Sharing my new article on an open-source project for automated signature detection in document processing. The article details:
- Dataset Engineering: Combining two public collections to create a hybrid dataset. - Architecture Benchmarking: Evaluating sota models such as YOLO series, DETR variants, and YOLOS for accuracy and efficiency. - Model Optimization: Using Optuna for hyperparameter tuning, achieving a 7.94% F1-score improvement. - Production Deployment: Implementing Triton Inference Server with an OpenVINO CPU backend for optimized inference.
It's not such a complex project, but I explore the training of the best current architectures for object detection and share all notebooks, data, models, and the repo with deployment and benchmarking details.