Accuracy Aligned. Concise Implementation of Swin Transformer

Overview

Swin Transformer

Accuracy Aligned. Concise Implementation of Swin Transformer

This repository contains the implementation of Swin Transformer, and the training codes on ImageNet datasets. We have aligned the output of our network with the official one, that is, using the same input and random seed, the output is identical to the official one.

Our implementation is highly based on einops, which makes the implementation more concise, and easy to be understand. (Intuitively, we use only 200 lines of codes compared with ~600 lines of official codes.) Besides, our implementation keeps the same training speed.

Model Epoch [email protected](our) [email protected](our) [email protected](official) [email protected](official) pretrained model
Swin-T 300 81.3 95.5 81.2 95.5 here

Usage

Train on ImageNet:

Train Swin-T

python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model Swin_T \
--batch-size 128 --drop-path 0.2 --data-path ~/ILSVRC2012/ --output_dir /data/SwinTransformer_exp/SwinT/

Train Swin-S

python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model Swin_S \
--batch-size 128 --drop-path 0.3 --data-path ~/ILSVRC2012/ --output_dir /data/SwinTransformer_exp/SwinS/

Train Swin-B

python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --model Swin_B \
--batch-size 128 --drop-path 0.5 --data-path ~/ILSVRC2012/ --output_dir /data/SwinTransformer_exp/SwinB/

Reference

The training process involves many training and augmentation tricks, such as stochastic depth, mixup, cutmix and random erasing. I borrow large from Deit (https://github.com/facebookresearch/deit).

Citations

@misc{liu2021swin,
      title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, 
      author={Ze Liu and Yutong Lin and Yue Cao and Han Hu and Yixuan Wei and Zheng Zhang and Stephen Lin and Baining Guo},
      year={2021},
      eprint={2103.14030},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Owner
FengWang
FengWang
YOLOv2 in PyTorch

YOLOv2 in PyTorch NOTE: This project is no longer maintained and may not compatible with the newest pytorch (after 0.4.0). This is a PyTorch implement

Long Chen 1.5k Jan 02, 2023
CS506-Spring2022 - Code and Slides for Boston University CS 506

CS 506 - Computational Tools for Data Science Code, slides, and notes for Boston

Lance Galletti 17 May 06, 2022
Code for the paper "Query Embedding on Hyper-relational Knowledge Graphs"

Query Embedding on Hyper-Relational Knowledge Graphs This repository contains the code used for the experiments in the paper Query Embedding on Hyper-

DimitrisAlivas 19 Jul 26, 2022
Using some basic methods to show linkages and transformations of robotic arms

roboticArmVisualizer Python GUI application to create custom linkages and adjust joint angles. In the future, I plan to add 2d inverse kinematics solv

Sandesh Banskota 1 Nov 19, 2021
existing and custom freqtrade strategies supporting the new hyperstrategy format.

freqtrade-strategies Description Existing and self-developed strategies, rewritten to support the new HyperStrategy format from the freqtrade-develop

39 Aug 20, 2021
2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup)智能人机交互自然语言理解赛道第二名参赛解决方案

2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup) 智能人机交互自然语言理解赛道第二名解决方案 比赛网址: CCIR-Cup-智能人机交互自然语言理解 1.依赖环境: python==3.8 torch==1.7.1+cu110 numpy==1.19.2 transformers=

JinXiang 22 Oct 29, 2022
A2LP for short, ECCV2020 spotlight, Investigating SSL principles for UDA problems

Label-Propagation-with-Augmented-Anchors (A2LP) Official codes of the ECCV2020 spotlight (label propagation with augmented anchors: a simple semi-supe

20 Oct 27, 2022
Pytorch implementation of "Neural Wireframe Renderer: Learning Wireframe to Image Translations"

Neural Wireframe Renderer: Learning Wireframe to Image Translations Pytorch implementation of ideas from the paper Neural Wireframe Renderer: Learning

Yuan Xue 7 Nov 14, 2022
Re-TACRED: Addressing Shortcomings of the TACRED Dataset

Re-TACRED Re-TACRED: Addressing Shortcomings of the TACRED Dataset

George Stoica 40 Dec 10, 2022
Finding Donors for CharityML

Finding-Donors-for-CharityML - Investigated factors that affect the likelihood of charity donations being made based on real census data.

Moamen Abdelkawy 1 Dec 30, 2021
StarGAN-ZSVC: Unofficial PyTorch Implementation

This repository is an unofficial PyTorch implementation of StarGAN-ZSVC by Matthew Baas and Herman Kamper. This repository provides both model architectures and the code to inference or train them.

Jirayu Burapacheep 11 Aug 28, 2022
When are Iterative GPs Numerically Accurate?

When are Iterative GPs Numerically Accurate? This is a code repository for the paper "When are Iterative GPs Numerically Accurate?" by Wesley Maddox,

Wesley Maddox 1 Jan 06, 2022
SberSwap Video Swap base on deep learning

SberSwap Video Swap base on deep learning

Sber AI 431 Jan 03, 2023
Segmentation for medical image.

EfficientSegmentation Introduction EfficientSegmentation is an open source, PyTorch-based segmentation framework for 3D medical image. Features A whol

68 Nov 28, 2022
Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV 2021).

AA-RMVSNet Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV 2021) in PyTorch. paper link: arXiv | CVF Change Log Ju

Qingtian Zhu 97 Dec 30, 2022
Final report with code for KAIST Course KSE 801.

Orthogonal collocation is a method for the numerical solution of partial differential equations

Chuanbo HUA 4 Apr 06, 2022
GUPNet - Geometry Uncertainty Projection Network for Monocular 3D Object Detection

GUPNet This is the official implementation of "Geometry Uncertainty Projection Network for Monocular 3D Object Detection". citation If you find our wo

Yan Lu 103 Dec 28, 2022
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
MegEngine implementation of YOLOX

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

旷视天元 MegEngine 77 Nov 22, 2022