Popular GitHub Repositories for Human Object Interaction

YOLOv5 ? in PyTorch > ONNX > CoreML > TFLite

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

OpenMMLab Detection Toolbox and Benchmark

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

deep learning for image processing including classification and object-detection etc.

Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

CVPR 2025 论文和开源项目合集

Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Creative Coding: Generative Art, Data visualization, Interaction Design, Resources.

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

A paper list of object detection using deep learning.

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

Refine high-quality datasets and visual AI models

Best Practices, code samples, and documentation for Computer Vision.

Techniques for deep learning with satellite & aerial imagery