object-detection

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

Techniques for deep learning with satellite & aerial imagery

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.

Best Practices, code samples, and documentation for Computer Vision.

A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.

RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026]

Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

Experience, Learn and Code the latest breakthrough innovations with Microsoft AI

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

Deep Learning and Reinforcement Learning Library for Scientists and Engineers

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Gluon CV Toolkit

A Unified Toolkit for Deep Learning Based Document Image Analysis

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

computer vision and sports

A PyTorch Implementation of Single Shot MultiBox Detector

[ECCV 2022] This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.