Update README.md

README.md

@@ -11,4 +11,192 @@ license: mit
 short_description: InceptionV3 sports ball classifier with Gradio.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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.