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model_list_npu.en.md

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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) Model Download Link
CLIP_vit_base_patch16_224 85.36 306.5 M Inference Model/Trained Model
CLIP_vit_large_patch14_224 88.1 1.04 G Inference Model/Trained Model
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
MobileNetV1_x0_5 63.5 4.8 M Inference Model/Trained Model
MobileNetV1_x0_25 51.4 1.8 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_5 65.0 7.1 M Inference Model/Trained Model
MobileNetV2_x0_25 53.2 5.5 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_5 69.2 9.6 M Inference Model/Trained Model
MobileNetV3_large_x0_35 64.3 7.5 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_5 59.2 6.8 M Inference Model/Trained Model
MobileNetV3_small_x0_35 53.0 6.0 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 79.9 37.6 M Inference Model/Trained Model
MobileNetV4_conv_small 74.6 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 79.18 22.6 M Inference Model/Trained Model
PP-HGNetV2-B2 81.74 39.9 M Inference Model/Trained Model
PP-HGNetV2-B3 82.98 57.9 M Inference Model/Trained Model
PP-HGNetV2-B4 83.57 70.4 M Inference Model/Trained Model
PP-HGNetV2-B5 84.75 140.8 M Inference Model/Trained Model
PP-HGNetV2-B6 86.30 268.4 M Inference Model/Trained Model
PP-LCNet_x0_5 63.14 6.7 M Inference Model/Trained Model
PP-LCNet_x0_25 51.86 5.5 M Inference Model/Trained Model
PP-LCNet_x0_35 58.09 5.9 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 77.05 23.7 M Inference Model/Trained Model
PP-LCNetV2_large 78.51 37.3 M Inference Model/Trained Model
PP-LCNetV2_small 73.97 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 76.5 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 80.9 266.0 M Inference Model/Trained Model
SwinTransformer_base_patch4_window7_224 83.37 310.5 M Inference Model/Trained Model
SwinTransformer_base_patch4_window12_384 84.17 311.4 M Inference Model/Trained Model
SwinTransformer_large_patch4_window7_224 86.19 694.8 M Inference Model/Trained Model
SwinTransformer_large_patch4_window12_384 87.06 696.1 M Inference Model/Trained Model
SwinTransformer_small_patch4_window7_224 83.21 175.6 M Inference Model/Trained Model
SwinTransformer_tiny_patch4_window7_224 81.10 100.1 M Inference Model/Trained Model
Note: The above accuracy metrics refer to Top-1 Accuracy on the ImageNet-1k validation set.

Image Multi-label Classification Module

Model Name mAP (%) Model Storage Size Model Download Link
CLIP_vit_base_patch16_448_ML 89.15 325.6 M Inference Model/Training Model
PP-HGNetV2-B0_ML 80.98 39.6 M Inference Model/Training Model
PP-HGNetV2-B4_ML 87.96 88.5 M Inference Model/Training Model
PP-HGNetV2-B6_ML 91.06 286.5 M Inference Model/Training Model
Note: The above accuracy metrics are for the multi-label classification task mAP of COCO2017.

