私的AI研究会 > YOLOv7_Colab6
追加学習において元になる学習モデルの違いによる推論結果の相違を検証してみる。
!python train.py --workers 8 --batch-size 16 --data janken4_dataset.yaml --cfg cfg/training/yolov7.yaml --weights 'yolov7.pt' --name yolov7_custom4_60 --hyp data/hyp.scratch.p5.yaml --epochs 60 --device 0
2023-08-12 04:39:06.464026: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-08-12 04:39:07.386965: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT YOLOR 🚀 v0.1-122-g3b41c2c torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB) Namespace(weights='yolov7.pt', cfg='cfg/training/yolov7.yaml', data='janken4_dataset.yaml', hyp='data/hyp.scratch.p5.yaml', epochs=60, batch_size=16, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='yolov7_custom4_60', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7_custom4_60', total_batch_size=16) tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, 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=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 wandb: Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended) Overriding model.yaml nc=80 with nc=3 from n params module arguments 0 -1 1 928 models.common.Conv [3, 32, 3, 1] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] 12 -1 1 0 models.common.MP [] 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] 16 [-1, -3] 1 0 models.common.Concat [1] 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] 25 -1 1 0 models.common.MP [] 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] 29 [-1, -3] 1 0 models.common.Concat [1] 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] 38 -1 1 0 models.common.MP [] 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] 42 [-1, -3] 1 0 models.common.Concat [1] 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] 55 [-1, -2] 1 0 models.common.Concat [1] 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 66 24 1 65792 models.common.Conv [512, 128, 1, 1] 67 [-1, -2] 1 0 models.common.Concat [1] 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] 76 -1 1 0 models.common.MP [] 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] 80 [-1, -3, 63] 1 0 models.common.Concat [1] 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 89 -1 1 0 models.common.MP [] 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] 93 [-1, -3, 51] 1 0 models.common.Concat [1] 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] 100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] 102 75 1 328704 models.common.RepConv [128, 256, 3, 1] 103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] 104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] 105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]] Model Summary: 415 layers, 37207344 parameters, 37207344 gradients Transferred 552/566 items from yolov7.pt Scaled weight_decay = 0.0005 Optimizer groups: 95 .bias, 95 conv.weight, 98 other train: Scanning 'data/janken4_dataset/train/labels.cache' images and labels... 480 found, 0 missing, 0 empty, 0 corrupted: 100% 480/480 [00:00<?, ?it/s] val: Scanning 'data/janken4_dataset/valid/labels.cache' images and labels... 120 found, 0 missing, 0 empty, 0 corrupted: 100% 120/120 [00:00<?, ?it/s] autoanchor: Analyzing anchors... anchors/target = 3.61, Best Possible Recall (BPR) = 1.0000 Image sizes 640 train, 640 test Using 2 dataloader workers Logging results to runs/train/yolov7_custom4_60 Starting training for 60 epochs... Epoch gpu_mem box obj cls total labels img_size 0/59 12.7G 0.0655 0.01588 0.02114 0.1025 41 640: 100% 30/30 [00:54<00:00, 1.81s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 0% 0/4 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:10<00:00, 2.60s/it] all 120 120 0.0253 0.0833 0.0119 0.00193 Epoch gpu_mem box obj cls total labels img_size 1/59 13.7G 0.05883 0.0134 0.02144 0.09367 52 640: 100% 30/30 [00:36<00:00, 1.22s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.36it/s] all 120 120 0.156 0.2 0.127 0.0331 Epoch gpu_mem box obj cls total labels img_size 2/59 12.2G 0.05407 0.01261 0.02084 0.08752 40 640: 100% 30/30 [00:33<00:00, 1.13s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.87it/s] all 120 120 0.181 0.45 0.209 0.0686 Epoch gpu_mem box obj cls total labels img_size 3/59 12.2G 0.04925 0.0118 0.01848 0.07953 42 640: 100% 30/30 [00:35<00:00, 1.18s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.43it/s] all 120 120 0.224 0.619 0.306 0.105 Epoch gpu_mem box obj cls total labels img_size 4/59 12.2G 0.0409 0.01026 0.0164 0.06756 41 640: 100% 30/30 [00:37<00:00, 1.24s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.54it/s] all 120 120 0.354 0.529 0.366 0.187 Epoch gpu_mem box obj cls total labels img_size 5/59 12.2G 0.04628 0.009895 0.01608 0.07226 45 640: 100% 30/30 [00:33<00:00, 1.12s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.29it/s] all 120 120 0.338 0.675 0.437 0.232 Epoch gpu_mem box obj cls total labels img_size 6/59 12.3G 0.04424 0.009543 0.01654 0.07032 38 640: 100% 30/30 [00:33<00:00, 1.13s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.21it/s] all 120 120 0.323 0.5 0.316 0.152 Epoch gpu_mem box obj cls total labels img_size 7/59 12.3G 0.04503 0.009459 0.01842 0.07291 46 640: 100% 30/30 [00:33<00:00, 1.13s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.58it/s] all 120 120 0.148 0.158 0.0994 0.0219 Epoch gpu_mem box obj cls total labels img_size 8/59 12.3G 0.04467 0.009455 0.01753 0.07165 46 640: 100% 30/30 [00:32<00:00, 1.10s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.57it/s] all 120 120 0.389 0.521 0.386 0.173 Epoch gpu_mem box obj cls total labels img_size 9/59 12.3G 0.03854 0.008963 0.01559 0.06309 47 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.78it/s] all 120 120 0.329 0.553 0.4 0.185 Epoch gpu_mem box obj cls total labels img_size 10/59 12.3G 0.04359 0.009271 0.0179 0.07076 46 640: 100% 30/30 [00:32<00:00, 1.10s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.73it/s] all 120 120 0.463 0.667 0.543 0.284 Epoch gpu_mem box obj cls total labels img_size 11/59 12.3G 0.04083 0.008669 0.01515 0.06465 61 640: 100% 30/30 [00:35<00:00, 1.18s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.71it/s] all 120 120 0.604 0.653 0.677 0.458 Epoch gpu_mem box obj cls total labels img_size 12/59 12.3G 0.03363 0.008123 0.01301 0.05477 39 640: 100% 30/30 [00:34<00:00, 1.16s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.65it/s] all 120 120 0.536 0.758 0.659 0.381 Epoch gpu_mem box obj cls total labels img_size 13/59 12.3G 0.03971 0.008277 0.0134 0.06138 49 640: 100% 30/30 [00:32<00:00, 1.07s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.62it/s] all 120 120 0.559 0.874 0.758 0.418 Epoch gpu_mem box obj cls total labels img_size 14/59 12.3G 0.0343 0.007569 0.01243 0.05429 34 640: 100% 30/30 [00:35<00:00, 1.17s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.62it/s] all 120 120 0.361 0.507 0.435 0.246 Epoch gpu_mem box obj cls total labels img_size 15/59 12.3G 0.04379 0.007736 0.01517 0.0667 38 640: 100% 30/30 [00:32<00:00, 1.08s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.09it/s] all 120 120 0.577 0.851 0.728 0.434 Epoch gpu_mem box obj cls total labels img_size 16/59 12.3G 0.03496 0.007798 0.01282 0.05558 54 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.45it/s] all 120 120 0.621 0.85 0.783 0.496 Epoch gpu_mem box obj cls total labels img_size 17/59 12.3G 0.03472 0.00784 0.01268 0.05524 51 640: 100% 