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yolo-world
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YoloDotNet - A C# .NET 8.0 project for Classification, Object Detection, OBB Detection, Segmentation and Pose Estimation in both images and videos.
Use this project to automatically annotate your dataset for free in CVAT
ROS package for SOTA Computer Vision Models including SAM, Cutie, GroundingDINO, YOLO-World, VLPart, DEVA and MaskDINO.
Run zero-shot prediction models on your data
Tensorrt codebase to inference in c++ for all major neural arch using onnx
EfficientSAM + YOLO World base model for use with Autodistill.
ODLabel is a powerful tool for zero-shot object detection, labeling and visualization. It provides an intuitive graphical user interface for labeling objects in images using the YOLO-World model and supports various output formats such as YOLO, COCO, CSV, and XML.
YOLO-World-v2 のGradioデモをColaboratoryで実行するノートブック
System designed to provide real-time assistance to visually impaired individuals by detecting obstacles in their path and helping them finding desire objects in their environment.
[ArXiv2024] ModPrompt: Visual Modality Prompt for Adapting Vision-Language Object Detectors
Class-Conditional self-reward mechanism for improved Text-to-Image models
Learning project for exploring generative AI for robotics action planning and control
YOLO World base module for use with Autodistill.
BrainHack 2024 competition repository for the TIL-AI category in the Novice track for Team dingdongs.
Description of YOLO-World along with it's application
Object detection and tracking in videos with YOLOWorld and ByteTrack, featuring a Gradio interface for easy interaction and multi-class support.
Repository containing implemetation and documentation of master's thesis Object detection and segmentation in historical encrypted manuscripts at at Faculty of Electrical Engineering and Information Technology of Slovak University of Technology in Bratislava (FEI STU).
This repository contains code for detecting Personal Protective Equipment (PPE) using YOLOv8 and YOLO-World's Custom Model with Custom Classes. The goal of this project is to identify whether individuals in images are wearing appropriate PPE such as helmets, safety vests, goggles, etc.