Status: Ongoing
Associated with: Thynk360
In the world of e-commerce, effective visual categorization of products is critical for improving searchability, personalization, and recommendation accuracy. As part of Thynk360’s mission to create practical, AI-powered solutions, I developed a Visual Product Tagging Engine—a computer vision system that automatically tags product images with descriptive labels such as color, type, and category.
Tools & Technologies Used
- Programming Language: Python
- Libraries & Frameworks: TensorFlow, Keras, NumPy, OpenCV
- Model Architecture: ResNet (Residual Networks) with Transfer Learning
- Dataset: Custom-labeled product image dataset + public retail datasets
- Techniques: Transfer learning, image preprocessing, fine-tuning
Description
This engine takes product images as input and automatically predicts relevant tags such as “red shirt,” “leather bag,” “sports shoes,” or “round sunglasses.” It’s designed to assist in e-commerce workflows like inventory management, search optimization, and catalog auto-generation.
At its core, the model is built using ResNet, a deep convolutional neural network architecture known for its ability to handle complex visual patterns. Rather than training a model from scratch, I used transfer learning, starting with a pre-trained ResNet model (trained on ImageNet) and fine-tuning it on a curated dataset of labeled retail product images.
The system includes a multi-label classification head, enabling the model to assign multiple tags to a single image (e.g., “casual,” “cotton,” “blue,” “shirt”). I used sigmoid activation and binary cross-entropy loss to make this possible. Data preprocessing included resizing, normalization, and augmentation (e.g., random flips and brightness adjustments) to make the model more robust.
Key Highlights
- Fine-tuned ResNet using transfer learning to classify multiple visual attributes.
- Implemented a multi-label classification pipeline to predict multiple tags per image.
- Achieved high precision on validation sets across product categories.
- Designed the model to be integrated into product listing workflows for marketplaces.
What You Learned / Achieved
This project gave me a deeper understanding of computer vision workflows in production settings. I learned how pre-trained architectures like ResNet can drastically speed up development while maintaining high accuracy through fine-tuning.
Through multi-label classification, I tackled challenges like imbalanced label distributions and co-occurrence of visual features, which required careful dataset balancing and regularization. I also explored methods to interpret model outputs using Grad-CAM, helping visualize which parts of an image influenced specific tag predictions—important for debugging and stakeholder trust.
From an application standpoint, I studied how e-commerce platforms manage visual metadata and realized how AI tagging could significantly reduce manual effort. The engine can help retailers quickly populate product descriptions, improve visual search accuracy, and even filter user recommendations based on visual preferences.
Deploying the model as an API also introduced me to real-time inference optimization using TensorFlow Lite and model quantization strategies—crucial for integration with mobile apps or web platforms.
Future Plans
Next, I aim to integrate this engine with a Streamlit dashboard where users can upload product images and receive tags in real-time, along with confidence scores. I also plan to expand the dataset with more product categories (e.g., electronics, furniture, cosmetics) to increase coverage.
In future versions, I will incorporate hierarchical tagging (e.g., distinguishing “t-shirt” under “clothing” under “fashion”) and possibly self-supervised learning techniques to reduce dependency on large labeled datasets.
This project is an exciting demonstration of how AI and computer vision can make e-commerce smarter, faster, and more scalable—core goals of Thynk360’s AI innovation strategy.
