--- comments: true --- # PaddleX Model List (Hygon DCU) PaddleX incorporates multiple pipelines, each containing several modules, and each module encompasses various models. The specific models to use can be selected based on the benchmark data below. If you prioritize model accuracy, choose models with higher accuracy. If you prioritize model storage size, select models with smaller storage sizes. ## Image Classification Module
Model Name Top-1 Accuracy (%) Model Storage Size (M) Model Download Link
ResNet18 71.0 41.5 M Inference Model/Training Model
ResNet34 74.6 77.3 M Inference Model/Training Model
ResNet50 76.5 90.8 M Inference Model/Training Model
ResNet101 77.6 158.7 M Inference Model/Training Model
ResNet152 78.3 214.2 M Inference Model/Training Model
PP-LCNet_x1_0 71.32 10.5 M Inference Model/Training Model
Note: The above accuracy metrics are Top-1 Accuracy on the [ImageNet-1k](https://www.image-net.org/index.php) validation set. ## [Image Multi-label Classification Module](../module_usage/tutorials/cv_modules/image_multilabel_classification.en.md)
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](https://cocodataset.org/#home). ## [Image Feature Module](../module_usage/tutorials/cv_modules/image_feature.en.md)
Model Name recall@1（%） Model Size Model Download Link
PP-ShiTuV2_rec_CLIP_vit_base 88.69 306.6 M Inference Model/Training Model
Note: The above accuracy metrics are for AliProducts recall@1。 ## Object Detection Module
Model Name mAP (%) Model Size (M) Model Download Link
PicoDet-L 42.6 20.9 M Inference Model/Training Model
PicoDet-M 37.5 16.8 M Inference Model/Training Model
PicoDet-S 29.1 4.4 M Inference Model/Training Model
PicoDet-XS 26.2 5.7M Inference Model/Training Model
PP-YOLOE_plus-L 52.9 185.3 M Inference Model/Training Model
PP-YOLOE_plus-M 49.8 83.2 M Inference Model/Training Model
PP-YOLOE_plus-S 43.7 28.3 M Inference Model/Training Model
PP-YOLOE_plus-X 54.7 349.4 M Inference Model/Training Model
RT-DETR-R18 46.5 70.7 M Inference Model/Training Model
FCOS-ResNet50 39.6 124.2 M Inference Model/Training Model
YOLOX-N 26.1 3.4M Inference Model/Training Model
FasterRCNN-ResNet34-FPN 37.8 137.5 M Inference Model/Training Model
YOLOv3-DarkNet53 39.1 219.7 M Inference ModelInference Model/Training Model
Cascade-FasterRCNN-ResNet50-FPN 41.1 245.4 M Inference Model/Training Model
Note: The above accuracy metrics are mAP(0.5:0.95) on the [COCO2017](https://cocodataset.org/#home) validation set. ## [Small Object Detection Module](../module_usage/tutorials/cv_modules/small_object_detection.en.md)
Model Name mAP（%） Model Size Model Download Link
PP-YOLOE_plus_SOD-S 25.1 77.3 M Inference Model/Training Model
PP-YOLOE_plus_SOD-L 31.9 325.0 M Inference Model/Training Model
PP-YOLOE_plus_SOD-largesize-L 42.7 340.5 M Inference Model/Training Model
Note: The above accuracy metrics are for [VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset) validation set mAP(0.5:0.95)。 ## [Semantic Segmentation Module](../module_usage/tutorials/cv_modules/semantic_segmentation.en.md)
Model Name mIoU (%) Model Storage Size (M) Model Download Link
Deeplabv3_Plus-R50 80.36 94.9 M Inference Model/Training Model
Deeplabv3_Plus-R101 81.10 162.5 M Inference Model/Training Model
PP-LiteSeg-T 73.10 28.5 M Inference Model/Training Model
Note: The above accuracy metrics are mIoU on the [Cityscapes](https://www.cityscapes-dataset.com/) dataset. ## [Abnormality Detection Module](../module_usage/tutorials/cv_modules/anomaly_detection.en.md)
Model Name Avg（%） Model Size Model Download Link
STFPM 96.2 21.5 M Inference Model/Training Model
Note: The above accuracy metrics are evaluated on the [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad) dataset using the average anomaly score. ## [Face Detection Module](../module_usage/tutorials/cv_modules/face_detection.en.md)
Model Name AP (%)
Easy/Medium/Hard		 Model Size Model Download Link
PicoDet_LCNet_x2_5_face 93.7/90.7/68.1 28.9 M Inference Model/Training Model
Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640. ## Text Detection Module
Model Name Detection Hmean (%) Model Size (M) Model Download Link
PP-OCRv4_mobile_det 77.79 4.2 M Inference Model/Training Model
PP-OCRv4_server_det 82.69 100.1M Inference Model/Training Model
Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, handwriting, and more 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/Training Model
PP-OCRv4_server_rec 79.20 71.2 M Inference Model/Training Model
Note: The evaluation set for the above accuracy metrics is PaddleOCR's self-built Chinese dataset, covering street scenes, web images, documents, handwriting, and more scenarios, with 11,000 images for text recognition. ## [Time Series Forecasting Module](../module_usage/tutorials/time_series_modules/time_series_forecasting.en.md)
Model Name mse mae Model Size (M) Model Download Link
DLinear 0.382 0.394 72K Inference Model/Training Model
NLinear 0.386 0.392 40K Inference Model/Training Model
RLinear 0.384 0.392 40K Inference Model/Training Model
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).