This repository holds all the code and resources related to the ATR (Automatic Target Recognition) project 2023. Here is a brief overview of the key sections in this repository.
The main directory consists of all the project's essential files and resources. It is the primary entry point into the various aspects of the ATR project.
This directory includes the Matlab implementation of PCR6 (Principal Component Regression) and the associated toolkits that are used in this project.
To get started, run the temporal_test.m file. Before running, make sure to add all the files under the root directory to the path.
This directory holds all the Python implementations related to this project.
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AirSim Simulation: This sub-directory contains the codes for utilizing the AirSim simulation environment. It includes codes for moving the camera model and storing data, which can be used for image data generation.
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DINO-v2 Pre-trained Model: Here, you can find the codes for leveraging the DINO-v2 pre-trained model. These scripts can handle the training and testing tasks across various datasets.
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PCR5 and Dynamic Bayesian Network: This section contains the Python implementation of PCR5 and its integration with a Dynamic Bayesian Network.
Please ensure that all dependencies are installed and paths are correctly set before executing any scripts.
Note: This README is meant to provide an overview of the repository. For detailed instructions on how to use each script or function, please refer to the comments in the scripts themselves.
If you encounter any issues or have any questions about this project, please create an issue on this repository. Contributions to improve the project are always welcome.