diff --git a/docs/support_list/model_list_gcu.en.md b/docs/support_list/model_list_gcu.en.md index f7f8e364b..f4d44c651 100644 --- a/docs/support_list/model_list_gcu.en.md +++ b/docs/support_list/model_list_gcu.en.md @@ -6,7 +6,7 @@ comments: true PaddleX incorporates multiple pipelines, each containing several modules, and each module encompasses various models. You can select the appropriate models based on the benchmark data below. If you prioritize model accuracy, choose models with higher accuracy. If you prioritize model size, select models with smaller storage requirements. -## Image Classification Module +## [Image Classification Module](../module_usage/tutorials/cv_modules/image_classification.en.md)
ConvNeXt_base_224 | +83.84 | +313.9 M | +Inference Model/Trained Model | |
ConvNeXt_base_384 | +84.90 | +313.9 M | +Inference Model/Trained Model | |
ConvNeXt_large_224 | +84.26 | +700.7 M | +Inference Model/Trained Model | |
ConvNeXt_large_384 | +85.27 | +700.7 M | +Inference Model/Trained Model | |
ConvNeXt_small | +83.13 | +178.0 M | +Inference Model/Trained Model | |
ConvNeXt_tiny | +82.03 | +101.4 M | +Inference Model/Trained Model | |
FasterNet-L | +83.5 | +357.1 M | +Inference Model/Trained Model | |
FasterNet-M | +82.9 | +204.6 M | +Inference Model/Trained Model | |
FasterNet-S | +81.3 | +119.3 M | +Inference Model/Trained Model | |
FasterNet-T0 | +71.8 | +15.1 M | +Inference Model/Trained Model | |
FasterNet-T1 | +76.2 | +29.2 M | +Inference Model/Trained Model | |
FasterNet-T2 | +78.8 | +57.4 M | +Inference Model/Trained Model | |
MobileNetV1_x0_25 | +51.4 | +1.8 M | +Inference Model/Trained Model | |
MobileNetV1_x0_5 | +63.5 | +4.8 M | +Inference Model/Trained Model | |
MobileNetV1_x0_75 | +68.8 | +9.3 M | +Inference Model/Trained Model | |
MobileNetV1_x1_0 | +71.0 | +15.2 M | +Inference Model/Trained Model | |
MobileNetV2_x0_25 | +53.2 | +5.5 M | +Inference Model/Trained Model | |
MobileNetV2_x0_5 | +65.0 | +7.1 M | +Inference Model/Trained Model | |
MobileNetV2_x1_0 | +72.2 | +12.6 M | +Inference Model/Trained Model | |
MobileNetV2_x1_5 | +74.1 | +25.0 M | +Inference Model/Trained Model | |
MobileNetV2_x2_0 | +75.2 | +41.2 M | +Inference Model/Trained Model | |
MobileNetV3_large_x0_35 | +64.3 | +7.5 M | +Inference Model/Trained Model | |
MobileNetV3_large_x0_5 | +69.2 | +9.6 M | +Inference Model/Trained Model | |
MobileNetV3_large_x0_75 | +73.1 | +14.0 M | +Inference Model/Trained Model | |
MobileNetV3_large_x1_0 | +75.3 | +19.5 M | +Inference Model/Trained Model | |
MobileNetV3_large_x1_25 | +76.4 | +26.5 M | +Inference Model/Trained Model | |
MobileNetV3_small_x0_35 | +53.0 | +6.0 M | +Inference Model/Trained Model | |
