AI Object Detection
Experimenting with AI vision systems capable of detecting and recognizing real-world objects.
Overview
A personal machine learning project developed in 2023 focused on object detection and real-time visual recognition.
The goal was to explore how AI models can identify, classify, and localize objects from visual input such as images or video streams.
The project involved dataset preparation, model experimentation, training workflows, testing, and accuracy evaluation.
This project also served as a practical exploration of computer vision, machine learning pipelines, and solving real-world detection problems.
Stack
Backend / API
ML / AI
Other
Inspiration
Driven by curiosity around artificial intelligence, automation, computer vision, and creating systems capable of understanding visual data.
Structure
Architecture
Framework
Python + TensorFlow. Models trained offline, inference run against image input or live video streams.
How it works
Data Collection
Collect and prepare training images / datasets for object recognition.
Preprocessing
Clean, organize, and format datasets for model training.
Model Training
Train an object detection model using machine learning techniques.
Evaluation
Measure prediction accuracy, detection quality, and performance.
Inference / Detection
Run the trained model against image or live visual input to detect objects.