OpenMMLab Detection Toolbox and Benchmark

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.

[TPAMI 2022 & CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

[CVPR 2022 Oral] Official implementation of DN-DETR

D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement 💥💥💥

[CVPR'2022] SAM-DETR & SAM-DETR++: Official PyTorch Implementation

This repository is an official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence". (https://arxiv.org/abs/2108.06152)

AI to Combat Environmental Pollution - detecting plastic waste in the environment to combat environmental pollution and promote circular economy (Deep Learning, PyTorch)

A User Interface for DETR built with Dash. 100% Python.

GRIT: Faster and Better Image-captioning Transformer (ECCV 2022)

A Tensorflow implementation of the DETR object detection architecture.

Reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer: Self-Attention with Linear Complexity)

An Efficient, Flexible, and General deep learning framework that retains minimal.

[ECCV2024 Oral] Official implementation of the paper "Relation DETR: Exploring Explicit Position Relation Prior for Object Detection"

A brand logo detection system by DETR

Official pytorch repository for CG-DETR "Correlation-guided Query-Dependency Calibration in Video Representation Learning for Temporal Grounding"

DETR - Faster RCNN implementation in tensorflow 2

[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.