Implementing and training SE-ResNet and SE-ResNeXt and including them in "torchvision.models"
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- Python
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Description
🚀 Feature
Inclusion of pretrained SE-ResNet and SE-ResNeXt models in torchvision.
Motivation
SE-ResNet and SE-ResNeXt outperform ResNet and ResNeXt respectively and have been released around 2 years ago already.
Pitch
Implementing SE blocks and creating ResNet and ResNeXt models that include them, which will be called SE-ResNet and SE-ResNeXt and will be available as a part of torchvision.models. The SE paper has been published in CVPR 2018.
Alternatives
Additional context
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 reviewing the existing torchvision.models implementations and the SE paper linked in the issue to determine the required SE-ResNet and SE-ResNeXt variants. Done means the models are implemented, trained or provided with pretrained weights, and exposed through torchvision.models with appropriate validation.
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
- 25/100