--- comments: true --- # PaddleX Model List (Huawei Ascend NPU) 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
Model Name Top-1 Accuracy (%) Model Size (M)
CLIP_vit_base_patch16_224 85.36 306.5 M
CLIP_vit_large_patch14_224 88.1 1.04 G
ConvNeXt_base_224 83.84 313.9 M
ConvNeXt_base_384 84.90 313.9 M
ConvNeXt_large_224 84.26 700.7 M
ConvNeXt_large_384 85.27 700.7 M
ConvNeXt_small 83.13 178.0 M
ConvNeXt_tiny 82.03 101.4 M
MobileNetV1_x0_5 63.5 4.8 M
MobileNetV1_x0_25 51.4 1.8 M
MobileNetV1_x0_75 68.8 9.3 M
MobileNetV1_x1_0 71.0 15.2 M
MobileNetV2_x0_5 65.0 7.1 M
MobileNetV2_x0_25 53.2 5.5 M
MobileNetV2_x1_0 72.2 12.6 M
MobileNetV2_x1_5 74.1 25.0 M
MobileNetV2_x2_0 75.2 41.2 M
MobileNetV3_large_x0_5 69.2 9.6 M
MobileNetV3_large_x0_35 64.3 7.5 M
MobileNetV3_large_x0_75 73.1 14.0 M
MobileNetV3_large_x1_0 75.3 19.5 M
MobileNetV3_large_x1_25 76.4 26.5 M
MobileNetV3_small_x0_5 59.2 6.8 M
MobileNetV3_small_x0_35 53.0 6.0 M
MobileNetV3_small_x0_75 66.0 8.5 M
MobileNetV3_small_x1_0 68.2 10.5 M
MobileNetV3_small_x1_25 70.7 13.0 M
MobileNetV4_conv_large 83.4 125.2 M
MobileNetV4_conv_medium 79.9 37.6 M
MobileNetV4_conv_small 74.6 14.7 M
PP-HGNet_base 85.0 249.4 M
PP-HGNet_small 81.51 86.5 M
PP-HGNet_tiny 79.83 52.4 M
PP-HGNetV2-B0 77.77 21.4 M
PP-HGNetV2-B1 79.18 22.6 M
PP-HGNetV2-B2 81.74 39.9 M
PP-HGNetV2-B3 82.98 57.9 M
PP-HGNetV2-B4 83.57 70.4 M
PP-HGNetV2-B5 84.75 140.8 M
PP-HGNetV2-B6 86.30 268.4 M
PP-LCNet_x0_5 63.14 6.7 M
PP-LCNet_x0_25 51.86 5.5 M
PP-LCNet_x0_35 58.09 5.9 M
PP-LCNet_x0_75 68.18 8.4 M
PP-LCNet_x1_0 71.32 10.5 M
PP-LCNet_x1_5 73.71 16.0 M
PP-LCNet_x2_0 75.18 23.2 M
PP-LCNet_x2_5 76.60 32.1 M
PP-LCNetV2_base 77.05 23.7 M
PP-LCNetV2_large 78.51 37.3 M
PP-LCNetV2_small 73.97 14.6 M
ResNet18_vd 72.3 41.5 M
ResNet18 71.0 41.5 M
ResNet34_vd 76.0 77.3 M
ResNet34 74.6 77.3 M
ResNet50_vd 79.1 90.8 M
ResNet50 76.5 90.8 M
ResNet101_vd 80.2 158.4 M
ResNet101 77.6 158.7 M
ResNet152_vd 80.6 214.3 M
ResNet152 78.3 214.2 M
ResNet200_vd 80.9 266.0 M
SwinTransformer_base_patch4_window7_224 83.37 310.5 M
SwinTransformer_base_patch4_window12_384 84.17 311.4 M
SwinTransformer_large_patch4_window7_224 86.19 694.8 M
SwinTransformer_large_patch4_window12_384 87.06 696.1 M
SwinTransformer_small_patch4_window7_224 83.21 175.6 M
SwinTransformer_tiny_patch4_window7_224 81.10 100.1 M
Note: The above accuracy metrics refer to Top-1 Accuracy on the [ImageNet-1k](https://www.image-net.org/index.php) validation set. ## [图像多标签分类模块](../module_usage/tutorials/cv_modules/image_multilabel_classification.md)
模型名称 mAP（%） 模型存储大小
CLIP_vit_base_patch16_448_ML 89.15 325.6 M
PP-HGNetV2-B0_ML 80.98 39.6 M
PP-HGNetV2-B4_ML 87.96 88.5 M
PP-HGNetV2-B6_ML 91.25 286.5 M
注：以上精度指标为 [COCO2017](https://cocodataset.org/#home) 的多标签分类任务mAP。 ## Object Detection Module
Model Name mAP (%) Model Size (M)
Cascade-FasterRCNN-ResNet50-FPN 41.1 245.4 M
Cascade-FasterRCNN-ResNet50-vd-SSLDv2-FPN 45.0 246.2 M
CenterNet-DLA-34 37.6 75.4 M
CenterNet-ResNet50 38.9 319.7 M
DETR-R50 42.3 159.3 M
FasterRCNN-ResNet34-FPN 37.8 137.5 M
FasterRCNN-ResNet50 36.7 120.2 M
FasterRCNN-ResNet50-FPN 38.4 148.1 M
