24 Repositories
Python detr Libraries
HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
HugsVision is an open-source and easy to use all-in-one huggingface wrapper for computer vision. The goal is to create a fast, flexible and user-frien
[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
DAB-DETR This is the official pytorch implementation of our ICLR 2022 paper DAB-DETR. Authors: Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer
OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh
Official Implementation of DE-DETR and DELA-DETR in "Towards Data-Efficient Detection Transformers"
DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-DETR and DELA-DETR in
Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics
[AAAI2022] Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics Overall pipeline of OCN. Paper Link: [arXiv] [AAAI
YOLOv7 - Framework Beyond Detection
🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)
Awesome Visual-Transformer Collect some Transformer with Computer-Vision (CV) papers. If you find some overlooked papers, please open issues or pull r
Transformer in Computer Vision
Transformer-in-Vision A paper list of some recent Transformer-based CV works. If you find some ignored papers, please open issues or pull requests. **
Fast convergence of detr with spatially modulated co-attention
Fast convergence of detr with spatially modulated co-attention Usage There are no extra compiled components in SMCA DETR and package dependencies are
Reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer: Self-Attention with Linear Complexity)
Linear Multihead Attention (Linformer) PyTorch Implementation of reproducing the Linear Multihead Attention introduced in Linformer paper (Linformer:
An official implementation of the Anchor DETR.
Anchor DETR: Query Design for Transformer-Based Detector Introduction This repository is an official implementation of the Anchor DETR. We encode the
AI4Good project for detecting waste in the environment
Detect waste AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in
Implementation of the paper Recurrent Glimpse-based Decoder for Detection with Transformer.
REGO-Deformable DETR By Zhe Chen, Jing Zhang, and Dacheng Tao. This repository is the implementation of the paper Recurrent Glimpse-based Decoder for
PyTorch Implementation of Sparse DETR
Sparse DETR By Byungseok Roh*, Jaewoong Shin*, Wuhyun Shin*, and Saehoon Kim at Kakao Brain. (*: Equal contribution) This repository is an official im
SMCA replication There are no extra compiled components in SMCA DETR and package dependencies are minimal
Usage There are no extra compiled components in SMCA DETR and package dependencies are minimal, so the code is very simple to use. We provide instruct
Implementation of ICCV21 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers
Implementation of ICCV 2021 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers arxiv This repository is based on detr Recently, DETR
A simple version of DeTR
DeTR-Lite A simple version of DeTR Before you enjoy this DeTR-Lite The purpose of this project is to allow you to learn the basic knowledge of DeTR. P
Official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".
The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings and that the spatial embeddings make minor contributions, increasing the need for high-quality content embeddings and thus increasing the training difficulty.
Moment-DETR code and QVHighlights dataset
Moment-DETR QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries Jie Lei, Tamara L. Berg, Mohit Bansal For dataset de
Deformable DETR is an efficient and fast-converging end-to-end object detector.
Deformable DETR: Deformable Transformers for End-to-End Object Detection.
PED: DETR for Crowd Pedestrian Detection
PED: DETR for Crowd Pedestrian Detection Code for PED: DETR For (Crowd) Pedestrian Detection Paper PED: DETR for Crowd Pedestrian Detection Installati
Code for PED: DETR For (Crowd) Pedestrian Detection
Code for PED: DETR For (Crowd) Pedestrian Detection
Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
UP-DETR: Unsupervised Pre-training for Object Detection with Transformers This is the official PyTorch implementation and models for UP-DETR paper: @a