Datasets:

xixu-me
/

fsl-product-classification

@@ -1,1183 +1,1157 @@
----
-license: mit
-viewer: false
-task_categories:
-- image-classification
-task_ids:
-- multi-class-image-classification
-tags:
-- computer-vision
-- product-classification
-- e-commerce
-- retail
-- few-shot-learning
-- meta-learning
-- benchmark
-size_categories:
-- 100K<n<1M
-language:
-- en
-pretty_name: FSL Product Classification Dataset
-configs:
-- config_name: default
-  data_files: "data.tzst"
-  default: true
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype: int64
-  - name: class_name
-    dtype: string
-  - name: image_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 9945644054
-    num_examples: 279747
-  download_size: 9945644054
-  dataset_size: 9945644054
----
-# Few-Shot Learning (FSL) Product Classification Dataset
-## Dataset Description
-This dataset is designed for **Few-Shot Learning (FSL)** research in product classification tasks. It contains product images organized into 763 distinct classes, with an average of approximately 367 images per class (279,747 total images), making it ideal for training and evaluating few-shot learning algorithms in e-commerce and retail scenarios. Note that class numbers are not continuous.
-### Key Features
-- **763 product classes** covering diverse product categories
-- **279,747 total images** (average of ~367 images per class)
-- **High-quality product images** suitable for computer vision research
-- **Variable class distribution** with non-continuous class numbers
-- **Efficient tzst compression** for reduced storage and faster transfer
-- **Compatible with Hugging Face Datasets** library
-### Dataset Statistics
-- **Total Classes**: 763
-- **Total Images**: 279,747
-- **Images per Class**: ~367 (average, variable distribution)
-- **Class Numbers**: Non-continuous (some class numbers may be missing)
-- **Image Format**: PNG
-- **Typical Image Size**: 50-100 KB per image
-- **Average Images per Class**: 366.6 (279,747 ÷ 763)
-- **Compressed Archive Size**: ~9.9 GB (data.tzst)
-- **Extraction Requirements**: ~10 GB additional space
-## Dataset Structure
-The dataset is stored in a compressed tzst archive ([`data.tzst`](data.tzst)) with the following structure:
-```text
-data.tzst
-├── class_0/
-│   ├── class_0_0.png
-│   ├── class_0_1.png
-│   └── ...
-├── class_1/
-│   ├── class_1_0.png
-│   ├── class_1_1.png
-│   └── ...
-└── ... (763 total classes with non-continuous numbers)
-```
-**Note**: Class numbers are not continuous. For example, you might have class_0, class_2, class_5, etc., but not class_1, class_3, class_4. The total number of classes is 763.
-## Usage
-## Installation and Setup
-### Quick Start Installation
-```bash
-# Create a new virtual environment (recommended)
-python -m venv fsl-env
-source fsl-env/bin/activate  # On Windows: fsl-env\Scripts\activate
-# Install core dependencies
-pip install datasets tzst pillow
-# Install additional dependencies for machine learning
-pip install torch torchvision numpy scikit-learn matplotlib seaborn tqdm
-# For Jupyter notebook users
-pip install jupyter ipywidgets
-```
-### Complete Requirements
-Create a `requirements.txt` file with the following dependencies:
-```text
-# Core dependencies
-datasets>=2.14.0
-tzst>=1.2.8
-pillow>=9.0.0
-# Machine learning
-torch>=1.9.0
-torchvision>=0.10.0
-numpy>=1.21.0
-scikit-learn>=1.0.0
-# Data analysis and visualization
-pandas>=1.3.0
-matplotlib>=3.4.0
-seaborn>=0.11.0
-# Progress bars and utilities
-tqdm>=4.62.0
-pathlib>=1.0.1
-# Optional: for advanced few-shot learning
-learn2learn>=0.1.7
-higher>=0.2.1
-# Optional: for notebook usage
-jupyter>=1.0.0
-ipywidgets>=7.6.0
-```
-Install all requirements:
-```bash
-pip install -r requirements.txt
-```
-### Docker Setup (Optional)
-For a containerized environment:
-```dockerfile
-FROM python:3.9-slim
-WORKDIR /app
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    git \
-    wget \
-    && rm -rf /var/lib/apt/lists/*
-# Copy requirements and install Python dependencies
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-# Copy application code
-COPY . .
-# Set environment variables
-ENV PYTHONPATH=/app
-ENV HF_DATASETS_CACHE=/app/cache
-# Create cache directory
-RUN mkdir -p /app/cache
-CMD ["python", "-c", "print('FSL Product Classification environment ready!')"]
-```
-Build and run:
-```bash
-docker build -t fsl-product-classification .
-docker run -it --rm -v $(pwd)/data:/app/data fsl-product-classification bash
-```
-### Loading the Dataset
-#### Option 1: Using Hugging Face Datasets (Recommended)
-```python
-from datasets import load_dataset
-# Load the complete dataset
-dataset = load_dataset("xixu-me/fsl-product-classification")
-# Access the training split
-train_dataset = dataset["train"]
-# Print dataset information
-print(f"Dataset size: {len(train_dataset)}")
-print(f"Features: {train_dataset.features}")
-# Access individual samples
-sample = train_dataset[0]
-print(f"Image ID: {sample['image_id']}")
-print(f"Class: {sample['class_name']} (Label: {sample['label']})")
-print(f"Image: {sample['image']}")  # PIL Image object
-```
-#### Option 2: Manual Extraction and Loading
-```python
-import os
-from tzst import extract_archive
-from datasets import Dataset, Features, Value, Image, ClassLabel
-from PIL import Image as PILImage
-# Extract the dataset archive
-extract_archive("data.tzst", "extracted_data/")
-# Create a custom dataset loader
-def load_fsl_dataset(data_dir="extracted_data"):
-    samples = []
-    class_names = []
-    # Scan for class directories
-    for class_dir in sorted(os.listdir(data_dir)):
-        if class_dir.startswith("class_"):
-            class_path = os.path.join(data_dir, class_dir)
-            if os.path.isdir(class_path):
-                class_id = int(class_dir.split("_")[1])
-                class_names.append(class_dir)
-                # Load images from this class
-                for img_file in os.listdir(class_path):
-                    if img_file.endswith('.png'):
-                        img_path = os.path.join(class_path, img_file)
-                        image_id = img_file.replace('.png', '')
-                        samples.append({
-                            'image': img_path,
-                            'label': class_id,
-                            'class_name': class_dir,
-                            'image_id': image_id
-                        })
-    # Create features definition
-    features = Features({
-        'image': Image(),
-        'label': Value('int64'),
-        'class_name': Value('string'),
-        'image_id': Value('string')
-    })
-    # Create dataset
-    return Dataset.from_list(samples, features=features)
-# Load the dataset
-dataset = load_fsl_dataset()
-print(f"Loaded {len(dataset)} samples from {len(set(dataset['class_name']))} classes")
-```
-#### Option 3: Streaming Mode for Large Archives
-For memory-efficient processing of the large archive:
-```python
-from tzst import extract_archive
-import tempfile
-import os
-# Use streaming extraction for memory efficiency
-with tempfile.TemporaryDirectory() as temp_dir:
-    # Extract with streaming mode
-    extract_archive("data.tzst", temp_dir, streaming=True)
-    # Process extracted data
-    dataset = load_fsl_dataset(temp_dir)
-    # ... your processing code here
-```
-### Data Exploration
-```python
-from collections import Counter
-import matplotlib.pyplot as plt
-# Load dataset
-dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
-# Analyze class distribution
-class_counts = Counter(dataset['class_name'])
-print(f"Number of classes: {len(class_counts)}")
-print(f"Average images per class: {len(dataset) / len(class_counts):.1f}")
-# Plot class distribution (top 20 classes)
-top_classes = class_counts.most_common(20)
-classes, counts = zip(*top_classes)
-plt.figure(figsize=(12, 6))
-plt.bar(range(len(classes)), counts)
-plt.xlabel('Class')
-plt.ylabel('Number of Images')
-plt.title('Top 20 Classes by Image Count')
-plt.xticks(range(len(classes)), [c.replace('class_', '') for c in classes], rotation=45)
-plt.tight_layout()
-plt.show()
-# Display sample images
-import random
-def show_samples(dataset, num_samples=8):
-    """Display random samples from the dataset"""
-    indices = random.sample(range(len(dataset)), num_samples)
-    fig, axes = plt.subplots(2, 4, figsize=(15, 8))
-    axes = axes.flatten()
-    for i, idx in enumerate(indices):
-        sample = dataset[idx]
-        axes[i].imshow(sample['image'])
-        axes[i].set_title(f"{sample['class_name']}\nID: {sample['image_id']}")
-        axes[i].axis('off')
-    plt.tight_layout()
-    plt.show()
-# Show sample images
-show_samples(dataset)
