🖼️ Image Classification Dataset

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

# 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

# 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

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

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:

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 file for details.

👤 Author

Molla Samser

• 🌐 Website: rskworld.in
• 📧 Email: help@rskworld.in
• 📱 Phone: +91 93305 39277

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

⭐ 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
• Contact Page: https://rskworld.in/contact.php

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