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This repository represents a web app with a multi-class classification ML model which creates a segmented image of rocks and plain land.

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ShrirangKanade/Obstacle_Detection_on_Lunar_Surface_using_U-Net

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Obstacle Detection on Lunar Surface using U-Net ✨

This repository represents a web app with a multi-class classification ML model which creates a segmented image of rocks and plain land.

📄 Description

  • This project is developed to solve the problem of detecting obstacles (eg. rocks) on lunar surface.

  • Implementation is based on the U-Net architecture which creates a segmented image from raw image as an input.

📁 Dataset

🛠 Installation

Requirements

  • Python 3.10.9
  • PyTorch 1.12.1 (GPU)
  • torchvision 0.13.1
  • OpenCV 4.6.0
  • Django 4.1.7
  • cudatoolkit 11.6.0

Rest of the packages are listed in lunar packages list.txt file.

👁Download the Obstacle Detection Model

  • Download the " final_model.pth " file from following Drive Link.
  • Download the file and add its path in views.py file in load_checkpoint() function.

🖥 Deployment

  • Install the dependencies locally.

  • To deploy this project open /lunarApp/views.py and run :

  python manage.py runserver
  • It will launch the webapp, then follow below steps :

    1. Click on Choose File.
    2. Upload any file from Input samples eg PCAM1.png and click on segment.
    3. The results are displayed on new webpage.🎉🎊

🧠 Hyperparameters

Hyperparameters Values
Epoch 30
Batch Size 16
Learning Rate 0.0001
Optimizer Adam
Scheduler ReduceLROnPlateau
Accuracy IoU
Loss Function Cross Entropy Loss

📷 Screenshot

Finaloutput

📄 Published Papers

😇 Feedback

If you have any feedback, please reach out to us at coder.shrirang.kanade@gmail.com

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This repository represents a web app with a multi-class classification ML model which creates a segmented image of rocks and plain land.

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