Web-Based AI Image Recognition

PHP, JavaScript, CSS, Machine Learning (ML), HTML, Docker, Computer Vision, REST API · I’m building a browser-accessible tool that can take an image uploaded by the user, run it through an AI model, and immediately return classification or detection results on-screen. All core logic must live server-side, exposed through a clean REST or GraphQL endpoint, so the front-end remains lightweight and responsive across modern web browsers.

Key expectations • Model accuracy matters: please start with a proven open-source architecture (e.g., YOLOv8, ResNet, EfficientDet) fine-tuned on a small sample set I’ll provide, then document how to retrain it when new data arrives. • One-click deploy: include a Dockerfile and concise README so I can spin everything up on a fresh VPS. • Results returned as JSON plus visual overlays (bounding boxes or masks) rendered on a simple HTML/React page for verification. • Security: images must be discarded after processing; no long-term storage. • Clean code and inline comments so I can extend the project later—potentially adding natural-language features or predictive analytics down the line.

Deliverables 1. Source code for back-end API and front-end demo page 2. Pre-trained model weights and data preprocessing scripts 3. Deplo