23 Star 225 Fork 88

PaddlePaddle / PaddleSeg

Create your Gitee Account
Explore and code with more than 12 million developers,Free private repositories !:)
Sign up
Clone or Download
contribute
Sync branch
Cancel
Notice: Creating folder will generate an empty file .keep, because not support in Git
Loading...
README_EN.md
Apache-2.0

English | 简体中文

A High-Efficient Development Toolkit for Image Segmentation Based on PaddlePaddle.

License Version python version support os stars

News

  • [2023-04-11] :fire: PaddleSeg v2.8 is released! Check more details in Release Notes.
    • Release Segment Anything Model based on PaddlePaddle. Demos are provided to demonstrate the function of automatic full-image segmentation and specified object segmentation with prompt input.
    • Release PP-MobileSeg, a lightweight semantic segmentation model for mobile devices. Comparing PP-MobileSeg with other models on the ADE20K dataset, the segmentation accuracy is improved by 1.5%, the inference speed is accelerated by 42.3%, and the number of parameters is decreased by 34.9%.
    • Release QualityInspector v0.5, a full-process solution for industrial quality inspection. It provides a unified and configurable pipeline for single-task and multi-task models, integrates detection and segmentation model libraries, and supports three unsupervised quality inspection methods.
    • Release PanopticSeg v0.5, a universal panoptic segmentation solution. It provides the full-process capabilities of panoptic segmentation, integrates two models, and has flexible secondary development capabilities.
  • [2022-11-30] PaddleSeg v2.7 released a real-time human matting model PP-MattingV2, a 3D medical image segmentation solution MedicalSegV2, and a real-time semantic segmentation model RTFormer.
  • [2022-07-20] PaddleSeg v2.6 released a real-time human segmentation SOTA solution PP-HumanSegV2, a stable-version semi-automatic segmentation annotation tool EISeg v1.0, a pseudo label pre-training method PSSL, and the source code of PP-MattingV1.
  • [2022-04-20] PaddleSeg v2.5 released a real-time semantic segmentation model PP-LiteSeg, a trimap-free image matting model PP-MattingV1, and an easy-to-use solution for 3D medical image segmentation MedicalSegV1.
  • [2022-01-20] We release PaddleSeg v2.4 with EISeg v0.4, and PP-HumanSegV1 including an open-sourced dataset PP-HumanSeg14K.

Introduction

PaddleSeg is an end-to-end high-efficent development toolkit for image segmentation based on PaddlePaddle, which helps both developers and researchers in the whole process of designing segmentation models, training models, optimizing performance and inference speed, and deploying models. A lot of well-trained models and various real-world applications in both industry and academia help users conveniently build hands-on experiences in image segmentation.

Features

  • High-Performance Model: Following the state of the art segmentation methods and using high-performance backbone networks, we provide 45+ models and 150+ high-quality pre-training models, which are better than other open-source implementations.

  • High Efficiency: PaddleSeg provides multi-process asynchronous I/O, multi-card parallel training, evaluation, and other acceleration strategies, combined with the memory optimization function of the PaddlePaddle, which can greatly reduce the training overhead of the segmentation model, all these allowing developers to train image segmentation models more efficiently and at a lower cost.

  • Modular Design: We build PaddleSeg with the modular design philosophy. Therefore, based on actual application scenarios, developers can assemble diversified training configurations with data augmentation strategies, segmentation models, backbone networks, loss functions, and other different components to meet different performance and accuracy requirements.

  • Complete Flow: PaddleSeg supports image labeling, model designing, model training, model compression, and model deployment. With the help of PaddleSeg, developers can easily finish all tasks in the entire workflow.

Community

  • If you have any questions, suggestions or feature requests, please do not hesitate to create an issue in GitHub Issues.
  • Please scan the following QR code to join PaddleSeg WeChat group to communicate with us:

Overview

Models Components Special Cases
Backbones
Losses
Metrics
  • mIoU
  • Accuracy
  • Kappa
  • Dice
  • AUC_ROC
Datasets
Data Augmentation
  • Flipping
  • Resize
  • ResizeByLong
  • ResizeByShort
  • LimitLong
  • ResizeRangeScaling
  • ResizeStepScaling
  • Normalize
  • Padding
  • PaddingByAspectRatio
  • RandomPaddingCrop
  • RandomCenterCrop
  • ScalePadding
  • RandomNoise
  • RandomBlur
  • RandomRotation
  • RandomScaleAspect
  • RandomDistort
  • RandomAffine
Segment Anything
Model Selection Tool
Human Segmentation
MedicalSeg
Cityscapes SOTA Model
CVPR Champion Model
Domain Adaptation

Industrial Segmentation Models

High Accuracy Semantic Segmentation Models

These models have good performance and costly inference time, so they are designed for GPU and Jetson devices.

Model Backbone Cityscapes mIoU(%) V100 TRT Inference Speed(FPS) Config File
FCN HRNet_W18 78.97 24.43 yml
FCN HRNet_W48 80.70 10.16 yml
DeepLabV3 ResNet50_OS8 79.90 4.56 yml
DeepLabV3 ResNet101_OS8 80.85 3.2 yml
DeepLabV3 ResNet50_OS8 80.36 6.58 yml
DeepLabV3 ResNet101_OS8 81.10 3.94 yml
OCRNet :star2: HRNet_w18 80.67 13.26 yml
OCRNet HRNet_w48 82.15 6.17 yml
CCNet ResNet101_OS8 80.95 3.24 yml

Note that:

  • We test the inference speed on Nvidia GPU V100. We use PaddleInference Python API with TensorRT enabled. The data type is FP32, and the shape of input tensor is 1x3x1024x2048.
Lightweight Semantic Segmentation Models

The segmentation accuracy and inference speed of these models are medium. They can be deployed on GPU, X86 CPU and ARM CPU.