30/30 [00:35<00:00, 1.18s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 2.00it/s] all 120 120 0.563 0.824 0.755 0.378 Epoch gpu_mem box obj cls total labels img_size 18/59 12.3G 0.03389 0.007578 0.01125 0.05272 46 640: 100% 30/30 [00:33<00:00, 1.11s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.47it/s] all 120 120 0.696 0.85 0.847 0.597 Epoch gpu_mem box obj cls total labels img_size 19/59 12.3G 0.03135 0.007049 0.01091 0.04931 41 640: 100% 30/30 [00:32<00:00, 1.07s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.80it/s] all 120 120 0.0354 0.025 0.0137 0.00365 Epoch gpu_mem box obj cls total labels img_size 20/59 12.3G 0.03758 0.007605 0.01135 0.05653 37 640: 100% 30/30 [00:34<00:00, 1.15s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.77it/s] all 120 120 0.127 0.308 0.128 0.0368 Epoch gpu_mem box obj cls total labels img_size 21/59 12.3G 0.03337 0.008026 0.01194 0.05333 66 640: 100% 30/30 [00:32<00:00, 1.07s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.16it/s] all 120 120 0.6 0.817 0.779 0.426 Epoch gpu_mem box obj cls total labels img_size 22/59 12.3G 0.02771 0.007421 0.009318 0.04445 57 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.61it/s] all 120 120 0.792 0.917 0.957 0.587 Epoch gpu_mem box obj cls total labels img_size 23/59 12.3G 0.03711 0.007451 0.011 0.05556 36 640: 100% 30/30 [00:33<00:00, 1.12s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.63it/s] all 120 120 0.883 0.838 0.931 0.611 Epoch gpu_mem box obj cls total labels img_size 24/59 12.3G 0.03473 0.008045 0.01299 0.05577 29 640: 100% 30/30 [00:34<00:00, 1.16s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.36it/s] all 120 120 0.431 0.432 0.398 0.18 Epoch gpu_mem box obj cls total labels img_size 25/59 12.3G 0.03577 0.00824 0.01158 0.05559 39 640: 100% 30/30 [00:32<00:00, 1.08s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.53it/s] all 120 120 0.662 0.47 0.559 0.32 Epoch gpu_mem box obj cls total labels img_size 26/59 12.3G 0.03351 0.007618 0.01082 0.05196 44 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.87it/s] all 120 120 0.933 0.833 0.932 0.594 Epoch gpu_mem box obj cls total labels img_size 27/59 12.3G 0.03254 0.007645 0.01069 0.05088 43 640: 100% 30/30 [00:33<00:00, 1.11s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.43it/s] all 120 120 0.848 0.868 0.895 0.494 Epoch gpu_mem box obj cls total labels img_size 28/59 12.3G 0.03056 0.007958 0.009669 0.04819 38 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.66it/s] all 120 120 0.765 0.738 0.83 0.463 Epoch gpu_mem box obj cls total labels img_size 29/59 12.3G 0.03073 0.007189 0.008787 0.04671 48 640: 100% 30/30 [00:31<00:00, 1.06s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.12it/s] all 120 120 0.727 0.775 0.865 0.488 Epoch gpu_mem box obj cls total labels img_size 30/59 12.3G 0.0359 0.008134 0.01183 0.05587 40 640: 100% 30/30 [00:31<00:00, 1.06s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.12it/s] all 120 0 0 0 0 0 Epoch gpu_mem box obj cls total labels img_size 31/59 12.3G 0.05076 0.009205 0.01713 0.07709 47 640: 100% 30/30 [00:33<00:00, 1.10s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.44it/s] all 120 120 8.7e-05 0.00833 2.39e-06 2.39e-07 Epoch gpu_mem box obj cls total labels img_size 32/59 12.3G 0.04147 0.009063 0.01496 0.06549 35 640: 100% 30/30 [00:34<00:00, 1.14s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.17it/s] all 120 120 0.289 0.525 0.366 0.154 Epoch gpu_mem box obj cls total labels img_size 33/59 12.3G 0.03584 0.008767 0.01396 0.05857 51 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.53it/s] all 120 120 0.663 0.547 0.539 0.352 Epoch gpu_mem box obj cls total labels img_size 34/59 12.3G 0.02592 0.008929 0.01188 0.04673 54 640: 100% 30/30 [00:35<00:00, 1.17s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.52it/s] all 120 120 0.379 0.2 0.0352 0.0119 Epoch gpu_mem box obj cls total labels img_size 35/59 12.3G 0.03416 0.008859 0.01416 0.05719 33 640: 100% 30/30 [00:34<00:00, 1.15s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.50it/s] all 120 120 0.0413 0.192 0.0302 0.00566 Epoch gpu_mem box obj cls total labels img_size 36/59 12.3G 0.0344 0.008209 0.01296 0.05557 47 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.74it/s] all 120 120 0.374 0.167 0.0193 0.00409 Epoch gpu_mem box obj cls total labels img_size 37/59 12.3G 0.03576 0.008361 0.01207 0.05619 34 640: 100% 30/30 [00:35<00:00, 1.19s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.60it/s] all 120 120 0.75 0.824 0.866 0.504 Epoch gpu_mem box obj cls total labels img_size 38/59 12.3G 0.03391 0.008713 0.01222 0.05484 47 640: 100% 30/30 [00:33<00:00, 1.12s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.47it/s] all 120 120 0.926 0.699 0.858 0.509 Epoch gpu_mem box obj cls total labels img_size 39/59 12.3G 0.03165 0.008483 0.01224 0.05237 51 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.03it/s] all 120 120 0.837 0.821 0.888 0.521 Epoch gpu_mem box obj cls total labels img_size 40/59 12.3G 0.03651 0.008153 0.01233 0.05699 50 640: 100% 30/30 [00:34<00:00, 1.13s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.61it/s] all 120 120 0.964 0.791 0.931 0.627 Epoch gpu_mem box obj cls total labels img_size 41/59 12.3G 0.03448 0.008189 0.01283 0.0555 43 640: 100% 30/30 [00:33<00:00, 1.13s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.62it/s] all 120 120 0.889 0.833 0.906 0.552 Epoch gpu_mem box obj cls total labels img_size 42/59 12.3G 0.03436 0.007916 0.0121 0.05438 54 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.35it/s] all 120 120 0.96 0.882 0.953 0.627 Epoch gpu_mem box obj cls total labels img_size 43/59 12.3G 0.03836 0.007986 0.01204 0.05839 56 640: 100% 30/30 [00:36<00:00, 1.22s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.12it/s] all 120 120 0.923 0.898 0.957 0.614 Epoch gpu_mem box obj cls total labels img_size 44/59 12.3G 0.03659 0.007802 0.0124 0.05679 32 640: 100% 30/30 [00:33<00:00, 1.10s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.08it/s] all 120 120 0.934 0.917 0.946 0.634 Epoch gpu_mem box obj cls total labels img_size 45/59 12.3G 0.03852 0.007942 0.01252 0.05898 50 640: 100% 30/30 [00:36<00:00, 1.21s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.25it/s] all 120 120 0.963 0.899 0.96 0.67 Epoch gpu_mem box obj cls total labels img_size 46/59 12.3G 0.02613 0.007814 0.009601 0.04354 28 640: 100% 30/30 [00:34<00:00, 1.15s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.08it/s] all 120 120 0.89 0.925 0.953 0.65 Epoch gpu_mem box obj cls total labels img_size 47/59 12.3G 0.02763 0.007562 0.00991 0.0451 35 640: 100% 30/30 [00:32<00:00, 1.07s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.46it/s] all 120 120 0.937 0.841 0.954 0.643 Epoch gpu_mem box obj cls total labels img_size 48/59 12.3G 0.03783 0.007672 0.01187 0.05738 45 640: 100% 30/30 [00:33<00:00, 1.12s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.72it/s] all 120 120 0.953 0.888 0.962 0.669 Epoch gpu_mem box obj cls total labels img_size 49/59 12.3G 0.03591 0.007994 0.01135 0.05525 53 640: 100% 30/30 [00:36<00:00, 1.20s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.74it/s] all 120 120 0.956 0.933 0.96 0.684 Epoch gpu_mem box obj cls total labels img_size 50/59 12.3G 0.03541 0.007798 0.01175 0.05496 51 640: 100% 30/30 [00:37<00:00, 1.26s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.49it/s] all 