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Starting from a pre-trained model on a custom dataset would help in faster convergence and better model performance. But currently when we try to use a pretrained model with num_classes other than 80, it fails and we have train the model from scratch instead.
One possible solution of this could be keeping strict=False while loading state dictionary in line 263. model.load_state_dict(state_dict, strict=False)
Can this be implemented?
The text was updated successfully, but these errors were encountered:
Starting from a pre-trained model on a custom dataset would help in faster convergence and better model performance.
Yep, This would be a very useful feature, but unfortunately the training mechanism is not fully developed yet. See #59 and #60 for more details. Maybe we need to do more preparation, the timing is not very clear to me at the moment, my focus is now moving to some other projects.
https://github.com/zhiqwang/yolov5-rt-stack/blob/b7cb695beacec273ea97cc0e3732797580ef37b5/yolort/models/yolo.py#L263
Starting from a pre-trained model on a custom dataset would help in faster convergence and better model performance. But currently when we try to use a pretrained model with num_classes other than 80, it fails and we have train the model from scratch instead.
One possible solution of this could be keeping strict=False while loading state dictionary in line 263.
model.load_state_dict(state_dict, strict=False)
Can this be implemented?
The text was updated successfully, but these errors were encountered: