RSK World - Image Classification Dataset - Project Files Browser | RSK World

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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.
===================================================================================
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                <span>Image Data</span>
                <span class="badge-new">NEW</span>
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            <h1 class="hero-title">
                <span class="title-line">Image Classification</span>
                <span class="gradient-text typing-effect">Dataset</span>
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            <p class="hero-description">
                Large image classification dataset with <strong>10,000+ images</strong> across <strong>15 categories</strong> 
                for training CNN models, transfer learning, and image recognition tasks.
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            <div class="hero-stats">
                <div class="stat-item" data-aos="fade-up">
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                    <span class="stat-value" data-count="10000">0</span>
                    <span class="stat-label">Images</span>
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                <div class="stat-item" data-aos="fade-up" data-aos-delay="100">
                    <div class="stat-icon"><i class="fas fa-th-large"></i></div>
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                    <div class="stat-icon"><i class="fas fa-percentage"></i></div>
                    <span class="stat-value" data-count="98">0</span>
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                    <div class="cube-face top"><i class="fas fa-building"></i></div>
                    <div class="cube-face bottom"><i class="fas fa-plane"></i></div>
                </div>
            </div>
            <div class="image-carousel">
                <div class="carousel-track">
                    <div class="carousel-item">
                        <div class="sample-image" style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);">
                            <i class="fas fa-dog"></i>
                        </div>
                        <span>Animals</span>
                    </div>
                    <div class="carousel-item">
                        <div class="sample-image" style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);">
                            <i class="fas fa-car"></i>
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                        <span>Vehicles</span>
                    </div>
                    <div class="carousel-item">
                        <div class="sample-image" style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);">
                            <i class="fas fa-tree"></i>
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                        <span>Nature</span>
                    </div>
                    <div class="carousel-item">
                        <div class="sample-image" style="background: linear-gradient(135deg, #43e97b 0%, #38f9d7 100%);">
                            <i class="fas fa-utensils"></i>
                        </div>
                        <span>Food</span>
                    </div>
                    <div class="carousel-item">
                        <div class="sample-image" style="background: linear-gradient(135deg, #fa709a 0%, #fee140 100%);">
                            <i class="fas fa-building"></i>
                        </div>
                        <span>Buildings</span>
                    </div>
                    <div class="carousel-item">
                        <div class="sample-image" style="background: linear-gradient(135deg, #a8edea 0%, #fed6e3 100%);">
                            <i class="fas fa-tshirt"></i>
                        </div>
                        <span>Fashion</span>
                    </div>
                </div>
            </div>
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            <span>🖼️ 10,000+ Images</span>
            <span>🏷️ 15 Categories</span>
            <span>🧠 CNN Ready</span>
            <span>📊 Pre-split Data</span>
            <span>🚀 Transfer Learning</span>
            <span>⚡ High Quality</span>
            <span>📁 Organized Structure</span>
            <span>🔧 Python Scripts</span>
            <span>🖼️ 10,000+ Images</span>
            <span>🏷️ 15 Categories</span>
            <span>🧠 CNN Ready</span>
            <span>📊 Pre-split Data</span>
        </div>
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    <!-- Features Section -->
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            <div class="section-header">
                <span class="section-tag"><i class="fas fa-star"></i> Why Choose Us</span>
                <h2 class="section-title">Powerful <span class="gradient-text">Features</span></h2>
                <p class="section-desc">Everything you need for your image classification projects</p>
            </div>
            <div class="features-grid">
                <div class="feature-card feature-card-large">
                    <div class="feature-glow"></div>
                    <div class="feature-icon">
                        <i class="fas fa-layer-group"></i>
                    </div>
                    <h3>Multiple Categories</h3>
                    <p>Diverse image categories covering animals, vehicles, nature, objects, food, buildings, fashion, and more for comprehensive training.</p>
                    <div class="feature-tags">
                        <span>15 Categories</span>
                        <span>Diverse</span>
                        <span>Balanced</span>
                    </div>
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                    <h3>Labeled Data</h3>
                    <p>All images properly labeled and organized by category.</p>
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                    <h3>Pre-split Sets</h3>
                    <p>Train, validation, and test sets following best practices.</p>
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                <div class="feature-card">
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                    <h3>Various Sizes</h3>
                    <p>Multiple resolutions for different model architectures.</p>
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                    <h3>CNN Ready</h3>
                    <p>Optimized format for Convolutional Neural Networks.</p>
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                    <h3>Transfer Learning</h3>
                    <p>Compatible with VGG, ResNet, and EfficientNet.</p>
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                    <h3>Augmentation</h3>
                    <p>Built-in augmentation scripts included.</p>
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                <span class="section-tag"><i class="fas fa-play-circle"></i> Interactive</span>