120 120 0.931 0.958 0.971 0.708 Epoch gpu_mem box obj cls total labels img_size 51/59 12.3G 0.03112 0.007489 0.009969 0.04858 51 640: 100% 30/30 [00:33<00:00, 1.13s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.53it/s] all 120 120 0.911 0.917 0.95 0.664 Epoch gpu_mem box obj cls total labels img_size 52/59 12.3G 0.02915 0.007615 0.008901 0.04567 51 640: 100% 30/30 [00:34<00:00, 1.14s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.59it/s] all 120 120 0.908 0.95 0.963 0.669 Epoch gpu_mem box obj cls total labels img_size 53/59 12.3G 0.03253 0.007544 0.01018 0.05025 56 640: 100% 30/30 [00:34<00:00, 1.16s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.14it/s] all 120 120 0.928 0.949 0.967 0.673 Epoch gpu_mem box obj cls total labels img_size 54/59 12.3G 0.03003 0.007436 0.01009 0.04757 56 640: 100% 30/30 [00:32<00:00, 1.09s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.50it/s] all 120 120 0.922 0.917 0.955 0.665 Epoch gpu_mem box obj cls total labels img_size 55/59 12.3G 0.02957 0.007782 0.01087 0.04822 59 640: 100% 30/30 [00:35<00:00, 1.18s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.68it/s] all 120 120 0.912 0.946 0.955 0.675 Epoch gpu_mem box obj cls total labels img_size 56/59 12.3G 0.03473 0.007387 0.01116 0.05328 46 640: 100% 30/30 [00:36<00:00, 1.22s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.63it/s] all 120 120 0.947 0.956 0.967 0.703 Epoch gpu_mem box obj cls total labels img_size 57/59 12.3G 0.03051 0.007138 0.00989 0.04754 44 640: 100% 30/30 [00:36<00:00, 1.21s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.52it/s] all 120 120 0.97 0.956 0.973 0.719 Epoch gpu_mem box obj cls total labels img_size 58/59 12.3G 0.03035 0.007344 0.01042 0.04812 47 640: 100% 30/30 [00:35<00:00, 1.18s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.52it/s] all 120 120 0.973 0.938 0.973 0.714 Epoch gpu_mem box obj cls total labels img_size 59/59 12.3G 0.03188 0.007449 0.01055 0.04988 49 640: 100% 30/30 [00:35<00:00, 1.18s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:04<00:00, 1.10s/it] all 120 120 0.935 0.967 0.979 0.698 goo 120 40 0.952 1 0.995 0.663 choki 120 40 0.926 0.95 0.962 0.645 par 120 40 0.927 0.95 0.981 0.784 60 epochs completed in 0.674 hours. Optimizer stripped from runs/train/yolov7_custom4_60/weights/last.pt, 74.8MB Optimizer stripped from runs/train/yolov7_custom4_60/weights/best.pt, 74.8MB
!python train.py --workers 8 --batch-size 16 --data janken3_dataset.yaml --cfg cfg/training/yolov7.yaml --weights 'yolov7.pt' --name yolov7_custom3_60 --hyp data/hyp.scratch.p5.yaml --epochs 60 --device 0
2023-08-12 05:22:20.932544: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-08-12 05:22:22.698835: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT YOLOR 🚀 v0.1-122-g3b41c2c torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB) Namespace(weights='yolov7.pt', cfg='cfg/training/yolov7.yaml', data='janken3_dataset.yaml', hyp='data/hyp.scratch.p5.yaml', epochs=60, batch_size=16, img_size=[640, 640], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='yolov7_custom3_60', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7_custom3_60', total_batch_size=16) tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, 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=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 wandb: Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended) Overriding model.yaml nc=80 with nc=3 from n params module arguments 0 -1 1 928 models.common.Conv [3, 32, 3, 1] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] 12 -1 1 0 models.common.MP [] 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] 16 [-1, -3] 1 0 models.common.Concat [1] 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] 25 -1 1 0 models.common.MP [] 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] 29 [-1, -3] 1 0 models.common.Concat [1] 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] 38 -1 1 0 models.common.MP [] 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] 42 [-1, -3] 1 0 models.common.Concat [1] 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] 55 [-1, -2] 1 0 models.common.Concat [1] 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 66 24 1 65792 models.common.Conv [512, 128, 1, 1] 67 [-1, -2] 1 0 models.common.Concat [1] 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] 76 -1 1 0 models.common.MP [] 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] 80 [-1, -3, 63] 1 0 models.common.Concat [1] 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 89 -1 1 0 models.common.MP [] 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] 93 [-1, -3, 51] 1 0 models.common.Concat [1] 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] 100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] 102 75 1 328704 models.common.RepConv [128, 256, 3, 1] 103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] 104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] 105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]] Model Summary: 415 layers, 37207344 parameters, 37207344 gradients Transferred 552/566 items from yolov7.pt Scaled weight_decay = 0.0005 Optimizer groups: 95 .bias, 95 conv.weight, 98 other train: Scanning 'data/janken3_dataset/train/labels.cache' images and labels... 480 found, 0 missing, 0 empty, 0 corrupted: 100% 480/480 [00:00<?, ?it/s] val: Scanning 'data/janken3_dataset/valid/labels.cache' images and labels... 120 found, 0 missing, 0 empty, 0 corrupted: 100% 120/120 [00:00<?, ?it/s] autoanchor: Analyzing anchors... anchors/target = 3.23, Best Possible Recall (BPR) = 1.0000 Image sizes 640 train, 640 test Using 2 dataloader workers Logging results to runs/train/yolov7_custom3_60 Starting training for 60 epochs... Epoch gpu_mem box obj cls total labels img_size 0/59 12.6G 0.06392 0.01608 0.02108 0.1011 41 640: 100% 30/30 [00:53<00:00, 1.79s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 0% 0/4 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:10<00:00, 2.54s/it] all 120 120 0.0135 0.2 0.00994 0.00177 Epoch gpu_mem box obj cls total labels img_size 1/59 13.7G 0.05595 0.01398 0.02087 0.09079 55 640: 100% 30/30 [00:33<00:00, 1.12s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.75it/s] all 120 120 0.0545 0.258 0.0456 0.0105 Epoch gpu_mem box obj cls total labels img_size 2/59 11.6G 0.05177 0.01314 0.01907 0.08398 44 640: 100% 30/30 [00:31<00:00, 1.04s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.57it/s] all 120 120 0.1 0.525 0.108 0.031 Epoch gpu_mem box obj cls total labels img_size 3/59 11.6G 0.04776 0.01193 0.01787 0.07757 44 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.02it/s] all 120 120 0.209 0.483 0.253 0.0867 Epoch gpu_mem box obj cls total labels img_size 4/59 11.6G 0.04004 0.01061 0.01551 0.06617 46 640: 100% 30/30 [00:33<00:00, 1.12s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.41it/s] all 120 120 0.318 0.665 0.312 0.163 Epoch gpu_mem box obj cls total labels img_size 5/59 11.6G 0.04804 0.01025 0.01631 0.0746 51 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.33it/s] all 120 120 0.294 0.642 0.339 0.112 Epoch gpu_mem box obj cls total labels img_size 6/59 11.6G 0.04171 0.01043 0.01615 0.06829 43 640: 100% 30/30 [00:30<00:00, 1.01s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.29it/s] all 120 120 0.43 0.631 0.51 0.265 