🌸 VisionAI — Image Classification using VGG16

VGG16 Transfer Learning — 7-class image classifier (flowers + gender detection) with FastAPI backend & React UI. 86% accuracy trained on T4 GPU

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🤖 AI Model

Category	Details
Architecture	VGG16 (Transfer Learning from ImageNet)
Framework	TensorFlow / Keras
Training	Google Colab T4 GPU
Epochs	20 (EarlyStopping + ReduceLROnPlateau)
Accuracy	86.27% validation accuracy
Dataset	6,300+ images across 7 classes

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🎯 Classes

Class	Type	Count
🌼 Daisy	Flower	~764
🌱 Dandelion	Flower	~1052
🌹 Rose	Flower	~784
🌻 Sunflower	Flower	~733
🌷 Tulip	Flower	~984
👨 Male	Gender	~1000
👩 Female	Gender	~1000

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✨ Features

• 📸 Real-time image classification — upload any image and get instant predictions
• 📊 Confidence scores — probability breakdown for all 7 classes
• 🎨 Modern React UI — dark navy theme with animated progress bars
• ⚡ FastAPI backend — high-performance REST API with image validation
• 🧠 Transfer Learning — VGG16 pretrained on ImageNet, fine-tuned for custom classes
• 🔄 Data Augmentation — rotation, zoom, flip for better generalization
• 🖱️ Drag & Drop — drag and drop image upload support

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🛠️ Tech Stack

ML & Backend

Frontend

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📁 Project Structure

vgg16-classifier/
├── backend/
│   ├── api/
│   │   └── routes.py          # FastAPI endpoints
│   ├── core/
│   │   └── config.py          # App configuration
│   ├── ml/
│   │   ├── inference/
│   │   │   └── predictor.py   # Model inference
│   │   ├── model/
│   │   │   └── vgg16_model.py # Model architecture
│   │   └── preprocessing/
│   │       └── preprocess.py  # Image preprocessing
│   └── main.py                # FastAPI app entry point
├── frontend/
│   └── src/
│       └── App.jsx            # React frontend
├── models/
│   └── saved/                 # Trained model files (not tracked)
├── data/
│   └── raw/                   # Dataset (not tracked)
├── logs/                      # Training plots
├── notebooks/                 # Jupyter notebooks
└── requirements.txt

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🚀 Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+
• TensorFlow 2.17+

1. Clone the repository

git clone https://github.com/Hamzah1507/Image-Classification-using-VGG16.git
cd Image-Classification-using-VGG16

2. Setup Backend

python -m venv venv
venv\Scripts\activate        # Windows
pip install -r requirements.txt

3. Add Model Files

Download trained model and place in models/saved/:

• vgg16_final.keras
• class_names.json

4. Run Backend

python -m backend.main
# API running at http://localhost:8000
# Swagger docs at http://localhost:8000/docs

5. Run Frontend

cd frontend
npm install
npm run dev
# UI running at http://localhost:5173

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📡 API Endpoints

Method	Endpoint	Description
GET	/api/	Health check
POST	/api/predict	Upload image → get prediction

Example Request

curl -X POST "http://localhost:8000/api/predict" \
  -H "accept: application/json" \
  -F "file=@sunflower.jpg"

Example Response

{
  "predicted_class": "sunflower",
  "confidence": 99.2,
  "all_predictions": {
    "daisy": 0.12,
    "dandelion": 0.28,
    "rose": 0.0,
    "sunflower": 99.2,
    "tulip": 0.4,
    "male": 0.0,
    "female": 0.0
  }
}

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🧪 Model Training

Training was done on Google Colab T4 GPU using ImageDataGenerator for memory-efficient batch loading.

Train/Val Split : 80% / 20%
Batch Size      : 32
Image Size      : 224 × 224
Optimizer       : Adam (lr=0.0001)
Loss            : Sparse Categorical Crossentropy
Callbacks       : ModelCheckpoint, EarlyStopping (patience=5), ReduceLROnPlateau

Training Results:

Best Epoch      : 18/20
Val Accuracy    : 86.27%
Train Accuracy  : 92.20%

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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