--- comments: true --- # PaddleX Model List（CPU/GPU） PaddleX incorporates multiple pipelines, each containing several modules, and each module includes various models. You can choose which models to use based on the benchmark data below. If you prioritize model accuracy, select models with higher accuracy. If you prioritize inference speed, choose models with faster inference. If you prioritize model storage size, select models with smaller storage sizes. ## [Image Classification Module](../module_usage/tutorials/cv_modules/image_classification.en.md)

Model Name	Top-1 Acc (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
CLIP_vit_base_patch16_224	85.36	13.1957	285.493	306.5 M	CLIP_vit_base_patch16_224.yaml
CLIP_vit_large_patch14_224	88.1	51.1284	1131.28	1.04 G	CLIP_vit_large_patch14_224.yaml
ConvNeXt_base_224	83.84	12.8473	1513.87	313.9 M	ConvNeXt_base_224.yaml
ConvNeXt_base_384	84.90	31.7607	3967.05	313.9 M	ConvNeXt_base_384.yaml
ConvNeXt_large_224	84.26	26.8103	2463.56	700.7 M	ConvNeXt_large_224.yaml
ConvNeXt_large_384	85.27	66.4058	6598.92	700.7 M	ConvNeXt_large_384.yaml
ConvNeXt_small	83.13	9.74075	1127.6	178.0 M	ConvNeXt_small.yaml
ConvNeXt_tiny	82.03	5.48923	672.559	101.4 M	ConvNeXt_tiny.yaml
FasterNet-L	83.5	23.4415	-	357.1 M	FasterNet-L.yaml
FasterNet-M	83.0	21.8936	-	204.6 M	FasterNet-M.yaml
FasterNet-S	81.3	13.0409	-	119.3 M	FasterNet-S.yaml
FasterNet-T0	71.9	12.2432	-	15.1 M	FasterNet-T0.yaml
FasterNet-T1	75.9	11.3562	-	29.2 M	FasterNet-T1.yaml
FasterNet-T2	79.1	10.703	-	57.4 M	FasterNet-T2.yaml
MobileNetV1_x0_5	63.5	1.86754	7.48297	4.8 M	MobileNetV1_x0_5.yaml
MobileNetV1_x0_25	51.4	1.83478	4.83674	1.8 M	MobileNetV1_x0_25.yaml
MobileNetV1_x0_75	68.8	2.57903	10.6343	9.3 M	MobileNetV1_x0_75.yaml
MobileNetV1_x1_0	71.0	2.78781	13.98	15.2 M	MobileNetV1_x1_0.yaml
MobileNetV2_x0_5	65.0	4.94234	11.1629	7.1 M	MobileNetV2_x0_5.yaml
MobileNetV2_x0_25	53.2	4.50856	9.40991	5.5 M	MobileNetV2_x0_25.yaml
MobileNetV2_x1_0	72.2	6.12159	16.0442	12.6 M	MobileNetV2_x1_0.yaml
MobileNetV2_x1_5	74.1	6.28385	22.5129	25.0 M	MobileNetV2_x1_5.yaml
MobileNetV2_x2_0	75.2	6.12888	30.8612	41.2 M	MobileNetV2_x2_0.yaml
MobileNetV3_large_x0_5	69.2	6.31302	14.5588	9.6 M	MobileNetV3_large_x0_5.yaml
MobileNetV3_large_x0_35	64.3	5.76207	13.9041	7.5 M	MobileNetV3_large_x0_35.yaml
MobileNetV3_large_x0_75	73.1	8.41737	16.9506	14.0 M	MobileNetV3_large_x0_75.yaml
MobileNetV3_large_x1_0	75.3	8.64112	19.1614	19.5 M	MobileNetV3_large_x1_0.yaml
MobileNetV3_large_x1_25	76.4	8.73358	22.1296	26.5 M	MobileNetV3_large_x1_25.yaml
MobileNetV3_small_x0_5	59.2	5.16721	11.2688	6.8 M	MobileNetV3_small_x0_5.yaml
MobileNetV3_small_x0_35	53.0	5.22053	11.0055	6.0 M	MobileNetV3_small_x0_35.yaml
MobileNetV3_small_x0_75	66.0	5.39831	12.8313	8.5 M	MobileNetV3_small_x0_75.yaml
MobileNetV3_small_x1_0	68.2	6.00993	12.9598	10.5 M	MobileNetV3_small_x1_0.yaml
MobileNetV3_small_x1_25	70.7	6.9589	14.3995	13.0 M	MobileNetV3_small_x1_25.yaml
MobileNetV4_conv_large	83.4	12.5485	51.6453	125.2 M	MobileNetV4_conv_large.yaml
MobileNetV4_conv_medium	79.9	9.65509	26.6157	37.6 M	MobileNetV4_conv_medium.yaml
MobileNetV4_conv_small	74.6	5.24172	11.0893	14.7 M	MobileNetV4_conv_small.yaml
MobileNetV4_hybrid_large	83.8	20.0726	213.769	145.1 M	MobileNetV4_hybrid_large.yaml
MobileNetV4_hybrid_medium	80.5	19.7543	62.2624	42.9 M	MobileNetV4_hybrid_medium.yaml
