---
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.

---
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





## Contact
* E-mail : abhiprd20@gmail.com

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**License:** Apache 2.0

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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference