231    0.02932    0.03379         37        640: 100%|██████████| 30/30 [00:03<00:00,  9.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120    0.00334      0.992     0.0603     0.0182
       :
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         99/99      3.98G    0.01874   0.009725    0.00775         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.787
   
   100 epochs completed in 0.093 hours.
   Optimizer stripped from runs\train\janken4_yolov5s\weights\last.pt, 14.4MB
   Optimizer stripped from runs\train\janken4_yolov5s\weights\best.pt, 14.4MB
   
   Validating runs\train\janken4_yolov5s\weights\best.pt...
   Fusing layers...
   Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.788
                      goo        120         40      0.984          1      0.995      0.754
                    choki        120         40          1      0.977      0.995      0.755
                      par        120         40      0.994          1      0.995      0.855
   Results saved to runs\train\janken4_yolov5s

   ▼「train.py」実行ログ詳細
   ▲「train.py」実行ログ詳細

   (py_learn) python train.py --epochs 100 --data data/janken4_dataset/janken4_dataset.yaml --weights yolov5s.pt --name janken4_yolov5s
   train: weights=yolov5s.pt, cfg=, data=data/janken4_dataset/janken4_dataset.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=janken4_yolov5s, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
   github:  YOLOv5 is out of date by 3 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
   Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
   TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
   Overriding model.yaml nc=80 with nc=3
   
                    from  n    params  module                                  arguments
     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
     1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
     2                -1  1     18816  models.common.C3                        [64, 64, 1]
     3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
     4                -1  2    115712  models.common.C3                        [128, 128, 2]
     5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]
     6                -1  3    625152  models.common.C3                        [256, 256, 3]
     7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]
     8                -1  1   1182720  models.common.C3                        [512, 512, 1]
     9                -1  1    656896  models.common.SPPF                      [512, 512, 5]
    10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]
    11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    12           [-1, 6]  1         0  models.common.Concat                    [1]
    13                -1  1    361984  models.common.C3                        [512, 256, 1, False]
    14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]
    15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    16           [-1, 4]  1         0  models.common.Concat                    [1]
    17                -1  1     90880  models.common.C3                        [256, 128, 1, False]
    18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]
    19          [-1, 14]  1         0  models.common.Concat                    [1]
    20                -1  1    296448  models.common.C3                        [256, 256, 1, False]
    21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]
    22          [-1, 10]  1         0  models.common.Concat                    [1]
    23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]
    24      [17, 20, 23]  1     21576  models.yolo.Detect                      [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
   Model summary: 214 layers, 7027720 parameters, 7027720 gradients, 16.0 GFLOPs
   
   Transferred 343/349 items from yolov5s.pt
   AMP: checks passed
   optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
   train: Scanning C:\anaconda_win\workspace_pylearn\yolov5\data\janken4_dataset\train\labels... 480 images, 0 backgrounds
   train: New cache created: C:\anaconda_win\workspace_pylearn\yolov5\data\janken4_dataset\train\labels.cache
   val: Scanning C:\anaconda_win\workspace_pylearn\yolov5\data\janken4_dataset\valid\labels... 120 images, 0 backgrounds,
   val: New cache created: C:\anaconda_win\workspace_pylearn\yolov5\data\janken4_dataset\valid\labels.cache
   
