Problems about Inference on Video-MME
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- bash, python
- Domain
- documentation, machine-learning
Research direction
Start with README evaluation instructions and scripts/eval/eval_ov_encoder.sh, focusing on how MODEL_PATH is selected for the VideoMME task. Check the repository and referenced Hugging Face organizations for the expected checkpoint. Done means the documentation or script clearly identifies an available LLaVA-integrated checkpoint, or explicitly explains that it is not released.
Written by the indexing model from the issue text.
Description
Brilliant work on OneVision-Encoder! 🎉
I'm trying to reproduce the LLaVA-NeXT-Video evaluation results following the instructions in the README.
For video benchmarks (e.g., VideoMME), I ran:
TASKS="videomme" bash scripts/eval/eval_ov_encoder.sh
However, I noticed this line in the script:
MODEL_PATH="${MODEL_PATH:-trained_model/must_contain_llava_in_name}"
I've searched through the repository and the Hugging Face organization (lmms-lab-encoder / lmms-lab), but I couldn't find a released model checkpoint whose name contains "llava".
❓ Could you clarify:
Am I misunderstanding the evaluation workflow?
Or is the LLaVA-integrated checkpoint not yet publicly released?
Any guidance would be greatly appreciated! Thanks again for the amazing work. 🙏
- Dominant language
- Python
- Stars
- 403
- Forks
- 20
- PR merge metrics
- No merged PRs in 30d
Getting set up
- Ships a Dockerfile or Docker Compose file
- No pull request template
- No contributing 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.
More from EvolvingLMMs-Lab/OneVision-Encoder
-
Difficulty 1/5 Under an hour Newbie friendliness 20/100
-
Difficulty 3/5 1-2 days Newbie friendliness 35/100
-
Difficulty 5/5 Over a week Newbie friendliness 25/100
-
Difficulty 2/5 1-3 hours Newbie friendliness 45/100
-
Difficulty 4/5 3-5 days Newbie friendliness 35/100
EvolvingLMMs-Lab/OneVision-Encoder#116 · 1 comment ·
All issues in EvolvingLMMs-Lab/OneVision-Encoder
Similar issues
-
Difficulty 2/5 1-3 hours Newbie friendliness 70/100
Maintainers usually reply within 1 day
-
area:docs
Difficulty 2/5 1-3 hours Newbie friendliness 72/100
RailtownAI/railtracks#1633 ·
Maintainers usually reply within 2 days
-
review-panel severity:low
Difficulty 1/5 Under an hour Newbie friendliness 85/100
kristovatlas/coin-accounting#152 ·
Maintainers usually reply within 1 day
-
documentation :blue_book:
Difficulty 1/5 Under an hour Newbie friendliness 88/100
PennyLaneAI/pennylane#10280 ·
Maintainers usually reply within 2 days
-
`pipx reinstall` prints a Python traceback when the reinstall failsPossibly taken @ParamTanna claimed this today. Openbug
Difficulty 2/5 1-3 hours Newbie friendliness 78/100
Maintainers usually reply within 1 day