                <h2 class="section-title">Live <span class="gradient-text">Prediction Demo</span></h2>
                <p class="section-desc">Upload an image and see the AI classification in action</p>
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                <div class="demo-upload-area" id="dropZone">
                    <div class="upload-content">
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                        <h4>Drag & Drop Image Here</h4>
                        <p>or click to browse</p>
                        <input type="file" id="imageInput" accept="image/*" hidden>
                        <button class="btn btn-secondary" onclick="document.getElementById('imageInput').click()">
                            <i class="fas fa-folder-open"></i> Choose File
                        </button>
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                        <img id="previewImage" src="" alt="Preview">
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                <div class="demo-results">
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                        <h4><i class="fas fa-chart-bar"></i> Classification Results</h4>
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                            <span class="spinner"></span> Processing...
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                            <p>Upload an image to see predictions</p>
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                        <div class="top-prediction">
                            <div class="prediction-label">
                                <i class="fas fa-trophy"></i>
                                <span id="topClass">Animals</span>
                            </div>
                            <span class="prediction-confidence" id="topConfidence">95.8%</span>
                        </div>
                        <div class="all-predictions" id="allPredictions">
                            <!-- Predictions will be inserted here -->
                        </div>
                        <div class="prediction-time">
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                            <span>Inference time: <strong id="inferenceTime">0.24s</strong></span>
                        </div>
                    </div>
                </div>
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            <div class="demo-samples">
                <h4>Try with sample images:</h4>
                <div class="sample-images">
                    <button class="sample-btn" data-category="Animals">
                        <i class="fas fa-dog"></i>
                        <span>Animals</span>
                    </button>
                    <button class="sample-btn" data-category="Vehicles">
                        <i class="fas fa-car"></i>
                        <span>Vehicles</span>
                    </button>
                    <button class="sample-btn" data-category="Nature">
                        <i class="fas fa-tree"></i>
                        <span>Nature</span>
                    </button>
                    <button class="sample-btn" data-category="Food">
                        <i class="fas fa-utensils"></i>
                        <span>Food</span>
                    </button>
                    <button class="sample-btn" data-category="Buildings">
                        <i class="fas fa-building"></i>
                        <span>Buildings</span>
                    </button>
                </div>
            </div>
        </div>
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                <span class="section-tag"><i class="fas fa-images"></i> Dataset Preview</span>
                <h2 class="section-title">Image <span class="gradient-text">Gallery</span></h2>
                <p class="section-desc">Explore sample images from each category</p>
            </div>
            <div class="gallery-filters">
                <button class="filter-btn active" data-filter="all">All</button>
                <button class="filter-btn" data-filter="animals">Animals</button>
                <button class="filter-btn" data-filter="vehicles">Vehicles</button>
                <button class="filter-btn" data-filter="nature">Nature</button>
                <button class="filter-btn" data-filter="food">Food</button>
                <button class="filter-btn" data-filter="buildings">Buildings</button>
            </div>
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                <!-- Gallery items will be generated by JS -->
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                <span class="page-info">Page <span id="currentPage">1</span> of <span id="totalPages">3</span></span>
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                <span class="section-tag"><i class="fas fa-chart-line"></i> Data Insights</span>
                <h2 class="section-title">Dataset <span class="gradient-text">Analytics</span></h2>
                <p class="section-desc">Comprehensive statistics and distribution analysis</p>
            </div>
            <div class="analytics-grid">
                <div class="analytics-card">
                    <div class="card-header">
                        <h4><i class="fas fa-chart-pie"></i> Category Distribution</h4>
                    </div>
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                        <canvas id="categoryChart"></canvas>
                    </div>
                </div>
                <div class="analytics-card">
                    <div class="card-header">
                        <h4><i class="fas fa-chart-bar"></i> Split Distribution</h4>
                    </div>
                    <div class="chart-container">
                        <canvas id="splitChart"></canvas>
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                    <div class="card-header">
                        <h4><i class="fas fa-info-circle"></i> Quick Stats</h4>
                    </div>
                    <div class="quick-stats">
                        <div class="quick-stat">
                            <i class="fas fa-images"></i>
                            <div>
                                <span class="stat-num">10,000</span>
                                <span class="stat-text">Total Images</span>
                            </div>
                        </div>
                        <div class="quick-stat">
                            <i class="fas fa-th-large"></i>
                            <div>
                                <span class="stat-num">15</span>
                                <span class="stat-text">Categories</span>
                            </div>
                        </div>
                        <div class="quick-stat">
                            <i class="fas fa-hdd"></i>
                            <div>
                                <span class="stat-num">~500MB</span>