   AutoAnchor: 3.12 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
   Plotting labels to runs\train\janken4_yolov5s\labels.jpg...
   Image sizes 640 train, 640 val
   Using 8 dataloader workers
   Logging results to runs\train\janken4_yolov5s
   Starting training for 100 epochs...
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          0/99      3.35G    0.08231    0.02932    0.03379         37        640: 100%|██████████| 30/30 [00:03<00:00,  9.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120    0.00334      0.992     0.0603     0.0182
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          1/99      3.98G     0.0572    0.02576    0.03066         39        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.183      0.392        0.2     0.0732
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          2/99      3.98G    0.04957    0.02153    0.02791         45        640: 100%|██████████| 30/30 [00:02<00:00, 13.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.164      0.358      0.145     0.0422
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          3/99      3.98G     0.0485    0.02054    0.02889         37        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.364      0.433      0.401      0.136
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          4/99      3.98G    0.04405    0.01775    0.02909         37        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.244       0.56      0.344      0.121
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          5/99      3.98G    0.04293    0.01668    0.02636         33        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.301      0.675      0.393        0.2
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          6/99      3.98G    0.04271    0.01557    0.02331         42        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.398      0.786      0.629      0.245
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          7/99      3.98G    0.03947    0.01544    0.01986         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.427       0.85      0.631      0.355
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          8/99      3.98G    0.03792    0.01511    0.02048         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.495      0.825      0.698      0.353
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
          9/99      3.98G    0.03979    0.01469    0.02054         42        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.579       0.93      0.802      0.442
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         10/99      3.98G    0.03555    0.01413     0.0197         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.669      0.775      0.806      0.458
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         11/99      3.98G      0.033    0.01385     0.0217         45        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.705      0.594      0.721      0.365
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         12/99      3.98G    0.03321    0.01327    0.01911         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120       0.69      0.739      0.799      0.421
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         13/99      3.98G    0.03502    0.01387    0.02147         43        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.833      0.875      0.917      0.531
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         14/99      3.98G    0.02933    0.01338    0.01939         46        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.893      0.848      0.957      0.617
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         15/99      3.98G     0.0358    0.01393    0.01933         38        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.954      0.953      0.979      0.604
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         16/99      3.98G    0.03375    0.01295    0.01561         41        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.855      0.913      0.911      0.523
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         17/99      3.98G    0.03741    0.01303    0.01721         41        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.911      0.928      0.988      0.602
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         18/99      3.98G     0.0341    0.01311    0.01504         36        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.979      0.958      0.986      0.605
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         19/99      3.98G     0.0325    0.01296    0.01394         44        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.838      0.948      0.931      0.567
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         20/99      3.98G    0.03098    0.01276    0.01328         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.784      0.636      0.723      0.397
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         21/99      3.98G    0.02741    0.01282     0.0139         33        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.807      0.867      0.925       0.55
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         22/99      3.98G     0.0297     0.0124    0.01551         42        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.965      0.975      0.993      0.709
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         23/99      3.98G    0.03286    0.01236    0.01528         36        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.881      0.951      0.973      0.648
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         24/99      3.98G     0.0304    0.01237    0.01601         36        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.706      0.599      0.627      0.219
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         25/99      3.98G    0.03226    0.01292    0.01424         41        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.926      0.925      0.958      0.534
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         26/99      3.98G    0.03152    0.01266    0.01315         32        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.988      0.992      0.995      0.675
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         27/99      3.98G    0.03078    0.01273    0.01316         47        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.923      0.914      0.973      0.634
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         28/99      3.98G    0.02773    0.01226    0.01355         43        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.689      0.682      0.738      0.446
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         29/99      3.98G    0.02957      0.012    0.01376         35        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.927      0.833      0.952      0.599
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         30/99      3.98G    0.02811    0.01192    0.01414         42        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120       0.99      0.995      0.995      0.718
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         31/99      3.98G    0.02548    0.01237    0.01207         37        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.961      0.975      0.988       0.66
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         32/99      3.98G    0.03024    0.01172    0.01306         37        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.985      0.985      0.993      0.687
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         33/99      3.98G    0.02691    0.01125    0.01007         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.954       0.98      0.993      0.732
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         34/99      3.98G    0.02743    0.01169    0.01167         43        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.986      0.988      0.994      0.706
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         35/99      3.98G     0.0286    0.01179     0.0153         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.945      0.979      0.976      0.663
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         36/99      3.98G    0.02537    0.01211     0.0127         43        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120       0.98      0.972      0.994      0.715
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         37/99      3.98G    0.02837    0.01169    0.01206         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.974      0.967      0.983      0.698
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         38/99      3.98G    0.02573    0.01186    0.01277         44        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.988          1      0.995      0.708
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         39/99      3.98G    0.02653    0.01211    0.01424         45        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.989      0.982       0.99      0.752
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         40/99      3.98G    0.02987    0.01177     0.0129         31        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.996      0.998      0.995      0.754
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         41/99      3.98G    0.02421    0.01095   0.008913         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.956      0.969      0.992      0.712
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         42/99      3.98G    0.02611    0.01091    0.01266         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.896      0.914      0.976      0.683
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         43/99      3.98G    0.02356    0.01097    0.01029         40        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.988      0.994      0.707
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         44/99      3.98G    0.02585    0.01176    0.01273         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.989      0.998      0.995      0.741
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         45/99      3.98G    0.02214    0.01103   0.009593         33        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.984      0.968      0.994      0.731
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         46/99      3.98G    0.02429    0.01134    0.01229         37        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.972      0.997      0.995      0.696
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         47/99      3.98G    0.02669    0.01131     0.0113         42        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.979       0.99      0.993      0.728