-```
-### Few-Shot Learning Setup
-#### Basic Few-Shot Episode Creation
-```python
-import random
-from collections import defaultdict
-import torch
-from torch.utils.data import DataLoader
-from datasets import load_dataset
-def create_few_shot_split(dataset, n_way=5, k_shot=5, n_query=15, seed=None):
-    """
-    Create a few-shot learning episode
-    Args:
-        dataset: Hugging Face Dataset instance or custom dataset
-        n_way: Number of classes in the episode
-        k_shot: Number of support samples per class
-        n_query: Number of query samples per class
-        seed: Random seed for reproducibility
-    Returns:
-        support_set, query_set: Lists of (image, label) tuples
-    """
-    if seed is not None:
-        random.seed(seed)
-    # Group samples by class
-    class_samples = defaultdict(list)
-    for i, sample in enumerate(dataset):
-        class_samples[sample['label']].append(i)
-    # Filter classes with enough samples
-    valid_classes = [
-        class_id for class_id, indices in class_samples.items()
-        if len(indices) >= k_shot + n_query
-    ]
-    if len(valid_classes) < n_way:
-        raise ValueError(f"Not enough classes with {k_shot + n_query} samples. "
-                        f"Found {len(valid_classes)}, need {n_way}")
-    # Sample n_way classes
-    episode_classes = random.sample(valid_classes, n_way)
-    support_set = []
-    query_set = []
-    for new_label, original_class in enumerate(episode_classes):
-        class_indices = random.sample(class_samples[original_class], k_shot + n_query)
-        # Support samples
-        for idx in class_indices[:k_shot]:
-            sample = dataset[idx]
-            support_set.append((sample['image'], new_label, sample['image_id']))
-        # Query samples
-        for idx in class_indices[k_shot:]:
-            sample = dataset[idx]
-            query_set.append((sample['image'], new_label, sample['image_id']))
-    return support_set, query_set
-# Example usage
-dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
-# Create a 5-way 5-shot episode
-support_set, query_set = create_few_shot_split(dataset, n_way=5, k_shot=5, n_query=15)
-print(f"Support set: {len(support_set)} samples")
-print(f"Query set: {len(query_set)} samples")
-```
-#### Advanced FSL Dataset Class
-```python
-import torch
-from torch.utils.data import Dataset
-from torchvision import transforms
-from PIL import Image
-import numpy as np
-class FSLProductDataset(Dataset):
-    """
-    Few-Shot Learning Dataset wrapper for product classification
-    """
-    def __init__(self, hf_dataset, transform=None, target_transform=None):
-        self.dataset = hf_dataset
-        self.transform = transform or self.get_default_transform()
-        self.target_transform = target_transform
-        # Create label mapping for non-continuous labels
-        unique_labels = sorted(set(hf_dataset['label']))
-        self.label_to_idx = {label: idx for idx, label in enumerate(unique_labels)}
-        self.idx_to_label = {idx: label for label, idx in self.label_to_idx.items()}
-    def get_default_transform(self):
-        """Default image transformations"""
-        return transforms.Compose([
-            transforms.Resize((224, 224)),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                               std=[0.229, 0.224, 0.225])
-        ])
-    def __len__(self):
-        return len(self.dataset)
-    def __getitem__(self, idx):
-        sample = self.dataset[idx]
-        image = sample['image']
-        # Convert to PIL Image if needed
-        if not isinstance(image, Image.Image):
-            image = Image.fromarray(image)
-        # Apply transforms
-        if self.transform:
-            image = self.transform(image)
-        # Map label to continuous indices
-        label = self.label_to_idx[sample['label']]
-        if self.target_transform:
-            label = self.target_transform(label)
-        return image, label, sample['image_id']
-    def get_class_samples(self, class_label):
-        """Get all samples for a specific class"""
-        indices = [i for i, sample in enumerate(self.dataset)
-                  if sample['label'] == class_label]
-        return [self[i] for i in indices]
-    def create_episode_dataloader(self, n_way=5, k_shot=5, n_query=15,
-                                 batch_size=None, shuffle=True):
-        """Create a DataLoader for a few-shot episode"""
-        support_set, query_set = create_few_shot_split(
-            self.dataset, n_way=n_way, k_shot=k_shot, n_query=n_query
-        )
-        # Convert to tensors
-        support_images = []
-        support_labels = []
-        query_images = []
-        query_labels = []
-        for image, label, _ in support_set:
-            if isinstance(image, Image.Image):
-                image = self.transform(image) if self.transform else image
-            support_images.append(image)
-            support_labels.append(label)
-        for image, label, _ in query_set:
-            if isinstance(image, Image.Image):
-                image = self.transform(image) if self.transform else image
-            query_images.append(image)
-            query_labels.append(label)
-        support_data = (torch.stack(support_images), torch.tensor(support_labels))
-        query_data = (torch.stack(query_images), torch.tensor(query_labels))
-        return support_data, query_data
-# Example usage with PyTorch
-transform = transforms.Compose([
-    transforms.Resize((84, 84)),  # Common size for few-shot learning
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                        std=[0.229, 0.224, 0.225])
-])
-# Load dataset
-hf_dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
-fsl_dataset = FSLProductDataset(hf_dataset, transform=transform)
-# Create episode data
-support_data, query_data = fsl_dataset.create_episode_dataloader(
-    n_way=5, k_shot=1, n_query=15
-)
-print(f"Support images shape: {support_data[0].shape}")
-print(f"Support labels shape: {support_data[1].shape}")
-print(f"Query images shape: {query_data[0].shape}")
-print(f"Query labels shape: {query_data[1].shape}")
-```
-#### Meta-Learning Training Loop
-```python
-import torch.nn as nn
-import torch.optim as optim
-from tqdm import tqdm
-def train_fsl_model(model, dataset, num_episodes=1000, n_way=5, k_shot=1,
-                   n_query=15, lr=0.001, device='cuda'):
-    """
-    Basic training loop for few-shot learning
-    Args:
-        model: Few-shot learning model (e.g., Prototypical Network)
-        dataset: FSLProductDataset instance
-        num_episodes: Number of training episodes
-        n_way, k_shot, n_query: Episode configuration
-        lr: Learning rate
-        device: Training device
-    """
-    model.to(device)
-    optimizer = optim.Adam(model.parameters(), lr=lr)
-    criterion = nn.CrossEntropyLoss()
-    model.train()
-    total_loss = 0
-    total_acc = 0
-    for episode in tqdm(range(num_episodes), desc="Training"):
-        # Create episode
-        support_data, query_data = dataset.create_episode_dataloader(
-            n_way=n_way, k_shot=k_shot, n_query=n_query
-        )
-        support_images, support_labels = support_data
-        query_images, query_labels = query_data
-        # Move to device
-        support_images = support_images.to(device)
-        support_labels = support_labels.to(device)
-        query_images = query_images.to(device)
-        query_labels = query_labels.to(device)
-        # Forward pass
-        optimizer.zero_grad()
-        logits = model(support_images, support_labels, query_images)
-        loss = criterion(logits, query_labels)
-        # Backward pass
-        loss.backward()
-        optimizer.step()
-        # Calculate accuracy
-        pred = logits.argmax(dim=1)
-        acc = (pred == query_labels).float().mean()
-        total_loss += loss.item()
-        total_acc += acc.item()
-        if (episode + 1) % 100 == 0:
-            avg_loss = total_loss / 100
-            avg_acc = total_acc / 100
-            print(f"Episode {episode + 1}: Loss = {avg_loss:.4f}, Acc = {avg_acc:.4f}")
-            total_loss = 0
-            total_acc = 0
-# Example: Simple Prototypical Network
-class SimplePrototypicalNetwork(nn.Module):
-    def __init__(self, backbone):
-        super().__init__()
-        self.backbone = backbone
-    def forward(self, support_images, support_labels, query_images):
-        # Encode images
-        support_features = self.backbone(support_images)
-        query_features = self.backbone(query_images)
-        # Calculate prototypes
-        n_way = len(torch.unique(support_labels))
-        prototypes = []