                                <span class="stat-text">Dataset Size</span>
                            </div>
                        </div>
                        <div class="quick-stat">
                            <i class="fas fa-expand"></i>
                            <div>
                                <span class="stat-num">224×224</span>
                                <span class="stat-text">Avg Resolution</span>
                            </div>
                        </div>
                        <div class="quick-stat">
                            <i class="fas fa-file-image"></i>
                            <div>
                                <span class="stat-num">JPG/PNG</span>
                                <span class="stat-text">Formats</span>
                            </div>
                        </div>
                        <div class="quick-stat">
                            <i class="fas fa-check-circle"></i>
                            <div>
                                <span class="stat-num">100%</span>
                                <span class="stat-text">Labeled</span>
                            </div>
                        </div>
                    </div>
                </div>
                <div class="analytics-card">
                    <div class="card-header">
                        <h4><i class="fas fa-chart-area"></i> Model Performance</h4>
                    </div>
                    <div class="chart-container">
                        <canvas id="performanceChart"></canvas>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Categories Section -->
    <section id="categories" class="categories-section">
        <div class="container">
            <div class="section-header">
                <span class="section-tag"><i class="fas fa-folder-open"></i> Dataset Overview</span>
                <h2 class="section-title">Image <span class="gradient-text">Categories</span></h2>
                <p class="section-desc">Explore all 15 diverse categories in the dataset</p>
            </div>
            <div class="categories-grid">
                <div class="category-card" data-category="animals">
                    <div class="category-image" style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);">
                        <i class="fas fa-dog"></i>
                    </div>
                    <div class="category-info">
                        <h4>Animals</h4>
                        <div class="category-meta">
                            <span class="category-count"><i class="fas fa-images"></i> 1,500+ images</span>
                            <div class="category-progress">
                                <div class="progress-bar" style="width: 100%"></div>
                            </div>
                        </div>
                    </div>
                </div>
                <div class="category-card" data-category="vehicles">
                    <div class="category-image" style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);">
                        <i class="fas fa-car"></i>
                    </div>
                    <div class="category-info">
                        <h4>Vehicles</h4>
                        <div class="category-meta">
                            <span class="category-count"><i class="fas fa-images"></i> 1,200+ images</span>
                            <div class="category-progress">
                                <div class="progress-bar" style="width: 80%"></div>
                            </div>
                        </div>
                    </div>
                </div>
                <div class="category-card" data-category="nature">
                    <div class="category-image" style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);">
                        <i class="fas fa-tree"></i>
                    </div>
                    <div class="category-info">
                        <h4>Nature</h4>
                        <div class="category-meta">
                            <span class="category-count"><i class="fas fa-images"></i> 1,000+ images</span>
                            <div class="category-progress">
                                <div class="progress-bar" style="width: 67%"></div>
                            </div>
                        </div>
                    </div>
                </div>
                <div class="category-card" data-category="food">
                    <div class="category-image" style="background: linear-gradient(135deg, #43e97b 0%, #38f9d7 100%);">
                        <i class="fas fa-utensils"></i>
                    </div>
                    <div class="category-info">
                        <h4>Food</h4>
                        <div class="category-meta">
                            <span class="category-count"><i class="fas fa-images"></i> 800+ images</span>
                            <div class="category-progress">
                                <div class="progress-bar" style="width: 53%"></div>
                            </div>
                        </div>
                    </div>
                </div>
                <div class="category-card" data-category="buildings">
                    <div class="category-image" style="background: linear-gradient(135deg, #fa709a 0%, #fee140 100%);">
                        <i class="fas fa-building"></i>
                    </div>
                    <div class="category-info">
                        <h4>Buildings</h4>
                        <div class="category-meta">
                            <span class="category-count"><i class="fas fa-images"></i> 900+ images</span>
                            <div class="category-progress">
                                <div class="progress-bar" style="width: 60%"></div>
                            </div>
                        </div>
                    </div>
                </div>
                <div class="category-card" data-category="fashion">
                    <div class="category-image" style="background: linear-gradient(135deg, #a8edea 0%, #fed6e3 100%);">
                        <i class="fas fa-tshirt"></i>
                    </div>
                    <div class="category-info">
                        <h4>Fashion</h4>
                        <div class="category-meta">
                            <span class="category-count"><i class="fas fa-images"></i> 700+ images</span>
                            <div class="category-progress">
                                <div class="progress-bar" style="width: 47%"></div>
                            </div>
                        </div>
                    </div>
                </div>
                <div class="category-card" data-category="aircraft">
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                        <pre><code id="pythonCode"><span class="comment"># Image Classification Dataset - rskworld.in</span>
<span class="comment"># Author: Molla Samser | Email: help@rskworld.in</span>

