C

computer-vision

Local-first, vision-LLM CAPTCHA solver for AI agents — MCP plugin + NopeCHA-style HTTP job API over any Chromium

Official Ultralytics YOLO Flutter plugin for real-time inference on Android and iOS across major vision tasks.

GitHub: https://github.com/ultralytics/yolo-flutter-app

https://pub.dev/packages/ultralytics_yolo

Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

GitHub: https://github.com/ultralytics/ultralytics

https://platform.ultralytics.com

YOLOE data pipeline for grounding and detection labels, predictions, text refinement, cache generation, and visualization.

GitHub: https://github.com/ultralytics/yoloe_data_engine

https://docs.ultralytics.com/models/yoloe/

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

GitHub: https://github.com/ultralytics/yolov5

https://docs.ultralytics.com/yolov5/

PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

GitHub: https://github.com/ultralytics/yolov3

https://docs.ultralytics.com/models/yolov3/

Docker container, inference pipeline, and submission tooling for deploying trained YOLOv3 object detection models in the xView satellite imagery challenge.

GitHub: https://github.com/ultralytics/xview-docker

https://challenge.xviewdataset.org/

PyTorch sandbox for testing convolutional networks, ResNets, and other architectures on MNIST digits.

GitHub: https://github.com/ultralytics/mnist

https://ultralytics.com

YOLOv3 training, preprocessing, validation, and inference for object detection in xView satellite imagery and the xView detection challenge.

GitHub: https://github.com/ultralytics/xview-yolov3

https://docs.ultralytics.com

An open-source computer vision framework for wildlife image analysis, featuring state-of-the-art models for species classification and detection.

Semantic Vectorizing and Masking Creator: A desktop application for AI-powered image segmentation, masking, and vectorization.

Turns ordinary photos into thermographic, X-ray, Kirlian, and night-vision renders via real physics simulation — not a filter.

Aligns portrait photographs onto a fixed 2048 face grid for mosaic work

Turn one crowded sheet of drawn icons into a clean, individually named, ready-to-use asset library. Local-first desktop app and CLI, with optional AI naming by a vision model running on your own machine.

Master thesis: free and open-source hands-free cursor control through gaze and facial gestures, for people with severe motor paralysis.

A pipeline for curating and sanitizing large-scale image datasets.

Rendering and point-cloud generation toolkit for the PartNet and ShapeNet 3D shape datasets (ICCV 2021).

Follow me - Vision is a vision-based control system designed to automate the navigation of remote-controlled objects like drones or cars. Using computer vision, the system detects a target object and generates commands to autonomously follow the subject in real time. Ideal for applications where a vehicle needs to track and follow a designated guide without manual intervention. For example, it could be used to help an elderly person by having a shopping cart follow them around a store.

This project is part of a larger, future initiative called Follow Me !

Real-time YOLOv7/YOLOv7‑Tiny object detection on low‑powered Jetson Orin Nano using TensorRT and a multi‑threaded ROS 1 pipeline for edge robotics, drone monitoring.

3D Hand Pose Estimation using Multi-View CNNs