Git Product home page Git Product logo

gram-ood's Introduction

On Out-of-Distribution Detection Algorithms with Deep Neural Skin CancerClassifiers

Code for the best paper of the ISIC Skin Image Analysis Workshop at CVPR 2020.

Paper Download: link

Workshop Talk: link

For the Mahalanobis, ODIN, and Baseline original codes, please, refer to the following repositories:

How to run

  • Install the dependencies: pip install -r requirements.txt
  • Download data and checkpoints running the setup.sh script.
  • Run the network notebook (ex: sk_mobilenet.ipynb)
  • The Mahalanobis, ODIN, and Baseline adaptations are available on mahalanobis_and_odin folder.

If you don't want to use the setup.shscript, you can download data from here and the checkpoints from here.

ISIC unknown detection is available on isic_submit folder.

Results

Unbiased performance in terms of TNR @ TPR 95%

Running time for Gram-OOD and Gram-OOD*

ISIC 2019 unknown detection - Resnet-50

Team on live leaderboard: Gram-OOD - UNK detection using Gram-OOD* - Resnet

The remaining results are available on this folder

gram-ood's People

Contributors

paaatcha avatar

Stargazers

avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar avatar

Watchers

avatar avatar avatar

Forkers

mitradp samunaai

gram-ood's Issues

model.feature_list function in OOD_Generate_Mahalanobis.py

Dear Andre,
Greetings. Hoping you are doing well!
I was hoping to ask one question about the model definitions used in mahalanobis_and_odin/OOD_Generate_Mahalanobis.py.
I noticed that in line 83, we are using a function called feature_list via temp_list = model.feature_list(temp_x)[1]?
However, this method is not currently defined in the models in my_models, such as my_models.resnet_50,
I was hoping to inquire regarding how you were able to define these functions? Just so I could maintain compatibility with the versions of PyTorch you used in torchvision.models.
Thank you so much!
Warm regards,

Training code

Hi,

Thank you for your work and for sharing the trained model on the ISIC dataset. I was wondering if you can share the training code or if you can tell me where the predictions for 8 classes are being computed in the repository.

Thank you for your work again,

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.