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PaddleX模型列表(昇腾 NPU)
PaddleX 内置了多条产线,每条产线都包含了若干模块,每个模块包含若干模型,具体使用哪些模型,您可以根据下边的 benchmark 数据来选择。如您更考虑模型精度,请选择精度较高的模型,如您更考虑模型存储大小,请选择存储大小较小的模型。
图像分类模块
| 模型名称 |
Top1 Acc(%) |
模型存储大小(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_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 |
| 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 |
| 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_small_patch4_window7_224 |
83.21 |
175.6 M |
| SwinTransformer_tiny_patch4_window7_224 |
81.10 |
100.1 M |
注:以上精度指标为ImageNet-1k验证集 Top1 Acc。
目标检测模块
| 模型名称 |
mAP(%) |
模型存储大小(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-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-FPN |
41.4 |
216.3 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 |
注:以上精度指标为COCO2017验证集 mAP(0.5:0.95)。
语义分割模块
| 模型名称 |
mloU(%) |
模型存储大小(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 |
注:以上精度指标为Cityscapes数据集 mloU。
实例分割模块
| 模型名称 |
Mask AP |
模型存储大小(M) |
| Mask-RT-DETR-H |
50.6 |
449.9 |
| Mask-RT-DETR-L |
45.7 |
113.6 |
| Mask-RT-DETR-M |
42.7 |
66.6 M |
| Cascade-MaskRCNN-ResNet50-FPN |
36.3 |
254.8 |
| Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN |
39.1 |
254.7 |
| PP-YOLOE_seg-S |
32.5 |
31.5 M |
注:以上精度指标为COCO2017验证集 Mask AP(0.5:0.95)。
文本检测模块
| 模型名称 |
检测Hmean(%) |
模型存储大小(M) |
| PP-OCRv4_mobile_det |
77.79 |
4.2 M |
| PP-OCRv4_server_det |
82.69 |
100.1M |
注:以上精度指标的评估集是 PaddleOCR 自建的中文数据集,覆盖街景、网图、文档、手写多个场景,其中检测包含 500 张图片。
文本识别模块
| 模型名称 |
识别Avg Accuracy(%) |
模型存储大小(M) |
| PP-OCRv4_mobile_rec |
78.20 |
10.6 M |
| PP-OCRv4_server_rec |
79.20 |
71.2 M |
注:以上精度指标的评估集是 PaddleOCR 自建的中文数据集,覆盖街景、网图、文档、手写多个场景,其中文本识别包含 1.1w 张图片。
| 模型名称 |
识别Avg Accuracy(%) |
模型存储大小(M) |
| ch_SVTRv2_rec |
68.81 |
73.9 M |
注:以上精度指标的评估集是 PaddleOCR算法模型挑战赛 - 赛题一:OCR端到端识别任务A榜。
| 模型名称 |
识别Avg Accuracy(%) |
模型存储大小(M) |
| ch_RepSVTR_rec |
65.07 |
22.1 M |
注:以上精度指标的评估集是 PaddleOCR算法模型挑战赛 - 赛题一:OCR端到端识别任务B榜。
表格结构识别模块
| 模型名称 |
精度(%) |
模型存储大小(M) |
| SLANet |
76.31 |
6.9 M |
注:以上精度指标测量自PubtabNet英文表格识别数据集。
版面区域分析模块
| 模型名称 |
mAP(%) |
模型存储大小(M) |
| PicoDet_layout_1x |
86.8 |
7.4M |
注:以上精度指标的评估集是 PaddleOCR 自建的版面区域分析数据集,包含 1w 张图片。
时序预测模块
| 模型名称 |
mse |
mae |
模型存储大小(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 |
注:以上精度指标测量自ETTH1数据集 (在测试集test.csv上的评测结果)。
时序异常检测模块
| 模型名称 |
precison |
recall |
f1_score |
模型存储大小(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 |
注:以上精度指标测量自PSM数据集。
时序分类模块
| 模型名称 |
acc(%) |
模型存储大小(M) |
| TimesNet_cls |
87.5 |
792K |
注:以上精度指标测量自UWaveGestureLibrary:训练、评测数据集。