How can I change dataset while training with PyTorch Hub code?

ghost commented 2 years ago

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Question

you already mentioned that below 2 examples are PyTorch Hub training codes.

model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) # load pretrained model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False, pretrained=False) # load scratch

further, how can I change dataset using above codes? must I only use coco128.yaml dataset? I want to use other dataset while using PyTorch Hub codes.

Additional

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github-actions[bot] commented 2 years ago

👋 Hello @jiho3013, 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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MartinPedersenpp commented 2 years ago

To load a YOLOv5 model for training rather than inference, set autoshape=False. To load a model with randomly initialized weights (to train from scratch) use pretrained=False. You must provide your own training script in this case. Alternatively see our YOLOv5 Train Custom Data Tutorial for model training.

github-actions[bot] commented 2 years ago

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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glenn-jocher commented 1 year ago

@MartinPedersenpp to load a YOLOv5 model with a different dataset, you can use the PyTorch Hub code for inference. However, for training with a custom dataset, you should provide your own training script. We recommend checking out our YOLOv5 Train Custom Data Tutorial for detailed instructions on training with a custom dataset. Thank you for your interest!

ultralytics / yolov5