| --- |
| title: Vehicle Classification Model |
| emoji: 🐨 |
| colorFrom: yellow |
| colorTo: red |
| sdk: gradio |
| sdk_version: 6.9.0 |
| app_file: app.py |
| pinned: false |
| license: apache-2.0 |
| short_description: A CNN for vehicle perception with 78.54% accuracy. |
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| --- |
| title: Vehicle Classification Demo - UTD |
| emoji: 🏎️ |
| colorFrom: blue |
| colorTo: indigo |
| sdk: gradio |
| sdk_version: 5.16.0 |
| app_file: app.py |
| pinned: true |
| license: apache-2.0 |
| short_description: Custom CNN for Autonomous Vehicle Perception |
| --- |
| |
| # 🏎️ Vehicle Classification: Autonomous Perception Demo |
| This application provides a real-time inference interface for a custom-built CNN architecture designed for safety-critical vehicle classification. |
| |
| ## 📊 Model Performance |
| * **Final Test Accuracy:** 78.54% (Target: >50%) |
| * **Generalization Gap:** 0.06% (Minimal variance between training and test sets) |
| * **Classes:** 8 (Bicycle, Bus, Car, Motorcycle, NonVehicles, Taxi, Truck, Van) |
| |
| ## 🏗️ Technical Architecture |
| Unlike generic transfer learning, this model utilizes a **Custom CNN** designed for high transparency: |
| * **Architecture:** 3-stage Convolutional blocks (16, 32, 64 filters). |
| * **Safety Regularization:** Integrated **Dropout (0.5)** and **BatchNorm2d** to ensure the model learns global geometric features rather than overfitting on pixel-level noise. |
| * **Preprocessing:** Standardized 224x224 input with ImageNet-standard normalization. |
| |
| ## 🛡️ AI Safety Analysis |
| * **Critical Detection:** The model is highly optimized to distinguish `NonVehicles` (1,775/1,800 correct), minimizing the risk of "false positive" emergency braking events. |
| * **Robustness:** Maintained high performance on the test set despite diverse lighting and perspective variations in the original dataset. |
| |
| ## 📚 More Work & Research Portfolio |
| *This project is part of my broader commitment to AI/CS research and open-source development.* |
| |
| ## 🔗 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 |
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| ## Contact |
| * E-mail : abhiprd20@gmail.com |
| |
| --- |
| **License:** Apache 2.0 |
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| --- |
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|
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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