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

Questions about your pretrained model #18

Closed
slcheng97 opened this issue Dec 5, 2022 · 1 comment
Closed

Questions about your pretrained model #18

slcheng97 opened this issue Dec 5, 2022 · 1 comment

Comments

@slcheng97
Copy link

Does the pre-trained model you provide cover the categories on LVIS data? If I want to do open-world object detection on the LVIS dataset, can I directly use your pre-trained model to generate the proposals or should I need to filter the dataset so that it doesn't contain any object in the LVIS dataset?

@mmaaz60
Copy link
Owner

mmaaz60 commented Dec 5, 2022

Hi @chengsilin,

Thank you for your interest in our work. Our MAVL model is trained on 1.3M aligned image-text pairs from from Flickr30k, MS-COCO (2014), and Visual Genome (VG). We refer this dataset as LMDet Dataset (See. 2 of paper). Note that we do not explicitly include LVIS categories in LMDet, however, it has many LVIS categories mentioned in the text used for training MAVL.

So for a fair Open World comparison on LVIS, it is recommended to train MAVL on a filtered dataset removing all the captions/text that mention any of the LVIS categories. We followed a similar setting for reporting ORE results on COCO using MAVL proposals (See. 4.2 of paper).

However, during our COCO Open-world OD experiments, we note a very little difference in results when using proposals from original MAVL and the MAVL trained on a filtered dataset.

I hope this would be helpful. Do let me know if you have any questions and face any difficulty on training MAVL. Thanks

@mmaaz60 mmaaz60 closed this as completed Apr 10, 2023
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

2 participants