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/
A Framework for Encrypted Machine Learning in TensorFlow

TF Encrypted is a framework for encrypted machine learning in TensorFlow. It looks and feels like TensorFlow, taking advantage of the ease-of-use of t

TF Encrypted 0 Jul 06, 2022
A 35mm camera, based on the Canonet G-III QL17 rangefinder, simulated in Python.

c is for Camera A 35mm camera, based on the Canonet G-III QL17 rangefinder, simulated in Python. The purpose of this project is to explore and underst

Daniele Procida 146 Sep 26, 2022
[ACM MM 2021] Yes, "Attention is All You Need", for Exemplar based Colorization

Transformer for Image Colorization This is an implemention for Yes, "Attention Is All You Need", for Exemplar based Colorization, and the current soft

Wang Yin 30 Dec 07, 2022
A platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.

Wilderness Scavenger: 3D Open-World FPS Game AI Challenge This is a platform for intelligent agent learning based on a 3D open-world FPS game develope

46 Nov 24, 2022
FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks

FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks This is our implementation for the paper: FinGAT: A Financial Graph At

Yu-Che Tsai 64 Dec 13, 2022
Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce

Bo Wan 32 Dec 23, 2022
Convolutional Neural Network to detect deforestation in the Amazon Rainforest

Convolutional Neural Network to detect deforestation in the Amazon Rainforest This project is part of my final work as an Aerospace Engineering studen

5 Feb 17, 2022
Estimation of human density in a closed space using deep learning.

Siemens HOLLZOF challenge - Human Density Estimation Add project description here. Installing Dependencies: Install Python3 either system-wide, user-w

3 Aug 08, 2021
4th place solution to datafactory challenge by Intermarché.

Solution to Datafactory challenge by Intermarché. 4th place solution to datafactory challenge by Intermarché. The objective of the challenge is to pre

Raphael Sourty 11 Mar 19, 2022
A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

CLEVR Dataset Generation This is the code used to generate the CLEVR dataset as described in the paper: CLEVR: A Diagnostic Dataset for Compositional

Facebook Research 503 Jan 04, 2023
NLP made easy

GluonNLP: Your Choice of Deep Learning for NLP GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you l

Distributed (Deep) Machine Learning Community 2.5k Jan 04, 2023
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
Source code for deep symbolic optimization.

Update July 10, 2021: This repository now supports an additional symbolic optimization task: learning symbolic policies for reinforcement learning. Th

Brenden Petersen 290 Dec 25, 2022
LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

This project is based on ultralytics/yolov3. LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image. The related paper is avai

26 Dec 13, 2022
[CVPR'22] Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

wseg Overview The Pytorch implementation of Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast. [arXiv] Though image-level weakly

Ye Du 96 Dec 30, 2022
Implementation of MA-Trace - a general-purpose multi-agent RL algorithm for cooperative environments.

Off-Policy Correction For Multi-Agent Reinforcement Learning This repository is the official implementation of Off-Policy Correction For Multi-Agent R

4 Aug 18, 2022
Class-Attentive Diffusion Network for Semi-Supervised Classification [AAAI'21] (official implementation)

Class-Attentive Diffusion Network for Semi-Supervised Classification Official Implementation of AAAI 2021 paper Class-Attentive Diffusion Network for

Jongin Lim 7 Sep 20, 2022
Repository for the NeurIPS 2021 paper: "Exploiting Domain-Specific Features to Enhance Domain Generalization".

meta-Domain Specific-Domain Invariant (mDSDI) Source code implementation for the paper: Manh-Ha Bui, Toan Tran, Anh Tuan Tran, Dinh Phung. "Exploiting

VinAI Research 12 Nov 25, 2022
Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

IterMVS official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo' Introduction IterMVS is a novel lear

Fangjinhua Wang 127 Jan 04, 2023
AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition.

AnimalAI 3 AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition. It aims to support AI research t

Matthew Crosby 58 Dec 12, 2022