Megvii-BaseDetection / Megvii-BaseDetection/BorderDet
Error at inference
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 429
- Forks
- 62
- PR merge metrics
- No merged PRs in 30d
Description
thanksf or opensourcing the work , when i try to run the borderdet r50 model i am getting the following error
command : pods_test --num-gpus 1 MODEL.WEIGHTS weights/BorderDet_R_50_FPN_1x.pth OUTPUT_DIR /BorderDet/op/
by going into the folder "BorderDet/playground/detection/coco/borderdet/borderdet.res50.fpn.coco.800size.1x"
Error :
09/09 11:50:28 c2.utils.env.env]: Using a generated random seed 28549847
[09/09 11:50:30 c2.checkpoint.checkpoint]: Loading checkpoint from /BorderDet/weights/BorderDet_R_50_FPN_1x.pth
WARNING [09/09 11:50:30 c2.checkpoint.checkpoint]: 'backbone.top_block.p6.weight' has shape (256, 2048, 3, 3) in the checkpoint but (256, 256, 3, 3) in the model! Skipped.
[09/09 11:50:30 c2.checkpoint.checkpoint]: Some model parameters are not in the checkpoint:
backbone.top_block.p6.weight
[09/09 11:50:30 c2.data.build]: TransformGens used: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')] in testing
[09/09 11:50:31 c2.data.datasets.coco]: Loaded 5000 images in COCO format from
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by rerunning the shown pods_test command from playground/detection/coco/borderdet/borderdet.res50.fpn.coco.800size.1x and capture the complete output after the COCO dataset-loading line. Inspect the checkpoint and model-shape mismatch for backbone.top_block.p6.weight; done means the reported inference error is reproduced and its cause or a successful inference result is documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100