FasterRCNN-ResNet50-vd-FPN 39.5 148.1 M
FasterRCNN-ResNet50-vd-SSLDv2-FPN 41.4 148.1 M
FasterRCNN-ResNet101 39.0 188.1 M
FasterRCNN-ResNet101-FPN 41.4 216.3 M
FasterRCNN-ResNeXt101-vd-FPN 43.4 360.6 M
FasterRCNN-Swin-Tiny-FPN 42.6 159.8 M
FCOS-ResNet50 39.6 124.2 M
PicoDet-L 42.6 20.9 M
PicoDet-M 37.5 16.8 M
PicoDet-S 29.1 4.4 M
PicoDet-XS 26.2 5.7M
PP-YOLOE_plus-L 52.9 185.3 M
PP-YOLOE_plus-M 49.8 83.2 M
PP-YOLOE_plus-S 43.7 28.3 M
PP-YOLOE_plus-X 54.7 349.4 M
RT-DETR-H 56.3 435.8 M
RT-DETR-L 53.0 113.7 M
RT-DETR-R18 46.5 70.7 M
RT-DETR-R50 53.1 149.1 M
RT-DETR-X 54.8 232.9 M
YOLOv3-DarkNet53 39.1 219.7 M
YOLOv3-MobileNetV3 31.4 83.8 M
YOLOv3-ResNet50_vd_DCN 40.6 163.0 M
Note: The above accuracy metrics are for [COCO2017](https://cocodataset.org/#home) validation set mAP(0.5:0.95). ## [小目标检测模块](../module_usage/tutorials/cv_modules/small_object_detection.md)
模型名称 mAP（%） 模型存储大小
PP-YOLOE_plus_SOD-S 25.1 77.3 M
PP-YOLOE_plus_SOD-L 31.9 325.0 M
PP-YOLOE_plus_SOD-largesize-L 42.7 340.5 M
注：以上精度指标为 [VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset) 验证集 mAP(0.5:0.95)。 ## [行人检测模块](../module_usage/tutorials/cv_modules/human_detection.md)
模型名称 mAP（%） 模型存储大小
PP-YOLOE-L_human 48.0 196.1 M
PP-YOLOE-S_human 42.5 28.8 M
注：以上精度指标为 [CrowdHuman](https://bj.bcebos.com/v1/paddledet/data/crowdhuman.zip) 验证集 mAP(0.5:0.95)。 ## Semantic Segmentation Module
Model Name mIoU (%) Model Size (M)
Deeplabv3_Plus-R50 80.36 94.9 M
Deeplabv3_Plus-R101 81.10 162.5 M
Deeplabv3-R50 79.90 138.3 M
Deeplabv3-R101 80.85 205.9 M
OCRNet_HRNet-W48 82.15 249.8 M
PP-LiteSeg-T 73.10 28.5 M
Note: The above accuracy metrics are for [Cityscapes](https://www.cityscapes-dataset.com/) dataset mIoU. ## Instance Segmentation Module
Model Name Mask AP Model Size (M)
Mask-RT-DETR-H 50.6 449.9 M
Mask-RT-DETR-L 45.7 113.6 M
Mask-RT-DETR-M 42.7 66.6 M
Mask-RT-DETR-S 41.0 51.8 M
Mask-RT-DETR-X 47.5 237.5 M
Cascade-MaskRCNN-ResNet50-FPN 36.3 254.8 M
Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN 39.1 254.7 M
MaskRCNN-ResNet50-FPN 35.6 157.5 M
MaskRCNN-ResNet50-vd-FPN 36.4 157.5 M
MaskRCNN-ResNet50 32.8 127.8 M
MaskRCNN-ResNet101-FPN 36.6 225.4 M
MaskRCNN-ResNet101-vd-FPN 38.1 225.1 M
MaskRCNN-ResNeXt101-vd-FPN 39.5 370.0 M
PP-YOLOE_seg-S 32.5 31.5 M
Note: The above accuracy metrics are for [COCO2017](https://cocodataset.org/#home) validation set Mask AP(0.5:0.95). ## [图像特征模块](../module_usage/tutorials/cv_modules/image_feature.md)
模型名称 recall@1（%） 模型存储大小
PP-ShiTuV2_rec_CLIP_vit_base 88.69 306.6 M
PP-ShiTuV2_rec_CLIP_vit_large 91.03 1.05 G
注：以上精度指标为 AliProducts recall@1。 ## [主体检测模块](../module_usage/tutorials/cv_modules/mainbody_detection.md)
模型名称 mAP（%） 模型存储大小
PP-ShiTuV2_det 41.5 27.6 M
注：以上精度指标为 [PaddleClas主体检测数据集](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/training/PP-ShiTu/mainbody_detection.md) mAP(0.5:0.95)。 ## [车辆检测模块](../module_usage/tutorials/cv_modules/vehicle_detection.md)
模型名称 mAP（%） 模型存储大小
PP-YOLOE-L_vehicle 63.9 196.1 M
PP-YOLOE-S_vehicle 61.3 28.8 M
