# PaddleX 模型列表

## 一、图像分类
### 1. ResNet 系列
| 模型名称 | config |
| :--- | :---: |
| ResNet18 | [ResNet18.yaml](../../../paddlex/configs/image_classification/ResNet18.yaml)|
| ResNet18_vd | [ResNet18_vd.yaml](../../../paddlex/configs/image_classification/ResNet18_vd.yaml)|
| ResNet34 | [ResNet34.yaml](../../../paddlex/configs/image_classification/ResNet34.yaml)|
| ResNet34_vd | [ResNet34_vd.yaml](../../../paddlex/configs/image_classification/ResNet34_vd.yaml)|
| ResNet50 | [ResNet50.yaml](../../../paddlex/configs/image_classification/ResNet50.yaml)|
| ResNet50_vd | [ResNet50_vd.yaml](../../../paddlex/configs/image_classification/ResNet50_vd.yaml)|
| ResNet101 | [ResNet101.yaml](../../../paddlex/configs/image_classification/ResNet101.yaml)|
| ResNet101_vd | [ResNet101_vd.yaml](../../../paddlex/configs/image_classification/ResNet101_vd.yaml)|
| ResNet152 | [ResNet152.yaml](../../../paddlex/configs/image_classification/ResNet152.yaml)|
| ResNet152_vd | [ResNet152_vd.yaml](../../../paddlex/configs/image_classification/ResNet152_vd.yaml)|
| ResNet200_vd | [ResNet200_vd.yaml](../../../paddlex/configs/image_classification/ResNet200_vd.yaml)|
### 2.PP-LCNet & PP-LCNetV2 系列
| 模型名称 | config |
| :--- | :---: |
| PP-LCNet_x0_25 | [PP-LCNet_x0_25.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x0_25.yaml)|
| PP-LCNet_x0_35 | [PP-LCNet_x0_35.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x0_35.yaml)|
| PP-LCNet_x0_5 | [PP-LCNet_x0_5.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x0_5.yaml)|
| PP-LCNet_x0_75 | [PP-LCNet_x0_75.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x0_75.yaml)|
| PP-LCNet_x1_0 | [PP-LCNet_x1_0.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x1_0.yaml)|
| PP-LCNet_x1_5 | [PP-LCNet_x1_5.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x1_5.yaml)|
| PP-LCNet_x2_0 | [PP-LCNet_x2_0.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x2_0.yaml)|
| PP-LCNet_x2_5 | [PP-LCNet_x2_5.yaml](../../../paddlex/configs/image_classification/PP-LCNet_x2_5.yaml)|
| PP-LCNetV2_small | [PP-LCNetV2_small.yaml](../../../paddlex/configs/image_classification/PP-LCNetV2_small.yaml)|
| PP-LCNetV2_base | [PP-LCNetV2_base.yaml](../../../paddlex/configs/image_classification/PP-LCNetV2_base.yaml)|
| PP-LCNetV2_large | [PP-LCNetV2_large.yaml](../../../paddlex/configs/image_classification/PP-LCNetV2_large.yaml)|
### 3.MobileNetV1 系列
| 模型名称 | config |
| :--- | :---: |
| MobileNetV1_x0_25 | [MobileNetV1_x0_25.yaml](../../../paddlex/configs/image_classification/MobileNetV1_x0_25.yaml)|
| MobileNetV1_x0_5 | [MobileNetV1_x0_5.yaml](../../../paddlex/configs/image_classification/MobileNetV1_x0_5.yaml)|
| MobileNetV1_x0_75 | [MobileNetV1_x0_75.yaml](../../../paddlex/configs/image_classification/MobileNetV1_x0_75.yaml)|
| MobileNetV1_x1_0 | [MobileNetV1_x1_0.yaml](../../../paddlex/configs/image_classification/MobileNetV1_x1_0.yaml)|
### 4.MobileNetV2 系列
| 模型名称 | config |
| :--- | :---: |
| MobileNetV2_x0_25 | [MobileNetV2_x0_25.yaml](../../../paddlex/configs/image_classification/MobileNetV2_x0_25.yaml)|