Epoch gpu_mem box obj cls total labels img_size 7/59 11.6G 0.04038 0.01014 0.01685 0.06737 46 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.50it/s] all 120 120 0.188 0.292 0.166 0.0383 Epoch gpu_mem box obj cls total labels img_size 8/59 11.6G 0.04188 0.0104 0.017 0.06928 50 640: 100% 30/30 [00:31<00:00, 1.06s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.03it/s] all 120 120 0.547 0.64 0.571 0.367 Epoch gpu_mem box obj cls total labels img_size 9/59 11.6G 0.03696 0.009458 0.0157 0.06211 57 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.35it/s] all 120 120 0.56 0.751 0.629 0.346 Epoch gpu_mem box obj cls total labels img_size 10/59 11.6G 0.04637 0.0103 0.01737 0.07403 53 640: 100% 30/30 [00:30<00:00, 1.01s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.56it/s] all 120 120 0.439 0.699 0.522 0.268 Epoch gpu_mem box obj cls total labels img_size 11/59 11.6G 0.04121 0.009586 0.0154 0.06619 66 640: 100% 30/30 [00:30<00:00, 1.01s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.33it/s] all 120 120 0.634 0.696 0.668 0.429 Epoch gpu_mem box obj cls total labels img_size 12/59 11.6G 0.03737 0.009223 0.01519 0.06179 42 640: 100% 30/30 [00:31<00:00, 1.04s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.07it/s] all 120 120 0.448 0.649 0.53 0.342 Epoch gpu_mem box obj cls total labels img_size 13/59 11.6G 0.03515 0.009031 0.01313 0.05731 47 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.73it/s] all 120 120 0.501 0.683 0.657 0.396 Epoch gpu_mem box obj cls total labels img_size 14/59 11.6G 0.03449 0.008316 0.01299 0.0558 39 640: 100% 30/30 [00:30<00:00, 1.01s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.80it/s] all 120 120 0.63 0.89 0.729 0.466 Epoch gpu_mem box obj cls total labels img_size 15/59 11.6G 0.04258 0.008812 0.01531 0.0667 41 640: 100% 30/30 [00:31<00:00, 1.04s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.16it/s] all 120 120 0.102 0.607 0.103 0.0211 Epoch gpu_mem box obj cls total labels img_size 16/59 11.6G 0.03593 0.009303 0.01429 0.05952 60 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.28it/s] all 120 120 0.318 0.45 0.369 0.214 Epoch gpu_mem box obj cls total labels img_size 17/59 11.6G 0.04171 0.00861 0.01399 0.06432 49 640: 100% 30/30 [00:29<00:00, 1.01it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.54it/s] all 120 120 0.572 0.697 0.631 0.399 Epoch gpu_mem box obj cls total labels img_size 18/59 12.7G 0.03829 0.008912 0.01394 0.06114 48 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.20it/s] all 120 120 0.61 0.0417 0.0585 0.0207 Epoch gpu_mem box obj cls total labels img_size 19/59 12.7G 0.0433 0.00849 0.01423 0.06602 46 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.22it/s] all 120 120 0.557 0.767 0.648 0.406 Epoch gpu_mem box obj cls total labels img_size 20/59 12.7G 0.03898 0.008777 0.01423 0.06199 41 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.76it/s] all 120 120 0.66 0.883 0.78 0.533 Epoch gpu_mem box obj cls total labels img_size 21/59 12.7G 0.03935 0.008717 0.01359 0.06165 74 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.91it/s] all 120 120 0.666 0.908 0.802 0.569 Epoch gpu_mem box obj cls total labels img_size 22/59 12.7G 0.03176 0.008584 0.01214 0.05249 59 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.13it/s] all 120 120 0.269 0.458 0.295 0.15 Epoch gpu_mem box obj cls total labels img_size 23/59 12.7G 0.03309 0.008728 0.0126 0.05442 43 640: 100% 30/30 [00:32<00:00, 1.08s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.50it/s] all 120 120 0.683 0.632 0.672 0.411 Epoch gpu_mem box obj cls total labels img_size 24/59 12.7G 0.04358 0.00911 0.01483 0.06752 34 640: 100% 30/30 [00:29<00:00, 1.00it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.80it/s] all 120 120 0.503 0.736 0.608 0.355 Epoch gpu_mem box obj cls total labels img_size 25/59 12.7G 0.0357 0.009229 0.01314 0.05807 39 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.30it/s] all 120 120 0.446 0.283 0.123 0.0195 Epoch gpu_mem box obj cls total labels img_size 26/59 12.7G 0.03058 0.008919 0.01234 0.05184 54 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.17it/s] all 120 120 0.604 0.85 0.808 0.62 Epoch gpu_mem box obj cls total labels img_size 27/59 12.7G 0.03078 0.009411 0.01305 0.05324 42 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.99it/s] all 120 120 0.766 0.478 0.662 0.316 Epoch gpu_mem box obj cls total labels img_size 28/59 12.7G 0.02816 0.009119 0.01116 0.04843 44 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.92it/s] all 120 120 0.889 0.808 0.904 0.684 Epoch gpu_mem box obj cls total labels img_size 29/59 12.7G 0.02871 0.00854 0.01069 0.04793 49 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.74it/s] all 120 120 0.853 0.882 0.933 0.679 Epoch gpu_mem box obj cls total labels img_size 30/59 12.7G 0.03509 0.008991 0.01122 0.0553 42 640: 100% 30/30 [00:31<00:00, 1.07s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.84it/s] all 120 120 0.935 0.843 0.941 0.711 Epoch gpu_mem box obj cls total labels img_size 31/59 12.7G 0.02851 0.008321 0.01069 0.04752 47 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.17it/s] all 120 120 0.825 0.836 0.915 0.677 Epoch gpu_mem box obj cls total labels img_size 32/59 12.7G 0.03077 0.008652 0.01101 0.05043 39 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.58it/s] all 120 120 0.929 0.883 0.966 0.704 Epoch gpu_mem box obj cls total labels img_size 33/59 12.7G 0.02479 0.008295 0.009527 0.04261 54 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.40it/s] all 120 120 0.997 0.939 0.978 0.731 Epoch gpu_mem box obj cls total labels img_size 34/59 12.7G 0.03003 0.008686 0.01057 0.04928 57 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.52it/s] all 120 120 0.882 0.9 0.968 0.748 Epoch gpu_mem box obj cls total labels img_size 35/59 12.7G 0.03529 0.00881 0.01252 0.05661 37 640: 100% 30/30 [00:31<00:00, 1.04s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.27it/s] all 120 120 0.92 0.958 0.972 0.737 Epoch gpu_mem box obj cls total labels img_size 36/59 12.7G 0.02979 0.008072 0.01026 0.04813 51 640: 100% 30/30 [00:29<00:00, 1.01it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.84it/s] all 120 120 0.941 0.889 0.965 0.721 Epoch gpu_mem box obj cls total labels img_size 37/59 12.7G 0.02517 0.008415 0.009834 0.04342 35 640: 100% 30/30 [00:31<00:00, 1.04s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.29it/s] all 120 120 0.91 0.958 0.979 0.767 Epoch gpu_mem box obj cls total labels img_size 38/59 12.7G 0.02472 0.008819 0.01013 0.04367 51 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.13it/s] all 120 120 0.403 0.474 0.44 0.311 Epoch gpu_mem box obj cls total labels img_size 39/59 12.7G 0.02786 0.008483 0.01069 0.04704 57 640: 100% 30/30 [00:29<00:00, 1.03it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.15it/s] all 120 120 0.687 0.717 0.834 0.592 Epoch gpu_mem box obj cls total labels img_size 40/59 12.7G 0.03067 0.008423 0.01033 0.04943 51 640: 100% 30/30 [00:31<00:00, 1.04s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.24it/s] all 120 120 0.874 0.83 0.91 0.702 Epoch gpu_mem box obj cls total labels img_size 41/59 12.7G 0.02497 0.008397 0.009489 0.04285 42 640: 100% 30/30 [00:29<00:00, 1.03it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.91it/s] all 120 120 0.939 0.942 0.973 0.702 Epoch gpu_mem box obj cls total labels img_size 42/59 12.7G 0.0359 0.008098 0.01275 0.05675 60 640: 100% 30/30 [00:28<00:00, 1.03it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.02it/s] all 120 120 0.927 0.926 0.982 0.785 Epoch gpu_mem box obj cls total labels img_size 43/59 12.7G 0.03463 0.008174 0.01061 0.05342 55 640: 100% 30/30 [00:30<00:00, 1.03s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.95it/s] all 120 120 0.983 0.951 0.981 0.79 Epoch gpu_mem box obj cls total labels img_size 44/59 12.7G 0.03099 0.008286 0.01069 0.04996 37 640: 100% 30/30 [00:31<00:00, 1.07s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.91it/s] all 120 120 0.897 0.933 0.954 0.703 Epoch gpu_mem box obj cls total labels img_size 45/59 12.7G 0.03582 0.008721 0.01145 0.05599 51 640: 100% 30/30 [00:30<00:00, 1.00s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.00it/s] all 120 120 0.932 0.813 0.909 0.616 Epoch gpu_mem box obj cls total labels img_size 46/59 12.7G 0.02606 0.008103 0.009442 0.0436 34 640: 100% 30/30 [00:29<00:00, 1.02it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.16it/s] all 120 120 0.952 0.942 0.98 0.788 Epoch gpu_mem box obj cls total labels img_size 47/59 12.7G 0.03226 0.007976 0.01042 0.05066 42 640: 100% 30/30 [00:29<00:00, 1.03it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.70it/s] all 120 120 0.983 0.967 0.99 0.79 Epoch gpu_mem box obj cls total labels img_size 48/59 12.7G 0.03126 0.00772 0.009925 0.04891 49 640: 100% 30/30 [00:32<00:00, 1.08s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.18it/s] all 120 120 0.934 0.975 0.984 0.791 Epoch gpu_mem box obj cls total labels img_size 49/59 12.7G 0.03024 0.008196 0.009821 0.04826 58 640: 100% 30/30 [00:29<00:00, 1.00it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.33it/s] all 120 120 0.767 0.821 0.871 0.534 Epoch gpu_mem box obj cls total labels img_size 50/59 12.7G 0.03229 0.008107 0.01037 0.05077 50 640: 100% 30/30 [00:30<00:00, 1.01s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.05it/s] all 120 120 0.945 0.939 0.987 0.799 Epoch gpu_mem box obj cls total labels img_size 51/59 12.7G 0.03327 0.008078 0.01024 0.05159 57 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.62it/s] all 120 120 0.959 0.964 0.991 0.792 Epoch gpu_mem box obj cls total labels img_size 52/59 12.7G 0.0292 0.008027 0.009851 0.04708 56 640: 100% 30/30 [00:29<00:00, 1.03it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.78it/s] all 120 120 0.973 0.932 0.992 0.814 Epoch gpu_mem box obj cls total labels img_size 53/59 12.7G 0.02977 0.008009 0.009735 0.04751 61 640: 100% 30/30 [00:30<00:00, 1.01s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.66it/s] all 120 120 0.945 0.767 0.925 0.652 Epoch gpu_mem box obj cls total labels img_size 54/59 12.7G 0.02548 0.008127 0.00901 0.04261 60 640: 100% 30/30 [00:29<00:00, 1.03it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.55it/s] all 120 120 0.973 0.942 0.984 0.8 Epoch gpu_mem box obj cls total labels img_size 55/59 12.7G 0.02712 0.007881 0.008697 0.0437 61 640: 100% 30/30 [00:31<00:00, 1.06s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.96it/s] all 120 120 0.975 0.961 0.99 0.811 Epoch gpu_mem box obj cls total labels img_size 56/59 12.7G 0.03204 0.007758 0.009953 0.04975 50 640: 100% 30/30 [00:30<00:00, 1.02s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.53it/s] all 120 120 0.981 0.991 0.994 0.816 Epoch gpu_mem box obj cls total labels img_size 57/59 12.7G 0.02626 0.007599 0.009218 0.04308 51 640: 100% 30/30 [00:31<00:00, 1.05s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 2.27it/s] all 120 120 0.966 0.986 0.993 0.821 Epoch gpu_mem box obj cls total labels img_size 58/59 12.7G 0.02802 0.00765 0.009315 0.04499 48 640: 100% 30/30 [00:33<00:00, 1.10s/it] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.58it/s] all 120 120 0.982 0.99 0.994 0.82 Epoch gpu_mem box obj cls total labels img_size 59/59 12.7G 0.02941 0.007589 0.009346 0.04634 49 640: 100% 30/30 [00:29<00:00, 1.00it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.32it/s] all 120 120 0.967 0.99 0.993 0.808 goo 120 40 0.975 0.994 0.994 0.809 choki 120 40 0.952 1 0.993 0.781 par 120 40 0.973 0.975 0.993 0.833 60 epochs completed in 0.619 hours. Optimizer stripped from runs/train/yolov7_custom3_60/weights/last.pt, 74.8MB Optimizer stripped from runs/train/yolov7_custom3_60/weights/best.pt, 74.8MB
yolov7_custom4_60 | yolov7x_custom4_60 | yolov7_custom3_60 | yolov7x_custom3_60 | |
janken3 .jpg | ||||
janken2 .jpg | ||||
janken .jpg | ||||
janken _test .mp4 | ||||
janken _test2 .mp4 | ||||
実行速度 fps※1 | 2.36 | 1.44 | 2.48 | 1.44 |
実行速度 fps※2 | 7.06 | 4.62 | 7.42 | 4.62 |
実行速度 fps※3 | 5.20 | 3.30 | 5.73 | 3.62 |
実行速度 fps※4 | 2.26 | 1.30 | 2.20 | 1.26 |
※ | GPU | CPU | 実行マシン |
1 | Intel® HD Graphics 530 | Intel® Core™ i7-6700 | HP EliteDesk800 G2 SFF (第6世代 Core™ i7 CPU搭載 デスクトップ) |
2 | Intel® Iris® Xe Graphics | Intel® Core™ i7-1260P | DELL XPS Plus 9320 (第12世代 Core™ i7 CPU搭載 ノート) |
3 | Intel® Iris® Xe Graphics | Intel® Core™ i7-1185G7 | DELL Latitude 7520 (第11世代 Core™ i7 CPU搭載 ノート) |
4 | Intel® UHD Graphics | Intel® Core™ i5-10210U | Intel® NUC BXNUC10I5FNH (第10世代 Core™ i5 CPU搭載 ミニPC) |
(py38a) PS > cd /anaconda_win/work/yolov7・学習済みモデル「yolov7_custom4_60/weights/best.onnx」
入力ソース | コマンド |
カメラ画像 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom4_60/weights/best.onnx -l janken.names_jp -i cam -d GPU |
janken3.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken3.jpg -d GPU |
janken2.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken2.jpg -d GPU |
janken.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken.jpg -d GPU |
janken_test.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test.mp4 -d GPU |
janken_test2.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test2.mp4 -d GPU |
入力ソース | コマンド |
カメラ画像 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom4_60/weights/best.onnx -l janken.names_jp -i cam -d GPU |
janken3.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken3.jpg -d GPU |
janken2.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken2.jpg -d GPU |
janken.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken.jpg -d GPU |
janken_test.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test.mp4 -d GPU |
janken_test2.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom4_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test2.mp4 -d GPU |
入力ソース | コマンド |
カメラ画像 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom3_60/weights/best.onnx -l janken.names_jp -i cam -d GPU |
janken3.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken3.jpg -d GPU |
janken2.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken2.jpg -d GPU |
janken.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken.jpg -d GPU |
janken_test.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test.mp4 -d GPU |
janken_test2.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test2.mp4 -d GPU |
入力ソース | コマンド |
カメラ画像 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom3_60/weights/best.onnx -l janken.names_jp -i cam -d GPU |
janken3.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken3.jpg -d GPU |
janken2.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken2.jpg -d GPU |
janken.jpg | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Images/janken.jpg -d GPU |
janken_test.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test.mp4 -d GPU |
