#COCO dataset

This project delivers an implementation of YOLOv3 using TensorFlow 2.0, ensuring compatibility and improvements over older versions. It facilitates rapid deployment with pre-trained models, supports training with custom datasets, and offers starting options with COCO weights. The well-documented scripts and guides make it accessible for both hobbyists and professionals interested in exploring YOLOv3's potential in TensorFlow.

This open-source project presents a unified transformer-based architecture that enhances object detection and segmentation. It supports panoptic, instance, and semantic segmentation, and demonstrates data cooperation across tasks. The project is compatible with COCO, ADE20K, and Cityscapes datasets, offering a range of pre-trained models and tools for various AI applications.

The project enhances the OpenPose algorithm for effective 2D multi-person pose estimation on CPU, maintaining accuracy. It employs a streamlined model to identify 18 keypoints per individual, achieving 40% AP on the COCO 2017 validation set. The repository includes essential code for training, validation, and conversion to OpenVINO format, with C++ and Python demo support, catering to developers seeking efficient pose estimation in limited-resource settings.

FCOS streamlines object detection by removing anchor boxes, enhancing both speed and accuracy over previous models such as Faster R-CNN. Utilizing ResNeXt backbones and deformable convolutions, FCOS reaches up to 49% AP on COCO datasets with multi-scale testing. Its efficient design allows its use on less powerful hardware and integrates seamlessly with Detectron2 and mmdetection for broader application.

The SOLO project introduces compelling instance segmentation algorithms that emphasize accuracy and efficiency. This includes SOLO and its advanced version, SOLOv2, both achieving significant improvements in accuracy on COCO test-dev datasets. The algorithms offer direct instance segmentation, producing fine, detailed masks without anchor dependency, and maintain high-speed operations, even in lightweight configurations. Originating in 2020, the project has undergone updates that accelerate training by 1.7 times and support integrations with frameworks such as detectron2, making it an attractive option for researchers and developers interested in advanced segmentation techniques.

The project uses an advanced framework to enhance real-time object detection across different tasks. By integrating models like YOLOR-CSP, YOLOR-CSP-X, and YOLOR-P6, the project shows significant improvements in Average Precision metrics on COCO datasets. It employs innovative features, offering enhanced processing speed and accuracy, making it a valuable tool for researchers and developers.

Discover how Residual Steps Network (RSN) advances multi-person pose estimation by effectively aggregating intra-level spatial features for precise keypoint localization. The Pose Refine Machine (PRM) enhances this precision by refining keypoint positions. As a winner of the COCO 2019 Keypoint Challenge, RSN demonstrates leading performance on both COCO and MPII benchmarks. Now part of the MMPose framework, RSN offers a substantial resource for pose estimation research and development.