WILDS WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping. Installation We recommend using pip to install WILDS: pip install wilds If you have already installed it, please check that you have the latest version: python -c “import wilds; print(wilds.__version__)” # This should print “1.1.0”. If it doesn’t, update by running: pip install -U wilds If you plan to edit or contribute to WILDS, you should […]

MGCNet Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency(ECCV 2020) This is an official python implementation of MGCNet. This is the pre-print version https://arxiv.org/abs/2007.12494. video image Full video can be seen in [YouTube] https://www.youtube.com/watch?v=DXzkO3OwlYQ 1. Code + Requirement + thirdlib We run the code with python3.7, tensorflow 1.13 git clone –recursive https://github.com/jiaxiangshang/MGCNet.git cd MGCNet (sudo) pip install -r requirement.txt (1) For render loss(reconstruction loss), we use the differential renderer named tf_mesh_render(thanks!) https://github.com/google/tf_mesh_renderer.I find many issue happens here, so […]

BMaskR-CNN This code is developed on Detectron2. Boundary-preserving Mask R-CNNECCV 2020Tianheng Cheng, Xinggang Wang, Lichao Huang, Wenyu Liu Abstract Tremendous efforts have been made to improve mask localization accuracy in instance segmentation.Modern instance segmentation methods relying on fully convolutional networks perform pixel-wise classification,which ignores object boundaries and shapes, leading coarse and indistinct mask prediction results and imprecise localization.To remedy these problems, we propose a conceptually simple yet effective Boundary-preserving Mask R-CNN (BMask R-CNN) toleverage object boundary information to improve mask […]

Ultralight-SimplePose Ultra-lightweight human body posture key point CNN model. ModelSize:2.3MB HUAWEI P40 NCNN benchmark: 6ms/img, Support NCNN mobile terminal deployment Based on MXNET(>=1.5.1) GLUON(>=0.7.0) framework Top-down strategy: The input image is the person ROI detected by the object detector Lightweight mobile terminal human body posture key point model(COCO 17 person_keypoints) Detector:https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3 Mobile inference frameworks benchmark (4*ARM_CPU) Network Resolution Inference time (NCNN/Kirin 990) FLOPS Weight size HeatmapAccuracy Ultralight-Nano-SimplePose W:192 H:256 ~5.4ms 0.224BFlops 2.3MB 74.3% COCO2017 val keypoints metrics evaluate Average Precision […]