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

How to deal with tables? #42

Open
Yashsethi24 opened this issue Feb 24, 2021 · 5 comments
Open

How to deal with tables? #42

Yashsethi24 opened this issue Feb 24, 2021 · 5 comments

Comments

@Yashsethi24
Copy link

Firstly, thank you so much @hpanwar08 for your contributions!
I was trying to use Mask_RCNN but I observed that whenever data is a little bit spreaded like in a table, then it fails to record it. There is no bbox in that case.
Consider the following picture as a reference.
Screenshot 2021-02-24 at 6 06 19 PM

Can you help me with this?
Thank you

@hpanwar08
Copy link
Owner

Yes, it seems to a problem with the data. Data might not have a lot of examples of this kind of table.

@Yashsethi24
Copy link
Author

Can you tell me the solution to this?
Can fine tuning the parameters help?

@hpanwar08
Copy link
Owner

Labeling data that has similar types of table might help. Then training on that data.

@Yashsethi24
Copy link
Author

Yashsethi24 commented Feb 25, 2021

How many images do I need to train it with? Can I train your model further? If yes, how?

@hpanwar08
Copy link
Owner

Yes you can train the existing model. Process is same for retraining is same as that of new model, just add the existing model path in the config file.

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