centerX accelerates object detection by leveraging the detectron2 framework and the CenterNet architecture. This repository provides implementations, training scripts, and evaluation results demonstrating efficient object detection capabilities. It addresses the need for faster training and competitive accuracy compared to existing object detection methods by optimizing training procedures and incorporating techniques like Knowledge Distillation. It is designed for researchers and practitioners seeking high-performance object detection solutions.
Key features include support for data augmentation using imgaug, stochastic weight averaging (SWA), and Knowledge Distillation for improved accuracy. The repository offers conversion scripts for models to Caffe, ONNX, and TensorRT formats. Performance comparisons showcase faster training times and competitive mAP scores compared to other object detection approaches like CenterNet-better. A comprehensive evaluation including crowd and widerface datasets is provided.
- Backbone Flexibility: Supports ResNet-18 and ResNet-50 backbones for adaptable performance and computational cost.
- Model Optimization: Implements techniques like Knowledge Distillation and SWA to enhance accuracy and generalization.
- Model Conversion: Provides scripts for converting models to Caffe, ONNX, and TensorRT for deployment in various environments.
The project is actively maintained with recent commits and continuous experiments exploring various optimization techniques. The codebase is well-documented, and included resources offer clear instructions for data preparation, training, and evaluation. The repository benefits from active community engagement and supports a variety of datasets, indicating a reliable foundational framework.
centerX benefits researchers and developers needing fast and accurate object detection solutions. It facilitates efficient model training and deployment, offering improved performance compared to traditional methods. It is suitable for applications requiring real-time or resource-constrained environments, providing a strong foundation with comprehensive evaluation and conversion tools.