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PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
Track-Anything is an AI-driven video object segmentation and tracking system. It utilizes the Segment Anything Model to isolate and mask multiple objects across video frames, providing tools for automated mask propagation and background-filling inpainting. The system distinguishes itself through a multi-object segmentation pipeline that can follow several distinct targets simultaneously. It includes a video inpainting utility to remove tracked objects and replace them with synthesized background content, as well as temporal mask refinement to correct tracking drift. The project covers broad
jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti
This project is a multi-object tracking framework designed to assign persistent identities to detected bounding boxes across consecutive video frames. It functions as a computer vision tracking algorithm that monitors multiple moving targets in real time by associating detections with consistent labels. The system utilizes a state estimation approach centered on a Kalman filter to predict future object positions and maintain identity during detection gaps. It employs the Hungarian algorithm for optimal data association and calculates intersection over union to match predicted track locations
This project is a computer vision system for object segmentation and tracking across images and videos. It employs models capable of identifying and masking objects using text prompts, bounding boxes, click points, or image exemplars.
The main features of facebookresearch/sam3 are: Video Object Tracking, Joint Detection-Embedding Architectures, Object Tracking Systems, Image Segmentation, Language-Based Segmentation, Prompt-Based Masking, Shared Spatial Memory, Trajectory Consistency.
Open-source alternatives to facebookresearch/sam3 include: paddlepaddle/paddledetection — PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of… gaomingqi/track-anything — Track-Anything is an AI-driven video object segmentation and tracking system. It utilizes the Segment Anything Model… dusty-nv/jetson-inference — jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU… abewley/sort — This project is a multi-object tracking framework designed to assign persistent identities to detected bounding boxes… dmlc/gluon-cv — Gluon-CV is an MXNet computer vision library that provides a comprehensive collection of pre-implemented vision… idea-research/grounded-segment-anything — Grounded-Segment-Anything is a suite of specialized tools for multimodal visual analysis, text-based segmentation, and…