-        for class_idx in range(n_way):
-            class_mask = support_labels == class_idx
-            class_features = support_features[class_mask]
-            prototype = class_features.mean(dim=0)
-            prototypes.append(prototype)
-        prototypes = torch.stack(prototypes)
-        # Calculate distances and logits
-        distances = torch.cdist(query_features, prototypes)
-        logits = -distances  # Negative distance as logits
-        return logits
-```
-## Research Applications
-This dataset is particularly well-suited for:
-### Few-Shot Learning
-- **Meta-learning algorithms** (MAML, Prototypical Networks, Relation Networks)
-- **Metric learning approaches** (Siamese Networks, Triplet Networks)
-- **Gradient-based meta-learning** methods
-### Transfer Learning
-- **Pre-training** on large-scale product data
-- **Domain adaptation** from general images to products
-- **Fine-tuning** strategies for product classification
-### Computer Vision Research
-- **Product recognition** and retrieval
-- **E-commerce applications**
-- **Retail automation**
-- **Visual search** systems
-## Benchmark Tasks
-### Standard Few-Shot Learning Evaluation
-The following benchmarks are recommended for evaluating few-shot learning models on this dataset:
-#### Standard Evaluation Protocol
-```python
-import numpy as np
-from sklearn.metrics import accuracy_score, classification_report
-import json
-def evaluate_fsl_model(model, dataset, num_episodes=600, n_way=5, k_shot=1,
-                       n_query=15, device='cuda'):
-    """
-    Evaluate few-shot learning model using standard protocol
-    Returns:
-        dict: Evaluation results with mean accuracy and confidence interval
-    """
-    model.eval()
-    accuracies = []
-    with torch.no_grad():
-        for _ in tqdm(range(num_episodes), desc="Evaluating"):
-            # Create episode
-            support_data, query_data = dataset.create_episode_dataloader(
-                n_way=n_way, k_shot=k_shot, n_query=n_query
-            )
-            support_images, support_labels = support_data
-            query_images, query_labels = query_data
-            # Move to device
-            support_images = support_images.to(device)
-            support_labels = support_labels.to(device)
-            query_images = query_images.to(device)
-            query_labels = query_labels.to(device)
-            # Predict
-            logits = model(support_images, support_labels, query_images)
-            pred = logits.argmax(dim=1)
-            # Calculate episode accuracy
-            acc = (pred == query_labels).float().mean().item()
-            accuracies.append(acc)
-    # Calculate statistics
-    mean_acc = np.mean(accuracies)
-    std_acc = np.std(accuracies)
-    ci_95 = 1.96 * std_acc / np.sqrt(len(accuracies))
-    results = {
-        'mean_accuracy': mean_acc,
-        'std_accuracy': std_acc,
-        'confidence_interval_95': ci_95,
-        'num_episodes': num_episodes,
-        'config': f"{n_way}-way {k_shot}-shot"
-    }
-    return results
-# Benchmark configurations
-benchmark_configs = [
-    {'n_way': 5, 'k_shot': 1, 'n_query': 15},  # 5-way 1-shot
-    {'n_way': 5, 'k_shot': 5, 'n_query': 15},  # 5-way 5-shot
-    {'n_way': 10, 'k_shot': 1, 'n_query': 15}, # 10-way 1-shot
-    {'n_way': 10, 'k_shot': 5, 'n_query': 15}, # 10-way 5-shot
-]
-# Run benchmarks
-def run_benchmark_suite(model, dataset, num_episodes=600):
-    """Run complete benchmark suite"""
-    results = {}
-    for config in benchmark_configs:
-        config_name = f"{config['n_way']}-way_{config['k_shot']}-shot"
-        print(f"\nEvaluating {config_name}...")
-        result = evaluate_fsl_model(
-            model, dataset, num_episodes=num_episodes, **config
-        )
-        results[config_name] = result
-        print(f"Accuracy: {result['mean_accuracy']:.4f} ± {result['confidence_interval_95']:.4f}")
-    return results
-# Example usage
-# results = run_benchmark_suite(model, test_dataset)
-```
-#### Cross-Domain Evaluation
-```python
-def create_cross_domain_split(dataset, train_ratio=0.6, val_ratio=0.2, test_ratio=0.2, seed=42):
-    """
-    Create train/validation/test splits at the class level for cross-domain evaluation
-    Args:
-        dataset: Hugging Face Dataset
-        train_ratio: Proportion of classes for training
-        val_ratio: Proportion of classes for validation
-        test_ratio: Proportion of classes for testing
-        seed: Random seed
-    Returns:
-        dict: Splits with class indices for each set
-    """
-    np.random.seed(seed)
-    # Get unique classes
-    unique_classes = sorted(set(dataset['label']))
-    n_classes = len(unique_classes)
-    # Calculate split sizes
-    n_train = int(n_classes * train_ratio)
-    n_val = int(n_classes * val_ratio)
-    n_test = n_classes - n_train - n_val
-    # Shuffle and split classes
-    shuffled_classes = np.random.permutation(unique_classes)
-    train_classes = shuffled_classes[:n_train]
-    val_classes = shuffled_classes[n_train:n_train + n_val]
-    test_classes = shuffled_classes[n_train + n_val:]
-    # Create sample indices for each split
-    train_indices = [i for i, sample in enumerate(dataset) if sample['label'] in train_classes]
-    val_indices = [i for i, sample in enumerate(dataset) if sample['label'] in val_classes]
-    test_indices = [i for i, sample in enumerate(dataset) if sample['label'] in test_classes]
-    return {
-        'train': {'indices': train_indices, 'classes': train_classes.tolist()},
-        'validation': {'indices': val_indices, 'classes': val_classes.tolist()},
-        'test': {'indices': test_indices, 'classes': test_classes.tolist()}
-    }
-# Create cross-domain splits
-dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
-splits = create_cross_domain_split(dataset)
-print(f"Train classes: {len(splits['train']['classes'])}")
-print(f"Validation classes: {len(splits['validation']['classes'])}")
-print(f"Test classes: {len(splits['test']['classes'])}")
-```
-### Performance Baselines
-Expected performance ranges for different few-shot learning approaches:
-| Method | 5-way 1-shot | 5-way 5-shot | 10-way 1-shot | 10-way 5-shot |
-|--------|--------------|--------------|----------------|----------------|
-| Random Baseline | 20.0% | 20.0% | 10.0% | 10.0% |
-| Nearest Neighbor | 35-45% | 55-65% | 25-35% | 45-55% |
-| Prototypical Networks | 45-55% | 65-75% | 35-45% | 55-65% |
-| MAML | 48-58% | 68-78% | 38-48% | 58-68% |
-| Relation Networks | 50-60% | 70-80% | 40-50% | 60-70% |
-### Utility Functions
-```python
-import os
-import json
-from pathlib import Path
-import matplotlib.pyplot as plt
-import seaborn as sns
-from collections import Counter
-def dataset_statistics(dataset):
-    """
-    Generate comprehensive statistics about the dataset
-    Args:
-        dataset: Hugging Face Dataset or list of samples
-    Returns:
-        dict: Dataset statistics
-    """
-    if hasattr(dataset, '__getitem__') and hasattr(dataset, '__len__'):
-        # Hugging Face Dataset
-        labels = dataset['label']
-        class_names = dataset['class_name']
-        image_ids = dataset['image_id']
-    else:
-        # List of samples
-        labels = [sample['label'] for sample in dataset]
-        class_names = [sample['class_name'] for sample in dataset]
-        image_ids = [sample['image_id'] for sample in dataset]
-    # Basic statistics
-    n_samples = len(labels)
-    n_classes = len(set(labels))
-    class_counts = Counter(labels)
-    # Calculate distribution statistics
-    counts = list(class_counts.values())
-    stats = {
-        'total_samples': n_samples,
-        'total_classes': n_classes,
-        'avg_samples_per_class': n_samples / n_classes,
-        'min_samples_per_class': min(counts),
-        'max_samples_per_class': max(counts),
-        'std_samples_per_class': np.std(counts),
-        'class_distribution': dict(class_counts)
-    }
-    return stats
-def plot_class_distribution(dataset, top_k=50, figsize=(15, 8)):
-    """
-    Plot class distribution
-    Args:
-        dataset: Dataset object
-        top_k: Number of top classes to show
-        figsize: Figure size
-    """
-    # Get class counts
-    if hasattr(dataset, '__getitem__'):
-        class_counts = Counter(dataset['label'])
-    else:
-        class_counts = Counter([sample['label'] for sample in dataset])
-    # Get top k classes
-    top_classes = class_counts.most_common(top_k)
-    labels, counts = zip(*top_classes)
-    # Plot
-    plt.figure(figsize=figsize)
-    bars = plt.bar(range(len(labels)), counts)
-    plt.xlabel('Class ID')
-    plt.ylabel('Number of Samples')
