PED: DETR for Crowd Pedestrian Detection

Related tags

Deep Learning PED-DETR-for-Pedestrian-Detection
Overview

PED: DETR for Crowd Pedestrian Detection

License: MIT

Code for PED: DETR For (Crowd) Pedestrian Detection

Paper

PED: DETR for Crowd Pedestrian Detection

Installation

The codebases are built on top of Detectron2, DETR, Deformable DETR and Fast-Transformer

License

PED is released under MIT License.

Citing

If you use PED in your research, please consider citing:

@misc{lin2021detr,
      title={DETR for Crowd Pedestrian Detection}, 
      author={Matthieu Lin and Chuming Li and Xingyuan Bu and Ming Sun and Chen Lin and Junjie Yan and Wanli Ouyang and Zhidong Deng},
      year={2021},
      eprint={2012.06785},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Comments
  • One import function does not exist.

    One import function does not exist.

    from dqrf.ops.functions.ms_deform_attn import SamplingAttention_RA, SamplingEncAttention, SamplingAttention_dec the function 'SamplingAttention_dec' seems missed in dqrf/ops/functions/ms_deform_attn.py, would you please kindly add this function to the script?

    opened by yexiguafuqihao 4
  • Setup help please

    Setup help please

    Hello, I am really interested in trying out your framework. Could you elaborate a little bit more on the installation process please? I did install Detectron2 and Fast-Transformer, but what should I do with DETR and Deformable-DETR, as you can't really install those as packages to your environment? Are these supposed to be added as submodules? Thank you for your time.

    opened by BeFranke 1
  • Lack of class

    Lack of class

    Thank you for sharing such a nice work! It would be very helpful if you can complement the definition of SamplingEncAttention_dec in PED-DETR-for-Pedestrian-Detection-master/dqrf/ops/functions/ms_deform_attn.py since there are only two classes(SamplingEncAttention and SamplingEncAttention_RA), which will lead to ImportError of SamplingEncAttention_dec.

    opened by us404898564 0
  • Instructions on train and inference.

    Instructions on train and inference.

    First of all very nice work.

    It would be very nice if you add minimum code/commands to train your model or do inference on it. It will help the fellow community.

    opened by VikasRajashekar 3
  • ModuleNotFoundError: No module named 'DenseQuerySelfAttention'

    ModuleNotFoundError: No module named 'DenseQuerySelfAttention'

    Dear Author. Firstly, Thanks for your outstanding work. I wanna follow your work, but I'm confused your code environment. could you inform me about it

    opened by CharlesChen24 0
Owner
lin matthieu
null
GitHub
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Pytorch Pedestrian Attribute Recognition: A strong PyTorch baseline of pedestrian attribute recognition and multi-label classification.

Jian 79 Dec 18, 2022
[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

null 336 Dec 25, 2022
Human Detection - Pedestrian Detection using OpenCV Python

Pedestrian Detection using OpenCV Python Follow us on Instagram for Machine Lear

Hrishikesh Dutta 1 Jan 23, 2022
Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination The offical implementation for the "NOH-NMS: Improving Pedestrian Detection by

Tencent YouTu Research 64 Nov 11, 2022
Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection This project provides an implementation for "LLA: Loss-aware Label Assignment for Dens

null 35 Dec 6, 2022
git《Beta R-CNN: Looking into Pedestrian Detection from Another Perspective》(NeurIPS 2020) GitHub:[fig3]

Beta R-CNN: Looking into Pedestrian Detection from Another Perspective This is the pytorch implementation of our paper "[Beta R-CNN: Looking into Pede

null 35 Sep 8, 2021
A light and fast one class detection framework for edge devices. We provide face detector, head detector, pedestrian detector, vehicle detector......

A Light and Fast Face Detector for Edge Devices Big News: LFD, which is a big update of LFFD, now is released (2021.03.09). It is strongly recommended

YonghaoHe 1.3k Dec 25, 2022
[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

dddzg 430 Dec 23, 2022
[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

Akshita Gupta 127 Dec 27, 2022
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

Hila Chefer 489 Jan 7, 2023
Deformable DETR is an efficient and fast-converging end-to-end object detector.

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

null 2k Jan 5, 2023
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

Jie Lei 雷杰 133 Dec 22, 2022
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.

null 281 Dec 30, 2022
PyTorch implementations of the paper: "Learning Independent Instance Maps for Crowd Localization"

IIM - Crowd Localization This repo is the official implementation of paper: Learning Independent Instance Maps for Crowd Localization. The code is dev

tao han 91 Nov 10, 2022
Jetson Nano-based smart camera system that measures crowd face mask usage in real-time.

MaskCam MaskCam is a prototype reference design for a Jetson Nano-based smart camera system that measures crowd face mask usage in real-time, with all

BDTI 212 Dec 29, 2022
Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting”

RGBT Crowd Counting Lingbo Liu, Jiaqi Chen, Hefeng Wu, Guanbin Li, Chenglong Li, Liang Lin. "Cross-Modal Collaborative Representation Learning and a L

null 37 Dec 8, 2022
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

LibraNet This repository includes the official implementation of LibraNet for crowd counting, presented in our paper: Weighing Counts: Sequential Crow

Hao Lu 18 Nov 5, 2022
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV, 2021)

DKPNet ICCV 2021 Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting Baseline of DKPNet is availa

null 19 Oct 14, 2022
LWCC: A LightWeight Crowd Counting library for Python that includes several pretrained state-of-the-art models.

LWCC: A LightWeight Crowd Counting library for Python LWCC is a lightweight crowd counting framework for Python. It wraps four state-of-the-art models

Matija Teršek 39 Dec 28, 2022