no anchor_grid in V9.6.0 yolov3.pt about yolov3 HOT 5 CLOSED

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from yolov3.

@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:

1. 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.

2. 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!

from yolov3.

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

from yolov3.

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!

from yolov3.

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from yolov3.