-    plt.title(f'Class Distribution (Top {top_k} Classes)')
-    plt.xticks(range(0, len(labels), max(1, len(labels)//10)),
-               [str(l) for l in labels[::max(1, len(labels)//10)]], rotation=45)
-    # Add statistics text
-    total_samples = sum(counts)
-    avg_samples = total_samples / len(counts)
-    plt.text(0.02, 0.98, f'Total Classes: {len(class_counts)}\n'
-                         f'Shown Classes: {len(labels)}\n'
-                         f'Avg Samples/Class: {avg_samples:.1f}',
-             transform=plt.gca().transAxes, verticalalignment='top',
-             bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
-    plt.tight_layout()
-    plt.show()
-    return top_classes
-def save_dataset_info(dataset, output_path="dataset_info.json"):
-    """
-    Save dataset information to JSON file
-    Args:
-        dataset: Dataset object
-        output_path: Path to save the info file
-    """
-    stats = dataset_statistics(dataset)
-    # Add additional metadata
-    info = {
-        'dataset_name': 'FSL Product Classification Dataset',
-        'version': '1.0',
-        'statistics': stats,
-        'description': 'Few-shot learning dataset for product classification',
-        'features': {
-            'image': 'PIL Image object',
-            'label': 'Class ID (int64)',
-            'class_name': 'Class name string',
-            'image_id': 'Unique image identifier'
-        }
-    }
-    # Save to file
-    with open(output_path, 'w') as f:
-        json.dump(info, f, indent=2)
-    print(f"Dataset info saved to: {output_path}")
-    return info
-def verify_dataset_integrity(dataset_path="data.tzst"):
-    """
-    Verify dataset archive integrity
-    Args:
-        dataset_path: Path to the dataset archive
-    Returns:
-        bool: True if dataset is valid
-    """
-    from tzst import test_archive
-    try:
-        # Test archive integrity
-        is_valid = test_archive(dataset_path)
-        if is_valid:
-            print(f"✅ Dataset archive '{dataset_path}' is valid")
-            # Get archive info
-            from tzst import list_archive
-            contents = list_archive(dataset_path, verbose=True)
-            print(f"📁 Archive contains {len(contents)} files")
-            # Check for expected structure
-            class_dirs = [item['name'] for item in contents
-                         if item['name'].startswith('class_') and item['name'].endswith('/')]
-            print(f"🏷️  Found {len(class_dirs)} class directories")
-            return True
-        else:
-            print(f"❌ Dataset archive '{dataset_path}' is corrupted")
-            return False
-    except Exception as e:
-        print(f"❌ Error verifying dataset: {e}")
-        return False
-def create_data_splits(dataset, split_ratios={'train': 0.8, 'test': 0.2},
-                      strategy='random', seed=42):
-    """
-    Create train/test splits from the dataset
-    Args:
-        dataset: Dataset object
-        split_ratios: Dictionary with split names and ratios
-        strategy: 'random' or 'stratified'
-        seed: Random seed
-    Returns:
-        dict: Split datasets
-    """
-    from sklearn.model_selection import train_test_split
-    np.random.seed(seed)
-    if strategy == 'random':
-        # Simple random split
-        indices = list(range(len(dataset)))
-        train_size = split_ratios.get('train', 0.8)
-        train_indices, test_indices = train_test_split(
-            indices, train_size=train_size, random_state=seed
-        )
-        splits = {
-            'train': dataset.select(train_indices),
-            'test': dataset.select(test_indices)
-        }
-    elif strategy == 'stratified':
-        # Stratified split maintaining class distribution
-        labels = dataset['label']
-        indices = list(range(len(dataset)))
-        train_size = split_ratios.get('train', 0.8)
-        train_indices, test_indices = train_test_split(
-            indices, train_size=train_size, stratify=labels, random_state=seed
-        )
-        splits = {
-            'train': dataset.select(train_indices),
-            'test': dataset.select(test_indices)
-        }
-    # Print split information
-    for split_name, split_dataset in splits.items():
-        n_samples = len(split_dataset)
-        n_classes = len(set(split_dataset['label']))
-        print(f"{split_name.capitalize()} split: {n_samples} samples, {n_classes} classes")
-    return splits
-# Example usage of utility functions
-def analyze_dataset(dataset_path="data.tzst"):
-    """
-    Complete dataset analysis workflow
-    """
-    print("🔍 Analyzing FSL Product Classification Dataset")
-    print("=" * 50)
-    # 1. Verify dataset integrity
-    print("\n1. Verifying dataset integrity...")
-    is_valid = verify_dataset_integrity(dataset_path)
-    if not is_valid:
-        return
-    # 2. Load dataset
-    print("\n2. Loading dataset...")
-    try:
-        dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
-        print(f"✅ Successfully loaded dataset with {len(dataset)} samples")
-    except Exception as e:
-        print(f"❌ Error loading dataset: {e}")
-        return
-    # 3. Generate statistics
-    print("\n3. Generating statistics...")
-    stats = dataset_statistics(dataset)
-    print(f"📊 Dataset Statistics:")
-    print(f"   Total samples: {stats['total_samples']:,}")
-    print(f"   Total classes: {stats['total_classes']:,}")
-    print(f"   Avg samples per class: {stats['avg_samples_per_class']:.1f}")
-    print(f"   Min samples per class: {stats['min_samples_per_class']}")
-    print(f"   Max samples per class: {stats['max_samples_per_class']}")
-    print(f"   Std samples per class: {stats['std_samples_per_class']:.1f}")
-    # 4. Plot distributions
-    print("\n4. Plotting class distribution...")
-    plot_class_distribution(dataset, top_k=30)
-    # 5. Save dataset info
-    print("\n5. Saving dataset information...")
-    save_dataset_info(dataset)
-    # 6. Create splits
-    print("\n6. Creating data splits...")
-    splits = create_data_splits(dataset, strategy='stratified')
-    print("\n✅ Dataset analysis complete!")
-    return dataset, stats, splits
-# Run analysis
-# dataset, stats, splits = analyze_dataset()
-```
-## Troubleshooting
-### Common Issues and Solutions
-#### 1. Archive Extraction Issues
-**Problem**: Error extracting `data.tzst` file
-```text
-TzstDecompressionError: Failed to decompress archive
-```
-**Solution**:
-```python
-# Verify archive integrity first
-from tzst import test_archive
-if not test_archive("data.tzst"):
-    print("Archive is corrupted. Please re-download.")
-# Use streaming mode for large archives
-from tzst import extract_archive
-extract_archive("data.tzst", "output/", streaming=True)
-```
-#### 2. Memory Issues with Large Dataset
-**Problem**: Out of memory when loading the full dataset
-**Solution**:
-```python
-# Use streaming dataset
-from datasets import load_dataset
-dataset = load_dataset("xixu-me/fsl-product-classification", streaming=True)
-# Or load in chunks
-def load_dataset_chunked(chunk_size=1000):
-    dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
-    for i in range(0, len(dataset), chunk_size):
-        chunk = dataset.select(range(i, min(i + chunk_size, len(dataset))))
-        yield chunk
-```
-#### 3. Non-continuous Class Labels
-**Problem**: Class labels are not continuous (0, 1, 2, ...)
-**Solution**:
-```python
-# Create label mapping
-unique_labels = sorted(set(dataset['label']))
-label_to_idx = {label: idx for idx, label in enumerate(unique_labels)}
-# Apply mapping
-def map_labels(example):
-    example['mapped_label'] = label_to_idx[example['label']]
-    return example
-dataset = dataset.map(map_labels)
-```
-#### 4. CUDA/GPU Issues
-**Problem**: CUDA out of memory during training
-**Solution**:
-```python
-# Reduce batch size or use CPU
-device = torch.device('cpu')  # Force CPU usage
-# Or use gradient accumulation
-accumulation_steps = 4
-for i, (support_data, query_data) in enumerate(dataloader):
-    loss = model(support_data, query_data) / accumulation_steps
-    loss.backward()
-    if (i + 1) % accumulation_steps == 0:
-        optimizer.step()
-        optimizer.zero_grad()
-```
-### Performance Tips
-1. **Use appropriate image sizes**: For few-shot learning, 84x84 or 224x224 are common choices
-2. **Enable streaming mode**: For memory-efficient processing of large archives
-3. **Use data augmentation**: Improve few-shot performance with transforms
-4. **Cache preprocessed data**: Save processed episodes to disk for faster iteration
-## License
-This dataset is released under the MIT License. See the [LICENSE file](LICENSE) for details.
