naszilla A repository to compare many popular NAS algorithms seamlessly across three popular benchmarks (NASBench 101, 201, and 301). You can implement your own NAS algorithm, and then easily compare it with eleven algorithms across three benchmarks. Installation Clone this repository and install its requirements (which includes nasbench,nas-bench-201, and nasbench301).It may take a few minutes. git clone https://github.com/naszilla/naszilla cd naszilla cat requirements.txt | xargs -n 1 -L 1 pip install pip install -e . You might need to replace line […]

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 […]

Backprop Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models. Solve a variety of tasks with pre-trained models or finetune them in one line for your own tasks. Out of the box tasks you can solve with Backprop: Conversational question answering in English Text Classification in 100+ languages Image Classification Text Vectorisation in 50+ languages Image Vectorisation Summarisation in English Emotion detection in English Text Generation For more specific use cases, you can adapt a task with […]

Torchsort Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.). Much of the code is copied from the original Numpy implementation at google-research/fast-soft-sort, with the isotonic regression solver rewritten as a PyTorch C++ and CUDA extension. Install pip install torchsort To build the CUDA extension you will need the CUDA toolchain installed. If youwant to build in an environment without a CUDA runtime (e.g. docker), you willneed to export the environment variableTORCH_CUDA_ARCH_LIST=”Pascal;Volta;Turing” before installing. Usage torchsort exposes […]

focal-frequency-loss This repository will provide the official code for the following paper: Focal Frequency Loss for Generative ModelsLiming Jiang, Bo Dai, Wayne Wu and Chen Change LoyarXiv preprint, 2020. Abstract: Despite the remarkable success of generative models in creating photorealistic images using deep neural networks, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we find that narrowing the frequency domain gap can ameliorate the image synthesis quality further. To this […]

ParallelStencil.jl ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding CUDA.jl Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance […]

Detectron2 This repo contains the training configurations, code and trained models trained on PubLayNet dataset using Detectron2 implementation.PubLayNet is a very large dataset for document layout analysis (document segmentation). It can be used to trained semantic segmentation/Object detection models. NOTE Models are trained on a portion of the dataset (train-0.zip, train-1.zip, train-2.zip, train-3.zip) Trained on total 191,832 images Models are evaluated on dev.zip (~11,000 images) Backbone pretrained on COCO dataset is used but trained from scratch on PubLayNet dataset Trained […]