Improve documentation for torchvision.models
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- Dominant language
- Python
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Description
Every model's architecture (as implemented in pytorch), number and names of layers (in the implementation of models done in pytorch, not the original model from the paper), and other important details that might help users (devs) should be included in documentation. Right now, the docs only list names of available models.
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 with the torchvision.models documentation linked in the issue and compare it with the implemented models in PyTorch. Document each model's implemented architecture, layer names and counts, and other user-relevant details; done when the documentation covers every available model rather than only listing names.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100