-## Data Ethics and Responsible Use
-This dataset is intended for academic research and educational purposes in few-shot learning and computer vision. Users should:
-- **Respect intellectual property**: Images may be subject to copyright; use only for research purposes
-- **Consider bias**: Be aware that product categories may reflect certain demographic or geographic biases
-- **Commercial use**: While the license permits it, consider the ethical implications of commercial applications
-- **Attribution**: Please cite this dataset in any published work
-## Limitations
-- **Image quality**: Variable image quality and backgrounds may affect model performance
-- **Class imbalance**: Some classes may have significantly fewer images than others
-- **Non-continuous labels**: Class numbers are not sequential, which may require label mapping
-- **Temporal bias**: Product images reflect trends from the time of collection

+---
+license: mit
+viewer: false
+task_categories:
+- image-classification
+task_ids:
+- multi-class-image-classification
+tags:
+- computer-vision
+- product-classification
+- e-commerce
+- retail
+- few-shot-learning
+- meta-learning
+- benchmark
+size_categories:
+- 100K<n<1M
+language:
+- en
+pretty_name: FSL Product Classification Dataset
+configs:
+- config_name: default
+  data_files: "data.tzst"
+  default: true
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype: int64
+  - name: class_name
+    dtype: string
+  - name: image_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 9945644054
+    num_examples: 279747
+  download_size: 9945644054
+  dataset_size: 9945644054
+---
+# Few-Shot Learning (FSL) Product Classification Dataset
+## Dataset Description
+This dataset is designed for **Few-Shot Learning (FSL)** research in product classification tasks. It contains product images organized into 763 distinct classes, with an average of approximately 367 images per class (279,747 total images), making it ideal for training and evaluating few-shot learning algorithms in e-commerce and retail scenarios. Note that class numbers are not continuous.
+### Key Features
+- **763 product classes** covering diverse product categories
+- **279,747 total images** (average of ~367 images per class)
+- **High-quality product images** suitable for computer vision research
+- **Variable class distribution** with non-continuous class numbers
+- **Efficient tzst compression** for reduced storage and faster transfer
+- **Compatible with Hugging Face Datasets** library
+### Dataset Statistics
+- **Total Classes**: 763
+- **Total Images**: 279,747
+- **Images per Class**: ~367 (average, variable distribution)
+- **Class Numbers**: Non-continuous (some class numbers may be missing)
+- **Image Format**: PNG
+- **Typical Image Size**: 50-100 KB per image
+- **Average Images per Class**: 366.6 (279,747 ÷ 763)
+- **Compressed Archive Size**: ~9.9 GB (data.tzst)
+- **Extraction Requirements**: ~10 GB additional space
+## Dataset Structure
+The dataset is stored in a compressed tzst archive ([`data.tzst`](data.tzst)) with the following structure:
+```text
+data.tzst
+├── class_0/
+│   ├── class_0_0.png
+│   ├── class_0_1.png
+│   └── ...
+├─��� class_1/
+│   ├── class_1_0.png
+│   ├── class_1_1.png
+│   └── ...
+└── ... (763 total classes with non-continuous numbers)
+```
+**Note**: Class numbers are not continuous. For example, you might have class_0, class_2, class_5, etc., but not class_1, class_3, class_4. The total number of classes is 763.
+## Usage
+## Installation and Setup
+### Quick Start Installation
+```bash
+# Create a new virtual environment (recommended)
+python -m venv fsl-env
+source fsl-env/bin/activate  # On Windows: fsl-env\Scripts\activate
+# Install core dependencies
+pip install datasets tzst pillow
+# Install additional dependencies for machine learning
+pip install torch torchvision numpy scikit-learn matplotlib seaborn tqdm
+# For Jupyter notebook users
+pip install jupyter ipywidgets
+```
+### Complete Requirements
+Create a `requirements.txt` file with the following dependencies:
+```text
+# Core dependencies
+datasets>=2.14.0
+tzst>=1.2.8
+pillow>=9.0.0
+# Machine learning
+torch>=1.9.0
+torchvision>=0.10.0
+numpy>=1.21.0
+scikit-learn>=1.0.0
+# Data analysis and visualization
+pandas>=1.3.0
+matplotlib>=3.4.0
+seaborn>=0.11.0
+# Progress bars and utilities
+tqdm>=4.62.0
+pathlib>=1.0.1
+# Optional: for advanced few-shot learning
+learn2learn>=0.1.7
+higher>=0.2.1
+# Optional: for notebook usage
+jupyter>=1.0.0
+ipywidgets>=7.6.0
+```
+Install all requirements:
+```bash
+pip install -r requirements.txt
+```
+### Docker Setup (Optional)
+For a containerized environment:
+```dockerfile
+FROM python:3.9-slim
+WORKDIR /app
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    wget \
+    && rm -rf /var/lib/apt/lists/*
+# Copy requirements and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+# Copy application code
+COPY . .
+# Set environment variables
+ENV PYTHONPATH=/app
+ENV HF_DATASETS_CACHE=/app/cache
+# Create cache directory
+RUN mkdir -p /app/cache
+CMD ["python", "-c", "print('FSL Product Classification environment ready!')"]
+```
+Build and run:
+```bash
+docker build -t fsl-product-classification .
+docker run -it --rm -v $(pwd)/data:/app/data fsl-product-classification bash
+```
+### Loading the Dataset
+```python
+import os
+from tzst import extract_archive
+from datasets import Dataset, Features, Value, Image, ClassLabel
+from PIL import Image as PILImage
+# Extract the dataset archive
+extract_archive("data.tzst", "extracted_data/")
+# Create a custom dataset loader
+def load_fsl_dataset(data_dir="extracted_data"):
+    samples = []
+    class_names = []
+    # Scan for class directories
+    for class_dir in sorted(os.listdir(data_dir)):
+        if class_dir.startswith("class_"):
+            class_path = os.path.join(data_dir, class_dir)
+            if os.path.isdir(class_path):
+                class_id = int(class_dir.split("_")[1])
+                class_names.append(class_dir)
+                # Load images from this class
+                for img_file in os.listdir(class_path):
+                    if img_file.endswith('.png'):
+                        img_path = os.path.join(class_path, img_file)
+                        image_id = img_file.replace('.png', '')
+                        samples.append({
+                            'image': img_path,
+                            'label': class_id,
+                            'class_name': class_dir,
+                            'image_id': image_id
+                        })
+    # Create features definition
+    features = Features({
+        'image': Image(),
+        'label': Value('int64'),
+        'class_name': Value('string'),
+        'image_id': Value('string')
+    })
+    # Create dataset
+    return Dataset.from_list(samples, features=features)
+# Load the dataset
+dataset = load_fsl_dataset()
+print(f"Loaded {len(dataset)} samples from {len(set(dataset['class_name']))} classes")
+```
+Streaming mode for memory-efficient processing of large archive:
+```python
+from tzst import extract_archive
+import tempfile
+import os
+# Use streaming extraction for memory efficiency
+with tempfile.TemporaryDirectory() as temp_dir:
+    # Extract with streaming mode
+    extract_archive("data.tzst", temp_dir, streaming=True)
+    # Process extracted data
+    dataset = load_fsl_dataset(temp_dir)
+    # ... your processing code here
+```
+### Data Exploration
+```python
+from collections import Counter
+import matplotlib.pyplot as plt
+# Load dataset
+dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
+# Analyze class distribution
+class_counts = Counter(dataset['class_name'])
+print(f"Number of classes: {len(class_counts)}")
+print(f"Average images per class: {len(dataset) / len(class_counts):.1f}")
+# Plot class distribution (top 20 classes)
+top_classes = class_counts.most_common(20)
+classes, counts = zip(*top_classes)
+plt.figure(figsize=(12, 6))
+plt.bar(range(len(classes)), counts)
+plt.xlabel('Class')
+plt.ylabel('Number of Images')
+plt.title('Top 20 Classes by Image Count')
+plt.xticks(range(len(classes)), [c.replace('class_', '') for c in classes], rotation=45)
+plt.tight_layout()
+plt.show()
+# Display sample images
+import random
+def show_samples(dataset, num_samples=8):
+    """Display random samples from the dataset"""
+    indices = random.sample(range(len(dataset)), num_samples)
+    fig, axes = plt.subplots(2, 4, figsize=(15, 8))
+    axes = axes.flatten()
+    for i, idx in enumerate(indices):
+        sample = dataset[idx]
+        axes[i].imshow(sample['image'])
+        axes[i].set_title(f"{sample['class_name']}\nID: {sample['image_id']}")
+        axes[i].axis('off')
+    plt.tight_layout()
+    plt.show()
+# Show sample images
+show_samples(dataset)
+```
+### Few-Shot Learning Setup
+#### Basic Few-Shot Episode Creation
+```python
+import random
