The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Gluon CV Toolkit

[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation

A Simple and Versatile Framework for Object Detection and Instance Recognition

Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.

CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch.

Semantic Segmentation in Pytorch

Caffe models (including classification, detection and segmentation) and deploy files for famouse networks

[CVPR 2021 & IJCV 2024] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Please choose the openseg.pytorch project for the updated code that achieve SOTA on 6 benchmarks!

SOS IROS 2018 GOOGLE; StereoNet ECCV2018 GOOGLE; ActiveStereoNet ECCV2018 Oral GOOGLE; HITNET CVPR2021 GOOGLE；PLUME Uber ATG

ECCV2020 - Official code repository for the paper : STAR - A Sparse Trained Articulated Human Body Regressor

Unofficial implementation of: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

[CVPR'20] AANet: Adaptive Aggregation Network for Efficient Stereo Matching

Code repository for Part Grouping Network, ECCV 2018

Fully Convolutional Networks for Panoptic Segmentation (CVPR2021 Oral)

CRF-RNN PyTorch version http://crfasrnn.torr.vision

Official implementation of paper "Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields" (CVPR2020)

This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.

This repository contains code and tools for reading, processing, evaluating on, and visualizing Panoptic Parts datasets. Moreover, it contains code for reproducing our CVPR 2021 paper results.

Very random, limited python script to convert a python dictionary into an xml file

Folds batch normalisation and the following scale layer into a single scale layer for networks trained in Caffe. This can be done at inference time to reduce memory consumption.