E-commerce Product Image Classification Dataset

Dataset Composition and Structure

The dataset is organized into 9 distinct classes, primarily reflecting major product categories prevalent on Amazon. These categories were chosen based on a balance between representation and practicality, ensuring sufficient diversity and relevance for training and testing computer vision models. The dataset’s structure includes:

• 18,175 images: Resized to 224×224 pixels, suitable for use in various pretrained CNN architectures.
• 9 Classes: Representing major e-commerce product categories, offering a broad spectrum of items typically found on online retail platforms.
• Train-Val-Check Sets: The dataset is split into training, validation, and check sets. The training and validation sets are designated for model training and hyperparameter tuning, while a smaller check set is reserved for model deployment, providing a visual evaluation of the model’s performance in a real-world scenario.

Application and Relevance

E-commerce platforms face significant challenges in product categorization due to the vast number of categories, the variety of products, and the need for precise classification. This dataset addresses these challenges by offering a well-balanced collection of images across multiple categories, allowing for robust model training and evaluation.