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English | 简体中文
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.
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.
Models | Components | Special Cases | |
Semantic SegmentationInteractive SegmentationImage MattingPanoptic Segmentation |
BackbonesLossesMetrics
|
DatasetsData Augmentation
|
Segment AnythingModel Selection ToolHuman SegmentationCityscapes SOTA ModelCVPR Champion ModelDomain Adaptation |
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 🌟 | 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:
Model | Backbone | Cityscapes mIoU(%) | V100 TRT Inference Speed(FPS) | Snapdragon 855 Inference Speed(FPS) | Config File |
---|---|---|---|---|---|
PP-LiteSeg 🌟 | STDC1 | 77.04 | 69.82 | 17.22 | yml |
PP-LiteSeg 🌟 | 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:
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 🌟 | StrideFormer-Base | 41.57 | 265.5 | 5.62 | config |
TopFormer-Tiny | TopTransformer-Tiny | 32.46 | 490.3 | 1.41 | config |
PP-MobileSeg-Tiny 🌟 | StrideFormer-Tiny | 36.39 | 215.3 | 1.61 | config |
Note that:
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 🌟 | 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:
Introductory Tutorials
Basic Tutorials
Data Preparation
Model Export
Model Deployment
Advanced Tutorials
Model Compression
Welcome to Contribute
Advanced Development
Pull Request
For more examples, see here.
PaddleSeg is released under the Apache 2.0 license.
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}
}
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