BrightXiaoHan/tari-product-image

Qwen2.5-VL Product Classification Dataset

This dataset is designed for fine-tuning the Qwen2.5-VL model on product classification tasks.

Dataset Description

The dataset consists of product images with 10 balanced categories:

• 蛋白粉 (Protein powder)
• 纸巾 (Tissue paper)
• 衣服 (Clothes)
• 膏药贴 (Medicinal plasters)
• 胶囊 (Capsules)
• 电脑 (Computer)
• 手表 (Watch)
• 弓箭 (Bow and arrow)
• 医疗器械 (Medical equipment)
• 包 (Bag)

Each item has two types of samples:

1. Image classification: Given a product image, determine its category
2. Title classification: Given a product title, determine its category

Dataset Structure

This dataset follows the ShareGPT format:

For image classification items:

{
    'id': 'classification_[unique_id]',
    'image_path': PIL.Image.Image(...),  # The product image
    'item_type': 'image_classification',
    'conversations': [
        {'from': 'human', 'value': 'Prompt text with <image> token'},
        {'from': 'assistant', 'value': 'Model expected response (class label)'}
    ]
}

For title classification items:

{
    'id': 'title_classification_[unique_id]',
    'item_type': 'title_classification',  
    'conversations': [
        {'from': 'human', 'value': 'Prompt text with product title'},
        {'from': 'assistant', 'value': 'Model expected response (class label)'}
    ]
}

Dataset Statistics

• Train samples: 3498
• Validation samples: 740
• Test samples: 760

Usage

from datasets import load_dataset

# Load dataset from Hugging Face Hub
dataset = load_dataset("BrightXiaoHan/tari-product-image")

# Access an example
example = dataset["train"][0]
print(example["conversations"][0]['value'])
print(example["conversations"][1]['value'])
if "image_path" in example:
    image = example["image_path"]

This dataset is intended for fine-tuning Qwen2.5-VL models for product classification tasks.