microsoft / microsoft/DAViD

Verify evals on Papers with Code

Open
#28 0 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Dominant language
Python
Stars
397
Forks
34
PR merge metrics
No merged PRs in 30d

Description

Hi,

Niels here from the open-source team at Hugging Face. Congratulations on your work!

I've made the paper and 3 paper-native evaluations available on Papers with Code.

The paper has results on Depth estimation, Image Matting, and Surface Normal Estimation task pages.

The DAViD Large results currently rank second on Goliath (Relative Depth) and PhotoMatte85.

The DAViD Large result currently ranks third on Hi4D.

Would it be possible to verify these results and let me know if any score, model name, benchmark protocol, or openness metadata should be corrected?

You can also edit the task, methods, project page, and GitHub URL directly from the paper page using your Hugging Face account.

If you'd like to showcase the results in your repository README, you can copy these live leaderboard badges (or use the “Copy PwC badge” button in the Results section):

Papers with Code: #2 on Goliath (Relative Depth)
Papers with Code: #2 on PhotoMatte85
Papers with Code: #3 on Hi4D

Kind regards,

Niels

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the linked paper and its three Papers with Code task and benchmark pages: Goliath, PhotoMatte85, and Hi4D. Compare the DAViD Large entries with the paper and repository information, checking each score, model name, benchmark protocol, and openness metadata. Done means reporting any corrections needed, with optional README badges based on the verified results.

Written by the indexing model from the issue text.

Assessment

Domain
documentation, machine-learning
Issue type
Documentation
Difficulty
3/5
Estimated time
1-2 days
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
50/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.