README.md

<!--
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Project: Image Classification Dataset
Description: Large image classification dataset with multiple categories
for training CNN models, transfer learning, and image recognition tasks.

Author: Molla Samser
Email: help@rskworld.in
Phone: +91 93305 39277
Website: https://rskworld.in

© 2025 RSK World. All rights reserved.
===================================================================================
-->

# 🖼️ Image Classification Dataset

[](https://opensource.org/licenses/MIT)
[](https://python.org)
[](https://tensorflow.org)
[](https://pytorch.org)

> Large image classification dataset with multiple categories for training CNN models, transfer learning, and image recognition tasks.

## 📋 Overview

This dataset contains thousands of labeled images across multiple categories, organized for image classification tasks. Perfect for training CNN models, transfer learning, image recognition, and computer vision applications.

## ✨ Features

- 🖼️ **Multiple Image Categories** - Diverse categories covering animals, vehicles, nature, objects, and more
- 🏷️ **Labeled Training Data** - All images properly labeled and organized by category
- 📊 **Test & Validation Sets** - Pre-split into training (70%), validation (15%), and test (15%) sets
- 📐 **Various Image Sizes** - Images in multiple resolutions for different model requirements
- 🧠 **Ready for CNN Training** - Optimized format for Convolutional Neural Networks

## 📁 Dataset Structure

```
image-classification/
├── dataset/
│ ├── train/ # 70% of data
│ │ ├── animals/
│ │ ├── vehicles/
│ │ ├── nature/
│ │ ├── food/
│ │ ├── buildings/
│ │ └── ...
│ ├── validation/ # 15% of data
│ │ ├── animals/
│ │ ├── vehicles/
│ │ └── ...
│ └── test/ # 15% of data
│ ├── animals/
│ ├── vehicles/
│ └── ...
├── scripts/
│ ├── data_loader.py
│ ├── train_model.py
│ ├── augmentation.py
│ └── evaluate.py
├── requirements.txt
├── LICENSE
└── README.md
```

## 🚀 Quick Start

### Installation

```bash
# Clone the repository
git clone https://github.com/yourusername/image-classification.git
cd image-classification

# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
```

### Basic Usage

```python
# Image Classification Dataset - rskworld.in
# Author: Molla Samser | Email: help@rskworld.in

import os
import numpy as np
from PIL import Image

def load_dataset(data_dir):
"""Load images from the dataset directory."""
images = []
labels = []
categories = os.listdir(data_dir)

for idx, category in enumerate(categories):
category_path = os.path.join(data_dir, category)
for img_name in os.listdir(category_path):
img_path = os.path.join(category_path, img_name)
img = Image.open(img_path).resize((224, 224))
images.append(np.array(img))
labels.append(idx)

return np.array(images), np.array(labels)

# Load training data
X_train, y_train = load_dataset('dataset/train')
print(f"Training samples: {len(X_train)}")
```

## 🛠️ Technologies

| Technology | Description |
|------------|-------------|
| **PNG/JPG** | Image formats used in the dataset |
| **NumPy** | Numerical computing for array operations |
| **PIL/Pillow** | Python Imaging Library for image processing |
| **OpenCV** | Computer vision library for advanced operations |
| **TensorFlow** | Deep learning framework |
| **PyTorch** | Deep learning framework |

## 📊 Dataset Statistics

| Category | Training | Validation | Test |
|----------|----------|------------|------|
| Animals | 1,050 | 225 | 225 |
| Vehicles | 840 | 180 | 180 |
| Nature | 700 | 150 | 150 |
| Food | 560 | 120 | 120 |
| Buildings | 630 | 135 | 135 |
| Fashion | 490 | 105 | 105 |
| Aircraft | 420 | 90 | 90 |
| Sports | 560 | 120 | 120 |
| Instruments | 350 | 75 | 75 |
| Electronics | 490 | 105 | 105 |
| Furniture | 420 | 90 | 90 |
| Plants | 490 | 105 | 105 |
| **Total** | **7,000** | **1,500** | **1,500** |

## 🧠 Model Training Examples

### TensorFlow/Keras

```python
import tensorflow as tf
from tensorflow.keras import layers, models

# Build CNN Model
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(128, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(256, activation='relu'),
layers.Dropout(0.5),
layers.Dense(num_classes, activation='softmax')
])

model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
```

### PyTorch

```python
import torch
import torch.nn as nn
from torchvision import transforms, datasets
from torch.utils.data import DataLoader

# Define transforms
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])

# Load dataset
train_dataset = datasets.ImageFolder('dataset/train', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
```

## 📈 Data Augmentation

The dataset includes augmentation scripts for expanding your training data:

```python
from tensorflow.keras.preprocessing.image import ImageDataGenerator

augmentation = ImageDataGenerator(
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
zoom_range=0.2,
shear_range=0.2,
fill_mode='nearest'
)
```

## 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 👤 Author

**Molla Samser**

- 🌐 Website: [rskworld.in](https://rskworld.in)
- 📧 Email: [help@rskworld.in](mailto:help@rskworld.in)
- 📱 Phone: +91 93305 39277

## 🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the [issues page](https://github.com/yourusername/image-classification/issues).

## ⭐ Show Your Support

Give a ⭐️ if this project helped you!

## 📞 Contact

For any inquiries or support, please reach out:

- **Email:** help@rskworld.in
- **Phone:** +91 93305 39277
- **Website:** [https://rskworld.in](https://rskworld.in)
- **Contact Page:** [https://rskworld.in/contact.php](https://rskworld.in/contact.php)

---

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