MobileNetV3_small_x0_5 | +59.2 | +6.8 M | +Inference Model/Trained Model | |
MobileNetV3_small_x0_75 | +66.0 | +8.5 M | +Inference Model/Trained Model | |
MobileNetV3_small_x1_0 | +68.2 | +10.5 M | +Inference Model/Trained Model | |
MobileNetV3_small_x1_25 | +70.7 | +13.0 M | +Inference Model/Trained Model | |
MobileNetV4_conv_large | +83.4 | +125.2 M | +Inference Model/Trained Model | |
MobileNetV4_conv_medium | +80.9 | +37.6 M | +Inference Model/Trained Model | |
MobileNetV4_conv_small | +74.4 | +14.7 M | +Inference Model/Trained Model | |
PP-HGNet_base | +85.0 | +249.4 M | +Inference Model/Trained Model | |
PP-HGNet_small | +81.51 | +86.5 M | +Inference Model/Trained Model | |
PP-HGNet_tiny | +79.83 | +52.4 M | +Inference Model/Trained Model | |
PP-HGNetV2-B0 | +77.77 | +21.4 M | +Inference Model/Trained Model | |
PP-HGNetV2-B1 | +78.90 | +22.6 M | +Inference Model/Trained Model | |
PP-HGNetV2-B2 | +81.57 | +39.9 M | +Inference Model/Trained Model | |
PP-HGNetV2-B3 | +82.92 | +57.9 M | +Inference Model/Trained Model | |
PP-HGNetV2-B4 | +83.68 | +70.4 M | +Inference Model/Trained Model | |
PP-HGNetV2-B5 | +84.75 | +140.8 M | +Inference Model/Trained Model | |
PP-HGNetV2-B6 | +86.20 | +268.4 M | +Inference Model/Trained Model | |
PP-LCNet_x0_25 | +51.86 | +5.5 M | +Inference Model/Trained Model | |
PP-LCNet_x0_35 | +58.10 | +5.9 M | +Inference Model/Trained Model | |
PP-LCNet_x0_5 | +63.14 | +6.7 M | +Inference Model/Trained Model | |
PP-LCNet_x0_75 | +68.18 | +8.4 M | +Inference Model/Trained Model | |
PP-LCNet_x1_0 | +71.32 | +10.5 M | +Inference Model/Trained Model | |
PP-LCNet_x1_5 | +73.71 | +16.0 M | +Inference Model/Trained Model | |
PP-LCNet_x2_0 | +75.18 | +23.2 M | +Inference Model/Trained Model | |
PP-LCNet_x2_5 | +76.60 | +32.1 M | +Inference Model/Trained Model | |
PP-LCNetV2_base | +77.04 | +23.7 M | +Inference Model/Trained Model | |
PP-LCNetV2_large | +78.51 | +37.3 M | +Inference Model/Trained Model | |
PP-LCNetV2_small | +73.96 | +14.6 M | +Inference Model/Trained Model | |
ResNet18_vd | +72.3 | +41.5 M | +Inference Model/Trained Model | |
ResNet18 | +71.0 | +41.5 M | +Inference Model/Trained Model | |
ResNet34_vd | +76.0 | +77.3 M | +Inference Model/Trained Model | |
ResNet34 | +74.6 | +77.3 M | +Inference Model/Trained Model | |
ResNet50_vd | +79.1 | +90.8 M | +Inference Model/Trained Model | |
ResNet50 | -76.96 | +76.5 | 90.8 M | Inference Model/Trained Model |
ResNet101_vd | +80.2 | +158.4 M | +Inference Model/Trained Model | |
ResNet101 | +77.6 | +158.7 M | +Inference Model/Trained Model | |
ResNet152_vd | +80.6 | +214.3 M | +Inference Model/Trained Model | |
ResNet152 | +78.3 | +214.2 M | +Inference Model/Trained Model | |
ResNet200_vd | +80.7 | +266.0 M | +Inference Model/Trained Model | |