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         48/99      3.98G    0.02624    0.01117    0.01391         35        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.981      0.991      0.994      0.731
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         49/99      3.98G     0.0216    0.01082    0.01195         40        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.979      0.997      0.994      0.749
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         50/99      3.98G    0.02802    0.01135    0.01317         35        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.987      0.992      0.995      0.753
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         51/99      3.98G    0.02481    0.01129    0.01008         33        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.979          1      0.995      0.734
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         52/99      3.98G    0.02087    0.01088    0.00971         39        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.991      0.976      0.995      0.742
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         53/99      3.98G    0.02397     0.0108    0.01014         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.965      0.987      0.993      0.737
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         54/99      3.98G    0.02143    0.01047    0.01007         39        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.999      0.968      0.994       0.76
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         55/99      3.98G    0.02357    0.01063    0.01068         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.982      0.965      0.992      0.732
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         56/99      3.98G    0.02155    0.01041   0.009341         32        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.997      0.997      0.995      0.734
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         57/99      3.98G    0.02413    0.01099    0.01143         43        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.992      0.998      0.995      0.741
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         58/99      3.98G    0.02416    0.01015    0.01208         46        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.989          1      0.995      0.752
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         59/99      3.98G    0.02315    0.01038   0.009973         36        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120       0.99      0.993      0.995      0.755
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         60/99      3.98G    0.02194    0.01087   0.008395         41        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.991      0.984      0.995      0.767
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         61/99      3.98G    0.02366    0.01112    0.01098         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.988      0.996      0.995      0.757
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         62/99      3.98G    0.02109    0.01104   0.008866         32        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.998      0.998      0.995       0.75
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         63/99      3.98G    0.02194    0.01008    0.01025         37        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.995          1      0.995      0.767
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         64/99      3.98G    0.01991     0.0104   0.009375         42        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.986          1      0.995      0.773
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         65/99      3.98G    0.02214    0.01088   0.008747         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.997      0.989      0.995      0.764
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         66/99      3.98G    0.02282    0.01059    0.01417         27        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.987      0.992      0.994       0.74
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         67/99      3.98G    0.02407    0.01053    0.01109         40        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.995       0.99      0.995      0.754
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         68/99      3.98G    0.02182    0.01021   0.008963         40        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.999      0.991      0.994      0.745
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         69/99      3.98G    0.02283    0.01069   0.008686         41        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.959      0.993      0.993      0.746
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         70/99      3.98G    0.02077    0.01068   0.008957         39        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120       0.96      0.987      0.994      0.754
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         71/99      3.98G    0.02087    0.01011    0.00913         27        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.986      0.992      0.993      0.749
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         72/99      3.98G    0.02065    0.01047    0.01085         42        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.946      0.961      0.985      0.713
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         73/99      3.98G    0.02052    0.01022   0.008233         46        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.765
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         74/99      3.98G    0.02427    0.01052    0.01263         39        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.976      0.997      0.993      0.744
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         75/99      3.98G    0.01907   0.009838   0.006526         36        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.996      0.999      0.995      0.767
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         76/99      3.98G    0.02121    0.01021   0.008985         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.997      0.993      0.995      0.771
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         77/99      3.98G    0.01806    0.01032   0.009292         37        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.999      0.995      0.767
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         78/99      3.98G     0.0211   0.009842   0.008575         42        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.994          1      0.995      0.759
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         79/99      3.98G    0.02148     0.0103   0.009642         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.995          1      0.995      0.759
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         80/99      3.98G    0.01837   0.009587   0.007205         33        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993          1      0.995      0.773
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         81/99      3.98G     0.0206    0.01032   0.007889         44        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.995          1      0.995      0.775
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         82/99      3.98G    0.02004     0.0102   0.007953         34        640: 100%|██████████| 30/30 [00:02<00:00, 11.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.996          1      0.995      0.768
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         83/99      3.98G    0.01952   0.009843   0.008777         28        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.997          1      0.995      0.764
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         84/99      3.98G    0.01898   0.009601   0.006989         33        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.999      0.995      0.777
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         85/99      3.98G    0.02144   0.009912   0.009352         37        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.995      0.992      0.995      0.775
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         86/99      3.98G    0.02084     0.0103    0.01001         32        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.991          1      0.995       0.77
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         87/99      3.98G    0.01999   0.009585   0.007808         33        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.995      0.999      0.995       0.78
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         88/99      3.98G    0.01913    0.01006   0.009588         31        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.997      0.993      0.995      0.779
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         89/99      3.98G    0.02166   0.009986   0.008898         34        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.777
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         90/99      3.98G    0.01914   0.009491   0.007874         40        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.991      0.992      0.995      0.771
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         91/99      3.98G    0.01811   0.009106   0.007113         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.988      0.992      0.995      0.774
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         92/99      3.98G     0.0193   0.009321   0.008117         47        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.778
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         93/99      3.98G    0.01848   0.009137   0.008004         32        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.991      0.991      0.995      0.781
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         94/99      3.98G    0.01892   0.009626   0.007458         34        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.774
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         95/99      3.98G    0.01856   0.009444   0.007062         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.992      0.999      0.995      0.776
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         96/99      3.98G    0.01887   0.009591   0.007504         35        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.996      0.993      0.995      0.784
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         97/99      3.98G    0.01569   0.009341    0.00873         46        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.995      0.992      0.995      0.776
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         98/99      3.98G    0.01845   0.009881   0.007536         36        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.783
   
         Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         99/99      3.98G    0.01874   0.009725    0.00775         38        640: 100%|██████████| 30/30 [00:02<00:00, 12.
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.787
   
   100 epochs completed in 0.093 hours.
   Optimizer stripped from runs\train\janken4_yolov5s\weights\last.pt, 14.4MB
   Optimizer stripped from runs\train\janken4_yolov5s\weights\best.pt, 14.4MB
   
   Validating runs\train\janken4_yolov5s\weights\best.pt...
   Fusing layers...
   Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
                    Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 4/4 [00:00<0
                      all        120        120      0.993      0.992      0.995      0.788
                      goo        120         40      0.984          1      0.995      0.754
                    choki        120         40          1      0.977      0.995      0.755
                      par        120         40      0.994          1      0.995      0.855
   Results saved to runs\train\janken4_yolov5s

   
   
4. メッセージの最後「Results saved to runs\train\janken4_yolov5s」を確認
   ・学習結果モデルは「runs/train/janken4_yolov5s/weights」評価指標は「runs/train/janken4_yolov5s/」
   

   F1 curve	P curve	PR curve	R curve

   
   
   
   
   
   
   
   
   
   
   
   
   

実行結果を使って推論 †

1. 「detect2.py」で推論実行（静止画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2.py --weights ./runs/train/janken4_yolov5s/weights/best.pt --source ../../Images/janken3.jpg --view-img

   
   ・実行ログ（結果は「runs/detect/exp*」*は順次更新）
   

   (py_learn) python detect2.py --weights ./runs/train/janken4_yolov5s/weights/best.pt --source ../../Images/janken3.jpg --view-img
   detect2: weights=['./runs/train/janken4_yolov5s/weights/best.pt'], source=../../Images/janken3.jpg, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=True, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
   Speed: 0.0ms pre-process, 53.6ms inference, 42.0ms NMS per image at shape (1, 3, 640, 640)
   Results saved to runs\detect\exp23

2. 「detect2.py」で推論実行（動画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2.py --weights ./runs/train/janken4_yolov5s/weights/best.pt --source ../../Videos/janken_test2.mp4 --view-img

   
   ・実行ログ（結果は「runs/detect/exp*」*は順次更新）
   

   (py_learn) python detect2.py --weights ./runs/train/janken4_yolov5s/weights/best.pt --source ../../Videos/janken_test2.mp4 --view-img
   detect2: weights=['./runs/train/janken4_yolov5s/weights/best.pt'], source=../../Videos/janken_test2.mp4, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=True, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
   YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
   Speed: 0.7ms pre-process, 8.4ms inference, 1.0ms NMS per image at shape (1, 3, 640, 640)
   Results saved to runs\detect\exp24

3. 「detect2.py」その他の実行例
   

   (py_learn) python detect2.py --weights ./runs/train/janken4_yolov5s/weights/best.pt --source ../../Images/janken.jpg --view-img
   (py_learn) python detect2.py --weights ./runs/train/janken4_yolov5s/weights/best.pt --source ../../Images/janken2.jpg --view-img

   
   