注：以上精度指标为 [PPVehicle](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppvehicle) 验证集 mAP(0.5:0.95)。 ## [异常检测模块](../module_usage/tutorials/cv_modules/anomaly_detection.md)
模型名称 Avg（%） 模型存储大小
STFPM 96.2 21.5 M
注：以上精度指标为 [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad) 验证集 平均异常分数。 ## Text Detection Module
Model Name Detection Hmean (%) Model Size (M)
PP-OCRv4_mobile_det 77.79 4.2 M
PP-OCRv4_server_det 82.69 100.1 M
Note: The above accuracy metrics are evaluated on PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, and handwritten scenarios, with 500 images for detection. ## Text Recognition Module
Model Name Recognition Avg Accuracy (%) Model Size (M)
PP-OCRv4_mobile_rec 78.20 10.6 M
PP-OCRv4_server_rec 79.20 71.2 M
Note: The above accuracy metrics are evaluated on PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, and handwritten scenarios, with 11,000 images for text recognition.
Model Name Recognition Avg Accuracy (%) Model Size (M)
ch_SVTRv2_rec 68.81 73.9 M
Note: The above accuracy metrics are evaluated on the [PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition](https://aistudio.baidu.com/competition/detail/1131/0/introduction) A-Rank.
Model Name Recognition Avg Accuracy (%) Model Size (M)
ch_RepSVTR_rec 65.07 22.1 M
Note: The above accuracy metrics are evaluated on the [PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition](https://aistudio.baidu.com/competition/detail/1131/0/introduction) B-Rank. ## Table Structure Recognition Module
Model Name Accuracy (%) Model Size (M)
SLANet 76.31 6.9 M
Note: The above accuracy metrics are measured on the PubtabNet English table recognition dataset. ## Layout Analysis Module
Model Name mAP (%) Model Size (M)
PicoDet_layout_1x 86.8 7.4M
PicoDet-L_layout_3cls 89.3 22.6 M
RT-DETR-H_layout_3cls 95.9 470.1 M
RT-DETR-H_layout_17cls 92.6 470.2 M
Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built layout analysis dataset, containing 10,000 images. ## Time Series Forecasting Module
Model Name MSE MAE Model Size (M)
DLinear 0.382 0.394 72K
NLinear 0.386 0.392 40K
Nonstationary 0.600 0.515 55.5 M
PatchTST 0.385 0.397 2.0M
RLinear 0.384 0.392 40K
TiDE 0.405 0.412 31.7M
TimesNet 0.417 0.431 4.9M
Note: The above accuracy metrics are measured on the [ETTH1](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/Etth1.tar) dataset (evaluation results on the test set test.csv). ## Time Series Anomaly Detection Module
Model Name Precision Recall F1-Score Model Size (M)
AutoEncoder_ad 99.36 84.36 91.25 52K
DLinear_ad 98.98 93.96 96.41 112K
Nonstationary_ad 98.55 88.95 93.51 1.8M
PatchTST_ad 98.78 90.70 94.57 320K
TimesNet_ad 98.37 94.80 96.56 1.3M
Note: The above accuracy metrics are measured on the [PSM](https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ts_anomaly_examples.tar) dataset. ## Time Series Classification Module
Model Name Acc (%) Model Size (M)
TimesNet_cls 87.5 792K
Note: The above accuracy metrics are measured on the UWaveGestureLibrary: [Training](https://paddlets.bj.bcebos.com/classification/UWaveGestureLibrary_TRAIN.csv), [Evaluation](https://paddlets.bj.bcebos.com/classification/UWaveGestureLibrary_TEST.csv) datasets.