StarNet-S1 | +73.5 | +11.2 M | +Inference Model/Trained Model | |
StarNet-S2 | +74.7 | +14.3 M | +Inference Model/Trained Model | |
StarNet-S3 | +77.4 | +22.2 M | +Inference Model/Trained Model | |
StarNet-S4 | +78.8 | +28.9 M | +Inference Model/Trained Model |
FCOS-ResNet50 | +39.6 | +124.2 M | +Inference Model/Trained Model |
PicoDet-L | +42.5 | +20.9 M | +Inference Model/Trained Model |
PicoDet-M | +37.4 | +16.8 M | +Inference Model/Trained Model |
PicoDet-S | +29.0 | +4.4 M | +Inference Model/Trained Model |
PicoDet-XS | +26.2 | +5.7M | +Inference Model/Trained Model |
PP-YOLOE_plus-L | 52.8 | 185.3 M | @@ -84,7 +454,31 @@ PaddleX incorporates multiple pipelines, each containing several modules, and ea
Model Name | +mAP(%) | +Model Size (M) | +Model Download Link |
---|---|---|---|
PP-YOLOE-L_human | +48.0 | +196.1 M | +Inference Model/Trained Model |
PP-YOLOE-S_human | +42.5 | +28.8 M | +Inference Model/Trained Model |
模型名称 | Top1 Acc(%) | -模型存储大小(M) | +模型存储大小(M) | 模型下载链接 |
---|---|---|---|---|
ConvNeXt_base_224 | +83.84 | +313.9 M | +推理模型/训练模型 | |
ConvNeXt_base_384 | +84.90 | +313.9 M | +推理模型/训练模型 | |
ConvNeXt_large_224 | +84.26 | +700.7 M | +推理模型/训练模型 | |
ConvNeXt_large_384 | +85.27 | +700.7 M | +推理模型/训练模型 | |
ConvNeXt_small | +83.13 | +178.0 M | +推理模型/训练模型 | |
ConvNeXt_tiny | +82.03 | +101.4 M | +推理模型/训练模型 | |
FasterNet-L | +83.5 | +357.1 M | +推理模型/训练模型 | |
FasterNet-M | +82.9 | +204.6 M | +推理模型/训练模型 | |
FasterNet-S | +81.3 | +119.3 M | +推理模型/训练模型 | |
FasterNet-T0 | +71.8 | +15.1 M | +推理模型/训练模型 | |
FasterNet-T1 | +76.2 | +29.2 M | +推理模型/训练模型 | |
FasterNet-T2 | +78.8 | +57.4 M | +推理模型/训练模型 | |
MobileNetV1_x0_25 | +51.4 | +1.8 M | +推理模型/训练模型 | |
MobileNetV1_x0_5 | +63.5 | +4.8 M | +推理模型/训练模型 | |
MobileNetV1_x0_75 | +68.8 | +9.3 M | +推理模型/训练模型 | |
MobileNetV1_x1_0 | +71.0 | +15.2 M | +推理模型/训练模型 | |
MobileNetV2_x0_25 | +53.2 | +5.5 M | +推理模型/训练模型 | |
MobileNetV2_x0_5 | +65.0 | +7.1 M | +推理模型/训练模型 | |
MobileNetV2_x1_0 | +72.2 | +12.6 M | +推理模型/训练模型 | |
MobileNetV2_x1_5 | +74.1 | +25.0 M | +推理模型/训练模型 | |
MobileNetV2_x2_0 | +75.2 | +41.2 M | +推理模型/训练模型 | |
MobileNetV3_large_x0_35 | +64.3 | +7.5 M | +推理模型/训练模型 | |
MobileNetV3_large_x0_5 | +69.2 | +9.6 M | +推理模型/训练模型 | |
MobileNetV3_large_x0_75 | +73.1 | +14.0 M | +推理模型/训练模型 | |
MobileNetV3_large_x1_0 | +75.3 | +19.5 M | +推理模型/训练模型 | |
MobileNetV3_large_x1_25 | +76.4 | +26.5 M | +推理模型/训练模型 | |
MobileNetV3_small_x0_35 | +53.0 | +6.0 M | +推理模型/训练模型 | |
MobileNetV3_small_x0_5 | +59.2 | +6.8 M | +推理模型/训练模型 | |
MobileNetV3_small_x0_75 | +66.0 | +8.5 M | +推理模型/训练模型 | |
MobileNetV3_small_x1_0 | +68.2 | +10.5 M | +推理模型/训练模型 | |
MobileNetV3_small_x1_25 | +70.7 | +13.0 M | +推理模型/训练模型 | |
