Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use.
We are your essential toolkit for computer vision. From data loading to real-time zone counting, we provide the building blocks so you can focus on building applications around your models. 🤝
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.
MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.
Ultralytics YOLOv5 🚀 is a fast, accurate, and easy-to-use computer vision model developed by Ultralytics. Based on the PyTorch framework, YOLOv5 is renowned for its speed, accuracy, and simplicity.
deep learning for image processing including classification and object-detection etc.
\ It is written in Python and uses Qt for its graphical interface.
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
NATTENCUDAARCH is the compute capability of your GPU. Print yours with
This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
To measure accuracy, download COCO-annotations for Pycocotools to the ./coco/annotations/instancesval2017.json