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AutoEncoders

This project aims to create a modular Autoencoder training and inference pipeline. Different architectures of Autoencoders can easily be added for image representation generation along with image reconstruction.

The pipeline is designed to read a config file to dynamically generate the relavant input, output layer dimensions along with bottleneck layer size. Other training related hyperparameters are also read from the config file.

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📜 Dataset

The dataset used for testing consists of images captured from the wrist camera of a Kinova3 robot arm inside a coppeliaSim environment.

Ani

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📜 Contributors

🎓 This codebase is part of the authors Master Thesis titled Visuomotor Policy Learning for Predictive Manipulation

:octocat: Anirudh NJ
      Email: anijaya9@gmail.com
      GitHub: @njanirudh

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📜 References

  1. https://discuss.pytorch.org/t/model-eval-vs-with-torch-no-grad/19615/10
  2. https://github.com/ma-shamshiri/Human-Activity-Recognition/blob/main/README.md