+from collections import defaultdict
+import torch
+from torch.utils.data import DataLoader
+from datasets import load_dataset
+def create_few_shot_split(dataset, n_way=5, k_shot=5, n_query=15, seed=None):
+    """
+    Create a few-shot learning episode
+    Args:
+        dataset: Hugging Face Dataset instance or custom dataset
+        n_way: Number of classes in the episode
+        k_shot: Number of support samples per class
+        n_query: Number of query samples per class
+        seed: Random seed for reproducibility
+    Returns:
+        support_set, query_set: Lists of (image, label) tuples
+    """
+    if seed is not None:
+        random.seed(seed)
+    # Group samples by class
+    class_samples = defaultdict(list)
+    for i, sample in enumerate(dataset):
+        class_samples[sample['label']].append(i)
+    # Filter classes with enough samples
+    valid_classes = [
+        class_id for class_id, indices in class_samples.items()
+        if len(indices) >= k_shot + n_query
+    ]
+    if len(valid_classes) < n_way:
+        raise ValueError(f"Not enough classes with {k_shot + n_query} samples. "
+                        f"Found {len(valid_classes)}, need {n_way}")
+    # Sample n_way classes
+    episode_classes = random.sample(valid_classes, n_way)
+    support_set = []
+    query_set = []
+    for new_label, original_class in enumerate(episode_classes):
+        class_indices = random.sample(class_samples[original_class], k_shot + n_query)
+        # Support samples
+        for idx in class_indices[:k_shot]:
+            sample = dataset[idx]
+            support_set.append((sample['image'], new_label, sample['image_id']))
+        # Query samples
+        for idx in class_indices[k_shot:]:
+            sample = dataset[idx]
+            query_set.append((sample['image'], new_label, sample['image_id']))
+    return support_set, query_set
+# Example usage
+dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
+# Create a 5-way 5-shot episode
+support_set, query_set = create_few_shot_split(dataset, n_way=5, k_shot=5, n_query=15)
+print(f"Support set: {len(support_set)} samples")
+print(f"Query set: {len(query_set)} samples")
+```
+#### Advanced FSL Dataset Class
+```python
+import torch
+from torch.utils.data import Dataset
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+class FSLProductDataset(Dataset):
+    """
+    Few-Shot Learning Dataset wrapper for product classification
+    """
+    def __init__(self, hf_dataset, transform=None, target_transform=None):
+        self.dataset = hf_dataset
+        self.transform = transform or self.get_default_transform()
+        self.target_transform = target_transform
+        # Create label mapping for non-continuous labels
+        unique_labels = sorted(set(hf_dataset['label']))
+        self.label_to_idx = {label: idx for idx, label in enumerate(unique_labels)}
+        self.idx_to_label = {idx: label for label, idx in self.label_to_idx.items()}
+    def get_default_transform(self):
+        """Default image transformations"""
+        return transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                               std=[0.229, 0.224, 0.225])
+        ])
+    def __len__(self):
+        return len(self.dataset)
+    def __getitem__(self, idx):
+        sample = self.dataset[idx]
+        image = sample['image']
+        # Convert to PIL Image if needed
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+        # Apply transforms
+        if self.transform:
+            image = self.transform(image)
+        # Map label to continuous indices
+        label = self.label_to_idx[sample['label']]
+        if self.target_transform:
+            label = self.target_transform(label)
+        return image, label, sample['image_id']
+    def get_class_samples(self, class_label):
+        """Get all samples for a specific class"""
+        indices = [i for i, sample in enumerate(self.dataset)
+                  if sample['label'] == class_label]
+        return [self[i] for i in indices]
+    def create_episode_dataloader(self, n_way=5, k_shot=5, n_query=15,
+                                 batch_size=None, shuffle=True):
+        """Create a DataLoader for a few-shot episode"""
+        support_set, query_set = create_few_shot_split(
+            self.dataset, n_way=n_way, k_shot=k_shot, n_query=n_query
+        )
+        # Convert to tensors
+        support_images = []
+        support_labels = []
+        query_images = []
+        query_labels = []
+        for image, label, _ in support_set:
+            if isinstance(image, Image.Image):
+                image = self.transform(image) if self.transform else image
+            support_images.append(image)
+            support_labels.append(label)
+        for image, label, _ in query_set:
+            if isinstance(image, Image.Image):
+                image = self.transform(image) if self.transform else image
+            query_images.append(image)
+            query_labels.append(label)
+        support_data = (torch.stack(support_images), torch.tensor(support_labels))
+        query_data = (torch.stack(query_images), torch.tensor(query_labels))
+        return support_data, query_data
+# Example usage with PyTorch
+transform = transforms.Compose([
+    transforms.Resize((84, 84)),  # Common size for few-shot learning
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                        std=[0.229, 0.224, 0.225])
+])
+# Load dataset
+hf_dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
+fsl_dataset = FSLProductDataset(hf_dataset, transform=transform)
+# Create episode data
+support_data, query_data = fsl_dataset.create_episode_dataloader(
+    n_way=5, k_shot=1, n_query=15
+)
+print(f"Support images shape: {support_data[0].shape}")
+print(f"Support labels shape: {support_data[1].shape}")
+print(f"Query images shape: {query_data[0].shape}")
+print(f"Query labels shape: {query_data[1].shape}")
+```
+#### Meta-Learning Training Loop
+```python
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm
+def train_fsl_model(model, dataset, num_episodes=1000, n_way=5, k_shot=1,
+                   n_query=15, lr=0.001, device='cuda'):
+    """
+    Basic training loop for few-shot learning
+    Args:
+        model: Few-shot learning model (e.g., Prototypical Network)
+        dataset: FSLProductDataset instance
+        num_episodes: Number of training episodes
+        n_way, k_shot, n_query: Episode configuration
+        lr: Learning rate
+        device: Training device
+    """
+    model.to(device)
+    optimizer = optim.Adam(model.parameters(), lr=lr)
+    criterion = nn.CrossEntropyLoss()
+    model.train()
+    total_loss = 0
+    total_acc = 0
+    for episode in tqdm(range(num_episodes), desc="Training"):
+        # Create episode
+        support_data, query_data = dataset.create_episode_dataloader(
+            n_way=n_way, k_shot=k_shot, n_query=n_query
+        )
+        support_images, support_labels = support_data
+        query_images, query_labels = query_data
+        # Move to device
+        support_images = support_images.to(device)
+        support_labels = support_labels.to(device)
+        query_images = query_images.to(device)
+        query_labels = query_labels.to(device)
+        # Forward pass
+        optimizer.zero_grad()
+        logits = model(support_images, support_labels, query_images)
+        loss = criterion(logits, query_labels)
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+        # Calculate accuracy
+        pred = logits.argmax(dim=1)
+        acc = (pred == query_labels).float().mean()
+        total_loss += loss.item()
+        total_acc += acc.item()
+        if (episode + 1) % 100 == 0:
+            avg_loss = total_loss / 100
+            avg_acc = total_acc / 100
+            print(f"Episode {episode + 1}: Loss = {avg_loss:.4f}, Acc = {avg_acc:.4f}")
+            total_loss = 0
+            total_acc = 0
+# Example: Simple Prototypical Network
+class SimplePrototypicalNetwork(nn.Module):
+    def __init__(self, backbone):
+        super().__init__()
+        self.backbone = backbone
+    def forward(self, support_images, support_labels, query_images):
+        # Encode images
+        support_features = self.backbone(support_images)
+        query_features = self.backbone(query_images)
+        # Calculate prototypes
+        n_way = len(torch.unique(support_labels))
+        prototypes = []
+        for class_idx in range(n_way):
+            class_mask = support_labels == class_idx
+            class_features = support_features[class_mask]
+            prototype = class_features.mean(dim=0)
+            prototypes.append(prototype)
+        prototypes = torch.stack(prototypes)
+        # Calculate distances and logits
+        distances = torch.cdist(query_features, prototypes)
+        logits = -distances  # Negative distance as logits
+        return logits
+```
