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3 years ago 👋 Hello @Asma-94, 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
Asma-94
❔Question
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15109MB)
Namespace(adam=False, batch_size=64, bucket='', cache_images=False, cfg='models/yolov5s.yaml', data='asl.yaml', device='', epochs=3, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, logdir='runs/', multi_scale=False, name='asl_example', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=64, weights='yolov5s.pt', workers=8, world_size=1) Start Tensorboard with "tensorboard --logdir runs/", view at http://localhost:6006/ 2021-02-19 17:18:24.635404: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1 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} Overriding model.yaml nc=80 with nc=28
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 89001 models.yolo.Detect [28, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model Summary: 191 layers, 7.32791e+06 parameters, 7.32791e+06 gradients, 17.0 GFLOPS
Transferred 362/370 items from yolov5s.pt Optimizer groups: 62 .bias, 70 conv.weight, 59 other Scanning labels asl_yolo/labels/train.cache (19113 found, 0 missing, 9 empty, 0 duplicate, for 19122 images): 19122it [00:01, 15994.32it/s] Scanning labels asl_yolo/labels/validation.cache (4779 found, 0 missing, 9 empty, 0 duplicate, for 4788 images): 4788it [00:00, 7887.93it/s] NumExpr defaulting to 2 threads.
Analyzing anchors... anchors/target = 2.52, Best Possible Recall (BPR) = 1.0000 Image sizes 640 train, 640 test Using 2 dataloader workers Logging results to runs/exp18_asl_example Starting training for 3 epochs...
0% 0/299 [00:00<?, ?it/s]Traceback (most recent call last): File "train.py", line 456, in
train(hyp, opt, device, tb_writer)
File "train.py", line 268, in train
loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size
File "/content/drive/My Drive/ASLR/yolov5/utils/general.py", line 525, in compute_loss
lbox = h['gioU'] s
KeyError: 'gioU'
0% 0/299 [00:02<?, ?it/s]
Additional context
how I can solve this error?