| MobileNetV2_x0_5 | [MobileNetV2_x0_5.yaml](../../../paddlex/configs/image_classification/MobileNetV2_x0_5.yaml)|
| MobileNetV2_x1_0 | [MobileNetV2_x1_0.yaml](../../../paddlex/configs/image_classification/MobileNetV2_x1_0.yaml)|
| MobileNetV2_x1_5 | [MobileNetV2_x1_5.yaml](../../../paddlex/configs/image_classification/MobileNetV2_x1_5.yaml)|
| MobileNetV2_x2_0 | [MobileNetV2_x2_0.yaml](../../../paddlex/configs/image_classification/MobileNetV2_x2_0.yaml)|
### 5.MobileNetV3 系列
| 模型名称 | config |
| :--- | :---: |
| MobileNetV3_small_x0_35 | [MobileNetV3_small_x0_35.yaml](../../../paddlex/configs/image_classification/MobileNetV3_small_x0_35.yaml)|
| MobileNetV3_small_x0_5 | [MobileNetV3_small_x0_5.yaml](../../../paddlex/configs/image_classification/MobileNetV3_small_x0_5.yaml)|
| MobileNetV3_small_x0_75 | [MobileNetV3_small_x0_75.yaml](../../../paddlex/configs/image_classification/MobileNetV3_small_x0_75.yaml)|
| MobileNetV3_small_x1_0 | [MobileNetV3_small_x1_0.yaml](../../../paddlex/configs/image_classification/MobileNetV3_small_x1_0.yaml)|
| MobileNetV3_small_x1_25 | [MobileNetV3_small_x1_25.yaml](../../../paddlex/configs/image_classification/MobileNetV3_small_x1_25.yaml)|
| MobileNetV3_large_x0_35 | [MobileNetV3_large_x0_35.yaml](../../../paddlex/configs/image_classification/MobileNetV3_large_x0_35.yaml)|
| MobileNetV3_large_x0_5 | [MobileNetV3_large_x0_5.yaml](../../../paddlex/configs/image_classification/MobileNetV3_large_x0_5.yaml)|
| MobileNetV3_large_x0_75 | [MobileNetV3_large_x0_75.yaml](../../../paddlex/configs/image_classification/MobileNetV3_large_x0_75.yaml)|
| MobileNetV3_large_x1_0 | [MobileNetV3_large_x1_0.yaml](../../../paddlex/configs/image_classification/MobileNetV3_large_x1_0.yaml)|
| MobileNetV3_large_x1_25 | [MobileNetV3_large_x1_25.yaml](../../../paddlex/configs/image_classification/MobileNetV3_large_x1_25.yaml)|
### 6.PP-HGNet 系列
| 模型名称 | config |
| :--- | :---: |
| PP-HGNet_tiny | [PP-HGNet_tiny.yaml](../../../paddlex/configs/image_classification/PP-HGNet_tiny.yaml)|
| PP-HGNet_small | [PP-HGNet_small.yaml](../../../paddlex/configs/image_classification/PP-HGNet_small.yaml)|
| PP-HGNet_base | [PP-HGNet_base.yaml](../../../paddlex/configs/image_classification/PP-HGNet_base.yaml)|
### 7.PP-HGNetV2 系列
| 模型名称 | config |
| :--- | :---: |
| PP-HGNetV2-B0 | [PP-HGNetV2-B0.yaml](../../../paddlex/configs/image_classification/PP-HGNetV2-B0.yaml)|
| PP-HGNetV2-B1 | [PP-HGNetV2-B1.yaml](../../../paddlex/configs/image_classification/PP-HGNetV2-B1.yaml)|
| PP-HGNetV2-B2 | [PP-HGNetV2-B2.yaml](../../../paddlex/configs/image_classification/PP-HGNetV2-B2.yaml)|
| PP-HGNetV2-B3 | [PP-HGNetV2-B3.yaml](../../../paddlex/configs/image_classification/PP-HGNetV2-B3.yaml)|
| PP-HGNetV2-B4 | [PP-HGNetV2-B4.yaml](../../../paddlex/configs/image_classification/PP-HGNetV2-B4.yaml)|
| PP-HGNetV2-B5 | [PP-HGNetV2-B5.yaml](../../../paddlex/configs/image_classification/PP-HGNetV2-B5.yaml)|
| PP-HGNetV2-B6 | [PP-HGNetV2-B6.yaml](../../../paddlex/configs/image_classification/PP-HGNetV2-B6.yaml)|
### 8.CLIP 系列
| 模型名称 | config |
| :--- | :---: |