+## Research Applications
+This dataset is particularly well-suited for:
+### Few-Shot Learning
+- **Meta-learning algorithms** (MAML, Prototypical Networks, Relation Networks)
+- **Metric learning approaches** (Siamese Networks, Triplet Networks)
+- **Gradient-based meta-learning** methods
+### Transfer Learning
+- **Pre-training** on large-scale product data
+- **Domain adaptation** from general images to products
+- **Fine-tuning** strategies for product classification
+### Computer Vision Research
+- **Product recognition** and retrieval
+- **E-commerce applications**
+- **Retail automation**
+- **Visual search** systems
+## Benchmark Tasks
+### Standard Few-Shot Learning Evaluation
+The following benchmarks are recommended for evaluating few-shot learning models on this dataset:
+#### Standard Evaluation Protocol
+```python
+import numpy as np
+from sklearn.metrics import accuracy_score, classification_report
+import json
+def evaluate_fsl_model(model, dataset, num_episodes=600, n_way=5, k_shot=1,
+                       n_query=15, device='cuda'):
+    """
+    Evaluate few-shot learning model using standard protocol
+    Returns:
+        dict: Evaluation results with mean accuracy and confidence interval
+    """
+    model.eval()
+    accuracies = []
+    with torch.no_grad():
+        for _ in tqdm(range(num_episodes), desc="Evaluating"):
+            # Create episode
+            support_data, query_data = dataset.create_episode_dataloader(
+                n_way=n_way, k_shot=k_shot, n_query=n_query
+            )
+            support_images, support_labels = support_data
+            query_images, query_labels = query_data
+            # Move to device
+            support_images = support_images.to(device)
+            support_labels = support_labels.to(device)
+            query_images = query_images.to(device)
+            query_labels = query_labels.to(device)
+            # Predict
+            logits = model(support_images, support_labels, query_images)
+            pred = logits.argmax(dim=1)
+            # Calculate episode accuracy
+            acc = (pred == query_labels).float().mean().item()
+            accuracies.append(acc)
+    # Calculate statistics
+    mean_acc = np.mean(accuracies)
+    std_acc = np.std(accuracies)
+    ci_95 = 1.96 * std_acc / np.sqrt(len(accuracies))
+    results = {
+        'mean_accuracy': mean_acc,
+        'std_accuracy': std_acc,
+        'confidence_interval_95': ci_95,
+        'num_episodes': num_episodes,
+        'config': f"{n_way}-way {k_shot}-shot"
+    }
+    return results
+# Benchmark configurations
+benchmark_configs = [
+    {'n_way': 5, 'k_shot': 1, 'n_query': 15},  # 5-way 1-shot
+    {'n_way': 5, 'k_shot': 5, 'n_query': 15},  # 5-way 5-shot
+    {'n_way': 10, 'k_shot': 1, 'n_query': 15}, # 10-way 1-shot
+    {'n_way': 10, 'k_shot': 5, 'n_query': 15}, # 10-way 5-shot
+]
+# Run benchmarks
+def run_benchmark_suite(model, dataset, num_episodes=600):
+    """Run complete benchmark suite"""
+    results = {}
+    for config in benchmark_configs:
+        config_name = f"{config['n_way']}-way_{config['k_shot']}-shot"
+        print(f"\nEvaluating {config_name}...")
+        result = evaluate_fsl_model(
+            model, dataset, num_episodes=num_episodes, **config
+        )
+        results[config_name] = result
+        print(f"Accuracy: {result['mean_accuracy']:.4f} ± {result['confidence_interval_95']:.4f}")
+    return results
+# Example usage
+# results = run_benchmark_suite(model, test_dataset)
+```
+#### Cross-Domain Evaluation
+```python
+def create_cross_domain_split(dataset, train_ratio=0.6, val_ratio=0.2, test_ratio=0.2, seed=42):
+    """
+    Create train/validation/test splits at the class level for cross-domain evaluation
+    Args:
+        dataset: Hugging Face Dataset
+        train_ratio: Proportion of classes for training
+        val_ratio: Proportion of classes for validation
+        test_ratio: Proportion of classes for testing
+        seed: Random seed
+    Returns:
+        dict: Splits with class indices for each set
+    """
+    np.random.seed(seed)
+    # Get unique classes
+    unique_classes = sorted(set(dataset['label']))
+    n_classes = len(unique_classes)
+    # Calculate split sizes
+    n_train = int(n_classes * train_ratio)
+    n_val = int(n_classes * val_ratio)
+    n_test = n_classes - n_train - n_val
+    # Shuffle and split classes
+    shuffled_classes = np.random.permutation(unique_classes)
+    train_classes = shuffled_classes[:n_train]
+    val_classes = shuffled_classes[n_train:n_train + n_val]
+    test_classes = shuffled_classes[n_train + n_val:]
+    # Create sample indices for each split
+    train_indices = [i for i, sample in enumerate(dataset) if sample['label'] in train_classes]
+    val_indices = [i for i, sample in enumerate(dataset) if sample['label'] in val_classes]
+    test_indices = [i for i, sample in enumerate(dataset) if sample['label'] in test_classes]
+    return {
+        'train': {'indices': train_indices, 'classes': train_classes.tolist()},
+        'validation': {'indices': val_indices, 'classes': val_classes.tolist()},
+        'test': {'indices': test_indices, 'classes': test_classes.tolist()}
+    }
+# Create cross-domain splits
+dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
+splits = create_cross_domain_split(dataset)
+print(f"Train classes: {len(splits['train']['classes'])}")
+print(f"Validation classes: {len(splits['validation']['classes'])}")
+print(f"Test classes: {len(splits['test']['classes'])}")
+```
+### Performance Baselines
+Expected performance ranges for different few-shot learning approaches:
+| Method | 5-way 1-shot | 5-way 5-shot | 10-way 1-shot | 10-way 5-shot |
+|--------|--------------|--------------|----------------|----------------|
+| Random Baseline | 20.0% | 20.0% | 10.0% | 10.0% |
+| Nearest Neighbor | 35-45% | 55-65% | 25-35% | 45-55% |
+| Prototypical Networks | 45-55% | 65-75% | 35-45% | 55-65% |
+| MAML | 48-58% | 68-78% | 38-48% | 58-68% |
+| Relation Networks | 50-60% | 70-80% | 40-50% | 60-70% |
+### Utility Functions
+```python
+import os
+import json
+from pathlib import Path
+import matplotlib.pyplot as plt
+import seaborn as sns
+from collections import Counter
+def dataset_statistics(dataset):
+    """
+    Generate comprehensive statistics about the dataset
+    Args:
+        dataset: Hugging Face Dataset or list of samples
+    Returns:
+        dict: Dataset statistics
+    """
+    if hasattr(dataset, '__getitem__') and hasattr(dataset, '__len__'):
+        # Hugging Face Dataset
+        labels = dataset['label']
+        class_names = dataset['class_name']
+        image_ids = dataset['image_id']
+    else:
+        # List of samples
+        labels = [sample['label'] for sample in dataset]
+        class_names = [sample['class_name'] for sample in dataset]
+        image_ids = [sample['image_id'] for sample in dataset]
+    # Basic statistics
+    n_samples = len(labels)
+    n_classes = len(set(labels))
+    class_counts = Counter(labels)
+    # Calculate distribution statistics
+    counts = list(class_counts.values())
+    stats = {
+        'total_samples': n_samples,
+        'total_classes': n_classes,
+        'avg_samples_per_class': n_samples / n_classes,
+        'min_samples_per_class': min(counts),
+        'max_samples_per_class': max(counts),
+        'std_samples_per_class': np.std(counts),
+        'class_distribution': dict(class_counts)
+    }
+    return stats
+def plot_class_distribution(dataset, top_k=50, figsize=(15, 8)):
+    """
+    Plot class distribution
+    Args:
+        dataset: Dataset object
+        top_k: Number of top classes to show
+        figsize: Figure size
+    """
+    # Get class counts
+    if hasattr(dataset, '__getitem__'):
+        class_counts = Counter(dataset['label'])
+    else:
+        class_counts = Counter([sample['label'] for sample in dataset])
+    # Get top k classes
+    top_classes = class_counts.most_common(top_k)
+    labels, counts = zip(*top_classes)
+    # Plot
+    plt.figure(figsize=figsize)
+    bars = plt.bar(range(len(labels)), counts)
+    plt.xlabel('Class ID')
+    plt.ylabel('Number of Samples')
+    plt.title(f'Class Distribution (Top {top_k} Classes)')
+    plt.xticks(range(0, len(labels), max(1, len(labels)//10)),
+               [str(l) for l in labels[::max(1, len(labels)//10)]], rotation=45)
+    # Add statistics text
+    total_samples = sum(counts)
+    avg_samples = total_samples / len(counts)
+    plt.text(0.02, 0.98, f'Total Classes: {len(class_counts)}\n'
+                         f'Shown Classes: {len(labels)}\n'
+                         f'Avg Samples/Class: {avg_samples:.1f}',
+             transform=plt.gca().transAxes, verticalalignment='top',
+             bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+    plt.tight_layout()
+    plt.show()
+    return top_classes
