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<<<<<<< HEAD English | 简体中文

PaddleSeg model zoo overview

Model zoo

CNN Series

|Model\Backbone Network|ResNet50|ResNet101|HRNetw18|HRNetw48|

PaddlePaddle-release/2.4

PaddleSeg模型库总览

模型库

CNN系列

|模型\骨干网络|ResNet50|ResNet101|HRNetw18|HRNetw48| <<<<<<< HEAD

9c8570af (add new models) PaddlePaddle-release/2.4 |-|-|-|-|-| |ANN|✔|✔||| |BiSeNetv2|-|-|-|-| |DANet|✔|✔||| |Deeplabv3|✔|✔||| |Deeplabv3P|✔|✔||| |Fast-SCNN|-|-|-|-| |FCN|||✔|✔| |GCNet|✔|✔||| |GSCNN|✔|✔||| |HarDNet|-|-|-|-| |OCRNet|||✔|✔| |PSPNet|✔|✔||| |U-Net|-|-|-|-| |U2-Net|-|-|-|-| |Att U-Net|-|-|-|-| |U-Net++|-|-|-|-| |U-Net3+|-|-|-|-| |DecoupledSegNet|✔|✔||| |EMANet|✔|✔|-|-| |ISANet|✔|✔|-|-| |DNLNet|✔|✔|-|-| |SFNet|✔|-|-|-| |PP-HumanSeg-Lite|-|-|-|-| |PortraitNet|-|-|-|-| |STDC|-|-|-|-| |GINet|✔|✔|-|-| |PointRend|✔|✔|-|-| |SegNet|-|-|-|-| |ESPNetV2|-|-|-|-| |HRNetW48Contrast|-|-|-|✔| <<<<<<< HEAD ======= <<<<<<< HEAD |DMNet|-|✔|-|-| |ESPNetV1|-|-|-|-| |ENCNet|-|✔|-|-| |PFPNNet|-|✔|-|-| |FastFCN|✔|-|-|-| |BiSeNetV1|-|-|-|-|

Transformer series

Model zoo benchmark

Based on the Cityscapes dataset, PaddleSeg supports 22+ series of segmentation algorithms and corresponding 30+ image segmentation pre-training models. The performance is evaluated as follows.

Test environment:

PaddlePaddle-release/2.4

Transformer系列

模型库Benchmark

基于Cityscapes数据集，PaddleSeg支持22+系列分割算法以及对应的30+个图像分割预训练模型，性能评估如下。

测试环境： <<<<<<< HEAD

9c8570af (add new models) PaddlePaddle-release/2.4

  • GPU: Tesla V100 16GB
  • CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
  • CUDA: 10.2
  • cuDNN: 7.6
  • Paddle: 2.1.3
  • PaddleSeg: 2.3

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<<<<<<< HEAD Test method:

  • Single GPU, Batch size is 1, the running time is pure model prediction time, and the predicted image size is 1024x512.
  • Use Paddle Inference's Python API to test the model after export.
  • Inference time is the result of averaging predictions using 100 images in the CityScapes dataset.
  • Some algorithms have only tested performance under the configuration that achieves the highest segmentation accuracy.

Accuracy vs Speed

Accuracy vs FLOPs

Accuracy vs Params

Summary table

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PaddlePaddle-release/2.4 测试方法:

  • 单GPU，Batch size为1，运行耗时为纯模型预测时间，预测图片尺寸为1024x512。
  • 模型导出后使用Paddle Inference的Python API测试。
  • 推理时间是使用CityScapes数据集中的100张图像进行预测取平均值的结果。
  • 部分算法只测试了取得最高分割精度的配置下的模型性能。

精度 vs 速度

精度 vs FLOPs

精度 vs Params

总表

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9c8570af (add new models) PaddlePaddle-release/2.4 |Model|Backbone|mIoU|Flops(G)|Params(M)|Inference Time(ms)|Preprocess Time(ms)|Postprocess Time(ms) |-|-|-|-|-|-|-|-| |BiSeNetv2|-|73.19%|16.14|2.33|16.00|167.45|0.013 |Fast-SCNN|-|69.31%|2.04|1.44|10.43|161.52|0.012 |HarDNet|-|79.03%|35.40|4.13|21.19|164.36|0.013 |U-Net|-|65.00%|253.75|13.41|29.11|137.75|0.012 |SegFormer_B0|-|76.73%|13.63|3.72|15.66|152.60|0.017 |SegFormer_B1|-|78.35%|26.55|13.68|21.48|152.40|0.017 |STDC1-Seg50|STDC1|74.74%|24.83|8.29|9.10|153.01|0.016 |STDC2-Seg50|STDC2|77.60%|38.05|12.33|10.88|152.64|0.015 |ANN|ResNet101|79.50%|564.43|67.70|94.91|143.35|0.013 |DANet|ResNet50|80.27%|398.48|47.52|95.08|134.78|0.015 |Deeplabv3|ResNet101_OS8|80.85%|481.00|58.17|114|141.65|0.014 |Deeplabv3P|ResNet50_OS8|81.10%|228.44|26.79|69.78|147.24|0.016 |FCN|HRNet_W48|80.70%|187.50|65.94|45.46|130.58|0.012 |GCNet|ResNet101_OS8|81.01%|570.74|68.73|90.28|119.38|0.013 |OCRNet|HRNet_W48|82.15%|324.66|70.47|61.88|138.48|0.014 |PSPNet|ResNet101_OS8|80.48%|686.89|86.97|115.93|115.94|0.012 |DecoupledSegNet|ResNet50_OS8|81.26%|395.10|41.71|66.89|136.28|0.013 |EMANet|ResNet101_OS8|80.00%|512.18|61.45|80.05|140.47|0.013 |ISANet|ResNet101_OS8|80.10%|474.13|56.81|91.72|129.12|0.012 |DNLNet|ResNet101_OS8|81.03%|575.04|69.13|97.81|138.95|0.014 |SFNet|ResNet18_OS8|78.72%|136.80|13.81|69.51|131.67|0.015 |SFNet|ResNet50_OS8|81.49%|394.37|42.03|121.35|160.45|0.013 |PointRend|ResNet50_OS8|76.54%|363.17|28.18|70.35|157.24|0.016 |SegFormer_B2|-|81.60%|113.71|27.36|47.08|155.45|0.016 |SegFormer_B3|-|82.47%|142.97|47.24|62.70|154.68|0.017 |SegFormer_B4|-|82.38%|171.05|64.01|73.26|151.11|0.017 |SegFormer_B5|-|82.58%|199.68|84.61|84.34|147.92|0.016 |SETR-Naive|Vision Transformer|77.29%|620.94|303.37|201.26|145.76|0.016 |SETR-PUP|Vision Transformer|78.08%|727.46|307.24|212.22|147.05|0.016 |SETR-MLA|Vision Transformer|76.52%|633.88|307.05|204.87|145.87|0.015