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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.
Thanks @SkalskiP , @nielsr , @sergiopaniego for the notebooks and resources that have been very helpful.
- https://huggingface.co/blog/samuellimabraz/signature-detection-model
- tech4humans/signature-detection-678b087d8b0ce22ae8c3f60e
- 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.
Thanks @SkalskiP , @nielsr , @sergiopaniego for the notebooks and resources that have been very helpful.
- https://huggingface.co/blog/samuellimabraz/signature-detection-model
- tech4humans/signature-detection-678b087d8b0ce22ae8c3f60e
- 1 reply