実行結果を使って推論（日本語表示） †

1. ラベルファイルを準備する
   ・「janken.names_jp」日本語
   

   グー
   チョキ
   パー

   ・「jankennames」英語
   

   gook
   choki
   par

2. 「detect2_yolov5.py」で推論実行（静止画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/janken4_yolov5s/weights/best.pt -i ../../Images/janken3.jpg -l janken.names_jp

   
   ・実行ログ
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/janken4_yolov5s/weights/best.pt -i ../../Images/janken3.jpg -l janken.names_jp
   
   Object detection YoloV5 in PyTorch Ver. 0.02: Starting application...
      OpenCV virsion : 4.9.0
   
      - Image File   :  ../../Images/janken3.jpg
      - YOLO v5      :  ultralytics/yolov5
      - Pretrained   :  ./runs/train/janken4_yolov5s/weights/best.pt
      - Label file   :  janken.names_jp
      - Program Title:  y
      - Speed flag   :  y
      - Processed out:  non
      - Use device   :  cuda:0
   
   Using cache found in C:\Users\izuts/.cache\torch\hub\ultralytics_yolov5_master
   YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
   Adding AutoShape...
   
   FPS average:       7.90
   
    Finished.

3. 「detect2_yolov5.py」で推論実行（動画）
   ・学習結果モデルを指定する
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/janken4_yolov5s/weights/best.pt -i ../../Videos/janken_test2.mp4 -l janken.names_jp

   
   ・実行ログ
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/janken4_yolov5s/weights/best.pt -i ../../Videos/janken_test2.mp4 -l janken.names_jp
   
   Object detection YoloV5 in PyTorch Ver. 0.02: Starting application...
      OpenCV virsion : 4.9.0
   
      - Image File   :  ../../Videos/janken_test2.mp4
      - YOLO v5      :  ultralytics/yolov5
      - Pretrained   :  ./runs/train/janken4_yolov5s/weights/best.pt
      - Label file   :  janken.names_jp
      - Program Title:  y
      - Speed flag   :  y
      - Processed out:  non
      - Use device   :  cuda:0
   
   Using cache found in C:\Users\izuts/.cache\torch\hub\ultralytics_yolov5_master
   YOLOv5  2024-4-9 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
   
   Fusing layers...
   Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs
   Adding AutoShape...
   
   FPS average:      47.30
   
    Finished.

4. 「detect2_yolov5.py」その他の実行例
   

   (py_learn) python detect2_yolov5.py -m ./runs/train/janken4_yolov5s/weights/best.pt -i ../../Images/janken.jpg -l janken.names_jp
   (py_learn) python detect2_yolov5.py -m ./runs/train/janken4_yolov5s/weights/best.pt -i ../../Images/janken2.jpg -l janken.names_jp

   
   

Official YOLOv5 まとめ †

各プログラムの機能 †

• 主なコマンドオプション（詳しくは各項参照）
  

  プログラム名	主なコマンドオプション	初期値	機能	種別
  detect2.py	--weights	yolov7s.pt	入力ソースを物体検出（推論）	YOLOv5 添付 detect.py 修正版
  	--source	data/images
  	--device	(cuda:0)
  	--view-img	False
  detect3_yolov5.py	-i , --image	'../../Videos/car_m.mp4'	PyTorch による 入力ソースの物体検出（推論） 複数モデル／日本語表示対応	新規作成
  	-y , --yolov5	'ultralytics/yolov5'
  	-m , --models	:'yolov5s'
  	-ms , --models2	''
  	-l , --labels	:'coco.names_jp'
  yolov5_OV2.py	-i, --input	cam	OpenVINO™ による 入力ソースの物体検出（推論） 日本語表示対応	新規作成
  	-m, --model	yolov5s_v7.xml
  	-d, --device	CPU
  	-l, --label	coco.names_jp
  export.py	--include	必須指定	学習済みモデルのフォーマット変換	YOLOv5 添付
  train.py	--weights	yolov7s.pt	学習プログラム	YOLOv5 添付
  	--cfg
  	--data	data/coco.yaml
  	--epochs	300
  	--batch-size	16
  	--device	(cuda:0)
  	--project	runs/train
  	--name	project/name