| CLIP_vit_base_patch16_224 | [CLIP_vit_base_patch16_224.yaml](../../../paddlex/configs/image_classification/CLIP_vit_base_patch16_224.yaml)|
| CLIP_vit_large_patch14_224 | [CLIP_vit_large_patch14_224.yaml](../../../paddlex/configs/image_classification/CLIP_vit_large_patch14_224.yaml)|
### 9.ConvNeXt 系列
| 模型名称 | config |
| :--- | :---: |
| ConvNeXt_tiny | [ConvNeXt_tiny.yaml](../../../paddlex/configs/image_classification/ConvNeXt_tiny.yaml)|
| ConvNeXt_small | [ConvNeXt_small.yaml](../../../paddlex/configs/image_classification/ConvNeXt_small.yaml)|
| ConvNeXt_base_224 | [ConvNeXt_base_224.yaml](../../../paddlex/configs/image_classification/ConvNeXt_base_224.yaml)|
| ConvNeXt_base_384 | [ConvNeXt_base_384.yaml](../../../paddlex/configs/image_classification/ConvNeXt_base_384.yaml)|
| ConvNeXt_large_224 | [ConvNeXt_large_224.yaml](../../../paddlex/configs/image_classification/ConvNeXt_large_224.yaml)|
| ConvNeXt_large_384 | [ConvNeXt_large_384.yaml](../../../paddlex/configs/image_classification/ConvNeXt_large_384.yaml)|
### 10.SwinTransformer系列
| 模型名称 | config |
| :--- | :---: |
| SwinTransformer_tiny_patch4_window7_224 | [SwinTransformer_tiny_patch4_window7_224.yaml](../../../paddlex/configs/image_classification/SwinTransformer_tiny_patch4_window7_224.yaml)|
| SwinTransformer_small_patch4_window7_224 | [SwinTransformer_small_patch4_window7_224.yaml](../../../paddlex/configs/image_classification/SwinTransformer_small_patch4_window7_224.yaml)|
| SwinTransformer_base_patch4_window7_224 | [SwinTransformer_base_patch4_window7_224.yaml](../../../paddlex/configs/image_classification/SwinTransformer_base_patch4_window7_224.yaml)|
| SwinTransformer_base_patch4_window12_384 | [SwinTransformer_base_patch4_window12_384.yaml](../../../paddlex/configs/image_classification/SwinTransformer_base_patch4_window12_384.yaml)|
| SwinTransformer_large_patch4_window7_224 | [SwinTransformer_large_patch4_window7_224.yaml](../../../paddlex/configs/image_classification/SwinTransformer_large_patch4_window7_224.yaml)|
| SwinTransformer_large_patch4_window12_384 | [SwinTransformer_large_patch4_window12_384.yaml](../../../paddlex/configs/image_classification/SwinTransformer_large_patch4_window12_384.yaml)|
## 二、目标检测
### 1. PP-YOLOE_plus 系列
| 模型名称 | config |
| :--- | :---: |
| PP-YOLOE_plus-S | [PP-YOLOE_plus-S.yaml](../../../paddlex/configs/object_detection/PP-YOLOE_plus-S.yaml)|
| PP-YOLOE_plus-M | [PP-YOLOE_plus-M.yaml](../../../paddlex/configs/object_detection/PP-YOLOE_plus-M.yaml)|
| PP-YOLOE_plus-L | [PP-YOLOE_plus-L.yaml](../../../paddlex/configs/object_detection/PP-YOLOE_plus-L.yaml)|
| PP-YOLOE_plus-X | [PP-YOLOE_plus-X.yaml](../../../paddlex/configs/object_detection/PP-YOLOE_plus-X.yaml)|
### 2. RT-DETR 系列
| 模型名称 | config |
| :--- | :---: |
| RT-DETR-R18 | [RT-DETR-R18.yaml](../../../paddlex/configs/object_detection/RT-DETR-R18.yaml)|
| RT-DETR-R50 | [RT-DETR-R50.yaml](../../../paddlex/configs/object_detection/RT-DETR-R50.yaml)|
| RT-DETR-L | [RT-DETR-L.yaml](../../../paddlex/configs/object_detection/RT-DETR-L.yaml)|
