Hi,
Niels here from the open-source team at Hugging Face. Congratulations on your work!
I've made the paper and 6 paper-native evaluations available on Papers with Code.
The paper has results on Object Detection, Image segmentation, and Image Understanding task pages.
The GLIPv2-H (fine-tuned) result currently ranks second on ODinW13 (subset of ODinW).
The GLIPv2-H (fine-tuned) results currently rank fourth on LVIS (Instance Segmentation) and LVIS (Object Detection).
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):
Kind regards,
Niels
Hi,
Niels here from the open-source team at Hugging Face. Congratulations on your work!
I've made the paper and 6 paper-native evaluations available on Papers with Code.
The paper has results on Object Detection, Image segmentation, and Image Understanding task pages.
The GLIPv2-H (fine-tuned) result currently ranks second on ODinW13 (subset of ODinW).
The GLIPv2-H (fine-tuned) results currently rank fourth on LVIS (Instance Segmentation) and LVIS (Object Detection).
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):
Kind regards,
Niels