投稿2022/10/26 06:05
編集2022/10/27 06:38リンク内容参考に6種類の食材の物体検出を試みています。
しかし、「スナックエンドウの画像を使って学習実行」のところでつまずいてしまいました。
仮想メモリ設定や空き量、環境設定の整理などしたのですが改善されません。
どうすればよいかアドバイスいただけると嬉しいです。
エラー文
(yolov5) C:\Users\isl\Desktop\yolov5>python train.py --data data.yaml --cfg yolov5m.yaml --weights '' --batch-size 8 --epochs 1000 train: weights='', cfg=yolov5m.yaml, data=data.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=1000, batch_size=8, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, 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 github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5 YOLOv5 2022-10-19 Python-3.8.13 torch-1.7.1+cu110 CUDA:0 (NVIDIA GeForce GTX 1050 Ti, 4096MiB) 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 ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 in ClearML 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=6 from n params module arguments 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2] 1 -1 1 41664 models.common.Conv [48, 96, 3, 2] 2 -1 2 65280 models.common.C3 [96, 96, 2] 3 -1 1 166272 models.common.Conv [96, 192, 3, 2] 4 -1 4 444672 models.common.C3 [192, 192, 4] 5 -1 1 664320 models.common.Conv [192, 384, 3, 2] 6 -1 6 2512896 models.common.C3 [384, 384, 6] 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2] 8 -1 2 4134912 models.common.C3 [768, 768, 2] 9 -1 1 1476864 models.common.SPPF [768, 768, 5] 10 -1 1 295680 models.common.Conv [768, 384, 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 2 1182720 models.common.C3 [768, 384, 2, False] 14 -1 1 74112 models.common.Conv [384, 192, 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 2 296448 models.common.C3 [384, 192, 2, False] 18 -1 1 332160 models.common.Conv [192, 192, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 2 1035264 models.common.C3 [384, 384, 2, False] 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 2 4134912 models.common.C3 [768, 768, 2, False] 24 [17, 20, 23] 1 44451 models.yolo.Detect [6, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]] YOLOv5m summary: 291 layers, 20891523 parameters, 20891523 gradients, 48.3 GFLOPs AMP: checks passed optimizer: SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias train: WARNING C:\Users\isl\Desktop\yolov5\data\Train\images\vegetable(162).JPG: ignoring corrupt image/label: could not convert string to float: '<annotation>' train: WARNING C:\Users\isl\Desktop\yolov5\data\Train\images\vegetable(163).JPG: ignoring corrupt image/label: could not convert string to float: '<annotation>' train: WARNING C:\Users\isl\Desktop\yolov5\data\Train\images\vegetable(164).JPG: ignoring corrupt image/label: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (62,) + inhomogeneous part. train: WARNING C:\Users\isl\Desktop\yolov5\data\Train\images\vegetable(98).JPG: ignoring corrupt image/label: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (26,) + inhomogeneous part. train: WARNING C:\Users\isl\Desktop\yolov5\data\Train\images\vegetable(99).JPG: ignoring corrupt image/label: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (50,) + inhomogeneous part. Traceback (most recent call last): File "train.py", line 630, in <module> main(opt) File "train.py", line 524, in main train(opt.hyp, opt, device, callbacks) File "train.py", line 185, in train train_loader, dataset = create_dataloader(train_path, File "C:\Users\isl\Desktop\yolov5\utils\dataloaders.py", line 123, in create_dataloader dataset = LoadImagesAndLabels( File "C:\Users\isl\Desktop\yolov5\utils\dataloaders.py", line 501, in __init__ labels, shapes, self.segments = zip(*cache.values()) ValueError: not enough values to unpack (expected 3, got 0)
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環境：Windows、Anaconda
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