| RT-DETR-H | [RT-DETR-H.yaml](../../../paddlex/configs/object_detection/RT-DETR-H.yaml)|
| RT-DETR-X | [RT-DETR-X.yaml](../../../paddlex/configs/object_detection/RT-DETR-X.yaml)|
### 3. PicoDet系列
| 模型名称 | config |
| :--- | :---: |
| PicoDet-S | [PicoDet-S.yaml](../../../paddlex/configs/object_detection/PicoDet-S.yaml)|
| PicoDet-L | [PicoDet-L.yaml](../../../paddlex/configs/object_detection/PicoDet-L.yaml)|
### 4. YOLOv3 系列
| 模型名称 | config |
| :--- | :---: |
| YOLOv3-DarkNet53 | [YOLOv3-DarkNet53.yaml](../../../paddlex/configs/object_detection/YOLOv3-DarkNet53.yaml)|
| YOLOv3-MobileNetV3 | [YOLOv3-MobileNetV3.yaml](../../../paddlex/configs/object_detection/YOLOv3-MobileNetV3.yaml)|
| YOLOv3-ResNet50_vd_DCN | [YOLOv3-ResNet50_vd_DCN.yaml](../../../paddlex/configs/object_detection/YOLOv3-ResNet50_vd_DCN.yaml)|
### 5. YOLOX 系列
| 模型名称 | config |
| :--- | :---: |
| YOLOX-L | [YOLOX-L.yaml](../../../paddlex/configs/object_detection/YOLOX-L.yaml)|
| YOLOX-M | [YOLOX-M.yaml](../../../paddlex/configs/object_detection/YOLOX-M.yaml)|
| YOLOX-N | [YOLOX-N.yaml](../../../paddlex/configs/object_detection/YOLOX-N.yaml)|
| YOLOX-S | [YOLOX-S.yaml](../../../paddlex/configs/object_detection/YOLOX-S.yaml)|
| YOLOX-T | [YOLOX-T.yaml](../../../paddlex/configs/object_detection/YOLOX-T.yaml)|
| YOLOX-X | [YOLOX-X.yaml](../../../paddlex/configs/object_detection/YOLOX-X.yaml)|
## 三、实例分割
### 1.Mask-RT-DETR 系列
| 模型名称 | config |
| :--- | :---: |
| Mask-RT-DETR-L | [Mask-RT-DETR-L.yaml](../../../paddlex/configs/instance_segmentation/Mask-RT-DETR-L.yaml)|
| Mask-RT-DETR-H | [Mask-RT-DETR-H.yaml](../../../paddlex/configs/instance_segmentation/Mask-RT-DETR-H.yaml)|
## 四、语义分割
### 1.Deeplabv3 系列
| 模型名称 | config |
| :--- | :---: |
| Deeplabv3-R50 | [Deeplabv3-R50.yaml](../../../paddlex/configs/semantic_segmentation/Deeplabv3-R50.yaml)|
| Deeplabv3-R101 | [Deeplabv3-R101.yaml](../../../paddlex/configs/semantic_segmentation/Deeplabv3-R101.yaml)|
| Deeplabv3_Plus-R50 | [Deeplabv3_Plus-R50.yaml](../../../paddlex/configs/semantic_segmentation/Deeplabv3_Plus-R50.yaml)|
| Deeplabv3_Plus-R101 | [Deeplabv3_Plus-R101.yaml](../../../paddlex/configs/semantic_segmentation/Deeplabv3_Plus-R101.yaml)|
### 2.OCRNet 系列
| 模型名称 | config |
| :--- | :---: |
| OCRNet_HRNet-W48 | [OCRNet_HRNet-W48.yaml](../../../paddlex/configs/semantic_segmentation/OCRNet_HRNet-W48.yaml)|
| OCRNet_HRNet-W18 | [OCRNet_HRNet-W18.yaml](../../../paddlex/configs/semantic_segmentation/OCRNet_HRNet-W18.yaml)|
### 3.PP-LiteSeg系列
| 模型名称 | config |
| :--- | :---: |
| PP-LiteSeg-T | [PP-LiteSeg-T.yaml](../../../paddlex/configs/semantic_segmentation/PP-LiteSeg-T.yaml)|
### 4.SegFormer 系列
| 模型名称 | config |
| :--- | :---: |
| SegFormer-B0 | [SegFormer-B0.yaml](../../../paddlex/configs/semantic_segmentation/SegFormer-B0.yaml)|
| SegFormer-B1 | [SegFormer-B1.yaml](../../../paddlex/configs/semantic_segmentation/SegFormer-B1.yaml)|
| SegFormer-B2 | [SegFormer-B2.yaml](../../../paddlex/configs/semantic_segmentation/SegFormer-B2.yaml)|
| SegFormer-B3 | [SegFormer-B3.yaml](../../../paddlex/configs/semantic_segmentation/SegFormer-B3.yaml)|