janken_test2.mp4 | python object_detect_yolo7.py -m ../yolov7-main/runs/train/yolov7x_custom3_60/weights/best.onnx -l janken.names_jp -i ../../Videos/janken_test2.mp4 -d GPU |
matching_matrix = torch.zeros_like(cost, device=device) → matching_matrix = torch.zeros_like(cost, device="cpu") matching_matrix = torch.zeros_like(cost) → matching_matrix = torch.zeros_like(cost, device="cpu")
!python train_aux.py --workers 8 --batch-size 4 --data janken4_dataset.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights 'yolov7-w6.pt' --name yolov7-w6_custom4_60 --hyp data/hyp.scratch.p6.yaml --epochs 60 --device 0
2023-08-12 10:42:36.275029: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-08-12 10:42:37.465947: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT YOLOR 🚀 v0.1-122-g3b41c2c torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB) Namespace(weights='yolov7-w6.pt', cfg='cfg/training/yolov7-w6.yaml', data='janken4_dataset.yaml', hyp='data/hyp.scratch.p6.yaml', epochs=60, batch_size=4, img_size=[1280, 1280], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='yolov7-w6_custom4_60', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7-w6_custom4_604', total_batch_size=4) tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, 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=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 wandb: Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended) Overriding model.yaml nc=80 with nc=3 from n params module arguments 0 -1 1 0 models.common.ReOrg [] 1 -1 1 7040 models.common.Conv [12, 64, 3, 1] 2 -1 1 73984 models.common.Conv [64, 128, 3, 2] 3 -1 1 8320 models.common.Conv [128, 64, 1, 1] 4 -2 1 8320 models.common.Conv [128, 64, 1, 1] 5 -1 1 36992 models.common.Conv [64, 64, 3, 1] 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] 9 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 10 -1 1 33024 models.common.Conv [256, 128, 1, 1] 11 -1 1 295424 models.common.Conv [128, 256, 3, 2] 12 -1 1 33024 models.common.Conv [256, 128, 1, 1] 13 -2 1 33024 models.common.Conv [256, 128, 1, 1] 14 -1 1 147712 models.common.Conv [128, 128, 3, 1] 15 -1 1 147712 models.common.Conv [128, 128, 3, 1] 16 -1 1 147712 models.common.Conv [128, 128, 3, 1] 17 -1 1 147712 models.common.Conv [128, 128, 3, 1] 18 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 19 -1 1 131584 models.common.Conv [512, 256, 1, 1] 20 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 21 -1 1 131584 models.common.Conv [512, 256, 1, 1] 22 -2 1 131584 models.common.Conv [512, 256, 1, 1] 23 -1 1 590336 models.common.Conv [256, 256, 3, 1] 24 -1 1 590336 models.common.Conv [256, 256, 3, 1] 25 -1 1 590336 models.common.Conv [256, 256, 3, 1] 26 -1 1 590336 models.common.Conv [256, 256, 3, 1] 27 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 28 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 29 -1 1 3540480 models.common.Conv [512, 768, 3, 2] 30 -1 1 295680 models.common.Conv [768, 384, 1, 1] 31 -2 1 295680 models.common.Conv [768, 384, 1, 1] 32 -1 1 1327872 models.common.Conv [384, 384, 3, 1] 33 -1 1 1327872 models.common.Conv [384, 384, 3, 1] 34 -1 1 1327872 models.common.Conv [384, 384, 3, 1] 35 -1 1 1327872 models.common.Conv [384, 384, 3, 1] 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 37 -1 1 1181184 models.common.Conv [1536, 768, 1, 1] 38 -1 1 7079936 models.common.Conv [768, 1024, 3, 2] 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 40 -2 1 525312 models.common.Conv [1024, 512, 1, 1] 41 -1 1 2360320 models.common.Conv [512, 512, 3, 1] 42 -1 1 2360320 models.common.Conv [512, 512, 3, 1] 43 -1 1 2360320 models.common.Conv [512, 512, 3, 1] 44 -1 1 2360320 models.common.Conv [512, 512, 3, 1] 45 [-1, -3, -5, -6] 1 0 models.common.Concat [1] 46 -1 1 2099200 models.common.Conv [2048, 1024, 1, 1] 47 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] 48 -1 1 197376 models.common.Conv [512, 384, 1, 1] 49 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 50 37 1 295680 models.common.Conv [768, 384, 1, 1] 51 [-1, -2] 1 0 models.common.Concat [1] 52 -1 1 295680 models.common.Conv [768, 384, 1, 1] 53 -2 1 295680 models.common.Conv [768, 384, 1, 1] 54 -1 1 663936 models.common.Conv [384, 192, 3, 1] 55 -1 1 332160 models.common.Conv [192, 192, 3, 1] 56 -1 1 332160 models.common.Conv [192, 192, 3, 1] 57 -1 1 332160 models.common.Conv [192, 192, 3, 1] 58[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 59 -1 1 590592 models.common.Conv [1536, 384, 1, 1] 60 -1 1 98816 models.common.Conv [384, 256, 1, 1] 61 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 62 28 1 131584 models.common.Conv [512, 256, 1, 1] 63 [-1, -2] 1 0 models.common.Concat [1] 64 -1 1 131584 models.common.Conv [512, 256, 1, 1] 65 -2 1 131584 models.common.Conv [512, 256, 1, 1] 66 -1 1 295168 models.common.Conv [256, 128, 3, 1] 67 -1 1 147712 models.common.Conv [128, 128, 3, 1] 68 -1 1 147712 models.common.Conv [128, 128, 3, 1] 69 -1 1 147712 models.common.Conv [128, 128, 3, 1] 70[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 71 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 72 -1 1 33024 models.common.Conv [256, 128, 1, 1] 73 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 74 19 1 33024 models.common.Conv [256, 128, 1, 1] 75 [-1, -2] 1 0 models.common.Concat [1] 76 -1 1 33024 models.common.Conv [256, 128, 1, 1] 77 -2 1 33024 models.common.Conv [256, 128, 1, 1] 78 -1 1 73856 models.common.Conv [128, 64, 3, 1] 79 -1 1 36992 models.common.Conv [64, 64, 3, 1] 80 -1 1 36992 models.common.Conv [64, 64, 3, 1] 81 -1 1 36992 models.common.Conv [64, 64, 3, 1] 82[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 83 -1 1 65792 models.common.Conv [512, 128, 1, 1] 84 -1 1 295424 models.common.Conv [128, 256, 3, 2] 85 [-1, 71] 1 0 models.common.Concat [1] 86 -1 1 131584 models.common.Conv [512, 256, 1, 1] 87 -2 1 131584 models.common.Conv [512, 256, 1, 1] 88 -1 1 295168 models.common.Conv [256, 128, 3, 1] 89 -1 1 147712 models.common.Conv [128, 128, 3, 1] 90 -1 1 147712 models.common.Conv [128, 128, 3, 1] 91 -1 1 147712 models.common.Conv [128, 128, 3, 1] 92[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 93 -1 1 262656 models.common.Conv [1024, 256, 1, 1] 94 -1 1 885504 models.common.Conv [256, 384, 3, 2] 95 [-1, 59] 1 0 models.common.Concat [1] 96 -1 1 295680 models.common.Conv [768, 384, 1, 1] 97 -2 1 295680 models.common.Conv [768, 384, 1, 1] 98 -1 1 663936 models.common.Conv [384, 192, 3, 1] 99 -1 1 332160 models.common.Conv [192, 192, 3, 1] 100 -1 1 332160 models.common.Conv [192, 192, 3, 1] 101 -1 1 332160 models.common.Conv [192, 192, 3, 1] 102[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 103 -1 1 590592 models.common.Conv [1536, 384, 1, 1] 104 -1 1 1770496 models.common.Conv [384, 512, 3, 2] 105 [-1, 47] 1 0 models.common.Concat [1] 106 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 107 -2 1 525312 models.common.Conv [1024, 512, 1, 1] 108 -1 1 1180160 models.common.Conv [512, 256, 3, 1] 109 -1 1 590336 models.common.Conv [256, 256, 3, 1] 110 -1 1 590336 models.common.Conv [256, 256, 3, 1] 111 -1 1 590336 models.common.Conv [256, 256, 3, 1] 112[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] 113 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] 114 83 1 295424 models.common.Conv [128, 256, 3, 1] 115 93 1 1180672 models.common.Conv [256, 512, 3, 1] 116 103 1 2655744 models.common.Conv [384, 768, 3, 1] 117 113 1 4720640 models.common.Conv [512, 1024, 3, 1] 118 83 1 369280 models.common.Conv [128, 320, 3, 1] 119 71 1 1475840 models.common.Conv [256, 640, 3, 1] 120 59 1 3319680 models.common.Conv [384, 960, 3, 1] 121 47 1 5900800 models.common.Conv [512, 1280, 3, 1] 122[114, 115, 116, 117, 118, 119, 120, 121] 1 141088 models.yolo.IAuxDetect [3, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [256, 512, 768, 1024, 320, 640, 960, 1280]] Model Summary: 477 layers, 80979104 parameters, 80979104 gradients Transferred 618/668 items from yolov7-w6.pt Scaled weight_decay = 0.0005 Optimizer groups: 115 .bias, 115 conv.weight, 115 other train: Scanning 'data/janken4_dataset/train/labels.cache' images and labels... 