Supporting "seasonal" transforms for image augmentation in torchvision
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Description
🚀 The feature
I was wondering if there's a plan to add seasonal transforms for real-world iaug to TV transforms. For example: https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library includes:
- Fog
- Sun Flare
- Snow
- Shadows (typically from buildings nearby)
Again motivated by the fact that these are things one expects to see in the real-world, and Albumentations includes those.
High level Q. Is there a plan to be somewhat transform compatible with Albumentations?
Follow-up from https://github.com/pytorch/vision/issues/7620#issuecomment-1562413892
Motivation, pitch
"Seasonal" transforms are useful for tasks that potentially involve real-world data outdoors.
Alternatives
No response
Additional context
Following up from https://github.com/pytorch/vision/issues/7620#issuecomment-1562413892
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
Review the torchvision transforms area, the linked follow-up discussion in issue #7620, and the referenced Automold and Albumentations implementations. Done would require a decided scope and acceptance criteria for seasonal transforms, but this issue names no files, tests, or bounded first change.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100