---
title: CarClassifierModel
emoji: 🚗
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "5.49.1"
app_file: app.py
pinned: false
---

# AiModelCarClassifier
Creating and Running a Car Classifier Model...

## Car Detector(YOLO + Custom Model)

This project uses **YOLOv5** for car detection and a **custom-trained classifier** for car model recognition and color identification.  
It takes in any image (JPEG/PNG), detects cars, classifies the car make & model, and outputs color and confidence scores.

Example output:
- **Cars detected: 1**
- **Car 1: Gray/Silver Dodge Dakota Crew Cab 2010 (98.7% confident)**

---

## Overview

This project combines **object detection** and **image classification** in one simple pipeline:

1. **YOLOv5** detects cars in the image.  
2. The detected car regions are cropped and passed into a **PyTorch classifier** (`car_classifier.pth`).  
3. A small color recognition helper determines the car’s dominant color.  
4. Results are displayed through a simple **Gradio UI** (or any frontend, e.g. HTML + Flask).

---

## Project Structure

│
├── YOLO.ipynb # Main notebook for YOLO + classification logic
├── car_classifier.pth # Trained PyTorch model for car model recognition
├── app.py # Gradio (or Flask) app for running the interface
├── class_names.json # (Optional) Human-readable class labels
├── requirements.txt # Python dependencies
└── README.md # Project description

---

---

## Works steps

1. **Image Upload** → User uploads an image.  
2. **YOLOv5 Detection** → Detects car bounding boxes.  
3. **Classification** → Each car crop is classified using `car_classifier.pth`.  
4. **Color Recognition** → Extracts car color from the cropped region.  
5. **Output** → Displays model name, color, and confidence percentage.

---
## Model Details

- **YOLOv5**: Handles object detection (pretrained on COCO dataset).  
- **Car Classifier (`car_classifier.pth`)**: Fine-tuned model trained on [Stanford Cars Dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-cars).  
- **Color Extractor**: Uses average RGB values to estimate color.

---
## install depencies
```
pip install -r requirements.txt
```


Then open the Gradio or local web interface that appears in your console.
---

## Setup & Run

Clone the repo:

Then open the Gradio or local web interface that appears in your console.
```bash
https://github.com/<Your-Username>/AiModelCarClassifier.git
cd car-detector-classifier
```



## run the app
```
python app.py
```