PyMAF This repository contains the code for the following paper: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback LoopHongwen Zhang*, Yating Tian*, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, Zhenan Sun Requirements packages necessary files mesh_downsampling.npz & DensePose UV data Run the following script to fetch mesh_downsampling.npz & DensePose UV data from other repositories. bash fetch_data.sh SMPL model files Fetch preprocessed data from SPIN. Download the pre-trained model and put it into the ./data/pretrained_model directory. After […]

AOT-GAN-for-Inpainting AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image InpaintingYanhong Zeng, Jianlong Fu, Hongyang Chao, and Baining Guo. AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image InpaintingYanhong Zeng, Jianlong Fu, Hongyang Chao, and Baining Guo. Citation If any part of our paper and code is helpful to your work,please generously cite and star us :kissing_heart: :kissing_heart: :kissing_heart: ! @inproceedings{yan2021agg, author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining}, title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting}, booktitle […]

ACSL This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection. Requirements 1. Environment: The requirements are exactly the same as BalancedGroupSoftmax. We tested on the following settings: python 3.7 cuda 10.0 pytorch 1.2.0 torchvision 0.4.0 mmcv 0.2.14 conda create -n mmdet python=3.7 -y conda activate mmdet pip install cython pip install numpy pip install torch pip install torchvision pip install pycocotools pip install matplotlib pip install terminaltables # download the source […]

carefree-learn carefree-learn is a minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch. Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. User Side import cflearn import numpy as np x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make().fit(x, y) Developer Side import cflearn import numpy as np cflearn.register_model(“wnd_full”, pipes=[cflearn.PipeInfo(“fcnn”), cflearn.PipeInfo(“linear”)]) x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make(“wnd_full”).fit(x, y) Please refer to Quick Start and Build Your Own […]