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aerial-image-segmentation

Aerial Image segmentation by PyTorch

alphapose_trt

基于AlphaPose的TensorRT加速

autoencodersegnet

Convolutional Autoencoder with SetNet in PyTorch

awesome-industrial-anomaly-detection

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

awesome-mvs

Awesome list of multi-view stereo papers

bdd100k

This repo has been moved to https://github.com/bdd100k/bdd100k permanently.

biblioteque

bit7z

A C++ static library offering a clean and simple interface to the 7-zip DLLs.

bts

From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

byol_ssl

c-3po

[PR] How to Reduce Change Detection to Semantic Segmentation

careg

cdae_multiscale

Image Denoising Using Deep Convolutional Autoencoder with Feature Pyramids

cityscapesscripts

README and scripts for the Cityscapes Dataset

classmix

coco_object_detection

cocostuff

The official homepage of the COCO-Stuff 10K dataset.

colmap

COLMAP - Structure-from-Motion and Multi-View Stereo

comic-book-reader-writer

comicreader

Qt本地漫画阅读软件，支持压缩文件双击解压

convdae

convolutional denoising autoencoder

crack_segmentation

This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu

cutmix-semisup-seg

Semi-supervised semantic segmentation needs strong, varied perturbations

deeplab-pytorch

PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

deeplab-v3plus

A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

deeplabv3plus-pytorch

DeepLabv3, DeepLabv3+ and pretrained weights on VOC & Cityscapes

deeplabv3plus-ssl

deeplearning-tensorflow2

本仓库我将使用谷歌TensorFlow2框架逐一复现经典的卷积神经网络：LeNet、AlexNet、VGG系列、GooLeNet、ResNet 系列、DenseNet 系列，以及现在比较经典的目标检测网络、语义分割网络等。

denoising-diffusion-pytorch

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

densecl

Dense Contrastive Learning (DenseCL) for self-supervised representation learning, CVPR 2021 Oral.