Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Cannot reproduce the results of the paper #36

Open
kinredon opened this issue Mar 31, 2023 · 3 comments
Open

Cannot reproduce the results of the paper #36

kinredon opened this issue Mar 31, 2023 · 3 comments

Comments

@kinredon
Copy link

Hi, thanks for your excellent work and the open-source.

I am trying to reproduce the results reported in the paper. Currently, I conduct the experiment of 1% COCO. I only obtained 18.5 AP, but the paper reported 19.64. Also, I use the large-scale jitter, setting the scale range to 400-1200. Still, I only obtained 20.7 AP (22.38 in the paper).

|   AP   |  AP50  |  AP75  |  APs  |  APm   |  APl   |
|:------:|:------:|:------:|:-----:|:------:|:------:|
| 20.679 | 35.090 | 21.424 | 9.386 | 22.590 | 28.191 |

I train the model on 8 3090 GPUs, and I pick the highest AP model of the teacher model.

Here is the full log when I use the large-scale jitter log.txt.

@ZRandomize
Copy link
Collaborator

The result on 1% data seems unstable, please check the performance not only on the last checkpoint. Besides, check whether you are testing the student model, we reported the performance of teacher model in the paper.

@Lyndon-wong
Copy link

Maybe the weight of unsupervised loss should be set as 4 ? (as the Table 7(b) in the paper said)

@PlutoQyl
Copy link

PlutoQyl commented Dec 8, 2023

Have you tried centerness branch?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

4 participants