A system that can detect and classify vehicles using deep learning.
- About the Project
- Getting Started
- Results and Demo
- Future Work
- Troubleshooting
- Contributors
- Acknowledgements and Resources
Our Project aims to analyse a large dataset of images containing various vehicle categories. We have built a Convolutional Neural Network utilizing LeNet Architecture to detect and classify vehicles from mulitple angles. The architecture consists of two sets of convolutional and average pooling layers, followed by a flattening convoutional layer, then two fully-connected layers finally use a softmax classifier.
Refer this documentation
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├── docs # Documentation files
│ ├── report.pdf # Project report
│ └── results # Video feed of the Working Model
├── src
├── hell.model # CNN Model
│ ├── main.py # Main File
│ └── train_network.py # Training Network
├── ...
├── test # Test files
│ ├── test_network.py # Testing
├── ...
├── LICENSE
├── README.md
├── Setup.md # Installation
└── todo.md # Future Developments
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Anaconda Environment
You can visit the Anaconda Website for the installation packages.
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Tensorflow-GPU version 2.1.0 (GPU version is recommended for faster performance)
Tensorflow installation in Conda Environment
Command for One Step installation (If the system has NVIDIA GPU):
conda create --name tensor_gpu tensorflow-gpu anaconda- OpenCV version 4.3.0
conda install -c conda-forge opencv- Clone the repo
git clone https://github.com/akshayb80/Multi-Class-Vehicle-Classification.gitA video demonstrating our working model
Working Model Video
- Integrate this project with the License Plate Rocognition System
- Mulitple epochs required to get the best accuracy
- Ensure there is no Tensorflow compatibilty issues with the GPU before training
- SRA VJTI Eklavya 2020
- Refered OpenCV Tutorials for Image Processing
- Refered MIT Deep Learning for bulding Neural Network Model
- Refered Pyimagesearch for additional OpenCV materials ...