<span class="keyword">import</span> os
<span class="keyword">import</span> numpy <span class="keyword">as</span> np
<span class="keyword">from</span> PIL <span class="keyword">import</span> Image
<span class="keyword">import</span> cv2

<span class="keyword">def</span> <span class="function">load_dataset</span>(data_dir, img_size=(<span class="number">224</span>, <span class="number">224</span>)):
    <span class="string">"""Load and preprocess images from dataset."""</span>
    images, labels = [], []
    categories = sorted(os.listdir(data_dir))
    
    <span class="keyword">for</span> idx, category <span class="keyword">in</span> enumerate(categories):
        category_path = os.path.join(data_dir, category)
        <span class="keyword">for</span> img_name <span class="keyword">in</span> os.listdir(category_path):
            img = Image.open(os.path.join(category_path, img_name))
            img = img.resize(img_size).convert(<span class="string">'RGB'</span>)
            images.append(np.array(img) / <span class="number">255.0</span>)
            labels.append(idx)
    
    <span class="keyword">return</span> np.array(images), np.array(labels), categories

<span class="comment"># Usage</span>
X_train, y_train, classes = load_dataset(<span class="string">'dataset/train'</span>)
<span class="keyword">print</span>(<span class="string">f"Loaded {len(X_train)} images from {len(classes)} classes"</span>)</code></pre>
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                        <pre><code id="tensorflowCode"><span class="comment"># TensorFlow Training - rskworld.in</span>
<span class="comment"># Author: Molla Samser | help@rskworld.in</span>

<span class="keyword">import</span> tensorflow <span class="keyword">as</span> tf
<span class="keyword">from</span> tensorflow.keras <span class="keyword">import</span> layers, models

<span class="comment"># Data Generator with Augmentation</span>
train_gen = tf.keras.preprocessing.image.ImageDataGenerator(
    rescale=<span class="number">1./255</span>,
    rotation_range=<span class="number">20</span>,
    horizontal_flip=<span class="keyword">True</span>,
    zoom_range=<span class="number">0.2</span>
)

train_data = train_gen.flow_from_directory(
    <span class="string">'dataset/train'</span>,
    target_size=(<span class="number">224</span>, <span class="number">224</span>),
    batch_size=<span class="number">32</span>
)

