Using any torchvision pretrained model as backbone for FasterRcnn
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- Dominant language
- Python
- Stars
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Description
🚀 The feature
Adding only the name of the backbone network, loads the pretrained model in the FasterRcnn model as backbone.
Motivation, pitch
Its possible to use any backbone, but we have to specify many things like feature size, feature_name etc., wont be it easy for the user to just give the name of the backbone and we internally do everything.
Alternatives
No response
Additional context
No response
cc @datumbox @YosuaMichael
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 the FasterRCNN API and the torchvision pretrained-model and backbone interfaces. Trace how the current feature size and feature name are supplied, then determine the supported model-name inputs and the expected behavior for automatic configuration. Done means a user can provide only a backbone name and FasterRCNN loads and configures it correctly, with coverage for the supported cases.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100