Grounded-sam-2 versus Florence-2 + sam2
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- computer-vision, machine-learning
Research direction
No files, tests, or entry points are named. Begin by checking how RAI currently downloads and runs Grounding DINO and SAM2, then assess Florence-2-base and Florence-2-large with SAM2 against the stated size, accuracy, and unified-model tradeoffs. Done when the project has a documented decision and, if selected, a scoped implementation plan.
Written by the indexing model from the issue text.
Description
RAI currently depends on Grounded-sam-2 which is a combination of two models:
- Grounding DINO, downloaded weights (661.85 MB)
- SAM2, sam2_hiera_large.pt, downloaded weights (856.35 MB)
A close equivalent to Grounded-sam-2 is Florence-2 + sam2. Florence-2 comes in two variants:
- Florence-2-base: https://huggingface.co/microsoft/Florence-2-base (less performant, lower accuracy)
- Florence-2-large: https://huggingface.co/microsoft/Florence-2-large, ~1.54 GB(?)
One advantage of Florence-2 is that it's a unified model, while Grounded-SAM-2 requires running two separate models in sequence (Grounding DINO for detection, then SAM 2 for segmentation). I'm curious on whether we'd considered the Florence-2 in the past and ruled it out.
- Dominant language
- Python
- Stars
- 589
- Forks
- 76
- Avg merge
- 1d 13h
- Merged PRs (30d)
- 3
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.
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