The fashion e-commerce industry moves fast. Every day, thousands of new products are uploaded to online stores, and each item must be correctly tagged — shirts, watches, shoes, handbags, and more. Traditionally, this process is manual, time-consuming, expensive, and prone to human error.
The Smart Fashion Classifier solves this problem with deep learning and cloud-native architecture. It is an end-to-end machine learning system that automatically classifies fashion products from images and exposes the model through a scalable microservice deployment pipeline.
This project is not just about training a neural network — it is about building a production-ready AI service.
The Problem
E-commerce platforms rely heavily on accurate product categorization for:
- Search and filtering
- Inventory management
- Product recommendations
- Customer experience
Manual tagging does not scale efficiently. As product catalogs grow, automation becomes essential.
The Solution
The Smart Fashion Classifier uses a Convolutional Neural Network (CNN) trained on the Fashion Product Images dataset from Kaggle to automatically predict product categories from images.
Once trained, the model is deployed as a cloud-ready microservice, allowing businesses or applications to send an image and instantly receive a predicted fashion category.
What Makes This Project Unique
Beyond building a classifier, this project implements a Gateway–Serving Architecture, separating image preprocessing from model inference. This design allows each service to scale independently in production environments like Kubernetes.
This mirrors real-world ML system design used in modern AI-powered products.
Key Features
Automated Fashion Classification
Predicts product categories such as T-shirts, Watches, Casual Shoes, Handbags, and more.
Transfer Learning with Xception
Uses a pre-trained Xception model on ImageNet, fine-tuned for fashion classification, ensuring strong accuracy with limited training data.
Microservice Architecture
- Gateway Service: Handles image input, preprocessing, and gRPC requests
- Model Service (TensorFlow Serving): Hosts the trained model and performs inference
REST + gRPC Integration
Combines Flask REST APIs with high-performance gRPC communication for efficient prediction serving.
Dockerized Deployment
Both services are containerized for consistent local and cloud execution.
Kubernetes & AWS EKS Ready
Built for scalable deployment in cloud environments.
Machine Learning Pipeline
- Model Architecture: Xception (transfer learning)
- Input Processing: Resize to 299×299 and normalize to [-1, 1]
- Output: 15 fashion product categories
- Training Environment: Jupyter Notebook with data augmentation and evaluation metrics
- Evaluation: Confusion matrix and classification report
This ensures the model is both accurate and production-aligned.
System Architecture
Gateway Service (Flask API)
- Accepts image URLs
- Downloads and preprocesses images
- Converts inputs to Protobuf
- Sends requests to TensorFlow Serving via gRPC
Model Service (TF Serving)
- Hosts the trained Xception model
- Returns prediction probabilities
This clean separation improves scalability, maintainability, and reliability.
Running Locally
The entire system can be launched with Docker Compose, spinning up both the Gateway and Model services together. Once running, developers can send test images and receive real-time predictions.
Cloud Deployment on AWS EKS
The project includes Kubernetes manifests for:
- Model deployment and service
- Gateway deployment and service
- Internal service discovery
- External LoadBalancer exposure
Deployment to AWS Elastic Kubernetes Service (EKS) allows the system to handle real-world traffic and scale automatically based on demand.
Real-World Use Cases
Retail Inventory Automation
Automatically tag thousands of new products uploaded by vendors.
Visual Product Search
Allow customers to take a photo of an item and find similar products.
Recommendation Systems
Improve personalization through accurate product categorization.
Why This Project Matters
Many ML projects stop at model training. This one goes further by addressing:
- Model serving
- API design
- Containerization
- Cloud deployment
- Scalable microservices
It demonstrates the complete journey from notebook to production, which is exactly how modern AI systems are built in industry.
Next Step: Interactive Web Dashboard
The proposed next phase is to build a Streamlit-based interactive frontend, allowing users to upload images and see classification results in real time — making the system accessible to non-technical users.
Final Thoughts
The Smart Fashion Classifier is a strong example of applying deep learning to a real business problem while following modern ML engineering best practices. It blends data science, backend engineering, and cloud deployment into a single, cohesive solution.
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