How to train YOLO in a different way instead of using '.yaml' file ?

[wzh506]

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Question

I don't want to train by using train and .yaml files. I currently want to do a dynamic occlusion task and hope to use yolo for self-supervised training. So the code is as follows: self.map_generator= YOLO("yolov8n-seg.pt") print('Make visibility map v3 : yolov8') for name, param in self.map_generator.named_parameters(): param.requires_grad = True and I already add parameters to optimizer l: self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) But by printing the model weights after the optimizer, optimizer.step() optimizer.zero_grad(set_to_none = True) after_update = {name: param.clone() for name, param in map_generator.named_parameters()} if iteration > opt.masks_iters: for name in before_update: print(f"{name}: ") print(f"Before update: {before_update[name]}") print(f"After update: {after_update[name]}") print(f"Difference: {after_update[name] - before_update[name]}") I found Difference is always zero,I wonder why?

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[github-actions[bot]]

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