Lab-Based-Project

Team Members: Suryansh Singh(20114096), Pranay Parashar(20114073), and Shrayash Prasad(20114088)

Details of Directories:

1. COD Model : Contains the code for the model alongwith code for data augmentation, image registration and feature extraction.

2. Literature Review : Contains various documents, research papers and pdfs regarding object detection, data fusion, camouflage, multispectral imaging etc.

3. Report and Ppt : Contains the Final Report and Presentation of our Project.

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How to execute the code :

1. Clone our repository from GitHub.

2. Download the required Python libraries as specified in ‘requirement.txt’ file.

3. Create folder naming ‘Dataset’ and create sub-folders naming ‘Train’ and ‘Test’ in it.

4. Create a ‘Model’ folder to store the weights of our model, and a ‘Result’ folder to store the resultant grayscale images obtained.

5. Then download the datasets from the drive links provided in ‘Dataset.txt’.

6. To run our model, the device should have CUDA support which is used to make training process faster by using GPUs for general-purpose computing using parallel computing.

7. CUDA Toolkit can be installed using the Microsoft Installation Docs for it.

8. Start training by running ‘MyTrain.py’

9. Test the model by running ‘MyTest.py’ file.