Object Detection Module

Model Name mAP (%) Model Size (M) Model Download Link
Cascade-FasterRCNN-ResNet50-FPN 41.1 245.4 M Inference Model/Trained Model
Cascade-FasterRCNN-ResNet50-vd-SSLDv2-FPN 45.0 246.2 M Inference Model/Trained Model
CenterNet-DLA-34 37.6 75.4 M Inference Model/Trained Model
CenterNet-ResNet50 38.9 319.7 M Inference Model/Trained Model
DETR-R50 42.3 159.3 M Inference Model/Trained Model
FasterRCNN-ResNet34-FPN 37.8 137.5 M Inference Model/Trained Model
FasterRCNN-ResNet50 36.7 120.2 M Inference Model/Trained Model
FasterRCNN-ResNet50-FPN 38.4 148.1 M Inference Model/Trained Model
FasterRCNN-ResNet50-vd-FPN 39.5 148.1 M Inference Model/Trained Model
FasterRCNN-ResNet50-vd-SSLDv2-FPN 41.4 148.1 M Inference Model/Trained Model
FasterRCNN-ResNet101 39.0 188.1 M Inference Model/Trained Model
FasterRCNN-ResNet101-FPN 41.4 216.3 M Inference Model/Trained Model
FasterRCNN-ResNeXt101-vd-FPN 43.4 360.6 M Inference Model/Trained Model
FasterRCNN-Swin-Tiny-FPN 42.6 159.8 M Inference Model/Trained Model
FCOS-ResNet50 39.6 124.2 M Inference Model/Trained Model
PicoDet-L 42.6 20.9 M Inference Model/Trained Model
PicoDet-M 37.5 16.8 M Inference Model/Trained Model
PicoDet-S 29.1 4.4 M Inference Model/Trained Model
PicoDet-XS 26.2 5.7M Inference Model/Trained Model
PP-YOLOE_plus-L 52.9 185.3 M Inference Model/Trained Model
PP-YOLOE_plus-M 49.8 83.2 M Inference Model/Trained Model
PP-YOLOE_plus-S 43.7 28.3 M Inference Model/Trained Model
PP-YOLOE_plus-X 54.7 349.4 M Inference Model/Trained Model
RT-DETR-H 56.3 435.8 M Inference Model/Trained Model
RT-DETR-L 53.0 113.7 M Inference Model/Trained Model
RT-DETR-R18 46.5 70.7 M Inference Model/Trained Model
RT-DETR-R50 53.1 149.1 M Inference Model/Trained Model
RT-DETR-X 54.8 232.9 M Inference Model/Trained Model
YOLOv3-DarkNet53 39.1 219.7 M Inference Model/Trained Model
YOLOv3-MobileNetV3 31.4 83.8 M Inference Model/Trained Model
YOLOv3-ResNet50_vd_DCN 40.6 163.0 M Inference Model/Trained Model
Note: The above accuracy metrics are for COCO2017 validation set mAP(0.5:0.95).

小目标检测模块

模型名称 mAP（%） 模型存储大小 Model Download Link
PP-YOLOE_plus_SOD-S 25.1 77.3 M Inference Model/Trained Model
PP-YOLOE_plus_SOD-L 31.9 325.0 M Inference Model/Trained Model
PP-YOLOE_plus_SOD-largesize-L 42.7 340.5 M Inference Model/Trained Model
注：以上精度指标为 VisDrone-DET 验证集 mAP(0.5:0.95)。

行人检测模块

模型名称 mAP（%） 模型存储大小 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
注：以上精度指标为 CrowdHuman 验证集 mAP(0.5:0.95)。

Semantic Segmentation Module

Model Name mIoU (%) Model Size (M) Model Download Link
Deeplabv3_Plus-R50 80.36 94.9 M Inference Model/Trained Model
Deeplabv3_Plus-R101 81.10 162.5 M Inference Model/Trained Model
Deeplabv3-R50 79.90 138.3 M Inference Model/Trained Model
Deeplabv3-R101 80.85 205.9 M Inference Model/Trained Model
OCRNet_HRNet-W48 82.15 249.8 M Inference Model/Trained Model
PP-LiteSeg-T 73.10 28.5 M Inference Model/Trained Model
Note: The above accuracy metrics are for Cityscapes dataset mIoU.

Instance Segmentation Module

Model Name Mask AP Model Size (M) Model Download Link
Mask-RT-DETR-H 50.6 449.9 M Inference Model/Trained Model
Mask-RT-DETR-L 45.7 113.6 M Inference Model/Trained Model
Mask-RT-DETR-M 42.7 66.6 M Inference Model/Trained Model
Mask-RT-DETR-S 41.0 51.8 M Inference Model/Trained Model
Mask-RT-DETR-X 47.5 237.5 M Inference Model/Trained Model
Cascade-MaskRCNN-ResNet50-FPN 36.3 254.8 M Inference Model/Trained Model
Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN 39.1 254.7 M Inference Model/Trained Model
MaskRCNN-ResNet50-FPN 35.6 157.5 M Inference Model/Trained Model
MaskRCNN-ResNet50-vd-FPN 36.4 157.5 M Inference Model/Trained Model
MaskRCNN-ResNet50 32.8 127.8 M Inference Model/Trained Model
MaskRCNN-ResNet101-FPN 36.6 225.4 M Inference Model/Trained Model
MaskRCNN-ResNet101-vd-FPN 38.1 225.1 M Inference Model/Trained Model
MaskRCNN-ResNeXt101-vd-FPN 39.5 370.0 M Inference Model/Trained Model
PP-YOLOE_seg-S 32.5 31.5 M Inference Model/Trained Model
Note: The above accuracy metrics are for COCO2017 validation set Mask AP(0.5:0.95).

