https://github.com/arash-keshavarz/car_brand_classification
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.9%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Arash-Keshavarz
- Language: Jupyter Notebook
- Default Branch: main
- Size: 0 Bytes
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Car Model Detection
This project focuses on detecting and classifying car models using the Stanford Car Dataset. The implementation is done in a Jupyter Notebook using transfer learning with ResNet18 to perform fine-grained classification of car images.
Project Structure
bash
car-model-detection/
├── car-model-detection.ipynb # Main notebook with the complete pipeline
└── README.md
Features
- Image preprocessing and data augmentation
- Transfer learning using ResNet18
- Training and validation on a labeled dataset of car models
- Visualization of accuracy and loss during training
- Evaluation on test images using confusion matrix and classification report
Requirements
Install the following Python libraries:
bash
pip install numpy pandas matplotlib seaborn opencv-python scikit-learn torch torchvision
Results
Training accuracy: ~98.74%
Validation accuracy: ~88.36%%
Confusion matrix for the first 20 classes:
Sample predictions on unseen test images:
Contributions
Contributions are welcome! Please feel free to open issues or pull requests.
Contact: arashkeshavarzx@gmail.com
Owner
- Name: Arash
- Login: Arash-Keshavarz
- Kind: user
- Repositories: 1
- Profile: https://github.com/Arash-Keshavarz
GitHub Events
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- Push event: 1
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Last Year
- Watch event: 2
- Push event: 1
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