This is an official implementation for "AS-MLP: An Axial Shifted MLP Architecture for Vision".

Related tags

Deep LearningAS-MLP
Overview

AS-MLP architecture for Image Classification

Model Zoo

Image Classification on ImageNet-1K

Network Resolution Top-1 (%) Params FLOPs Throughput (image/s) model
AS-MLP-T 224x224 81.3 28M 4.4G 1047 onedrive
AS-MLP-S 224x224 83.1 50M 8.5G 619 onedrive
AS-MLP-B 224x224 83.3 88M 15.2G 455 onedrive

Usage

Install

  • Clone this repo:
git clone https://github.com/svip-lab/AS-MLP
cd AS-MLP
  • Create a conda virtual environment and activate it:
conda create -n asmlp python=3.7 -y
conda activate asmlp
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
  • Install timm==0.3.2:
pip install timm==0.3.2
  • Install cupy-cuda101:
pip install cupy-cuda101
  • Install Apex:
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  • Install other requirements:
pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8

Evaluation

To evaluate a pre-trained AS-MLP on ImageNet val, run:

bash train_scripts/test.sh

Training from scratch

To train a AS-MLP on ImageNet from scratch, run:

bash train_scripts/train.sh

You can easily reproduce our results. Enjoy!

Throughput

To measure the throughput, run:

bash train_scripts/get_throughput.sh

Citation

If this project is helpful for you, you can cite our paper:

@article{Lian_2021_ASMLP,
  author = {Lian, Dongze and Yu, Zehao and Sun, Xing and Gao, Shenghua},
  title = {AS-MLP: An Axial Shifted MLP Architecture for Vision},
  journal={arXiv preprint arXiv:2107.08391},
  year = {2021}
}

Acknowledgement

The code is built upon Swin-Transformer

Owner
SVIP Lab
ShanghaiTech Vision and Intelligent Perception Lab
SVIP Lab
(NeurIPS 2020) Wasserstein Distances for Stereo Disparity Estimation

Wasserstein Distances for Stereo Disparity Estimation Accepted in NeurIPS 2020 as Spotlight. [Project Page] Wasserstein Distances for Stereo Disparity

Divyansh Garg 92 Dec 12, 2022
Unofficial PyTorch implementation of Guided Dropout

Unofficial PyTorch implementation of Guided Dropout This is a simple implementation of Guided Dropout for research. We try to reproduce the algorithm

2 Jan 07, 2022
Implementation of Deep Deterministic Policy Gradiet Algorithm in Tensorflow

ddpg-aigym Deep Deterministic Policy Gradient Implementation of Deep Deterministic Policy Gradiet Algorithm (Lillicrap et al.arXiv:1509.02971.) in Ten

Steven Spielberg P 247 Dec 07, 2022
Implementation of ReSeg using PyTorch

Implementation of ReSeg using PyTorch ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation Pascal-Part Annotations Pascal VOC 2010

Onur Kaplan 46 Nov 23, 2022
Lightweight library to build and train neural networks in Theano

Lasagne Lasagne is a lightweight library to build and train neural networks in Theano. Its main features are: Supports feed-forward networks such as C

Lasagne 3.8k Dec 29, 2022
The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue. How do I cite D-REX? For now, cite

Alon Albalak 6 Mar 31, 2022
Catbird is an open source paraphrase generation toolkit based on PyTorch.

Catbird is an open source paraphrase generation toolkit based on PyTorch. Quick Start Requirements and Installation The project is based on PyTorch 1.

Afonso Salgado de Sousa 5 Dec 15, 2022
An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data

GLOM TensorFlow This Python package attempts to implement GLOM in TensorFlow, which allows advances made by several different groups transformers, neu

Rishit Dagli 32 Feb 21, 2022
Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN Introduction Image super-resolution (SR) is the process of recovering high-resoluti

8 Apr 15, 2022
Session-based Recommendation, CoHHN, price preferences, interest preferences, Heterogeneous Hypergraph, Co-guided Learning, SIGIR2022

This is our implementation for the paper: Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation Xiaokun Zhang, Bo

Xiaokun Zhang 27 Dec 02, 2022
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

PyVarInf PyVarInf provides facilities to easily train your PyTorch neural network models using variational inference. Bayesian Deep Learning with Vari

342 Dec 02, 2022
Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems

WideLinears Pytorch parallel Neural Networks A package of pytorch modules for fast paralellization of separate deep neural networks. Ideal for agent-b

1 Dec 17, 2021
Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation

Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation By: Zayd Hammoudeh and Daniel Lowd Paper: Arxiv Preprint Coming soo

Zayd Hammoudeh 2 Oct 08, 2022
Spectral Tensor Train Parameterization of Deep Learning Layers

Spectral Tensor Train Parameterization of Deep Learning Layers This repository is the official implementation of our AISTATS 2021 paper titled "Spectr

Anton Obukhov 12 Oct 23, 2022
Measuring and Improving Consistency in Pretrained Language Models

ParaRel 🤘 This repository contains the code and data for the paper: Measuring and Improving Consistency in Pretrained Language Models as well as the

Yanai Elazar 26 Dec 02, 2022
Edison AT is software Depression Assistant personal.

Edison AT Edison AT is software / program Depression Assistant personal. Feature: Analyze emotional real-time from face. Audio Edison(Comingsoon relea

Ananda Rauf 2 Apr 24, 2022
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

Holy Wu 35 Jan 01, 2023
A PyTorch Implementation of "Neural Arithmetic Logic Units"

Neural Arithmetic Logic Units [WIP] This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Ra

Kevin Zakka 181 Nov 18, 2022
IRON Kaggle project done while doing IRONHACK Bootcamp where we had to analyze and use a Machine Learning Project to predict future sales

IRON Kaggle project done while doing IRONHACK Bootcamp where we had to analyze and use a Machine Learning Project to predict future sales. In this case, we ended up using XGBoost because it was the o

1 Jan 04, 2022
In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021

In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021. Balestriero et

Sean M. Hendryx 1 Jan 27, 2022