glenn-jocher
commented
2 years ago @karl-gardner š hi, thanks for letting us know about this possible problem with YOLOv5 š. We've created a few short guidelines below to help users provide what we need in order to start investigating a possible problem.
How to create a Minimal, Reproducible Example
When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to reproduce the problem. This is referred to by community members as creating a minimum reproducible example. Your code that reproduces the problem should be:
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- ā Complete ā Provide all parts someone else needs to reproduce the problem
- ā Reproducible ā Test the code you're about to provide to make sure it reproduces the problem
For Ultralytics to provide assistance your code should also be:
- ā
Current ā Verify that your code is up-to-date with GitHub master, and if necessary
git pullorgit clonea new copy to ensure your problem has not already been solved in master. - ā Unmodified ā Your problem must be reproducible using official YOLOv5 code without changes. Ultralytics does not provide support for custom code ā ļø.
If you believe your problem meets all the above criteria, please close this issue and raise a new one using the š Bug Report template with a minimum reproducible example to help us better understand and diagnose your problem.
Thank you! š
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YOLOv5 Component
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Bug
Hello,
I transferred all contents of the yolov5 v6.1 repository over to my directory on github on 4/28/2022:
https://github.com/karl-gardner/droplet_detection/tree/master/yolov5
At this time the custom training was working. However, now I try to train on my custom dataset and I receive an error:
/content/droplet_detection/yolov5 train: weights=, cfg=./models/yolov5m.yaml, data=../yaml_files/droplet_model.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=5, batch_size=32, imgsz=544, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, 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, 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 š 2022-9-6 torch 1.12.1+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)
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 Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 š runs (RECOMMENDED) TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf... 100% 755k/755k [00:00<00:00, 28.5MB/s] Overriding model.yaml nc=80 with nc=4
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 36369 models.yolo.Detect [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]] YOLOv5m summary: 369 layers, 20883441 parameters, 20883441 gradients, 48.3 GFLOPs
Scaled weight_decay = 0.0005 optimizer: SGD with parameter groups 79 weight (no decay), 82 weight, 82 bias albumentations: Blur(always_apply=False, p=0.01, blur_limit=(3, 7)), MedianBlur(always_apply=False, p=0.01, blur_limit=(3, 7)), ToGray(always_apply=False, p=0.01), CLAHE(always_apply=False, p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) train: Scanning '/content/droplet_detection/yolov5/../train/labels' images and labels...412 found, 0 missing, 0 empty, 0 corrupt: 100% 412/412 [00:00<00:00, 1303.58it/s] train: New cache created: /content/droplet_detection/yolov5/../train/labels.cache train: Caching images (0.4GB ram): 100% 412/412 [00:01<00:00, 262.75it/s] val: Scanning '/content/droplet_detection/yolov5/../valid/labels' images and labels...103 found, 0 missing, 0 empty, 0 corrupt: 100% 103/103 [00:00<00:00, 571.20it/s] val: WARNING: /content/droplet_detection/yolov5/../valid/images/test_016241_png.rf.661d45690d6a99569e6ae72cd95aced7.jpg: 1 duplicate labels removed val: New cache created: /content/droplet_detection/yolov5/../valid/labels.cache val: Caching images (0.1GB ram): 100% 103/103 [00:01<00:00, 100.24it/s] Plotting labels to runs/train/exp/labels.jpg...
AutoAnchor: 5.92 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ā Image sizes 544 train, 544 val Using 2 dataloader workers Logging results to runs/train/exp Starting training for 5 epochs...
0% 0/13 [00:06<?, ?it/s] Traceback (most recent call last): File "train.py", line 668, in
main(opt)
File "train.py", line 563, in main
train(opt.hyp, opt, device, callbacks)
File "train.py", line 350, in train
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
File "/content/droplet_detection/yolov5/utils/loss.py", line 125, in call
tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
File "/content/droplet_detection/yolov5/utils/loss.py", line 229, in buildtargets
indices.append((b, a, gj.clamp(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
RuntimeError: result type Float can't be cast to the desired output type long int
Environment
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Minimal Reproducible Example
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Additional
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Are you willing to submit a PR?