PASSL包含 SimCLR，MoCo，BYOL，CLIP等基于对比学习的图像自监督算法以及 Vision-Transformer，Swin-Transformer，BEiT，CVT，T2T，MLP_Mixer等视觉Transformer算法

Related tags

Deep Learningdeep-learningclippaddlemocoself-supervised-learningcvtsimclrbeitvision-transformermoco-v2swin-transformer
Overview

PASSL

Introduction

PASSL is a Paddle based vision library for state-of-the-art Self-Supervised Learning research with PaddlePaddle. PASSL aims to accelerate research cycle in self-supervised learning: from designing a new self-supervised task to evaluating the learned representations.

  • Reproducible implementation of SOTA in Self-Supervision: Existing SOTA in Self-Supervision are implemented - SimCLR, MoCo(v1),MoCo(v2), MoCo-BYOL, CLIP. BYOL is coming soon. Also supports supervised trainings.
  • Modular: Easy to build new tasks and reuse the existing components from other tasks (Trainer, models and heads, data transforms, etc.).

Installation

Implemented Models

Benchmark Linear Image Classification on ImageNet-1K

epochs official results passl results Backbone Model
MoCo 200 60.6 60.64 ResNet-50 download
SimCLR 100 64.5 65.3 ResNet-50 download
MoCo v2 200 67.7 67.72 ResNet-50 download
MoCo-BYOL 300 71.56 72.10 ResNet-50 download
BYOL 300 72.50 71.62 ResNet-50 download

Getting Started

Please see GETTING_STARTED.md for the basic usage of PASSL.

Tutorials

Comments
  • MLP-Mixer: An all-MLP Architecture for Vision

    MLP-Mixer: An all-MLP Architecture for Vision

    readme文件里的两个模型的TOP1 是不是写反了？模型大的准确度比模型小的准确度小一些？

    Arch | Weight | Top-1 Acc | Top-5 Acc | Crop ratio | # Params -- | -- | -- | -- | -- | -- mlp_mixer_b16_224 | pretrain 1k | 76.60 | 92.23 | 0.875 | 60.0M mlp_mixer_l16_224 | pretrain 1k | 72.06 | 87.67 | 0.875 | 208.2M

    opened by gaorui999 3
  • 我很关注图像分类的自监督进展

    我很关注图像分类的自监督进展

    小弟想问问,对于图像分类的自监督,目前是什么进展呢?比如猫狗分类这种典型的二分类准确率如何?imagenet1k分类准确率如何?PASSL里面的关于图像分类的自监督算法或者模型,有哪些?能给个例子,让我知道如何使用吗?目前看到PASSLissues才1条,文档完全没看到.方便加个微信或者QQ聊几句吗?小弟对于图像分类的自监督高度重视.还有一个疑问,关于图像分类的自监督模型,是不是我给一堆图片,模型运行后,就会把图片归类呢?我需不需要给出类别的数量呢?说白了,我想知道图像分类的自监督的一个使用流程.现在都1.0了,该有点用处了吧.如果一个模型运行后,图像就分好类了,归纳为N类,我有什么办法判断分类的正确性呢?这方面有算法吗? 提了很多问题,跪求每个问题都回答一下,谢谢大佬.

    opened by yuwoyizhan 2
  • Unintended behavior in clip_logit_scale

    Unintended behavior in clip_logit_scale

    https://github.com/PaddlePaddle/PASSL/blob/83c49e6a5ba3444cee7f054122559d7759152764/passl/modeling/backbones/clip.py#L317

    check this issue for reference https://github.com/PaddlePaddle/Paddle/issues/43710

    Suggested approach (with non-public API)

    logit_scale_buffer = self.logit_scale.clip(-4.6, 4.6)
logit_scale_buffer._share_buffer_to(self.logit_scale)
    opened by minogame 1
  • 建议

    建议

    1.passl很多文字都是英文的,包括快速使用等文档,希望可以提供中文文档. 2.希望知道图像分类自监督学习的技术研究目前到达什么程度了.比如猫狗这种二分类准确率如何,imagenet准确率如何,使用passl进行图像分类,需要给类别总数量吗? 3.能加个QQ或者微信聊几句吗?有些疑问,拜托了,大佬. QQ:1226194560 微信:18820785964

    opened by yuwoyizhan 1
  • fix bug of mixup for DeiT

    fix bug of mixup for DeiT

    DeiT/B-16 pretrained on ImageNet1K:

    [01/21 02:54:46] passl.engine.trainer INFO: Validate Epoch [290] acc1 (81.336), acc5 (95.544)
[01/21 03:02:31] passl.engine.trainer INFO: Validate Epoch [291] acc1 (81.328), acc5 (95.580)
[01/21 03:10:20] passl.engine.trainer INFO: Validate Epoch [292] acc1 (81.390), acc5 (95.608)
[01/21 03:18:10] passl.engine.trainer INFO: Validate Epoch [293] acc1 (81.484), acc5 (95.636)
[01/21 03:26:00] passl.engine.trainer INFO: Validate Epoch [294] acc1 (81.452), acc5 (95.600)
[01/21 03:33:52] passl.engine.trainer INFO: Validate Epoch [295] acc1 (81.354), acc5 (95.528)
[01/21 03:41:38] passl.engine.trainer INFO: Validate Epoch [296] acc1 (81.338), acc5 (95.562)
[01/21 03:49:25] passl.engine.trainer INFO: Validate Epoch [297] acc1 (81.344), acc5 (95.542)
[01/21 03:57:15] passl.engine.trainer INFO: Validate Epoch [298] acc1 (81.476), acc5 (95.550)
[01/21 04:05:03] passl.engine.trainer INFO: Validate Epoch [299] acc1 (81.476), acc5 (95.572)
[01/21 04:12:51] passl.engine.trainer INFO: Validate Epoch [300] acc1 (81.386), acc5 (95.536)
    opened by GuoxiaWang 1
  • BYOL的预训练中好像使用了gt_label？

    BYOL的预训练中好像使用了gt_label？

    • 在byol的config 中设置了 num_classes=1000: https://github.com/PaddlePaddle/PASSL/blob/9d7a9fd4af41772e29120553dddab1c162e4cb70/configs/byol/byol_r50_IM.yaml#L34
    • 在model中设置了self.classifier = nn.Linear(embedding_dim, num_classes)，并且forward中将classif_out和label一起传给了head

    image

    https://github.com/PaddlePaddle/PASSL/blob/9d7a9fd4af41772e29120553dddab1c162e4cb70/passl/modeling/architectures/BYOL.py#L263

    • 在L2 Head中将对比loss和有监督的CE loss加在了一起返回

    image

    https://github.com/PaddlePaddle/PASSL/blob/9d7a9fd4af41772e29120553dddab1c162e4cb70/passl/modeling/heads/l2_head.py#L43

    opened by youqingxiaozhua 0
  • [飞桨论文复现挑战赛(第六期)] (85) Emerging Properties in Self-Supervised Vision Transformers

    [飞桨论文复现挑战赛(第六期)] (85) Emerging Properties in Self-Supervised Vision Transformers

    PR types

    New features

    PR changes

    APIs

    Describe

    • Task: https://github.com/PaddlePaddle/Paddle/issues/41482
    • 添加 passl.model.architectures.dino

    Peformance

    | Model | Official | Passl | | ---- | ---- | ---- | | DINO | 74.0 | 73.6 |

    • [x] 预训练和linear probe代码
    • [ ] 预训练和linear probe权重
    • [ ] 文档
    • [ ] TIPC
    opened by fuqianya 0
Releases(v1.0.0)

  • v1.0.0(Feb 24, 2022)

    • 新增 XCiT 视觉 Transformer 模型 xcit_nano_12_p8_224 蒸馏模型训练指标对齐，感谢 @BrilliantYuKaimin 的高质量贡献 🎉 🎉 🎉

    PASSL飞桨自监督领域核心学习库，提供大量高精度的视觉自监督模型、视觉 Transformer 模型，并支持超大视觉模型分布式训练功能，旨在提升飞桨开发者在自监督领域建模效率，并提供基于飞桨框架2.2的超大视觉模型领域最佳实践