480 found, 0 missing, 0 empty, 0 corrupted: 100% 480/480 [00:00<?, ?it/s] val: Scanning 'data/janken4_dataset/valid/labels.cache' images and labels... 120 found, 0 missing, 0 empty, 0 corrupted: 100% 120/120 [00:00<?, ?it/s] autoanchor: Analyzing anchors... anchors/target = 5.81, Best Possible Recall (BPR) = 1.0000 Image sizes 1280 train, 1280 test Using 2 dataloader workers Logging results to runs/train/yolov7-w6_custom4_604 Starting training for 60 epochs... Epoch gpu_mem box obj cls total labels img_size 0/59 8.92G 0.07193 0.05038 0.02343 0.1457 6 1280: 100% 120/120 [01:36<00:00, 1.25it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 0% 0/15 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:13<00:00, 1.08it/s] all 120 120 0.0351 0.367 0.0358 0.00549 Epoch gpu_mem box obj cls total labels img_size 1/59 8.85G 0.06132 0.02975 0.02315 0.1142 10 1280: 100% 120/120 [01:29<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.98it/s] all 120 120 0.201 0.525 0.176 0.0499 Epoch gpu_mem box obj cls total labels img_size 2/59 8.85G 0.05102 0.02268 0.02054 0.09424 4 1280: 100% 120/120 [01:29<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.68it/s] all 120 120 0.191 0.567 0.229 0.0714 Epoch gpu_mem box obj cls total labels img_size 3/59 8.85G 0.05071 0.01989 0.02076 0.09136 12 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.26it/s] all 120 120 0.335 0.492 0.371 0.181 Epoch gpu_mem box obj cls total labels img_size 4/59 8.85G 0.04434 0.01715 0.01972 0.0812 19 1280: 100% 120/120 [01:25<00:00, 1.40it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.27it/s] all 120 120 0.276 0.582 0.323 0.134 Epoch gpu_mem box obj cls total labels img_size 5/59 8.85G 0.04004 0.01502 0.01873 0.07378 9 1280: 100% 120/120 [01:25<00:00, 1.41it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.86it/s] all 120 120 0.296 0.672 0.409 0.177 Epoch gpu_mem box obj cls total labels img_size 6/59 8.85G 0.03984 0.01412 0.01886 0.07282 5 1280: 100% 120/120 [01:24<00:00, 1.42it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.32it/s] all 120 120 0.308 0.889 0.461 0.247 Epoch gpu_mem box obj cls total labels img_size 7/59 8.85G 0.0434 0.0126 0.01879 0.0748 9 1280: 100% 120/120 [01:24<00:00, 1.42it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.26it/s] all 120 120 0.22 0.783 0.266 0.0967 Epoch gpu_mem box obj cls total labels img_size 8/59 8.85G 0.04369 0.01102 0.01832 0.07303 5 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.18it/s] all 120 120 0.337 0.624 0.413 0.215 Epoch gpu_mem box obj cls total labels img_size 9/59 8.85G 0.03473 0.01087 0.01714 0.06274 10 1280: 100% 120/120 [01:22<00:00, 1.45it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.88it/s] all 120 120 0.366 0.783 0.449 0.152 Epoch gpu_mem box obj cls total labels img_size 10/59 8.85G 0.03986 0.01053 0.01824 0.06863 8 1280: 100% 120/120 [01:26<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.24it/s] all 120 120 0.402 0.843 0.524 0.307 Epoch gpu_mem box obj cls total labels img_size 11/59 8.85G 0.0349 0.01096 0.0181 0.06397 19 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.76it/s] all 120 120 0.377 0.914 0.491 0.292 Epoch gpu_mem box obj cls total labels img_size 12/59 8.85G 0.03348 0.01093 0.01796 0.06237 10 1280: 100% 120/120 [01:25<00:00, 1.41it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.29it/s] all 120 120 0.414 0.7 0.513 0.318 Epoch gpu_mem box obj cls total labels img_size 13/59 8.85G 0.03596 0.01072 0.01884 0.06552 11 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.31it/s] all 120 120 0.382 0.815 0.57 0.39 Epoch gpu_mem box obj cls total labels img_size 14/59 8.85G 0.03398 0.0101 0.0182 0.06228 7 1280: 100% 120/120 [01:26<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.91it/s] all 120 120 0.354 0.898 0.598 0.425 Epoch gpu_mem box obj cls total labels img_size 15/59 8.85G 0.03744 0.01164 0.01922 0.0683 7 1280: 100% 120/120 [01:29<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.28it/s] all 120 120 0.355 0.913 0.556 0.367 Epoch gpu_mem box obj cls total labels img_size 16/59 8.85G 0.03361 0.01138 0.01874 0.06373 11 1280: 100% 120/120 [01:24<00:00, 1.42it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.90it/s] all 120 120 0.399 0.717 0.531 0.345 Epoch gpu_mem box obj cls total labels img_size 17/59 8.85G 0.03469 0.01107 0.01951 0.06527 12 1280: 100% 120/120 [01:24<00:00, 1.43it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.30it/s] all 120 120 0.373 0.892 0.609 0.409 Epoch gpu_mem box obj cls total labels img_size 18/59 8.85G 0.03235 0.01084 0.01942 0.06262 13 1280: 100% 120/120 [01:26<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.93it/s] all 120 120 0.394 0.942 0.646 0.484 Epoch gpu_mem box obj cls total labels img_size 19/59 8.85G 0.0285 0.01026 0.01884 0.0576 10 1280: 100% 120/120 [01:25<00:00, 1.40it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.15it/s] all 120 120 0.396 0.853 0.601 0.456 Epoch gpu_mem box obj cls total labels img_size 20/59 8.85G 0.0313 0.01091 0.01926 0.06147 11 1280: 100% 120/120 [01:24<00:00, 1.42it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.05it/s] all 120 120 0.359 0.932 0.662 0.481 Epoch gpu_mem box obj cls total labels img_size 21/59 8.85G 0.02865 0.01078 0.01944 0.05886 14 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.30it/s] all 120 120 0.425 0.833 0.711 0.507 Epoch gpu_mem box obj cls total labels img_size 22/59 8.85G 0.03151 0.01096 0.0195 0.06197 7 1280: 100% 120/120 [01:25<00:00, 1.40it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.34it/s] all 120 120 0.517 0.848 0.741 0.551 Epoch gpu_mem box obj cls total labels img_size 23/59 8.85G 0.02962 0.01058 0.01921 0.05942 17 1280: 100% 120/120 [01:27<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.02it/s] all 120 120 0.571 0.886 0.769 0.557 Epoch gpu_mem box obj cls total labels img_size 24/59 8.85G 0.02797 0.01027 0.01908 0.05733 16 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.96it/s] all 120 120 0.634 0.9 0.789 0.576 Epoch gpu_mem box obj cls total labels img_size 25/59 8.85G 0.02549 0.009864 0.01722 0.05257 13 1280: 100% 120/120 [01:28<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.94it/s] all 120 120 0.628 0.956 0.797 0.58 Epoch gpu_mem box obj cls total labels img_size 26/59 8.85G 0.03115 0.009762 0.01782 0.05873 8 1280: 100% 120/120 [01:28<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.86it/s] all 120 120 0.62 0.966 0.801 0.593 Epoch gpu_mem box obj cls total labels img_size 27/59 8.85G 0.02908 0.01015 0.01732 0.05655 9 1280: 100% 120/120 [01:28<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.34it/s] all 