Implementation of ConvMixer-Patches Are All You Need? in TensorFlow and Keras

Last update: Oct 03, 2022

Overview

Patches Are All You Need? - ConvMixer

ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on patches as input, separates the mixing of spatial and channel dimensions, and maintains equal size and resolution throughout the network. In contrast, however, the ConvMixer uses only standard convolutions to achieve the mixing steps. Despite its simplicity, we show that the ConvMixer outperforms the ViT, MLP-Mixer, and some of their variants for similar parameter counts and data set sizes, in addition to outperforming classical vision models such as the ResNet.

Official GitHub Link: https://github.com/tmp-iclr/convmixer

Paper Link: https://openreview.net/pdf?id=TVHS5Y4dNvM

Note: Paper is under review for ICLR 2022

Model Architechture

Installation

pip install -q tensorflow-addons

Note: We are using TensorFlow-Addons for using the AdamW optimizer and GeLU activation function.

Results

TensorBoard Link: https://tensorboard.dev/experiment/bkhqOz0RQ1Cv5dwrDQySMQ/

Note: Trained 25 Epochs and got a top-5-accuracy of 64.41%

Future Work

To train on 150 epochs
To train model on ImageNet dataset

Citation

@inproceedings{
anonymous2022patches,
title={Patches Are All You Need?},
author={Anonymous},
booktitle={Submitted to The Tenth International Conference on Learning Representations },
year={2022},
url={https://openreview.net/forum?id=TVHS5Y4dNvM},
note={under review}
}

License

MIT License

Copyright (c) 2021 Sayan Nath

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

You might also like...

Implementation of Vaswani, Ashish, et al. "Attention is all you need."

Attention Is All You Need Paper Implementation This is my from-scratch implementation of the original transformer architecture from the following pape

195 Dec 30, 2022

Unofficial implementation of HiFi-GAN+ from the paper "Bandwidth Extension is All You Need" by Su, et al.

HiFi-GAN+ This project is an unoffical implementation of the HiFi-GAN+ model for audio bandwidth extension, from the paper Bandwidth Extension is All

134 Dec 30, 2022

BasicRL: easy and fundamental codes for deep reinforcement learning。It is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up.

BasicRL: easy and fundamental codes for deep reinforcement learning BasicRL is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up. It is

12 Apr 28, 2022

Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

79 Dec 28, 2022

Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

16 Jul 16, 2022

Releases(0.0.1)

0.0.1(Oct 15, 2021)

ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on patches as input, separates the mixing of spatial and channel dimensions, and maintains equal size and resolution throughout the network. In contrast, however, the ConvMixer uses only standard convolutions to achieve the mixing steps. Despite its simplicity, we show that the ConvMixer outperforms the ViT, MLP-Mixer, and some of their variants for similar parameter counts and data set sizes, in addition to outperforming classical vision models such as the ResNet.

View the TensorBoard here.

Note: Trained on 25 Epochs.
Source code(tar.gz)
Source code(zip)
convmixer-model.h5(6.94 MB)
convmixer.zip(6.21 MB)
train-logs.csv(2.94 KB)

Implementation of ConvMixer-Patches Are All You Need? in TensorFlow and Keras

Related tags

Overview

Patches Are All You Need? - ConvMixer

Model Architechture

Installation

Results

Future Work

Citation

License

You might also like...

Implementation of Vaswani, Ashish, et al. "Attention is all you need."

Unofficial implementation of HiFi-GAN+ from the paper "Bandwidth Extension is All You Need" by Su, et al.

BasicRL: easy and fundamental codes for deep reinforcement learning。It is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up.

Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Makes patches from huge resolution .svs slide files using openslide

Patches desktop steam to look like the new steamdeck ui.

Code for "Diffusion is All You Need for Learning on Surfaces"

Releases(0.0.1)

0.0.1(Oct 15, 2021)

Owner

Sayan Nath

Server files for UltimateLabeling

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility ICCV2021

PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020).

Official Repository for Machine Learning class - Physics Without Frontiers 2021

PyTorch implementation of Deformable Convolution

The project is associated with the recently-launched ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) to provide participants with baseline systems for speech recognition and speaker diarization in conference scenario.

Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

Implementation of CaiT models in TensorFlow and ImageNet-1k checkpoints. Includes code for inference and fine-tuning.

Easy to use Audio Tagging in PyTorch

Implementation of UNet on the Joey ML framework

Implementation of the final project of the course DDA6309 Probabilistic Graphical Model

A map update dataset and benchmark

Leaderboard, taxonomy, and curated list of few-shot object detection papers.

This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

李云龙二次元风格化!打滚卖萌，使用了animeGANv2进行了视频的风格迁移

A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning

constructing maps of intellectual influence from publication data

Code for ICE-BeeM paper - NeurIPS 2020