BMW-Semantic-Segmentation-Inference-API-GPU-CPU This is a repository for a Semantic Segmentation inference API using the Gluoncv CV toolkit. The training GUI (also based on the Gluoncv CV toolkit ) for the Semantic Segmentation workflow will be published soon. A sample inference model is provided with this repository for testing purposes. This repository can be deployed using docker. Prerequisites Ubuntu 18.04 or 20.04 LTS Windows 10 pro with hyper-v enabled and docker desktop NVIDIA Drivers (410.x or higher) Docker CE latest stable release […]

restyle-encoder Official Implementation of our ReStyle paper for both training and evaluation. ReStyle introduces an iterative refinement mechanism which can be applied over different StyleGAN encoders for solving the StyleGAN inversion task. Different from conventional encoder-based inversion techniques, our residual-based ReStyle scheme incorporates an iterative refinement mechanism to progressively converge to an accurate inversion of real images. For each domain, we show the input image on the left followed by intermediate inversion outputs. Getting Started Prerequisites Linux or macOS NVIDIA […]

NeX We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce NeXt-level view-dependent effects—in real time. Unlike traditional MPI that uses a set of simple RGBα planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated […]

LoFTR Code for “LoFTR: Detector-Free Local Feature Matching with Transformers”, CVPR 2021 LoFTR: Detector-Free Local Feature Matching with TransformersJiaming Sun*, Zehong Shen*, Yu’ang Wang*, Hujun Bao, Xiaowei ZhouCVPR 2021 TODO List and ETA The entire codebase for data pre-processing, training and validation is under major refactoring and will be released around June.Please subscribe to this discussion thread if you wish to be notified of the code release.In the meanwhile, discussions about the paper are welcomed in the discussion panel. [x] […]

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 […]

swin-transformer-pytorch Implementation of the Swin Transformer architecture. This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with shifted […]

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer projects: [email protected] Highlights End-to-End, Post-processing Free STARK is an end-to-end tracking approach, which directly predicts one accurate bounding box as the tracking result.Besides, STARK does not use any hyperparameters-sensitive post-processing, leading to stable performances. Real-Time Speed STARK-ST50 and STARK-ST101 run at 40FPS and 30FPS respectively on a Tesla V100 GPU. Strong performance Tracker LaSOT (AUC) GOT-10K (AO) TrackingNet (AUC) STARK 67.1 […]