A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners

Related tags

Deep Learningkerasautoencoderself-supervised-learningtensorflow2masked-image-modeling
Overview

Masked Autoencoders Are Scalable Vision Learners

Open In Colab

A TensorFlow implementation of Masked Autoencoders Are Scalable Vision Learners [1]. Our implementation of the proposed method is available in mae-pretraining.ipynb notebook. It includes evaluation with linear probing as well. Furthermore, the notebook can be fully executed on Google Colab. Our main objective is to present the core idea of the proposed method in a minimal and readable manner. We have also prepared a blog for getting started with Masked Autoencoder easily.


With just 100 epochs of pre-training and a fairly lightweight and asymmetric Autoencoder architecture we achieve 49.33%% accuracy with linear probing on the CIFAR-10 dataset. Our training logs and encoder weights are released in Weights and Logs. For comparison, we took the encoder architecture and trained it from scratch (refer to regular-classification.ipynb) in a fully supervised manner. This gave us ~76% test top-1 accuracy.

We note that with further hyperparameter tuning and more epochs of pre-training, we can achieve a better performance with linear-probing. Below we present some more results:

Config Masking
proportion		 LP
performance		 Encoder weights
& logs
Encoder & decoder layers: 3 & 1
Batch size: 256		 0.6 44.25% Link
Do 0.75 46.84% Link
Encoder & decoder layers: 6 & 2
Batch size: 256		 0.75 48.16% Link
Encoder & decoder layers: 9 & 3
Batch size: 256
Weight deacy: 1e-5		 0.75 49.33% Link

LP denotes linear-probing. Config is mostly based on what we define in the hyperparameters section of this notebook: mae-pretraining.ipynb.

Acknowledgements

References

[1] Masked Autoencoders Are Scalable Vision Learners; He et al.; arXiv 2021; https://arxiv.org/abs/2111.06377.

You might also like...
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]

PINTO_model_zoo Please read the contents of the LICENSE file located directly under each folder before using the model. My model conversion scripts ar

Katsuya Hyodo 2.4k Jan 5, 2023
Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax
Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax

Clockwork VAEs in JAX/Flax Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax, ported

Julius Kunze 26 Oct 5, 2022
Official implementation of the paper
Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders"

AAVAE Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders" Abstract Recent methods for self-supervised learnin

Grid AI Labs 48 Dec 12, 2022
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning

This is a release of our VIMPAC paper to illustrate the implementations. The pretrained checkpoints and scripts will be soon open-sourced in HuggingFace transformers.

Hao Tan 74 Dec 3, 2022
EMNLP 2021 - Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

Frustratingly Simple Pretraining Alternatives to Masked Language Modeling This is the official implementation for "Frustratingly Simple Pretraining Al

Atsuki Yamaguchi 31 Nov 18, 2022
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu

AI2 114 Jan 6, 2023
SimMIM: A Simple Framework for Masked Image Modeling
SimMIM: A Simple Framework for Masked Image Modeling

SimMIM By Zhenda Xie*, Zheng Zhang*, Yue Cao*, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai and Han Hu*. This repo is the official implementation of

Microsoft 181 Dec 10, 2021
SeMask: Semantically Masked Transformers for Semantic Segmentation.
SeMask: Semantically Masked Transformers for Semantic Segmentation.

SeMask: Semantically Masked Transformers Jitesh Jain, Anukriti Singh, Nikita Orlov, Zilong Huang, Jiachen Li, Steven Walton, Humphrey Shi This repo co

Picsart AI Research (PAIR) 186 Dec 30, 2022
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition

FocusFace This is the official repository of "FocusFace: Multi-task Contrastive Learning for Masked Face Recognition" accepted at IEEE International C

Pedro Neto 21 Nov 17, 2022
Comments
  • Excellent work (`mae.ipynb`)!

    Excellent work (`mae.ipynb`)!

    @ariG23498 this is fantastic stuff. Super clean, readable, and coherent with the original implementation. A couple of suggestions that would likely make things even better:

    • Since you have already implemented masking visualization utilities how about making them part of the PatchEncoder itself? That way you could let it accept a test image, apply random masking, and plot it just like the way you are doing in the earlier cells. This way I believe the notebook will be cleaner.
    • AdamW (tfa.optimizers.adamw) is a better choice when it comes to training Transformer-based models.
    • Are we taking the loss on the correct component? I remember you mentioning it being dealt with differently.

    After these points are addressed I will take a crack at porting the training loop to TPUs along with other performance monitoring callbacks.

    opened by sayakpaul 7
  • Unshuffle the patches?

    Unshuffle the patches?

    Your code helps me a lot! However, I still have some questions. In the paper, the authors say they unshuffle the full list before applying the deocder. In the MaskedAutoencoder class of your implementation, decoder_inputs = tf.concat([encoder_outputs, masked_embeddings], axis=1)
    no unshuffling is used. I wonder if you can tell me the purpose of doing so? Thanks a lot!

    opened by changtaoli 2
  • Could you also share the weight of the pretrained decoder?

    Could you also share the weight of the pretrained decoder?

    Hi,

    Thanks for your excellent implementation! I found that you have shared the weights of the encoder, but if we want to replicate the reconstruction, the pretrained decoder is still needed. So, could you also share the weight of the pretrained decoder?

