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2 years ago 👋 Hello @AvishayDev, 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>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txtEnvironments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Google Colab and Kaggle notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
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AvishayDev
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Question
hi, i follow to steps in the colab but when i tryto run: !python train.py --img 640 --batch 16 --epochs 150 --data {dataset.location}/data.yaml --weights yolov5m.pt --cache its write: train: weights=yolov5m.pt, cfg=, data=/content/datasets/final-hebrew-1/data.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=150, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, linear_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: up to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v6.0-144-gc9a46a6 torch 1.10.0+cu111 CPU
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=0.5, 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 🚀 runs (RECOMMENDED) TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Overriding model.yaml nc=80 with nc=1
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 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]] Model Summary: 369 layers, 20871318 parameters, 20871318 gradients, 48.0 GFLOPs
Transferred 475/481 items from yolov5m.pt Scaled weight_decay = 0.0005 optimizer: SGD with parameter groups 79 weight, 82 weight (no decay), 82 bias albumentations: version 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed train: Scanning '/content/datasets/final-hebrew-1/train/labels.cache' images and labels... 100 found, 0 missing, 0 empty, 0 corrupted: 100% 100/100 [00:00<?, ?it/s] train: Caching images (0.1GB ram): 100% 100/100 [00:00<00:00, 168.93it/s] val: Scanning '/content/datasets/final-hebrew-1/valid/labels.cache' images and labels... 25 found, 0 missing, 0 empty, 0 corrupted: 100% 25/25 [00:00<?, ?it/s] val: Caching images (0.0GB ram): 100% 25/25 [00:00<00:00, 52.24it/s] Plotting labels to runs/train/exp4/labels.jpg...
AutoAnchor: 6.01 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Image sizes 640 train, 640 val Using 2 dataloader workers Logging results to runs/train/exp4 Starting training for 150 epochs...
0% 0/7 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
and its semms like it tryto train but with no success.. someone can help me? Thanks!
Additional
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