<span class="comment"># Build Model</span>
model = models.Sequential([
    layers.Conv2D(<span class="number">32</span>, <span class="number">3</span>, activation=<span class="string">'relu'</span>, input_shape=(<span class="number">224</span>,<span class="number">224</span>,<span class="number">3</span>)),
    layers.MaxPooling2D(),
    layers.Conv2D(<span class="number">64</span>, <span class="number">3</span>, activation=<span class="string">'relu'</span>),
    layers.MaxPooling2D(),
    layers.Conv2D(<span class="number">128</span>, <span class="number">3</span>, activation=<span class="string">'relu'</span>),
    layers.GlobalAveragePooling2D(),
    layers.Dense(<span class="number">256</span>, activation=<span class="string">'relu'</span>),
    layers.Dropout(<span class="number">0.5</span>),
    layers.Dense(<span class="number">15</span>, activation=<span class="string">'softmax'</span>)
])

model.compile(optimizer=<span class="string">'adam'</span>, loss=<span class="string">'categorical_crossentropy'</span>, metrics=[<span class="string">'accuracy'</span>])</code></pre>
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                        <pre><code id="pytorchCode"><span class="comment"># PyTorch Training - rskworld.in</span>
<span class="comment"># Author: Molla Samser | help@rskworld.in</span>

<span class="keyword">import</span> torch
<span class="keyword">import</span> torch.nn <span class="keyword">as</span> nn
<span class="keyword">from</span> torchvision <span class="keyword">import</span> transforms, datasets
<span class="keyword">from</span> torch.utils.data <span class="keyword">import</span> DataLoader

<span class="comment"># Transforms</span>
transform = transforms.Compose([
    transforms.Resize((<span class="number">224</span>, <span class="number">224</span>)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize([<span class="number">0.485</span>, <span class="number">0.456</span>, <span class="number">0.406</span>], 
                        [<span class="number">0.229</span>, <span class="number">0.224</span>, <span class="number">0.225</span>])
])

<span class="comment"># Dataset</span>
train_data = datasets.ImageFolder(<span class="string">'dataset/train'</span>, transform)
train_loader = DataLoader(train_data, batch_size=<span class="number">32</span>, shuffle=<span class="keyword">True</span>)

<span class="comment"># Model</span>
<span class="keyword">class</span> <span class="function">CNN</span>(nn.Module):
    <span class="keyword">def</span> <span class="function">__init__</span>(self, num_classes=<span class="number">15</span>):
        <span class="keyword">super</span>().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(<span class="number">3</span>, <span class="number">32</span>, <span class="number">3</span>), nn.ReLU(), nn.MaxPool2d(<span class="number">2</span>),
            nn.Conv2d(<span class="number">32</span>, <span class="number">64</span>, <span class="number">3</span>), nn.ReLU(), nn.MaxPool2d(<span class="number">2</span>),
        )
        self.classifier = nn.Linear(<span class="number">64</span>*<span class="number">54</span>*<span class="number">54</span>, num_classes)</code></pre>
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                            <span><i class="fas fa-cubes"></i> transfer_learning.py</span>
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                        <pre><code id="kerasCode"><span class="comment"># Transfer Learning - rskworld.in</span>
<span class="comment"># Author: Molla Samser | help@rskworld.in</span>

<span class="keyword">from</span> tensorflow.keras.applications <span class="keyword">import</span> ResNet50
<span class="keyword">from</span> tensorflow.keras <span class="keyword">import</span> layers, Model

<span class="comment"># Load pre-trained ResNet50</span>
base_model = ResNet50(
    weights=<span class="string">'imagenet'</span>,
    include_top=<span class="keyword">False</span>,
    input_shape=(<span class="number">224</span>, <span class="number">224</span>, <span class="number">3</span>)
)

<span class="comment"># Freeze base layers</span>
base_model.trainable = <span class="keyword">False</span>

<span class="comment"># Add custom classifier</span>
x = layers.GlobalAveragePooling2D()(base_model.output)
x = layers.Dense(<span class="number">512</span>, activation=<span class="string">'relu'</span>)(x)
x = layers.Dropout(<span class="number">0.5</span>)(x)
x = layers.Dense(<span class="number">256</span>, activation=<span class="string">'relu'</span>)(x)
output = layers.Dense(<span class="number">15</span>, activation=<span class="string">'softmax'</span>)(x)

model = Model(base_model.input, output)
model.compile(
    optimizer=<span class="string">'adam'</span>,
    loss=<span class="string">'categorical_crossentropy'</span>,
    metrics=[<span class="string">'accuracy'</span>]
)</code></pre>
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