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

Create a way to generate images to boost inaccurate classes #65

Open
mrdbourke opened this issue Jan 31, 2023 · 1 comment
Open

Create a way to generate images to boost inaccurate classes #65

mrdbourke opened this issue Jan 31, 2023 · 1 comment
Labels
database Issues to do with Nutrify's database(s)

Comments

@mrdbourke
Copy link
Owner

Image generation is getting very good.

For samples where gathering images is hard, could just generate them on the fly.

This blog post showed good results with generated images: https://blog.ml6.eu/using-synthetic-data-to-boost-the-performance-of-your-object-detection-model-351a7f2171e2

Things to note:

  • Be sure to tag generated images with image_source: "generated" so I can run experiments on generated/not generated.
  • Only add generated images to training data, evaluate on real-world, taken with phone images (similar to Tesla's training in simulation but testing in real-world).
@mrdbourke mrdbourke added the database Issues to do with Nutrify's database(s) label Jan 31, 2023
@SahilJain8
Copy link

Good Greetings sir
i would like to work on this could my approach would be to use GANS i will generated images food related and save them in a folder name "generated" i will store these images on cloud (AWS) but i can even use other platform to store them , but i would need a little guidance from you sir
Thank you

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
database Issues to do with Nutrify's database(s)
Projects
None yet
Development

No branches or pull requests

2 participants