BCNet Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [BCNet, CVPR 2021] This is the official pytorch implementation of BCNet built on the open-source detectron2. Lei Ke, Yu-Wing Tai, Chi-Keung TangCVPR 2021 Two-stage instance segmentation with state-of-the-art performance. Image formation as composition of two overlapping layers. Bilayer decoupling for the occluder and occludee. Efficacy on both the FCOS and Faster R-CNN detectors. Under construction. Our code and pretrained model will be fully released in two months. Visualization of Occluded Objects Qualitative […]

Distribuuuu The pure and clear PyTorch Distributed Training Framework. Distribuuuu is a Distributed Classification Training Framework powered by native PyTorch. Please check tutorial for detailed Distributed Training tutorials: Single Node Single GPU Card Training [snsc.py] Single Node Multi-GPU Crads Training (with DataParallel) [snmc_dp.py] Multiple Nodes Multi-GPU Cards Training (with DistributedDataParallel) ImageNet training example [imagenet.py] For the complete training framework, please see distribuuuu. Requirements and Usage Dependency Install PyTorch>= 1.5 (has been tested on 1.5, 1.7.1 and 1.8) Install other dependencies: […]

ClearML ClearML – Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, ML-Ops and Data-Management ClearML is a ML/DL development and production suite, it contains three main modules: Experiment Manager – Automagical experiment tracking, environments and results ML-Ops – Automation, Pipelines & Orchestration solution for ML/DL jobs (K8s / Cloud / bare-metal) Data-Management – Fully differentiable data management & version control solution on top of object-storage(S3/GS/Azure/NAS) ClearML Experiment Manager Adding only 2 lines to your code gets you […]

DSM The source code for paper Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion 1. Introduction (scene-dominated to motion-dominated) Video datasets are usually scene-dominated, We propose to decouple the scene and the motion (DSM) with two simple operations, so that the model attention towards the motion information is better paid. The generated triplet is as below: What DSM learned? With DSM pretrain, the model learn to focus on motion region (Not necessarily actor) powerful without one […]

Hessian AWare Quantization HAWQ is an advanced quantization library written for PyTorch. HAWQ enables low-precision and mixed-precision uniform quantization, with direct hardware implementation through TVM. Installation PyTorch version >= 1.4.0 Python version >= 3.6 For training new models, you’ll also need NVIDIA GPUs and NCCL To install HAWQ and develop locally: git clone https://github.com/Zhen-Dong/HAWQ.git cd HAWQ pip install -r requirements.txt Getting Started Quantization-Aware Training An example to run uniform 8-bit quantization for resnet50 on ImageNet. export CUDA_VISIBLE_DEVICES=0 python quant_train.py -a […]