    Best Regards, Hongxin

    opened by hongxin001 1
  • Issue with the plotting utility `show_masked_image`

    Issue with the plotting utility `show_masked_image`

    Should be:

    def show_masked_image(self, patches):
        # Utility function that helps visualize maksed images.
        _, unmask_indices = self.get_random_indices()
        unmasked_patches = tf.gather(patches, unmask_indices, axis=1, batch_dims=1)

        # Necessary for plotting.
        ids = tf.argsort(unmask_indices)
        sorted_unmask_indices = tf.sort(unmask_indices)
        unmasked_patches = tf.gather(unmasked_patches, ids, batch_dims=1)

        # Select a random index for visualization.
        idx = np.random.choice(len(sorted_unmask_indices))
        print(f"Index selected: {idx}.")

        n = int(np.sqrt(NUM_PATCHES))
        unmask_index = sorted_unmask_indices[idx]
        unmasked_patch = unmasked_patches[idx]

        plt.figure(figsize=(4, 4))

        count = 0
        for i in range(NUM_PATCHES):
            ax = plt.subplot(n, n, i + 1)

            if count < unmask_index.shape[0] and unmask_index[count].numpy() == i:
                patch = unmasked_patch[count]
                patch_img = tf.reshape(patch, (PATCH_SIZE, PATCH_SIZE, 3))
                plt.imshow(patch_img)
                plt.axis("off")
                count = count + 1
            else:
                patch_img = tf.zeros((PATCH_SIZE, PATCH_SIZE, 3))
                plt.imshow(patch_img)
                plt.axis("off")
        plt.show()

        # Return the random index to validate the image outside the method.
        return idx
    opened by ariG23498 1
Releases(v1.0.0)
Owner
Aritra Roy Gosthipaty
Learning with a learning rate of 1e-10.
Aritra Roy Gosthipaty
GitHub Repository https://keras.io/examples/vision/masked_image_modeling/
Implementation of Nalbach et al. 2017 paper.

Deep Shading Convolutional Neural Networks for Screen-Space Shading Our project is based on Nalbach et al. 2017 paper. In this project, a set of buffe

Marcel Santana 17 Sep 08, 2022
Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

kongdebug 14 Oct 14, 2022
Score refinement for confidence-based 3D multi-object tracking

Score refinement for confidence-based 3D multi-object tracking Our video gives a brief explanation of our Method. This is the official code for the pa

Cognitive Systems Research Group 47 Dec 26, 2022
Example repository for custom C++/CUDA operators for TorchScript

Custom TorchScript Operators Example This repository contains examples for writing, compiling and using custom TorchScript operators. See here for the

106 Dec 14, 2022
SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements (CVPR 2021)

SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements (CVPR 2021) This repository contains the official PyTorch implementa

Qianli Ma 133 Jan 05, 2023
PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

Foley Music: Learning to Generate Music from Videos This repo holds the code for the framework presented on ECCV 2020. Foley Music: Learning to Genera

Chuang Gan 30 Nov 03, 2022
Python based Advanced AI Assistant

Knick is a virtual artificial intelligence project, fully developed in python. The objective of this project is to develop a virtual assistant that can handle our minor, intermediate as well as heavy

19 Nov 15, 2022
The coda and data for "Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach" (ACL '21)

We propose a hierarchical core-fringe learning framework to measure fine-grained domain relevance of terms – the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., de

Jie Huang 14 Oct 21, 2022
The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight).

Curriculum by Smoothing (NeurIPS 2020) The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight). For any questions reg

PAIR Lab 36 Nov 23, 2022
OverFeat is a Convolutional Network-based image classifier and feature extractor.

OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat

593 Dec 08, 2022
This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-grained Classification".

HA-in-Fine-Grained-Classification This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-g

16 Oct 29, 2022
Automatically erase objects in the video, such as logo, text, etc.

Video-Auto-Wipe Read English Introduction：Here   本人不定期的基于生成技术制作一些好玩有趣的算法模型，这次带来的作品是“视频擦除”方向的应用模型，它实现的功能是自动感知到视频中我们不想看见的部分（譬如广告、水印、字幕、图标等等）然后进行擦除。由于图标擦

seeprettyface.com 141 Dec 26, 2022
EgGateWayGetShell py脚本

EgGateWayGetShell_py 免责声明 由于传播、利用此文所提供的信息而造成的任何直接或者间接的后果及损失，均由使用者本人负责，作者不为此承担任何责任。 使用 python3 eg.py urls.txt 目标 title:锐捷网络-EWEB网管系统 port:4430 漏洞成因 ?p

榆木 61 Nov 09, 2022
Official implementation of Few-Shot and Continual Learning with Attentive Independent Mechanisms

Few-Shot and Continual Learning with Attentive Independent Mechanisms This repository is the official implementation of Few-Shot and Continual Learnin

Chikan_Huang 25 Dec 08, 2022
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Generative Models Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Note: Gen

Agustinus Kristiadi 7k Jan 02, 2023
LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

Deep-Leafsnap Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhev

Sujith Vishwajith 48 Nov 27, 2022
performing moving objects segmentation using image processing techniques with opencv and numpy

Moving Objects Segmentation On this project I tried to perform moving objects segmentation using background subtraction technique. the introduced meth

Mohamed Magdy 15 Dec 12, 2022
Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer) Introduction By applying the

Son Gyo Jung 1 Jul 09, 2022
Codebase for Inducing Causal Structure for Interpretable Neural Networks

Interchange Intervention Training (IIT) Codebase for Inducing Causal Structure for Interpretable Neural Networks Release Notes 12/01/2021: Code and Pa

Zen 6 Oct 10, 2022
Differentiable Annealed Importance Sampling (DAIS)

Differentiable Annealed Importance Sampling (DAIS) This repository contains the code to reproduce the DAIS results from the paper Differentiable Annea

Guodong Zhang 6 Dec 26, 2021