Jiawei-AnyVision

dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

DeepFAS

🔥Deep Learning for Face Anti-Spoofing

Mask2Former

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

semantic-segmentation-pytorch

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

unilm

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

Swin-Transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

image-app

Image Processing App deployed on Heroku Platform

dash-image-processing

Dash Demo App - Image Processing App

automl

Google Brain AutoML

U-2-Net

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

ConvNeXt

Code release for ConvNeXt model

ffhq-dataset

Flickr-Faces-HQ Dataset (FFHQ)

DPR

Code for Deep Single-image Portrait Image Relighting

Grad-CAM-pytorch

mmaction2

OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

awesome-face

An awesome face technology repository.

PFLD_68points_Pytorch

Implementation of PFLD For 68 Facial Landmarks By Pytorch

pipreqs

pipreqs - Generate pip requirements.txt file based on imports of any project. Looking for maintainers to move this project forward.

.tmux

🇫🇷 Oh my tmux! My self-contained, pretty & versatile tmux configuration made with ❤️

PFLD-pytorch

PFLD pytorch Implementation

pytorch_face_landmark

Fast and accurate face landmark detection library using PyTorch; Support 68-point semi-frontal and 39-point profile landmark detection; Support both coordinate-based and heatmap-based inference; Up to 100 FPS landmark inference speed with SOTA face detector on CPU.

apricot

apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

pycococreator

Helper functions to create COCO datasets

OpenNMT-py

Open Source Neural Machine Translation and (Large) Language Models in PyTorch

halonet-pytorch

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

RD3D

RD3D: RGB-D Salient Object Detection via 3D Convolutional Neural Networks

once-for-all

[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment

Awesome-FAS

Paper collection of about the face anti-spoofing