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 […]

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 […]