Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
conda env create -f yolo.yaml -yconda activate yolo detect.py runs inference on a variety of sources, downloading models automatically from
the latest YOLOv5 release and saving results to runs/detect.
python detect.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streamTraining
The commands below reproduce YOLOv5 COCO
results. Models
and datasets download automatically from the latest
YOLOv5 release. Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). Use the
largest --batch-size possible, or pass --batch-size -1 for
YOLOv5 AutoBatch. Batch sizes shown for V100-16GB.
python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the --data argument. To start training on MNIST for example use --data mnist.
python train.py --weights models/weights/xxxx.pt --batch-size 16 --project landSlideByPoly/TrainYolov5n --data data/xxx.yaml一般要使用weights.pt 文件进行预训练，如果已经训练完毕的文件也可以继续训练， 不过要在训练时添加“-resume”才能保证下一次能恢复训练；下一次想要继续上一次训练结果继续训练时，将--weights 中.pt文件改成最last.pt即可
# Single-GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
# Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validateUse pretrained YOLOv5s-cls.pt to predict bus.jpg:
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpgmodel = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch HubExport a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
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