• 利用できる 事前トレーニングモデル
  

  Model	size (pixels)	mAPval 50-95	mAPval 50	Speed CPU b1 (ms)	Speed V100 b1 (ms)	Speed V100 b32 (ms)	params (M)	FLOPs @640 (B)
  YOLOv5n	640	28.0	45.7	45	6.3	0.6	1.9	4.5
  YOLOv5s	640	37.4	56.8	98	6.4	0.9	7.2	16.5
  YOLOv5m	640	45.4	64.1	224	8.2	1.7	21.2	49.0
  YOLOv5l	640	49.0	67.3	430	10.1	2.7	46.5	109.1
  YOLOv5x	640	50.7	68.9	766	12.1	4.8	86.7	205.7

  　※ YOLOv5:Pretrained Checkpoints より転載
  

学習（Training） †

• 学習にかかった時間　GPU: GeForce RTX 4070 Ti 12GB
  

  データセット	データ数	yolov5s 学習回数			yolov5x 学習回数
  		30	100	300	100
  交通標識 (Traffic Signs Dataset)	741	2分30秒	7分1秒	20分36秒
  マスク着用 (Mask Wearing Dataset)	853		8分35秒
  じゃんけん (janken Dataset)	600		5分35秒		2時間4分41秒※

  　※テストしたマシンは共有GPUメモリー 32GB で、専用GPUメモリー 12GB を超えても共有サイズまでは動作するが速度は極端に低下する
  　　高速動作のためには 専用GPUメモリーサイズ内で動作させる必要がある
  　　→ ※ GPUメモリ（VRAM）とは？| グラフィックボードのメモリの役割、システムメモリとの違い！【簡単な言葉で解説!!】
  

対処したエラー詳細 †

OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized. †

• エラー内容
  

  (py_learn) python train.py --data data/ts_dataset/ts.yaml --weights yolov5s.pt --img 640 --epochs 30
  train: weights=yolov5s.pt, cfg=, data=data/ts_dataset/ts.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=30, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data\hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
  remote: Enumerating objects: 8, done.
  remote: Counting objects: 100% (6/6), done.
  remote: Compressing objects: 100% (3/3), done.
  remote: Total 8 (delta 3), reused 5 (delta 3), pack-reused 2
  Unpacking objects: 100% (8/8), 3.77 KiB | 226.00 KiB/s, done.
  From https://github.com/ultralytics/yolov5
     db125a20..ae4ef3b2  master     -> origin/master
  github:  YOLOv5 is out of date by 2 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
  YOLOv5  v7.0-294-gdb125a20 Python-3.11.8 torch-2.2.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 12282MiB)
      :
  Plotting labels to runs\train\exp\labels.jpg...
  OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
  OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.

  
• 対処方法
  1. C:\Users\<USER>\anaconda3\envs\py_learn で「libiomp5md.dll」を検索
  

  "C:\Users\<USER>\anaconda3\envs\py_learn\Lib\site-packages\torch\lib\libiomp5md.dll"
  "C:\Users\<USER>\anaconda3\envs\py_learn\Library\bin\libiomp5md.dll"

  2. 2つ目の「libiomp5md.dll」を「temp/」（どこでもよい）へ移動
  
  3. 再度実行する
  

• コメント
  「libiomp5md.dll」が複数存在する場合に起こるらしい
  

• 参考サイト:
  ・https://www.programmersought.com/article/53286415201/
  

libpng warning: iCCP: Not recognizing known sRGB profile that has been edited †

• エラー内容
  

  (py_learn) python train.py --epochs 100 --data data/mask_dataset/mask.yaml --weights yolov5s.pt --name mask_yolov5s
      :
  Starting training for 100 epochs...
  
        Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
         0/99      3.35G     0.1201     0.0557    0.04686        158        640:  12%|█▏        | 5/43 [00:01<00:06,  6.1libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
         0/99      3.35G     0.1187    0.05642     0.0466        156        640:  28%|██▊       | 12/43 [00:01<00:03,  9.libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
         0/99      3.35G     0.1165    0.05713    0.04507        126        640:  40%|███▉      | 17/43 [00:02<00:02,  9.libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
      :

• 対処方法
  1.「ImageMagic」をインストールする
  　画像処理ソフト ImageMagick 7, 動画像処理ソフト FFmpeg のインストール（Windows 上）
  2. インストールの確認
  　適当なフォルダ（temp/）にテスト用画像ファイルを置いてコマンドプロンプトで実行する
  

