DCSL - Generalizable Crowd Counting via Diverse Context Style Learning

Last update: Jun 13, 2022

Related tags

Deep Learning DCSL

Overview

DCSL

Generalizable Crowd Counting via Diverse Context Style Learning

Requirement:

python==3.8

pytorch==1.9.1

Test_datasets

链接: https://pan.baidu.com/s/1kChZgPQRCF4x0rAskaDpZw 提取码: ddqp

Parameters:

链接: https://pan.baidu.com/s/1wkXIho0P3HQXBCOMuGopXA 提取码: gap8

Test:

You can run test.py to test the model.

When loading the training parameters trained on which dataset to test the model,please modify models/backbones/model.py: num_class=num_class_SHA or num_class_SHB or num_class_QNRF.

For example,when use SHA.pth,please set num_class=num_class_SHA

Owner

GitHub Repository

Discord Multi Tool that focuses on design and easy usage

Multi-Tool-v1.0 Discord Multi Tool that focuses on design and easy usage Delete webhook Block all friends Spam webhook Modify webhook Webhook info Tok

24 May 23, 2022

Official repository for "Orthogonal Projection Loss" (ICCV'21)

Orthogonal Projection Loss (ICCV'21) Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, & Fahad Shahbaz Khan Paper Link | Project Page

83 Dec 26, 2022

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting This is the origin Pytorch implementation of Informer in the followin

3.1k Dec 29, 2022

Deep High-Resolution Representation Learning for Human Pose Estimation

Deep High-Resolution Representation Learning for Human Pose Estimation (accepted to CVPR2019) News If you are interested in internship or research pos

167 Dec 27, 2022

Official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

DPGNN This repository is an official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Im

18 Oct 12, 2022

Conditional Generative Adversarial Networks (CGAN) for Mobility Data Fusion

This code implements the paper, Kim et al. (2021). Imputing Qualitative Attributes for Trip Chains Extracted from Smart Card Data Using a Conditional Generative Adversarial Network. Transportation Re

2 Feb 03, 2022

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

PackNet: https://arxiv.org/abs/1711.05769 Pretrained models are available here: https://uofi.box.com/s/zap2p03tnst9dfisad4u0sfupc0y1fxt Datasets in Py

216 Jan 05, 2023

PyTorch implementation of the ideas presented in the paper Interaction Grounded Learning (IGL)

Interaction Grounded Learning This repository contains a simple PyTorch implementation of the ideas presented in the paper Interaction Grounded Learni

4 Aug 31, 2022

Collection of machine learning related notebooks to share.

ML_Notebooks Collection of machine learning related notebooks to share. Notebooks GAN_distributed_training.ipynb In this Notebook, TensorFlow's tutori

14 Dec 22, 2022

Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Angora Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without s

833 Jan 07, 2023

VQMIVC - Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion

VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion (Interspeech

262 Dec 31, 2022

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors, CVPR 2021

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors Human POSEitioning System (H

66 Dec 21, 2022

The first dataset of composite images with rationality score indicating whether the object placement in a composite image is reasonable.

Object-Placement-Assessment-Dataset-OPA Object-Placement-Assessment (OPA) is to verify whether a composite image is plausible in terms of the object p

53 Nov 15, 2022