Vehicle Classification — UTD Deep Learning Assessment

A Convolutional Neural Network (CNN) trained to classify 8 vehicle types: Bicycle, Bus, Car, Motorcycle, NonVehicles, Taxi, Truck, Van

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Requirements

• Python 3.11 (see .python-version)
• pip

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Setup

1. Clone the repository

git clone https://git.keshavanand.net/KeshavAnandCode/vehicle-classification.git
cd utd-vehicle-classification

2. Create and activate a virtual environment

python3.11 -m venv .venv

Mac/Linux:

source .venv/bin/activate

Windows:

.venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Download the dataset

• Download vehicle_classification.zip
• Place and extract it so the structure looks exactly like this:

data/
└── raw/
    └── vehicle_classification/
        ├── Bicycle/
        ├── Bus/
        ├── Car/
        ├── Motorcycle/
        ├── NonVehicles/
        ├── Taxi/
        ├── Truck/
        └── Van/

5. Run the notebook

jupyter notebook notebooks/submission.ipynb

Once open: Kernel → Restart & Run All

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Project Structure

utd-vehicle-classification/
├── notebooks/
│   ├── submission.ipynb          ← main submission, run this
│   └── experiments/              ← exploratory notebooks (ignore)
├── data/
│   └── raw/
│       └── vehicle_classification/   ← dataset goes here
├── models/                       ← saved model weights (auto-created on run)
├── results/                      ← training curves (auto-created on run)
├── requirements.txt
└── README.md

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Expected Output

Final Train Accuracy : ~88%
Final Test  Accuracy : ~82%

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Notes

• Device is detected automatically — runs on NVIDIA GPU, Apple Silicon (MPS), or CPU with no code changes required
• models/ and results/ directories are created automatically when the notebook runs