This can come in handy in various scenarios, like when the training process has been unexpectedly interrupted, or when you wish to continue training a model with new data or for more epochs.
Resuming training from a previously saved state is a crucial feature when working with deep learning models. For more detailed guidance and advanced configuration options, please refer to the PyTorch MPS documentation.
While leveraging the computational power of the M1/M2 chips, this enables more efficient processing of the training tasks. # Start training from a pretrained *.pt model using GPUs 0 and 1 yolo detect train data =coco128.yaml model =yolov8n.pt epochs = 100 imgsz = 640 device =mps
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