Add quantization support for BackboneWithFPN
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 17.9k
- Forks
- 7.3k
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 13
Description
🚀 The feature
Add quantization support for BackboneWithFPN.
Motivation, pitch
Currently, it is possible to use from torchvision.models.detection.backbone_utils.resnet_fpn_backbone/BackboneWithFPN in order to produce a backbone fpn network, given some sort of network that can produce features.
While the original resnet/other base networks you want to use (ex. efficientnet), have quantized variants supported (with pretrained weights too), it would be nice to support QAT in the context of FPN networks, to speed up the detectors even more.
Alternatives
No response
Additional context
No response
Contributor guide
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 reading torchvision.models.detection.backbone_utils.resnet_fpn_backbone and BackboneWithFPN, then examine how the existing quantized base networks and QAT support are integrated. Done means BackboneWithFPN can support quantization-aware training for detector backbones, with the relevant behavior verified by tests identified during that review.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100