| SegFormer-B4 | [SegFormer-B4.yaml](../../../paddlex/configs/semantic_segmentation/SegFormer-B4.yaml)|
| SegFormer-B5 | [SegFormer-B5.yaml](../../../paddlex/configs/semantic_segmentation/SegFormer-B5.yaml)|
### 5.SeaFormer 系列
| 模型名称 | config |
| :--- | :---: |
| SeaFormer_tiny | [SeaFormer_tiny.yaml](../../../paddlex/configs/semantic_segmentation/SeaFormer_tiny.yaml)|
| SeaFormer_small | [SeaFormer_small.yaml](../../../paddlex/configs/semantic_segmentation/SeaFormer_small.yaml)|
| SeaFormer_base | [SeaFormer_base.yaml](../../../paddlex/configs/semantic_segmentation/SeaFormer_base.yaml)|
| SeaFormer_large | [SeaFormer_large.yaml](../../../paddlex/configs/semantic_segmentation/SeaFormer_large.yaml)|
## 五、表格识别
| 模型名称 | config |
| :--- | :---: |
| SLANet | [SLANet.yaml](../../../paddlex/configs/table_recognition/SLANet.yaml)|
## 六、文本检测
### 1.PP-OCRv4 系列
| 模型名称 | config |
| :--- | :---: |
| PP-OCRv4_server_det | [PP-OCRv4_server_det.yaml](../../../paddlex/configs/text_detection/PP-OCRv4_server_det.yaml)|
| PP-OCRv4_mobile_det | [PP-OCRv4_mobile_det.yaml](../../../paddlex/configs/text_detection/PP-OCRv4_mobile_det.yaml)|
## 七、文本识别
### 1.PP-OCRv4 系列
| 模型名称 | config |
| :--- | :---: |
| PP-OCRv4_server_rec | [PP-OCRv4_server_rec.yaml](../../../paddlex/configs/text_recognition/PP-OCRv4_server_rec.yaml)|
| PP-OCRv4_mobile_rec | [PP-OCRv4_mobile_rec.yaml](../../../paddlex/configs/text_recognition/PP-OCRv4_mobile_rec.yaml)|
## 八、公式识别
| 模型名称 | config |
| :--- | :---: |
| LaTeX_OCR_rec | [LaTeX_OCR_rec.yml](../../../paddlex/configs/text_recognition/LaTeX_OCR_rec.yml)|
## 九、版面分析
| 模型名称 | config |
| :--- | :---: |
| PicoDet_layout_1x | [PicoDet_layout_1x.yaml](../../../paddlex/configs/structure_analysis/PicoDet_layout_1x.yaml)|
## 十、时序异常检测
| 模型名称 | config |
| :--- | :---: |
| DLinear_ad | [DLinear_ad.yaml](../../../paddlex/configs/ts_anomaly_detection/DLinear_ad.yaml)|
| PatchTST_ad | [PatchTST_ad.yaml](../../../paddlex/configs/ts_anomaly_detection/PatchTST_ad.yaml)|
| TimesNet_ad | [TimesNet_ad.yaml](../../../paddlex/configs/ts_anomaly_detection/TimesNet_ad.yaml)|
| AutoEncoder_ad | [AutoEncoder_ad.yaml](../../../paddlex/configs/ts_anomaly_detection/AutoEncoder_ad.yaml)|
| Nonstationary_ad | [Nonstationary_ad.yaml](../../../paddlex/configs/ts_anomaly_detection/Nonstationary_ad.yaml)|
## 十一、时序分类
| 模型名称 | config |
| :--- | :---: |
| TimesNet_cls | [TimesNet_cls.yaml](../../../paddlex/configs/ts_classification/TimesNet_cls.yaml)|
## 十二、时序预测
| 模型名称 | config |
| :--- | :---: |
| DLinear | [DLinear.yaml](../../../paddlex/configs/ts_forecast/DLinear.yaml)|
| RLinear | [RLinear.yaml](../../../paddlex/configs/ts_forecast/RLinear.yaml)|
| NLinear | [NLinear.yaml](../../../paddlex/configs/ts_forecast/NLinear.yaml)|
| PatchTST | [PatchTST.yaml](../../../paddlex/configs/ts_forecast/PatchTST.yaml)|
| TimesNet | [TimesNet.yaml](../../../paddlex/configs/ts_forecast/TimesNet.yaml)|
| Nonstationary | [Nonstationary.yaml](../../../paddlex/configs/ts_forecast/Nonstationary.yaml)|
| TiDE | [TiDE.yaml](../../../paddlex/configs/ts_forecast/TiDE.yaml)|