图像特征模块

模型名称 recall@1（%） 模型存储大小 Model Download Link
PP-ShiTuV2_rec_CLIP_vit_base 88.69 306.6 M Inference Model/Trained Model
PP-ShiTuV2_rec_CLIP_vit_large 91.03 1.05 G Inference Model/Trained Model
注：以上精度指标为 AliProducts recall@1。

主体检测模块

模型名称 mAP（%） 模型存储大小 Model Download Link
PP-ShiTuV2_det 41.5 27.6 M Inference Model/Trained Model
注：以上精度指标为 PaddleClas主体检测数据集 mAP(0.5:0.95)。

车辆检测模块

模型名称 mAP（%） 模型存储大小 Model Download Link
PP-YOLOE-L_vehicle 63.9 196.1 M Inference Model/Trained Model
PP-YOLOE-S_vehicle 61.3 28.8 M Inference Model/Trained Model
注：以上精度指标为 PPVehicle 验证集 mAP(0.5:0.95)。

异常检测模块

模型名称 Avg（%） 模型存储大小 Model Download Link
STFPM 96.2 21.5 M Inference Model/Trained Model
注：以上精度指标为 MVTec AD 验证集 平均异常分数。

Text Detection Module

Model Name Detection Hmean (%) Model Size (M) Model Download Link
PP-OCRv4_mobile_det 77.79 4.2 M Inference Model/Trained Model
PP-OCRv4_server_det 82.69 100.1 M Inference Model/Trained Model
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) Model Download Link
PP-OCRv4_mobile_rec 78.20 10.6 M Inference Model/Trained Model
PP-OCRv4_server_rec 79.20 71.2 M Inference Model/Trained Model
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) Model Download Link
ch_SVTRv2_rec 68.81 73.9 M Inference Model/Trained Model
Note: The above accuracy metrics are evaluated on the PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition A-Rank.
Model Name Recognition Avg Accuracy (%) Model Size (M) Model Download Link
ch_RepSVTR_rec 65.07 22.1 M Inference Model/Trained Model
Note: The above accuracy metrics are evaluated on the PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition B-Rank.

Table Structure Recognition Module

Model Name Accuracy (%) Model Size (M) Model Download Link
SLANet 76.31 6.9 M Inference Model/Trained Model
Note: The above accuracy metrics are measured on the PubtabNet English table recognition dataset.

Layout Analysis Module

Model Name mAP (%) Model Size (M) Model Download Link
PicoDet_layout_1x 86.8 7.4M Inference Model/Trained Model
PicoDet-L_layout_3cls 89.3 22.6 M Inference Model/Trained Model
RT-DETR-H_layout_3cls 95.9 470.1 M Inference Model/Trained Model
RT-DETR-H_layout_17cls 92.6 470.2 M Inference Model/Trained Model
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) Model Download Link
DLinear 0.382 0.394 72K Inference Model/Trained Model
NLinear 0.386 0.392 40K Inference Model/Trained Model
Nonstationary 0.600 0.515 55.5 M Inference Model/Trained Model
PatchTST 0.385 0.397 2.0M Inference Model/Trained Model
RLinear 0.384 0.392 40K Inference Model/Trained Model
TiDE 0.405 0.412 31.7M Inference Model/Trained Model
TimesNet 0.417 0.431 4.9M Inference Model/Trained Model
Note: The above accuracy metrics are measured on the ETTH1 dataset (evaluation results on the test set test.csv).

Time Series Anomaly Detection Module

Model Name Precision Recall F1-Score Model Size (M) Model Download Link
AutoEncoder_ad 99.36 84.36 91.25 52K Inference Model/Trained Model
DLinear_ad 98.98 93.96 96.41 112K Inference Model/Trained Model
Nonstationary_ad 98.55 88.95 93.51 1.8M Inference Model/Trained Model
PatchTST_ad 98.78 90.70 94.57 320K Inference Model/Trained Model
TimesNet_ad 98.37 94.80 96.56 1.3M Inference Model/Trained Model
Note: The above accuracy metrics are measured on the PSM dataset.

Time Series Classification Module

Model Name Acc (%) Model Size (M) Model Download Link
TimesNet_cls 87.5 792K Inference Model/Trained Model
Note: The above accuracy metrics are measured on the UWaveGestureLibrary: Training, Evaluation datasets.