A PyTorch re-implementation of Mask Autoencoder training. SimpleMAE tries to be small, clean, interpretable and extensible.
pip install -r requirements.txt
(base) ➜ python main.py -c configs/imagenet.yaml --debug
Outputs will be saved to ../mae_out/imagenet-1116211657
Found 0 gpu(s)
Param num: 76445440
Epoch 0 Train loss 1.5653417110443115
Output has been saved to ../mae_out/imagenet-1116211657
DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained
PPO-Continuous-Pytorch I found the current implementation of PPO on continuous action space is whether somewhat complicated or not stable. And this is
VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key
SSL models are Strong UDA learners Introduction This is the official code of paper "Semi-supervised Models are Strong Unsupervised Domain Adaptation L
Self-Supervised Models are Continual Learners This is the official repository for the paper: Self-Supervised Models are Continual Learners Enrico Fini
Clockwork VAEs in JAX/Flax Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax, ported
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
AAVAE Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders" Abstract Recent methods for self-supervised learnin
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.
img_recon = rearrange('(h w) b (s1 s2 c) -> b c (h s1) (w s2)', h=img.shape[2]//self.patch_size, s1=self.patch_size, s2=self.patch_size) TypeError: rearrange() missing 1 required positional argument: 'pattern'
An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners This is a coarse version for MAE, only make the pretrain model, the fine
mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/
Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training [Arxiv] VideoMAE: Masked Autoencoders are Data-Efficient Learne
ConvMAE ConvMAE: Masked Convolution Meets Masked Autoencoders Peng Gao1, Teli Ma1, Hongsheng Li2, Jifeng Dai3, Yu Qiao1, 1 Shanghai AI Laboratory, 2 M
MultiMAE: Multi-modal Multi-task Masked Autoencoders Roman Bachmann*, David Mizrahi*, Andrei Atanov, Amir Zamir Website | arXiv | BibTeX Official PyTo
Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project ?? ⚡ ?? Click on Use this template to initialize new re
Vision Transformers are Robust Learners This repository contains the code for the paper Vision Transformers are Robust Learners by Sayak Paul* and Pin
pytorch-made This code is an implementation of "Masked AutoEncoder for Density Estimation" by Germain et al., 2015. The core idea is that you can turn
Pytorch implementation of MaskGIT: Masked Generative Image Transformer
PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen
15 Dec 27, 2022
215 Dec 7, 2022
26 Dec 26, 2022
73 Dec 18, 2022
26 Oct 5, 2022
12.6k Jan 9, 2023
48 Dec 12, 2022
74 Dec 3, 2022
214 Dec 29, 2022
1 Dec 14, 2021
697 Jan 7, 2023
345 Jan 8, 2023
385 Jan 6, 2023
1 Dec 20, 2021
103 Jan 5, 2023
498 Dec 30, 2022
247 Dec 16, 2022
8 Nov 21, 2022