README.md

---
title: Sports Ball Classification Inceptionv3
emoji: ⚽
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 6.6.0
app_file: app.py
pinned: false
license: mit
short_description: InceptionV3 sports ball classifier with Gradio.
models:
  - AIOmarRehan/Sports_Balls_Classification_InceptionV3
datasets:
  - AIOmarRehan/Sports-Balls
---

# Sports Ball Classification using InceptionV3

A deep learning image classification model that identifies different types of sports balls using transfer learning with InceptionV3. The model achieves high accuracy through careful data preprocessing, augmentation, and a two-stage training strategy.

## Overview

This project demonstrates a production-ready approach to image classification, focusing on data quality, preprocessing pipelines, and comprehensive evaluation. The model can classify various sports balls including basketballs, soccer balls, tennis balls, baseballs, and more.

## Key Features

- Transfer learning with InceptionV3 pre-trained on ImageNet
- Comprehensive data preprocessing and quality analysis
- Automated data balancing through augmentation
- Two-stage training: feature extraction followed by fine-tuning
- FastAPI deployment for easy inference
- Docker support for containerized deployment
- Rigorous evaluation with multiple metrics (precision, recall, F1-score, ROC curves)

## Project Structure

```
InceptionV3_Sports_Balls_Classification/
├── app/
│   ├── main.py              # FastAPI application
│   └── model.py             # Model loading and prediction logic
├── Notebook and Py File/
│   ├── Sports_Balls_Classification.ipynb
├── saved_model/
│   └── Sports_Balls_Classification.h5
├── Results/
│   └── InceptionV3_Sports_Balls_Classification.mp4
├── requirements.txt
├── Dockerfile
└── README.md
```

## Installation

### Local Setup

1. Clone the repository:
```bash
git clone https://github.com/yourusername/sports-ball-classifier.git
cd sports-ball-classifier
```

2. Install dependencies:
```bash
pip install -r requirements.txt
```

### Docker Setup

Build and run using Docker:
```bash
docker build -t sports-ball-classifier .
docker run -p 8000:8000 sports-ball-classifier
```

## Usage

### Running the API

Start the FastAPI server:
```bash
uvicorn app.main:app --host 0.0.0.0 --port 8000
```

The API will be available at `http://localhost:8000`

### Making Predictions

Send a POST request to the `/predict` endpoint:

```python
import requests

url = "http://localhost:8000/predict"
files = {"file": open("path/to/sports_ball_image.jpg", "rb")}
response = requests.post(url, files=files)
print(response.json())
```

Response format:
```json
{
  "predicted_label": "basketball",
  "confidence": 0.985,
  "probabilities": {
    "basketball": 0.985,
    "soccer_ball": 0.012,
    "tennis_ball": 0.003
  }
}
```

### Using the Notebook

Open and run the Jupyter notebook for training and evaluation:
```bash
jupyter notebook "Notebook / Sports_Balls_Classification.ipynb"
```

## Model Architecture

The model uses InceptionV3 as a feature extractor with custom classification layers:

- Base: InceptionV3 (pre-trained on ImageNet, frozen initially)
- Global Average Pooling 2D
- Dense layer (512 units, ReLU activation)
- Dropout (0.5)
- Output layer (softmax activation)
```python
x = GlobalAveragePooling2D()(inception.output)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
prediction = Dense(len(le.classes_), activation='softmax')(x)
model = Model(inputs=inception.input, outputs=prediction)
model.summary()
```

### Training Strategy

**Stage 1: Feature Extraction (5 epochs)**
- Freeze InceptionV3 base layers
- Train only top classification layers
- Learn task-specific patterns

**Stage 2: Fine-Tuning (10 epochs)**
- Unfreeze last 30 layers of InceptionV3
- Train entire model with lower learning rate
- Adapt deep features to sports ball classification

## Data Preprocessing

The preprocessing pipeline includes:

1. Corruption and quality checks
2. Brightness and contrast analysis
3. Class balancing through augmentation
4. Normalization and resizing
5. TensorFlow data pipeline optimization (prefetching, caching, parallel processing)

## Evaluation Metrics

The model is evaluated using:

- Accuracy
- Precision, Recall, F1-Score (per class and macro-averaged)
- Confusion Matrix
- ROC Curves (one-vs-rest)
- Classification Report

![Confusion Matrix](https://files.catbox.moe/ror363.png)
![ROC Curves](https://files.catbox.moe/0w580o.png)

## Requirements

![Python](https://img.shields.io/badge/Python-3.8+-3776AB?logo=python&logoColor=white)
![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-FF6F00?logo=tensorflow&logoColor=white)
![FastAPI](https://img.shields.io/badge/FastAPI-009688?logo=fastapi&logoColor=white)
![Uvicorn](https://img.shields.io/badge/Uvicorn-ASGI-499848)
![Pillow](https://img.shields.io/badge/Pillow-Image%20Processing-8CAAE6)
![NumPy](https://img.shields.io/badge/NumPy-013243?logo=numpy&logoColor=white)
![scikit--learn](https://img.shields.io/badge/scikit--learn-F7931E?logo=scikitlearn&logoColor=white)
![Matplotlib](https://img.shields.io/badge/Matplotlib-11557C)
![Seaborn](https://img.shields.io/badge/Seaborn-Statistical%20Viz-4C72B0)
![pandas](https://img.shields.io/badge/pandas-150458?logo=pandas&logoColor=white)

See `requirements.txt` for complete dependencies.

## Performance

The model achieves strong performance across all classes after addressing:
- Class imbalance through augmentation
- Image quality issues (dark, bright, low contrast images)
- Proper train/validation/test splits (80/10/10)

Detailed metrics available in the notebook evaluation section.

## Check the Results

<a href="https://files.catbox.moe/gn2xut.mp4">
  <img src="https://files.catbox.moe/851c5y.avif" width="300">
</a>

## License

This project is licensed under the MIT License.