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PASSL包含 SimCLR,MoCo v1/v2,BYOL,CLIP,PixPro,simsiam, SwAV, BEiT,MAE 等图像自监督算法以及 Vision Transformer,DEiT,Swin Transformer,CvT,T2T-ViT,MLP-Mixer,XCiT,ConvNeXt,PVTv2 等基础视觉算法

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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.

Key features of PASSL:

  • Reproducible implementation of SOTA in Self-Supervision

    Existing SOTA in Self-Supervision are implemented - SimCLR, MoCo(v1), MoCo(v2), MoCo-BYOL, BYOL, BEiT. Supervised classification training is also supported.

  • Modular Design

    Easy to build new tasks and reuse the existing components from other tasks (Trainer, models and heads, data transforms, etc.)

🛠️ The ultimate goal of PASSL is to use self-supervised learning to provide more appropriate pre-training weights for downstream tasks while significantly reducing the cost of data annotation.

📣 Recent Update:

  • (2022-2-9): Refactoring README
  • 🔥 Now:

Implemented Models

  • Self-Supervised Learning Models

PASSL implements a series of self-supervised learning algorithms, See Document for details on its use

Epochs Official results PASSL results Backbone Model Document
MoCo 200 60.6 60.64 ResNet-50 download Train MoCo
SimCLR 100 64.5 65.3 ResNet-50 download Train SimCLR
MoCo v2 200 67.7 67.72 ResNet-50 download Train MoCo
MoCo-BYOL 300 71.56 72.10 ResNet-50 download Train MoCo-BYOL
BYOL 300 72.50 71.62 ResNet-50 download Train BYOL
PixPro 100 55.1(fp16) 57.2(fp32) ResNet-50 download Train PixPro
SimSiam 100 68.3 68.4 ResNet-50 download Train SimSiam
DenseCL 200 63.62 63.37 ResNet-50 download Train DenseCL
SwAV 100 72.1 72.4 ResNet-50 download Train SwAV

Benchmark Linear Image Classification on ImageNet-1K.

Comming Soon:More algorithm implementations are already in our plans ...

  • Classification Models

PASSL implements influential image classification algorithms such as Visual Transformer, and provides corresponding pre-training weights. Designed to support the construction and research of self-supervised, multimodal, large-model algorithms. See Classification_Models_Guide.md for more usage details

Detail Tutorial
ViT / PaddleEdu
Swin Transformer / PaddleEdu
CaiT config PaddleFleet
T2T-ViT config PaddleFleet
CvT config PaddleFleet
BEiT config unofficial
MLP-Mixer config PaddleFleet
ConvNeXt config PaddleFleet

🔥 PASSL provides a detailed dissection of the algorithm, see Tutorial for details.

Installation

See INSTALL.md.

Getting Started

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

Awesome SSL

Self-Supervised Learning (SSL) is a rapidly growing field, and some influential papers are listed here for research use.PASSL seeks to implement self-supervised algorithms with application potential

Contributing

PASSL is still young. It may contain bugs and issues. Please report them in our bug track system. Contributions are welcome. Besides, if you have any ideas about PASSL, please let us know.

Citation

If PASSL is helpful to your research, feel free to cite

@misc{=passl,
    title={PASSL: A visual Self-Supervised Learning Library},
    author={PASSL Contributors},
    howpublished = {\url{https://github.com/PaddlePaddle/PASSL}},
    year={2022}
}

License

As shown in the LICENSE.txt file, PASSL uses the Apache 2.0 copyright agreement.

About

PASSL包含 SimCLR,MoCo v1/v2,BYOL,CLIP,PixPro,simsiam, SwAV, BEiT,MAE 等图像自监督算法以及 Vision Transformer,DEiT,Swin Transformer,CvT,T2T-ViT,MLP-Mixer,XCiT,ConvNeXt,PVTv2 等基础视觉算法

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