Object Detection
Showing 36 of 39 projects
Open Source Computer Vision Library providing real-time image processing and AI capabilities.
An open-source library with over 2500 optimized algorithms for real-time computer vision and machine learning.
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
A cutting-edge framework for training and deploying state-of-the-art YOLO models for object detection, segmentation, classification, and pose estimation.
A PyTorch-based platform for state-of-the-art object detection, segmentation, and visual recognition tasks.
An open source neural network framework in C and CUDA, known for YOLO real-time object detection models.
Facebook AI Research's software system implementing state-of-the-art object detection algorithms like Mask R-CNN and RetinaNet.
A fast and flexible Python library for image augmentation in computer vision tasks like classification, segmentation, and object detection.
A Python library for building custom machine learning models for tasks like image classification, object detection, and recommendations.
A PyTorch implementation of YOLOv3 for real-time object detection, supporting export to ONNX, CoreML, and TFLite.
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A comprehensive resource of deep learning techniques and models for analyzing satellite and aerial imagery.
A self-hosted photo management service with automatic face recognition, object detection, and semantic search.
A curated list of papers, code, and resources for object detection algorithms in computer vision.
Go language bindings for OpenCV 4, enabling computer vision applications with support for CUDA, DNN, and OpenVINO.
An open-source PyTorch toolbox for general 3D object detection, supporting LiDAR, camera, and multi-modal models.
A TensorFlow implementation of YOLO for real-time object detection, supporting weight conversion, training, and mobile deployment.
A PyTorch-based toolbox for LiDAR-based 3D object detection, supporting multiple state-of-the-art models and datasets.
A PyTorch-based toolbox for LiDAR-based 3D object detection, supporting multiple state-of-the-art models and datasets.
A fully convolutional neural network for real-time instance segmentation, achieving high speed and accuracy on COCO.
OpenCV bindings for Node.js enabling real-time computer vision applications in JavaScript.
A large-scale dataset of images with object segmentation, bounding boxes, and visual relationship annotations.
A curated list of resources for action recognition, video understanding, object detection, and pose estimation in computer vision.
A curated list of satellite and aerial imagery datasets with annotations for computer vision and deep learning tasks.
A PyTorch-based framework for visual object tracking and video object segmentation, featuring implementations of state-of-the-art trackers like TaMOs, RTS, and DiMP.
A simple and versatile framework for object detection and instance recognition with extensive model coverage and distributed training.
A JavaScript library for real-time hand detection and pose classification directly in the browser using TensorFlow.js.
A Python toolkit for working with the nuScenes and nuImages autonomous driving datasets, providing data loading, visualization, and evaluation utilities.
A JavaScript application framework for machine learning and its engineering, designed for Web developers.
An open-source deep learning API and server written in C++ that supports multiple backends like PyTorch, TensorRT, and TensorFlow for training and inference.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
A pioneering object detection system that combines region proposals with convolutional neural network features, significantly advancing detection accuracy.
An open source Python library and framework for building computer vision models on satellite, aerial, and large imagery sets.
An end-to-end 3D object detection network that uses deep point set networks and Hough voting to directly detect objects in point clouds.
TensorFlow implementation of YOLO for real-time object detection using pretrained YOLO_small, YOLO_tiny, and YOLO_face models.
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