120 120 0.651 0.967 0.811 0.609 Epoch gpu_mem box obj cls total labels img_size 28/59 8.85G 0.02722 0.009631 0.01656 0.05341 12 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.22it/s] all 120 120 0.615 1 0.794 0.591 Epoch gpu_mem box obj cls total labels img_size 29/59 8.85G 0.02681 0.01002 0.01635 0.05318 7 1280: 100% 120/120 [01:27<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.89it/s] all 120 120 0.657 0.986 0.812 0.611 Epoch gpu_mem box obj cls total labels img_size 30/59 8.85G 0.02727 0.009484 0.01679 0.05355 8 1280: 100% 120/120 [01:28<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.07it/s] all 120 120 0.652 1 0.818 0.602 Epoch gpu_mem box obj cls total labels img_size 31/59 8.85G 0.02733 0.009465 0.01619 0.05298 5 1280: 100% 120/120 [01:23<00:00, 1.43it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.28it/s] all 120 120 0.639 0.997 0.811 0.599 Epoch gpu_mem box obj cls total labels img_size 32/59 8.85G 0.02681 0.008779 0.01627 0.05185 19 1280: 100% 120/120 [01:25<00:00, 1.40it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.28it/s] all 120 120 0.659 0.997 0.818 0.616 Epoch gpu_mem box obj cls total labels img_size 33/59 8.85G 0.02612 0.009449 0.01593 0.0515 12 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.22it/s] all 120 120 0.649 0.992 0.823 0.635 Epoch gpu_mem box obj cls total labels img_size 34/59 8.85G 0.02528 0.008885 0.01521 0.04937 7 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.33it/s] all 120 120 0.653 1 0.803 0.625 Epoch gpu_mem box obj cls total labels img_size 35/59 8.85G 0.03001 0.009354 0.01686 0.05623 8 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.09it/s] all 120 120 0.651 1 0.82 0.622 Epoch gpu_mem box obj cls total labels img_size 36/59 8.85G 0.02564 0.009036 0.01529 0.04997 17 1280: 100% 120/120 [01:24<00:00, 1.42it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.14it/s] all 120 120 0.643 1 0.817 0.624 Epoch gpu_mem box obj cls total labels img_size 37/59 8.85G 0.02765 0.009619 0.01618 0.05345 11 1280: 100% 120/120 [01:26<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.23it/s] all 120 120 0.652 0.965 0.818 0.632 Epoch gpu_mem box obj cls total labels img_size 38/59 8.85G 0.02639 0.009675 0.016 0.05206 10 1280: 100% 120/120 [01:24<00:00, 1.41it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.58it/s] all 120 120 0.654 1 0.849 0.666 Epoch gpu_mem box obj cls total labels img_size 39/59 8.85G 0.02614 0.0103 0.01617 0.05261 12 1280: 100% 120/120 [01:28<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.13it/s] all 120 120 0.698 0.915 0.86 0.655 Epoch gpu_mem box obj cls total labels img_size 40/59 8.85G 0.0247 0.009746 0.0149 0.04934 6 1280: 100% 120/120 [01:26<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.26it/s] all 120 120 0.782 0.857 0.892 0.687 Epoch gpu_mem box obj cls total labels img_size 41/59 8.85G 0.02454 0.009114 0.01487 0.04852 9 1280: 100% 120/120 [01:26<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.89it/s] all 120 120 0.73 0.95 0.939 0.726 Epoch gpu_mem box obj cls total labels img_size 42/59 8.85G 0.02538 0.01016 0.01585 0.05139 20 1280: 100% 120/120 [01:30<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.84it/s] all 120 120 0.808 0.925 0.949 0.737 Epoch gpu_mem box obj cls total labels img_size 43/59 8.85G 0.02543 0.01007 0.01576 0.05126 10 1280: 100% 120/120 [01:27<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.31it/s] all 120 120 0.826 0.95 0.967 0.747 Epoch gpu_mem box obj cls total labels img_size 44/59 8.85G 0.02414 0.009589 0.0145 0.04823 7 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.29it/s] all 120 120 0.853 0.939 0.963 0.738 Epoch gpu_mem box obj cls total labels img_size 45/59 8.85G 0.02389 0.009356 0.01378 0.04703 19 1280: 100% 120/120 [01:28<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.85it/s] all 120 120 0.869 0.982 0.974 0.758 Epoch gpu_mem box obj cls total labels img_size 46/59 8.85G 0.02464 0.009571 0.01408 0.04829 12 1280: 100% 120/120 [01:29<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.95it/s] all 120 120 0.898 0.975 0.988 0.743 Epoch gpu_mem box obj cls total labels img_size 47/59 8.85G 0.02323 0.009343 0.01312 0.0457 7 1280: 100% 120/120 [01:25<00:00, 1.40it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.31it/s] all 120 120 0.885 0.983 0.977 0.743 Epoch gpu_mem box obj cls total labels img_size 48/59 8.85G 0.02214 0.008673 0.0117 0.04252 6 1280: 100% 120/120 [01:24<00:00, 1.41it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.12it/s] all 120 120 0.909 0.958 0.97 0.755 Epoch gpu_mem box obj cls total labels img_size 49/59 8.85G 0.02403 0.00917 0.01216 0.04537 13 1280: 100% 120/120 [01:24<00:00, 1.41it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.07it/s] all 120 120 0.945 0.977 0.989 0.75 Epoch gpu_mem box obj cls total labels img_size 50/59 8.85G 0.02422 0.009305 0.01206 0.04558 9 1280: 100% 120/120 [01:28<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.06it/s] all 120 120 0.964 0.984 0.993 0.756 Epoch gpu_mem box obj cls total labels img_size 51/59 8.85G 0.0243 0.009152 0.01185 0.0453 5 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.20it/s] all 120 120 0.943 0.999 0.994 0.76 Epoch gpu_mem box obj cls total labels img_size 52/59 8.85G 0.02312 0.008929 0.01093 0.04297 13 1280: 100% 120/120 [01:27<00:00, 1.38it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.12it/s] all 120 120 0.964 0.992 0.996 0.75 Epoch gpu_mem box obj cls total labels img_size 53/59 8.85G 0.02246 0.009356 0.00974 0.04156 6 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.30it/s] all 120 120 0.967 0.994 0.996 0.755 Epoch gpu_mem box obj cls total labels img_size 54/59 8.85G 0.02408 0.00926 0.01121 0.04456 9 1280: 100% 120/120 [01:26<00:00, 1.39it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.87it/s] all 120 120 0.962 1 0.996 0.751 Epoch gpu_mem box obj cls total labels img_size 55/59 8.85G 0.02279 0.009419 0.01005 0.04226 13 1280: 100% 120/120 [01:25<00:00, 1.40it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.27it/s] all 120 120 0.997 0.982 0.996 0.775 Epoch gpu_mem box obj cls total labels img_size 56/59 8.85G 0.02372 0.009141 0.01047 0.04333 5 1280: 100% 120/120 [01:30<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.12it/s] all 120 120 0.988 1 0.996 0.771 Epoch gpu_mem box obj cls total labels img_size 57/59 8.85G 0.02274 0.00875 0.009518 0.04101 13 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.02it/s] all 120 120 0.965 0.992 0.995 0.767 Epoch gpu_mem box obj cls total labels img_size 58/59 8.85G 0.02321 0.008917 0.01005 0.04217 7 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:04<00:00, 3.30it/s] all 120 120 0.974 0.97 0.99 0.762 Epoch gpu_mem box obj cls total labels img_size 59/59 8.85G 0.02205 0.008874 0.009621 0.04054 5 1280: 100% 120/120 [01:27<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 15/15 [00:05<00:00, 2.62it/s] all 120 120 0.987 0.992 0.996 0.747 goo 120 40 0.973 1 0.997 0.674 choki 120 40 1 0.975 0.996 0.732 par 120 40 0.988 1 0.997 0.836 60 epochs completed in 1.632 hours. Optimizer stripped from runs/train/yolov7-w6_custom4_604/weights/last.pt, 162.6MB Optimizer stripped from runs/train/yolov7-w6_custom4_604/weights/best.pt, 162.6MB
!python export.py --weights runs/train/yolov7-w6_custom4_60/weights/best.pt --img 1280 1280
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