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3 years ago 👋 Hello @Zeroh023, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
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Requirements
Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:
$ pip install -r requirements.txtEnvironments
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glenn-jocher
roberthobblebottom
masterpeas
❔Question
HI! I am trying to train my Custom Data.I followed the "Train Custom Data Tutorial" ,made the labels with lambllmg(label format is YOLO) .And I am sure I put all images and labels in right places. But while i start training , always got this error:
Scanning 'D:\yolo\yolov5-WBunderv5\Dateset\Score\image\train.cache' for images and labels... 0 found, 71 missing, 0 empty, 0 corrupted: 100%|██████████| 71/71 [00:00<?, ?it/s]
And this is the whole tip:
Using torch 1.7.1+cu110 CUDA:0 (GeForce GTX 1650, 4096MB)
Namespace(adam=False, batch_size=4, bucket='', cache_images=False, cfg='models/helmet.yaml', data='data/custom_data.yaml', device='0', epochs=10, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs\train\exp43', single_cls=False, sync_bn=False, total_batch_size=4, weights='weights/yolov5s.pt', workers=8, world_size=1) Start Tensorboard 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': 0.5, '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}
0 -1 1 3520 models.common.Focus [3, 32, 3]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False]
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 378624 models.common.BottleneckCSP [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 95104 models.common.BottleneckCSP [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 313088 models.common.BottleneckCSP [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 1248768 models.common.BottleneckCSP [512, 512, 1, False]
24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model Summary: 283 layers, 7255094 parameters, 7255094 gradients, 16.8 GFLOPS
Transferred 362/370 items from weights/yolov5s.pt Optimizer groups: 62 .bias, 70 conv.weight, 59 other Scanning 'D:\yolo\yolov5-WBunderv5\Dateset\Score\image\train.cache' for images and labels... 0 found, 71 missing, 0 empty, 0 corrupted: 100%|██████████| 71/71 [00:00<?, ?it/s]Traceback (most recent call last): File "D:/yolo/yolov5-WBunderv5/train.py", line 491, in
train(hyp, opt, device, tb_writer, wandb)
File "D:/yolo/yolov5-WBunderv5/train.py", line 183, in train
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
File "D:\yolo\yolov5-WBunderv5\utils\datasets.py", line 61, in create_dataloader
dataset = LoadImagesAndLabels(path, imgsz, batch_size,
File "D:\yolo\yolov5-WBunderv5\utils\datasets.py", line 377, in init
assert nf > 0 or not augment, f'No labels found in {cache_path}. Can not train without labels. See {help_url}'
AssertionError: No labels found in D:\yolo\yolov5-WBunderv5\Dateset\Score\image\train.cache. Can not train without labels. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
Scanning 'D:\yolo\yolov5-WBunderv5\Dateset\Score\image\train.cache' for images and labels... 0 found, 71 missing, 0 empty, 0 corrupted: 100%|██████████| 71/71 [00:00<?, ?it/s]
Additional context
Give me some advice ,thanks!