no anchor_grid in V9.6.0 yolov3.pt

zhoujiawei3 commented 1 year ago

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Question

why this happen?model .yaml is from yolov3.pt too

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github-actions[bot] commented 1 year ago

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glenn-jocher commented 1 year ago

@zhoujiawei3 this issue may occur if you are using a newer version of YOLOv3 (v9.6.0), but your model weights file (yolov3.pt) is from an older version. The no anchor_grid error suggests that the model structure has changed between the versions.

To resolve this issue, you can try one of the following:

Use the compatible version of the model weights file (yolov3.pt) that matches your YOLOv3 version (v9.6.0 in this case). Make sure the model weights file is from the same release or commit as the version you are currently using.
Train the model using the new version (v9.6.0) with your own dataset. This ensures that the model weights and structure are consistent.

If you have any further questions or need assistance, please don't hesitate to ask. The YOLO community and the Ultralytics team are here to help!

zhoujiawei3 commented 1 year ago

ckpt = torch.load(weights, map_location=device) # load checkpoint model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not resume else [] # exclude keys csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load logger.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report

The code to load checkpoint is from Yolov3.9.6，my opt.cfg is empty still 439/440

glenn-jocher commented 1 year ago

Hi there! It seems that when loading the checkpoint using YOLOv3 version 9.6, the model is still showing 439 out of 440 items. This could be due to the mismatch between the checkpoint and the model state, possibly caused by differences in the model architecture or configurations.

To troubleshoot this, you can ensure that the checkpoint matches the exact architecture and configurations of the model, or you may need to adjust the loading process to handle any discrepancies between the checkpoint and the model state.

If you have any further questions or need additional assistance, feel free to ask. We're here to help!

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