Скачать или смотреть pytorch yolov5 github

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Sure thing! Let's dive into a basic tutorial on using PyTorch YOLOv5 from GitHub. Keep in mind that the information might be slightly outdated, so always refer to the latest documentation on the official GitHub repository.
Make sure you have Python installed on your system. Create a virtual environment and install the required packages.
Clone the YOLOv5 repository from GitHub.
Download pre-trained weights for YOLOv5. You can choose from various models like YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x. Replace yolov5s with your desired model.
Organize your dataset in the YOLO format, i.e., each image should have a corresponding text file with bounding box annotations. The annotations should be in the format: class x_center y_center width height.
Now it's time to train your YOLOv5 model. Modify the data.yaml file with your dataset configuration.
Train the model:
Adjust the parameters according to your requirements.
Once training is complete, you can run inference on images or videos. Use the following command:
Replace /path/to/test/images with the path to the directory containing the images you want to test.
Congratulations! You've successfully trained and run inference with YOLOv5 using PyTorch. Remember to check the official documentation for more advanced options and features.
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