Reproduction of Vision Transformer in Tensorflow2. Train from scratch and Finetune.

Related tags

Deep Learningvision-transformer-tf
Overview

Vision Transformer(ViT) in Tensorflow2

Tensorflow2 implementation of the Vision Transformer(ViT).

This repository is for An image is worth 16x16 words: Transformers for image recognition at scale and How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

Limitations.

  • Due to memory limitations, only the ti/16, s/16, and b/16 models were tested.
  • Due to memory limitations, batch_size 2048 in s16 and 1024 in b/16 (in paper, 4096).
  • Due to computational resource limitations, only reproduce using imagenet1k.

All experimental results and graphs are opend in Wandb.

Model weights

Since this is personal project, it is hard to train with large datasets like imagenet21k. For a pretrain model with good performance, see the official repo. But if you really need it, contact me.

Install dependencies

pip install -r requirements

All experiments were done on tpu_v3-8 with the support of TRC. But you can experiment on GPU. Check conf/config.yaml and conf/downstream.yaml

  # TPU options
  env:
    mode: tpu
    gcp_project: {your_project}
    tpu_name: node-1
    tpu_zone: europe-west4-a
    mixed_precision: True
  # GPU options
  # env:
  #   mode: gpu
  #   mixed_precision: True

Train from scratch

python run.py experiment=vit-s16-aug_light1-bs_2048-wd_0.1-do_0.1-dp_0.1-lr_1e-3 base.project_name=vit-s16-aug_light1-bs_2048-wd_0.1-do_0.1-dp_0.1-lr_1e-3 base.save_dir={your_save_dir} base.env.gcp_project={your_gcp_project} base.env.tpu_name={your_tpu_name} base.debug=False

Downstream

python run.py --config-name=downstream experiment=downstream-imagenet-ti16_384 base.pretrained={your_checkpoint} base.project_name={your_project_name} base.save_dir={your_save_dir} base.env.gcp_project={your_gcp_project} base.env.tpu_name={your_tpu_name} base.debug=False

Board

To track metics, you can use wandb or tensorboard (default: wandb). You can change in conf/callbacks/{filename.yaml}.

modules:
  - type: MonitorCallback
  - type: TerminateOnNaN
  - type: ProgbarLogger
    params:
      count_mode: steps
  - type: ModelCheckpoint
    params:
      filepath: ???
      save_weights_only: True
  - type: Wandb
    project: vit
    nested_dict: False
    hide_config: True
    params: 
      monitor: val_loss
      save_model: False
  # - type: TensorBoard
  #   params:
  #     log_dir: ???
  #     histogram_freq: 1

TFC

This open source was assisted by TPU Research Cloud (TRC) program

Thank you for providing the TPU.

Citations

@article{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@article{steiner2021train,
  title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers},
  author={Steiner, Andreas and Kolesnikov, Alexander and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},
  journal={arXiv preprint arXiv:2106.10270},
  year={2021}
}
Owner
sungjun lee
AI Researcher
sungjun lee
GitHub Repository
Source code of the paper Meta-learning with an Adaptive Task Scheduler.

ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the

Huaxiu Yao 16 Dec 26, 2022
PaRT: Parallel Learning for Robust and Transparent AI

PaRT: Parallel Learning for Robust and Transparent AI This repository contains the code for PaRT, an algorithm for training a base network on multiple

Mahsa 0 May 02, 2022
TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently.

Adversarial Chess TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently. Requirements To run

Muthu Chidambaram 30 Sep 07, 2021
Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow 201 Dec 21, 2022
Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation（Unfinished）

Keras-FCN Fully convolutional networks and semantic segmentation with Keras. Models Models are found in models.py, and include ResNet and DenseNet bas

645 Dec 29, 2022
Official repository for the ICCV 2021 paper: UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model.

UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model Official repository for the ICCV 2021 paper: UltraPose: Syn

MomoAILab 92 Dec 21, 2022
Gif-caption - A straightforward GIF Captioner written in Python

Broksy's GIF Captioner Have you ever wanted to easily caption a GIF without havi

3 Apr 09, 2022
SAS: Self-Augmentation Strategy for Language Model Pre-training

SAS: Self-Augmentation Strategy for Language Model Pre-training This repository

Alibaba 5 Nov 02, 2022
Pytorch tutorials for Neural Style transfert

PyTorch Tutorials This tutorial is no longer maintained. Please use the official version: https://pytorch.org/tutorials/advanced/neural_style_tutorial

Alexis David Jacq 135 Jun 26, 2022
Auxiliary data to the CHIIR paper Searching to Learn with Instructional Scaffolding

Searching to Learn with Instructional Scaffolding This is the data and analysis code for the paper "Searching to Learn with Instructional Scaffolding"

Arthur Câmara 2 Mar 02, 2022
Awesome-AI-books - Some awesome AI related books and pdfs for learning and downloading

Awesome AI books Some awesome AI related books and pdfs for downloading and learning. Preface This repo only used for learning, do not use in business

luckyzhou 1k Jan 01, 2023
Resco: A simple python package that report the effect of deep residual learning

resco Description resco is a simple python package that report the effect of dee

Pierre-Arthur Claudé 1 Jun 28, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
[CVPR2021] De-rendering the World's Revolutionary Artefacts

De-rendering the World's Revolutionary Artefacts Project Page | Video | Paper In CVPR 2021 Shangzhe Wu1,4, Ameesh Makadia4, Jiajun Wu2, Noah Snavely4,

49 Nov 06, 2022
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8.1k Jan 02, 2023
[SIGGRAPH 2021 Asia] DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning

DeepVecFont This is the official Pytorch implementation of the paper: Yizhi Wang and Zhouhui Lian. DeepVecFont: Synthesizing High-quality Vector Fonts

Yizhi Wang 146 Dec 18, 2022
Official implementation of the ICLR 2021 paper

You Only Need Adversarial Supervision for Semantic Image Synthesis Official PyTorch implementation of the ICLR 2021 paper "You Only Need Adversarial S

Bosch Research 272 Dec 28, 2022
Spatiotemporal resampling methods for mlr3

mlr3spatiotempcv Package website: release | dev Spatiotemporal resampling methods for mlr3. This package extends the mlr3 package framework with spati

45 Nov 21, 2022
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository will provide the official PyTorch implementa

Liming Jiang 238 Nov 25, 2022
This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations at CVPR'21. According to some product reasons, we are not planning to release the training/testing codes and models. However, we will release the dataset and the scripts to prepare the dataset.

TransFill-Reference-Inpainting This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transf

Yuqian Zhou 80 Dec 08, 2022