  > cd temp
  > magick mogrify -identify *.png
  
  image01.png PNG 512x366 512x366+0+0 8-bit TrueColor sRGB 329899B 0.007u 0:00.006
  9mage02.png PNG 301x400 301x400+0+0 8-bit TrueColorAlpha sRGB 181960B 0.006u 0:00.005

  ・Imagemagickで画像の情報を取得する
  
  3.「png」ファイルのプロファイルを削除する
  

  > cd train
  > magick mogrify -strip *.png
  
  > cd train
  > magick mogrify -strip *.png

• 参考サイト:
  ・dvipdfmxで「warning: iCCP: known incorrect sRGB profile」というエラーが出る場合の対処方法
  ・warning: pdflatex＞libpng warning: iCCP: known incorrect sRGB profile (警告: pdflatex libpng 警告: iCCP: 既知の不正な sRGB プロファイル
  ・【ImageMagick】コマンドで画像を一括処理（形式変換・リサイズ）する方法【Windows11対応】
  

• コメント
  「libpngl」の問題だそうで画像ファイル内に付属の プロファイルやその他のメタデータ を削除するとエラーが出なくなる
  

github: ⚠️ YOLOv5 is out of date by 6 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update. †

• エラー内容
  

      :
  github: ⚠️ YOLOv5 is out of date by 6 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.
  YOLOv5  v7.0-297-gd07d0cf6 Python-3.11.7 torch-2.2.0+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti, 11987MiB)
      :

• 対処方法
  

1. リモート（GitHub)の情報を確認する
   

   (py_learn) git remote -v
   origin  https://github.com/ultralytics/yolov5 (fetch)
   origin  https://github.com/ultralytics/yolov5 (push)
   
   (py_learn) git branch -vv
   * master d07d0cf6 [origin/master: behind 6] Create cla.yml (#12899)

2. pull を実行する
   

   (py_learn) git pull
   Updating d07d0cf6..cf8b67b7
   Fast-forward
    .github/workflows/merge-main-into-prs.yml | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
    pyproject.toml                            |  2 +-
    2 files changed, 57 insertions(+), 1 deletion(-)
    create mode 100644 .github/workflows/merge-main-into-prs.yml

• 参考サイト:
  ・gitのremote URLを確認する
  ・【Git&GitHub】リモート（GitHub)の情報を確認する（git remoteコマンド）
  ・git pullとは
  ・Git - リモートの最新情報をローカルに反映する(fetchとpull)
  

更新履歴 †

• 2024/04/05 初版
• 2024/04/18 学習内容追加
   

参考資料 †

• 【復習】物体検出アルゴリズム「YOLO V5」
  

• YOLO V5
  
  • YOLOV5 By Ultralytics
    
  • PyTorch Hub -Ultralytics YOLOv8 ドキュメント
    
  • YOLOv5 -Ultralytics YOLOv8 ドキュメント
    

• 【物体検出】YOLOとは？ PyTorchでYOLOv5を動作させよう！
  
• YOLOv5 で物体検出をしてみよう
  

• YOLO V5 training
  
  • 物体検出，物体検出のための追加学習の実行（YOLOv5，PyTorch，Python を使用）（Windows 上）
    
  • AIを使っての画像認識による物体検出をYOLOv5を使ってやってみた
    
  • YOLOv5を使った物体検出
    
  • YOLOv5の学習時に指定可能なオプションについての解説
    

• Face Mask Detection
  
• 【物体検出2022】YOLOv7まとめ第1回 　マスク着用をリアルタイムに判定
  

• AI を自分好みに調整できる、追加学習まとめ (その１ : 概要)
  
• 転移学習とは？ファインチューニングとの違いや活用例をご紹介
  
• 転移学習とは？AI実装でよく聞くファインチューニングとの違いも紹介
  
• 実装方法から読み解くファインチューニングと転移学習の違いとは
  

• ImageMagick
  
  • 画像処理ソフト ImageMagick 7, 動画像処理ソフト FFmpeg のインストール（Windows 上）
    
  • Imagemagickで画像の情報を取得する
    
  • 【ImageMagick】コマンドで画像を一括処理（形式変換・リサイズ）する方法【Windows11対応】
    

• Dataset
  
  • Traffic Signs Dataset
    
  • Face Mask Detection Dataset
    
  • Mask Wearing Dataset
    

• PascalVOC形式のファイルをYOLO形式に変換
  
   

Site admin: Masahiro Izutsu

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