Counterfeit Medicine Vision Model
Deep learning system for detecting counterfeit medicine from a photo, transfer-learning image classifier with a full deployment stack around it.
Key metrics
1,367
Training images
Roboflow Universe, CC BY 4.0
3
Services
React · Express · FastAPI
Binary
Task
Authentic vs counterfeit
Research question
Verifying medicine authenticity today requires expert inspection or lab testing, neither scalable to a pharmacy counter, a supply-chain checkpoint, or a consumer checking a suspicious package.
Method
A ResNet-18 model, transfer-learned to classify medicine images as authentic or counterfeit with a confidence score, deployed as a proper microservices stack rather than a notebook demo.
FastAPI handles inference. Express handles JWT auth and role-based access. React handles real-time upload and verification.
Architecture
- 01
ResNet-18 transfer learning for authentic vs counterfeit classification with confidence
- 02
FastAPI owns inference
- 03
Express owns JWT auth and role-based access around the inference endpoint
- 04
React handles real-time upload and verification
- 05
Docker Compose packages the full stack
Findings
- Full three-service microservices architecture (React → Express → FastAPI) shipped end-to-end, not just a trained model
- JWT-authenticated API with role-based access control around the inference endpoint
- Trained on a Roboflow counterfeit-medicine dataset (1,367 training images) with a documented train/val/test split
Method details
- Task
- Binary image classification, authentic vs. counterfeit
- Model
- ResNet-18, transfer learning
- Architecture
- FastAPI (ML inference) + Express (auth/API) + React (frontend)
- Delivery
- Dockerized, docker-compose for full stack
- Dataset
- Roboflow Universe counterfeit-medicine dataset, CC BY 4.0
Stack
References
- Roboflow Universe counterfeit-medicine dataset (CC BY 4.0)
- ResNet-18 transfer learning (PyTorch)