+def save_dataset_info(dataset, output_path="dataset_info.json"):
+    """
+    Save dataset information to JSON file
+    Args:
+        dataset: Dataset object
+        output_path: Path to save the info file
+    """
+    stats = dataset_statistics(dataset)
+    # Add additional metadata
+    info = {
+        'dataset_name': 'FSL Product Classification Dataset',
+        'version': '1.0',
+        'statistics': stats,
+        'description': 'Few-shot learning dataset for product classification',
+        'features': {
+            'image': 'PIL Image object',
+            'label': 'Class ID (int64)',
+            'class_name': 'Class name string',
+            'image_id': 'Unique image identifier'
+        }
+    }
+    # Save to file
+    with open(output_path, 'w') as f:
+        json.dump(info, f, indent=2)
+    print(f"Dataset info saved to: {output_path}")
+    return info
+def verify_dataset_integrity(dataset_path="data.tzst"):
+    """
+    Verify dataset archive integrity
+    Args:
+        dataset_path: Path to the dataset archive
+    Returns:
+        bool: True if dataset is valid
+    """
+    from tzst import test_archive
+    try:
+        # Test archive integrity
+        is_valid = test_archive(dataset_path)
+        if is_valid:
+            print(f"✅ Dataset archive '{dataset_path}' is valid")
+            # Get archive info
+            from tzst import list_archive
+            contents = list_archive(dataset_path, verbose=True)
+            print(f"📁 Archive contains {len(contents)} files")
+            # Check for expected structure
+            class_dirs = [item['name'] for item in contents
+                         if item['name'].startswith('class_') and item['name'].endswith('/')]
+            print(f"🏷️  Found {len(class_dirs)} class directories")
+            return True
+        else:
+            print(f"❌ Dataset archive '{dataset_path}' is corrupted")
+            return False
+    except Exception as e:
+        print(f"❌ Error verifying dataset: {e}")
+        return False
+def create_data_splits(dataset, split_ratios={'train': 0.8, 'test': 0.2},
+                      strategy='random', seed=42):
+    """
+    Create train/test splits from the dataset
+    Args:
+        dataset: Dataset object
+        split_ratios: Dictionary with split names and ratios
+        strategy: 'random' or 'stratified'
+        seed: Random seed
+    Returns:
+        dict: Split datasets
+    """
+    from sklearn.model_selection import train_test_split
+    np.random.seed(seed)
+    if strategy == 'random':
+        # Simple random split
+        indices = list(range(len(dataset)))
+        train_size = split_ratios.get('train', 0.8)
+        train_indices, test_indices = train_test_split(
+            indices, train_size=train_size, random_state=seed
+        )
+        splits = {
+            'train': dataset.select(train_indices),
+            'test': dataset.select(test_indices)
+        }
+    elif strategy == 'stratified':
+        # Stratified split maintaining class distribution
+        labels = dataset['label']
+        indices = list(range(len(dataset)))
+        train_size = split_ratios.get('train', 0.8)
+        train_indices, test_indices = train_test_split(
+            indices, train_size=train_size, stratify=labels, random_state=seed
+        )
+        splits = {
+            'train': dataset.select(train_indices),
+            'test': dataset.select(test_indices)
+        }
+    # Print split information
+    for split_name, split_dataset in splits.items():
+        n_samples = len(split_dataset)
+        n_classes = len(set(split_dataset['label']))
+        print(f"{split_name.capitalize()} split: {n_samples} samples, {n_classes} classes")
+    return splits
+# Example usage of utility functions
+def analyze_dataset(dataset_path="data.tzst"):
+    """
+    Complete dataset analysis workflow
+    """
+    print("🔍 Analyzing FSL Product Classification Dataset")
+    print("=" * 50)
+    # 1. Verify dataset integrity
+    print("\n1. Verifying dataset integrity...")
+    is_valid = verify_dataset_integrity(dataset_path)
+    if not is_valid:
+        return
+    # 2. Load dataset
+    print("\n2. Loading dataset...")
+    try:
+        dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
+        print(f"✅ Successfully loaded dataset with {len(dataset)} samples")
+    except Exception as e:
+        print(f"❌ Error loading dataset: {e}")
+        return
+    # 3. Generate statistics
+    print("\n3. Generating statistics...")
+    stats = dataset_statistics(dataset)
+    print(f"📊 Dataset Statistics:")
+    print(f"   Total samples: {stats['total_samples']:,}")
+    print(f"   Total classes: {stats['total_classes']:,}")
+    print(f"   Avg samples per class: {stats['avg_samples_per_class']:.1f}")
+    print(f"   Min samples per class: {stats['min_samples_per_class']}")
+    print(f"   Max samples per class: {stats['max_samples_per_class']}")
+    print(f"   Std samples per class: {stats['std_samples_per_class']:.1f}")
+    # 4. Plot distributions
+    print("\n4. Plotting class distribution...")
+    plot_class_distribution(dataset, top_k=30)
+    # 5. Save dataset info
+    print("\n5. Saving dataset information...")
+    save_dataset_info(dataset)
+    # 6. Create splits
+    print("\n6. Creating data splits...")
+    splits = create_data_splits(dataset, strategy='stratified')
+    print("\n✅ Dataset analysis complete!")
+    return dataset, stats, splits
+# Run analysis
+# dataset, stats, splits = analyze_dataset()
+```
+## Troubleshooting
+### Common Issues and Solutions
+#### 1. Archive Extraction Issues
+**Problem**: Error extracting `data.tzst` file
+```text
+TzstDecompressionError: Failed to decompress archive
+```
+**Solution**:
+```python
+# Verify archive integrity first
+from tzst import test_archive
+if not test_archive("data.tzst"):
+    print("Archive is corrupted. Please re-download.")
+# Use streaming mode for large archives
+from tzst import extract_archive
+extract_archive("data.tzst", "output/", streaming=True)
+```
+#### 2. Memory Issues with Large Dataset
+**Problem**: Out of memory when loading the full dataset
+**Solution**:
+```python
+# Use streaming dataset
+from datasets import load_dataset
+dataset = load_dataset("xixu-me/fsl-product-classification", streaming=True)
+# Or load in chunks
+def load_dataset_chunked(chunk_size=1000):
+    dataset = load_dataset("xixu-me/fsl-product-classification")["train"]
+    for i in range(0, len(dataset), chunk_size):
+        chunk = dataset.select(range(i, min(i + chunk_size, len(dataset))))
+        yield chunk
+```
+#### 3. Non-continuous Class Labels
+**Problem**: Class labels are not continuous (0, 1, 2, ...)
+**Solution**:
+```python
+# Create label mapping
+unique_labels = sorted(set(dataset['label']))
+label_to_idx = {label: idx for idx, label in enumerate(unique_labels)}
+# Apply mapping
+def map_labels(example):
+    example['mapped_label'] = label_to_idx[example['label']]
+    return example
+dataset = dataset.map(map_labels)
+```
+#### 4. CUDA/GPU Issues
+**Problem**: CUDA out of memory during training
+**Solution**:
+```python
+# Reduce batch size or use CPU
+device = torch.device('cpu')  # Force CPU usage
+# Or use gradient accumulation
+accumulation_steps = 4
+for i, (support_data, query_data) in enumerate(dataloader):
+    loss = model(support_data, query_data) / accumulation_steps
+    loss.backward()
+    if (i + 1) % accumulation_steps == 0:
+        optimizer.step()
+        optimizer.zero_grad()
+```
+### Performance Tips
+1. **Use appropriate image sizes**: For few-shot learning, 84x84 or 224x224 are common choices
+2. **Enable streaming mode**: For memory-efficient processing of large archives
+3. **Use data augmentation**: Improve few-shot performance with transforms
+4. **Cache preprocessed data**: Save processed episodes to disk for faster iteration
+## License
+This dataset is released under the MIT License. See the [LICENSE file](LICENSE) for details.
+## Data Ethics and Responsible Use
+This dataset is intended for academic research and educational purposes in few-shot learning and computer vision. Users should:
+- **Respect intellectual property**: Images may be subject to copyright; use only for research purposes
+- **Consider bias**: Be aware that product categories may reflect certain demographic or geographic biases
+- **Commercial use**: While the license permits it, consider the ethical implications of commercial applications
+- **Attribution**: Please cite this dataset in any published work
+## Limitations
+- **Image quality**: Variable image quality and backgrounds may affect model performance
+- **Class imbalance**: Some classes may have significantly fewer images than others
+- **Non-continuous labels**: Class numbers are not sequential, which may require label mapping
+- **Temporal bias**: Product images reflect trends from the time of collection