Computer Vision
Dog Breed Prediction System
A computer vision application for dog breed classification using deep learning, image preprocessing, and prediction delivery through an application interface.
System Architecture
Problem Statement
Image classification problems require robust preprocessing, model design, and prediction handling to produce meaningful outputs for end users. This project was built to classify dog breeds from images using deep learning and serve predictions through an accessible interface.
Tech Stack
PythonTensorFlowCNNFlaskComputer Vision
Key Contributions
- Built an end-to-end image classification workflow for breed prediction
- Used CNN-based deep learning for feature learning and inference
- Integrated trained model outputs into an application-level prediction flow
Results
- Delivered an end-to-end computer vision pipeline
- Connected image input, inference, and result delivery in a single workflow
- Demonstrated applied deep learning beyond notebook-only experimentation
Engineering Decisions
- Used TensorFlow and CNNs because image classification required hierarchical feature learning
- Added Flask to make the project accessible beyond model training alone
- Structured the system to separate preprocessing, inference, and interface logic
Challenges Faced
- Managing image preprocessing consistency was critical to prediction quality
- Training deep learning models with limited resources required practical constraints
- Bridging the gap between model experimentation and usable application delivery required extra backend work