Update README.md

README.md

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-title: Vehicle Classification Model-utd
+title: Vehicle Classification Model
 emoji: 🐨
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 colorTo: red
@@ -8,7 +8,65 @@ 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.
+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.*
+
+## 🛠️ 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
+
+
+## 🔗 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)**
+
+
+## Contact
+* E-mail : abhiprd20@gmail.com
+
+---
+**License:** Apache 2.0
+
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference