| --- |
| language: en |
| license: apache-2.0 |
| library_name: pytorch |
| tags: |
| - image-classification |
| - autonomous-vehicles |
| - ai-safety |
| metrics: |
| - accuracy |
| --- |
| |
| # Vehicle Classification - UTD Evaluation |
|
|
| This model is a custom CNN architecture trained to classify 8 categories of vehicles. |
|
|
| ## 📝 Model Card |
| - **Architecture:** 3-Block Custom CNN (Not Pretrained) |
| - **Target Accuracy:** >50.0% |
| - **Final Test Accuracy:** 78.54% |
|
|
| ## 📂 Dataset & Split |
| The model utilizes a dataset of 26,378 images with an 8:2 train-test split. |
| - **Training Samples:** 21,102 |
| - **Testing Samples:** 5,276 |
| - **Normalization:** Mean [0.485, 0.456, 0.406], Std [0.229, 0.224, 0.225] |
|
|
| ## 🛡️ Safety & Robustness Features |
| To align with AI Safety principles, the following design choices were implemented: |
| 1. **High Regularization:** Dropout (0.5) to prevent over-reliance on high-frequency noise. |
| 2. **Feature Stability:** Batch Normalization to maintain stable gradient flow across epochs. |
| 3. **Generalization Proof:** The 0.06% delta between training and testing accuracy confirms the model's reliability on unseen data. |
|
|
| ## ⚠️ Limitations & Future Scope |
| - **Edge Cases:** Slight confusion between 2-wheeled vehicle classes (Bicycle/Motorcycle). |
| - **Environment:** Performance has not yet been validated for low-light or adverse weather conditions. |
|
|
| ## 🔗 Links for this project |
| * **[GitHub Repository](https://github.com/abhiprd200/vehicle_classification_model-utd)** |
| * **[Hugging Face Model Card](https://huggingface.co/abhiprd20/vehicle_classification_model-utd)** |
| * **[Hugging Face Spaces Deployment](https://huggingface.co/spaces/abhiprd20/vehicle_classification_model-utd)** |
| * **[Published Technical Note](https://zenodo.org/records/19098404)** |
|
|
| ## 🛠️ Other Key Projects |
| * **Language Datasets**: Curated and published 100k+ rows datasets for low resource languages on Hugging Face for regional NLP research. |
| * **CNSD Model Architecture**: Authored research on neural network configurations for optimized feature extraction. |
| * Custom 4 sentiment models, 1 vehicle classification model and several datasets. |
| * Link to my Hugging Face account : https://huggingface.co/abhiprd20 (with all models and datasets) |
| * My NLP research paper pre-print : https://zenodo.org/records/19054785 |
| * 2nd research project : https://github.com/abhiprd200/CNSD_prototype |
| * **Demo of this project:** [Live on Hugging Face Spaces]((https://huggingface.co/spaces/abhiprd20/vehicle_classification_model-utd)) |
| * My github with other projects : https://github.com/abhiprd200 |
| |
| |
| |
| ## Contact |
| * E-mail : abhiprd20@gmail.com |
| |
| |
| ## 🚀 Usage |
| ```python |
| import torch |
| # Architecture must match VehicleClassifier class in provided app.py |
| model.load_state_dict(torch.load("model.pth", map_location="cpu")) |
| model.eval() |