MobileNetV4_conv_large | +83.4 | +125.2 M | +推理模型/训练模型 | |
MobileNetV4_conv_medium | +80.9 | +37.6 M | +推理模型/训练模型 | |
MobileNetV4_conv_small | +74.4 | +14.7 M | +推理模型/训练模型 | |
PP-HGNet_base | +85.0 | +249.4 M | +推理模型/训练模型 | |
PP-HGNet_small | +81.51 | +86.5 M | +推理模型/训练模型 | |
PP-HGNet_tiny | +79.83 | +52.4 M | +推理模型/训练模型 | |
PP-HGNetV2-B0 | +77.77 | +21.4 M | +推理模型/训练模型 | |
PP-HGNetV2-B1 | +78.90 | +22.6 M | +推理模型/训练模型 | |
PP-HGNetV2-B2 | +81.57 | +39.9 M | +推理模型/训练模型 | |
PP-HGNetV2-B3 | +82.92 | +57.9 M | +推理模型/训练模型 | |
PP-HGNetV2-B4 | +83.68 | +70.4 M | +推理模型/训练模型 | |
PP-HGNetV2-B5 | +84.75 | +140.8 M | +推理模型/训练模型 | |
PP-HGNetV2-B6 | +86.20 | +268.4 M | +推理模型/训练模型 | |
PP-LCNet_x0_25 | +51.86 | +5.5 M | +推理模型/训练模型 | |
PP-LCNet_x0_35 | +58.10 | +5.9 M | +推理模型/训练模型 | |
PP-LCNet_x0_5 | +63.14 | +6.7 M | +推理模型/训练模型 | |
PP-LCNet_x0_75 | +68.18 | +8.4 M | +推理模型/训练模型 | |
PP-LCNet_x1_0 | +71.32 | +10.5 M | +推理模型/训练模型 | |
PP-LCNet_x1_5 | +73.71 | +16.0 M | +推理模型/训练模型 | |
PP-LCNet_x2_0 | +75.18 | +23.2 M | +推理模型/训练模型 | |
PP-LCNet_x2_5 | +76.60 | +32.1 M | +推理模型/训练模型 | |
PP-LCNetV2_base | +77.04 | +23.7 M | +推理模型/训练模型 | |
PP-LCNetV2_large | +78.51 | +37.3 M | +推理模型/训练模型 | |
PP-LCNetV2_small | +73.96 | +14.6 M | +推理模型/训练模型 | |
ResNet18_vd | +72.3 | +41.5 M | +推理模型/训练模型 | |
ResNet18 | +71.0 | +41.5 M | +推理模型/训练模型 | |
ResNet34_vd | +76.0 | +77.3 M | +推理模型/训练模型 | |
ResNet34 | +74.6 | +77.3 M | +推理模型/训练模型 | |
ResNet50_vd | +79.1 | +90.8 M | +推理模型/训练模型 | |
ResNet50 | -76.96 | +76.5 | 90.8 M | 推理模型/训练模型 |
ResNet101_vd | +80.2 | +158.4 M | +推理模型/训练模型 | |
ResNet101 | +77.6 | +158.7 M | +推理模型/训练模型 | |
ResNet152_vd | +80.6 | +214.3 M | +推理模型/训练模型 | |
ResNet152 | +78.3 | +214.2 M | +推理模型/训练模型 | |
ResNet200_vd | +80.7 | +266.0 M | +推理模型/训练模型 | |
StarNet-S1 | +73.5 | +11.2 M | +推理模型/训练模型 | |
StarNet-S2 | +74.7 | +14.3 M | +推理模型/训练模型 | |
StarNet-S3 | +77.4 | +22.2 M | +推理模型/训练模型 | |
StarNet-S4 | +78.8 | +28.9 M | +推理模型/训练模型 |
模型名称 | mAP(%) | -模型存储大小(M) | +模型存储大小(M) | 模型下载链接 |
---|---|---|---|---|
FCOS-ResNet50 | +39.6 | +124.2 M | +推理模型/训练模型 | |
PicoDet-L | +42.5 | +20.9 M | +推理模型/训练模型 | |
PicoDet-M | +37.4 | +16.8 M | +推理模型/训练模型 | |
PicoDet-S | +29.0 | +4.4 M | +推理模型/训练模型 | |
PicoDet-XS | +26.2 | +5.7M | +推理模型/训练模型 | |
PP-YOLOE_plus-L | 52.8 | 185.3 M | @@ -84,13 +454,37 @@ PaddleX 内置了多条产线,每条产线都包含了若干模块,每个模
模型名称 | +mAP(%) | +模型存储大小 | +模型下载链接 |
---|---|---|---|
PP-YOLOE-L_human | +48.0 | +196.1 M | +推理模型/训练模型 |
PP-YOLOE-S_human | +42.5 | +28.8 M | +推理模型/训练模型 |
模型名称 | 检测Hmean(%) | -模型存储大小(M) | +模型存储大小(M) | 模型下载链接 |
---|---|---|---|---|
PP-OCRv4_server_det | 82.69 | -100.1M | +100.1 M | 推理模型/训练模型 |
模型名称 | 识别Avg Accuracy(%) | -模型存储大小(M) | +模型存储大小(M) | 模型下载链接 |
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