    Source code(tar.gz)
    Source code(zip)
Owner
GitHub Repository
AITUS - An atomatic notr maker for CYTUS

AITUS an automatic note maker for CYTUS. 利用AI根据指定乐曲生成CYTUS游戏谱面。 效果展示：https://www

GradiusTwinbee 6 Feb 24, 2022
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)

Self-supervised Augmentation Consistency for Adapting Semantic Segmentation This repository contains the official implementation of our paper: Self-su

Visual Inference Lab @TU Darmstadt 132 Dec 21, 2022
Custom TensorFlow2 implementations of forward and backward computation of soft-DTW algorithm in batch mode.

Batch Soft-DTW(Dynamic Time Warping) in TensorFlow2 including forward and backward computation Custom TensorFlow2 implementations of forward and backw

19 Aug 30, 2022
Implementation for Learning to Track with Object Permanence

Learning to Track with Object Permanence A video-based MOT approach capable of tracking through full occlusions: Learning to Track with Object Permane

Toyota Research Institute - Machine Learning 91 Jan 03, 2023
Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations"

Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations" this repository is maintained by bo

Yuhan Liu 24 Nov 29, 2022
Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Deep Causal Reasoning for Recommender Systems The codes are associated with the following paper: Deep Causal Reasoning for Recommendations, Yaochen Zh

Yaochen Zhu 22 Oct 15, 2022
Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

Spatio-Temporal Entropy Model A Pytorch Reproduction of Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression. More details can

16 Nov 28, 2022
GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks This repository implements a capsule model Inten

Joel Huang 15 Dec 24, 2022
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

4.8k Jan 07, 2023
CLIP (Contrastive Language–Image Pre-training) trained on Indonesian data

CLIP-Indonesian CLIP (Radford et al., 2021) is a multimodal model that can connect images and text by training a vision encoder and a text encoder joi

Galuh 17 Mar 10, 2022
The official codes for the ICCV2021 Oral presentation "Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework"

P2PNet (ICCV2021 Oral Presentation) This repository contains codes for the official implementation in PyTorch of P2PNet as described in Rethinking Cou

Tencent YouTu Research 208 Dec 26, 2022
The project covers common metrics for super-resolution performance evaluation.

Super-Resolution Performance Evaluation Code The project covers common metrics for super-resolution performance evaluation. Metrics support The script

xmy 10 Aug 03, 2022
The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier')

The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography

James 135 Dec 23, 2022
Self-driving car env with PPO algorithm from stable baseline3

Self-driving car with RL stable baseline3 Most of the project develop from https://github.com/GerardMaggiolino/Gym-Medium-Post Please check it out! Th

Sornsiri.P 7 Dec 22, 2022
Collective Multi-type Entity Alignment Between Knowledge Graphs (WWW'20)

CG-MuAlign A reference implementation for "Collective Multi-type Entity Alignment Between Knowledge Graphs", published in WWW 2020. If you find our pa

Bran Zhu 28 Dec 11, 2022
A proof of concept ai-powered Recaptcha v2 solver

Recaptcha Fullauto I've decided to open source my old Recaptcha v2 solver. My latest version will be opened sourced this summer. I am hoping this proj

Nate 60 Dec 20, 2022
catch-22: CAnonical Time-series CHaracteristics

catch22 - CAnonical Time-series CHaracteristics About catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Ma

Carl H Lubba 229 Oct 21, 2022
The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert.

The accompanying codes reproduce all figures and statistics presented in "Controlling for multiple covariates" by Mark Tygert. This repository also pr

Meta Research 1 Dec 02, 2021
LIVECell - A large-scale dataset for label-free live cell segmentation

LIVECell dataset This document contains instructions of how to access the data associated with the submitted manuscript "LIVECell - A large-scale data

Sartorius Corporate Research 112 Jan 07, 2023
SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP

scdlpicker SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP Objective This is a simple deep learning (DL) repicker module

Joachim Saul 6 May 13, 2022