Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

Related tags

Deep LearningLLA
Overview

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection

GitHub

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

LLA is the first one-stage detector that surpasses two-stage detectors (e.g., Faster R-CNN) on CrowdHuman dataset. Experiments in the paper were conducted on the internal framework, thus we reimplement them on cvpods and report details as below.

Requirements

Get Started

  • install cvpods locally (requires cuda to compile)
python3 -m pip install 'git+https://github.com/Megvii-BaseDetection/cvpods.git'
# (add --user if you don't have permission)

# Or, to install it from a local clone:
git clone https://github.com/Megvii-BaseDetection/cvpods.git
python3 -m pip install -e cvpods

# Or,
pip install -r requirements.txt
python3 setup.py build develop
  • prepare datasets
cd /path/to/cvpods/datasets
ln -s /path/to/your/crowdhuman/dataset crowdhuman
  • Train & Test
git clone https://github.com/Megvii-BaseDetection/LLA.git
cd LLA/playground/detection/crowdhuman/lla.res50.fpn.crowdhuman.800size.30k  # for example

# Train
pods_train --num-gpus 8

# Test
pods_test --num-gpus 8 \
    MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
    OUTPUT_DIR /path/to/your/save_dir # optional

# Multi node training
## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"

Results on CrowdHuman val set

Model Backbone LR Sched. Aux. Branch NMS Thr. MR AP50 Recall Download
FCOS Res50 30k CenterNess 0.6 54.4 86.0 94.1 weights
ATSS Res50 30k CenterNess 0.6 49.4 87.3 94.1 weights
Faster R-CNN Res50 30k - 0.5 48.5 84.3 87.1 weights
LLA.FCOS Res50 30k IoU 0.6 47.5 88.2 94.4 weights

Acknowledgement

This repo is developed based on cvpods. Please check cvpods for more details and features.

License

This repo is released under the Apache 2.0 license. Please see the LICENSE file for more information.

Citing

If you use this work in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:

@article{ge2021lla,
  title={LLA: Loss-aware Label Assignment for Dense Pedestrian Detection},
  author={Ge, Zheng and Wang, Jianfeng and Huang, Xin and Liu, Songtao and Yoshie, Osamu},
  journal={arXiv preprint arXiv:2101.04307},
  year={2021}
}
Owner
BaseDetection Team of Megvii
Semantically Contrastive Learning for Low-light Image Enhancement

Semantically Contrastive Learning for Low-light Image Enhancement Here, we propose an effective semantically contrastive learning paradigm for Low-lig

48 Dec 16, 2022
[v1 (ISBI'21) + v2] MedMNIST: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification

MedMNIST Project (Website) | Dataset (Zenodo) | Paper (arXiv) | MedMNIST v1 (ISBI'21) Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bili

683 Dec 28, 2022
Implementation of ICLR 2020 paper "Revisiting Self-Training for Neural Sequence Generation"

Self-Training for Neural Sequence Generation This repo includes instructions for running noisy self-training algorithms from the following paper: Revi

Junxian He 45 Dec 31, 2022
code for ICCV 2021 paper 'Generalized Source-free Domain Adaptation'

G-SFDA Code (based on pytorch 1.3) for our ICCV 2021 paper 'Generalized Source-free Domain Adaptation'. [project] [paper]. Dataset preparing Download

Shiqi Yang 84 Dec 26, 2022
Implementation of Convolutional LSTM in PyTorch.

ConvLSTM_pytorch This file contains the implementation of Convolutional LSTM in PyTorch made by me and DavideA. We started from this implementation an

Andrea Palazzi 1.3k Dec 29, 2022
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021

HyperSPN This repository contains code for the paper: HyperSPNs: Compact and Expressive Probabilistic Circuits "HyperSPNs: Compact and Expressive Prob

8 Nov 08, 2022
Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features

MediumVC MediumVC is an utterance-level method towards any-to-any VC. Before that, we propose SingleVC to perform A2O tasks(Xi → Ŷi) , Xi means utter

谷下雨 47 Dec 25, 2022
a reimplementation of Holistically-Nested Edge Detection in PyTorch

pytorch-hed This is a personal reimplementation of Holistically-Nested Edge Detection [1] using PyTorch. Should you be making use of this work, please

Simon Niklaus 375 Dec 06, 2022
Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-Supervised Learning for Domain Adaptation on Point-Clouds Introduction Self-supervised learning (SSL) allows to learn useful representations from

Idan Achituve 66 Dec 20, 2022
This repository contains the source code for the paper First Order Motion Model for Image Animation

!!! Check out our new paper and framework improved for articulated objects First Order Motion Model for Image Animation This repository contains the s

13k Jan 09, 2023
Data and extra materials for the food safety publications classifier

Data and extra materials for the food safety publications classifier The subdirectories contain detailed descriptions of their contents in the README.

1 Jan 20, 2022
Learning Off-Policy with Online Planning, CoRL 2021

LOOP: Learning Off-Policy with Online Planning Accepted in Conference of Robot Learning (CoRL) 2021. Harshit Sikchi, Wenxuan Zhou, David Held Paper In

Harshit Sikchi 24 Nov 22, 2022
Py-FEAT: Python Facial Expression Analysis Toolbox

Py-FEAT is a suite for facial expressions (FEX) research written in Python. This package includes tools to detect faces, extract emotional facial expressions (e.g., happiness, sadness, anger), facial

Computational Social Affective Neuroscience Laboratory 147 Jan 06, 2023
Codebase for Time-series Generative Adversarial Networks (TimeGAN)

Codebase for Time-series Generative Adversarial Networks (TimeGAN)

Jinsung Yoon 532 Dec 31, 2022
Code for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".

Topology-Imbalance Learning for Semi-Supervised Node Classification Introduction Code for NeurIPS 2021 paper "Topology-Imbalance Learning for Semi-Sup

Victor Chen 40 Nov 23, 2022
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection

SAGA Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection Please refer to the Jupyter notebook (Example.ipynb) for an example of using t

9 Dec 28, 2022
Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Christopher T. Chubb 35 Dec 21, 2022
Super Resolution for images using deep learning.

Neural Enhance Example #1 — Old Station: view comparison in 24-bit HD, original photo CC-BY-SA @siv-athens. As seen on TV! What if you could increase

Alex J. Champandard 11.7k Dec 29, 2022
Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks".

SURGE: Sequential Recommendation with Graph Neural Networks This is our TensorFlow implementation for the paper: Sequential Recommendation with Graph

FIB LAB, Tsinghua University 53 Dec 26, 2022