Model Backbone Cityscapes mIoU(%) V100 TRT Inference Speed(FPS) Snapdragon 855 Inference Speed(FPS) Config File
PP-LiteSeg :star2: STDC1 77.04 69.82 17.22 yml
PP-LiteSeg :star2: STDC2 79.04 54.53 11.75 yml
BiSeNetV1 - 75.19 14.67 1.53 yml
BiSeNetV2 - 73.19 61.83 13.67 yml
STDCSeg STDC1 74.74 62.24 14.51 yml
STDCSeg STDC2 77.60 51.15 10.95 yml
DDRNet_23 - 79.85 42.64 7.68 yml
HarDNet - 79.03 30.3 5.44 yml
SFNet ResNet18_OS8 78.72 10.72 - yml

Note that:

  • We test the inference speed on Nvidia GPU V100. We use PaddleInference Python API with TensorRT enabled. The data type is FP32, and the shape of input tensor is 1x3x1024x2048.
  • We test the inference speed on Snapdragon 855. We use PaddleLite CPP API with 1 thread, and the shape of input tensor is 1x3x256x256.
Super Lightweight Semantic Segmentation Models

These super lightweight semantic segmentation models are designed for X86 CPU and ARM CPU.

Model Backbone ADE20K mIoU(%) Snapdragon 855 Inference latency(ms) params(M) Links
TopFormer-Base TopTransformer-Base 38.28 480.6 5.13 config
PP-MobileSeg-Base :star2: StrideFormer-Base 41.57 265.5 5.62 config
TopFormer-Tiny TopTransformer-Tiny 32.46 490.3 1.41 config
PP-MobileSeg-Tiny :star2: StrideFormer-Tiny 36.39 215.3 1.61 config

Note that:

  • We test the inference speed on Snapdragon 855. We use PaddleLite CPP API with 1 thread, and the shape of input tensor is 1x3x512x512. We test the latency with the final argmax operator on.
Model Backbone Cityscapes mIoU(%) V100 TRT Inference Speed(FPS) Snapdragon 855 Inference Speed(FPS) Config File
MobileSeg MobileNetV2 73.94 67.57 27.01 yml
MobileSeg :star2: MobileNetV3 73.47 67.39 32.90 yml
MobileSeg Lite_HRNet_18 70.75 10.5 13.05 yml
MobileSeg ShuffleNetV2_x1_0 69.46 37.09 39.61 yml
MobileSeg GhostNet_x1_0 71.88 35.58 38.74 yml

Note that:

  • We test the inference speed on Nvidia GPU V100. We use PaddleInference Python API with TensorRT enabled. The data type is FP32, and the shape of input tensor is 1x3x1024x2048.
  • We test the inference speed on Snapdragon 855. We use PaddleLite CPP API with 1 thread, and the shape of input tensor is 1x3x256x256.

Tutorials

Introductory Tutorials

Basic Tutorials

Advanced Tutorials

Welcome to Contribute

Special Features

Industrial Tutorial Examples

For more examples, see here.

License

PaddleSeg is released under the Apache 2.0 license.

Acknowledgement

  • Thanks jm12138 for contributing U2-Net.
  • Thanks zjhellofss (Fu Shenshen) for contributing Attention U-Net, and Dice Loss.
  • Thanks liuguoyu666, geoyee for contributing U-Net++ and U-Net3+.
  • Thanks yazheng0307 (LIU Zheng) for contributing quick-start document.
  • Thanks CuberrChen for contributing STDC(rethink BiSeNet), PointRend and DetailAggregateLoss.
  • Thanks stuartchen1949 for contributing SegNet.
  • Thanks justld (Lang Du) for contributing UPerNet, DDRNet, CCNet, ESPNetV2, DMNet, ENCNet, HRNet_W48_Contrast, FastFCN, BiSeNetV1, SECrossEntropyLoss and PixelContrastCrossEntropyLoss.
  • Thanks Herman-Hu-saber (Hu Huiming) for contributing ESPNetV2.
  • Thanks zhangjin12138 for contributing RandomCenterCrop.
  • Thanks simuler for contributing ESPNetV1.
  • Thanks ETTR123(Zhang Kai) for contributing ENet, PFPNNet.

Citation

If you find our project useful in your research, please consider citing:

@misc{liu2021paddleseg,
      title={PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation},
      author={Yi Liu and Lutao Chu and Guowei Chen and Zewu Wu and Zeyu Chen and Baohua Lai and Yuying Hao},
      year={2021},
      eprint={2101.06175},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{paddleseg2019,
    title={PaddleSeg, End-to-end image segmentation kit based on PaddlePaddle},
    author={PaddlePaddle Contributors},
    howpublished = {\url{https://github.com/PaddlePaddle/PaddleSeg}},
    year={2019}
}
Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

About

End-to-End Image Segmentation Suite Based on PaddlePaddle. (『飞桨』图像分割开发套件) expand collapse
Python and 6 more languages
Apache-2.0
Cancel

Releases

No release

Contributors

All

Activities

Load More
can not load any more
Python
1
https://gitee.com/paddlepaddle/PaddleSeg.git
git@gitee.com:paddlepaddle/PaddleSeg.git
paddlepaddle
PaddleSeg
PaddleSeg
release/2.8

Search

F2647f8d 8189591 Bbdfb06e 8189591