PP-HGNet_base	85.0	14.2969	327.114	249.4 M	PP-HGNet_base.yaml
PP-HGNet_small	81.51	5.50661	119.041	86.5 M	PP-HGNet_small.yaml
PP-HGNet_tiny	79.83	5.22006	69.396	52.4 M	PP-HGNet_tiny.yaml
PP-HGNetV2-B0	77.77	6.53694	23.352	21.4 M	PP-HGNetV2-B0.yaml
PP-HGNetV2-B1	79.18	6.56034	27.3099	22.6 M	PP-HGNetV2-B1.yaml
PP-HGNetV2-B2	81.74	9.60494	43.1219	39.9 M	PP-HGNetV2-B2.yaml
PP-HGNetV2-B3	82.98	11.0042	55.1367	57.9 M	PP-HGNetV2-B3.yaml
PP-HGNetV2-B4	83.57	9.66407	54.2462	70.4 M	PP-HGNetV2-B4.yaml
PP-HGNetV2-B5	84.75	15.7091	115.926	140.8 M	PP-HGNetV2-B5.yaml
PP-HGNetV2-B6	86.30	21.226	255.279	268.4 M	PP-HGNetV2-B6.yaml
PP-LCNet_x0_5	63.14	3.67722	6.66857	6.7 M	PP-LCNet_x0_5.yaml
PP-LCNet_x0_25	51.86	2.65341	5.81357	5.5 M	PP-LCNet_x0_25.yaml
PP-LCNet_x0_35	58.09	2.7212	6.28944	5.9 M	PP-LCNet_x0_35.yaml
PP-LCNet_x0_75	68.18	3.91032	8.06953	8.4 M	PP-LCNet_x0_75.yaml
PP-LCNet_x1_0	71.32	3.84845	9.23735	10.5 M	PP-LCNet_x1_0.yaml
PP-LCNet_x1_5	73.71	3.97666	12.3457	16.0 M	PP-LCNet_x1_5.yaml
PP-LCNet_x2_0	75.18	4.07556	16.2752	23.2 M	PP-LCNet_x2_0.yaml
PP-LCNet_x2_5	76.60	4.06028	21.5063	32.1 M	PP-LCNet_x2_5.yaml
PP-LCNetV2_base	77.05	5.23428	19.6005	23.7 M	PP-LCNetV2_base.yaml
PP-LCNetV2_large	78.51	6.78335	30.4378	37.3 M	PP-LCNetV2_large.yaml
PP-LCNetV2_small	73.97	3.89762	13.0273	14.6 M	PP-LCNetV2_small.yaml
ResNet18_vd	72.3	3.53048	31.3014	41.5 M	ResNet18_vd.yaml
ResNet18	71.0	2.4868	27.4601	41.5 M	ResNet18.yaml
ResNet34_vd	76.0	5.60675	56.0653	77.3 M	ResNet34_vd.yaml
ResNet34	74.6	4.16902	51.925	77.3 M	ResNet34.yaml
ResNet50_vd	79.1	10.1885	68.446	90.8 M	ResNet50_vd.yaml
ResNet50	76.5	9.62383	64.8135	90.8 M	ResNet50.yaml
ResNet101_vd	80.2	20.0563	124.85	158.4 M	ResNet101_vd.yaml
ResNet101	77.6	19.2297	121.006	158.7 M	ResNet101.yaml
ResNet152_vd	80.6	29.6439	181.678	214.3 M	ResNet152_vd.yaml
ResNet152	78.3	30.0461	177.707	214.2 M	ResNet152.yaml
ResNet200_vd	80.9	39.1628	235.185	266.0 M	ResNet200_vd.yaml
StarNet-S1	73.6	9.895	23.0465	11.2 M	StarNet-S1.yaml
StarNet-S2	74.8	7.91279	21.9571	14.3 M	StarNet-S2.yaml
StarNet-S3	77.0	10.7531	30.7656	22.2 M	StarNet-S3.yaml
StarNet-S4	79.0	15.2868	43.2497	28.9 M	StarNet-S4.yaml
SwinTransformer_base_patch4_window7_224	83.37	16.9848	383.83	310.5 M	SwinTransformer_base_patch4_window7_224.yaml
SwinTransformer_base_patch4_window12_384	84.17	37.2855	1178.63	311.4 M	SwinTransformer_base_patch4_window12_384.yaml
SwinTransformer_large_patch4_window7_224	86.19	27.5498	689.729	694.8 M	SwinTransformer_large_patch4_window7_224.yaml
SwinTransformer_large_patch4_window12_384	87.06	74.1768	2105.22	696.1 M	SwinTransformer_large_patch4_window12_384.yaml
SwinTransformer_small_patch4_window7_224	83.21	16.3982	285.56	175.6 M	SwinTransformer_small_patch4_window7_224.yaml
SwinTransformer_tiny_patch4_window7_224	81.10	8.54846	156.306	100.1 M	SwinTransformer_tiny_patch4_window7_224.yaml

Note: The above accuracy metrics are Top-1 Acc 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 (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
CLIP_vit_base_patch16_448_ML	89.15	-	-	325.6 M	CLIP_vit_base_patch16_448_ML.yaml
PP-HGNetV2-B0_ML	80.98	-	-	39.6 M	PP-HGNetV2-B0_ML.yaml
PP-HGNetV2-B4_ML	87.96	-	-	88.5 M	PP-HGNetV2-B4_ML.yaml
PP-HGNetV2-B6_ML	91.25	-	-	286.5 M	PP-HGNetV2-B6_ML.yaml
PP-LCNet_x1_0_ML	77.96	-	-	29.4 M	PP-LCNet_x1_0_ML.yaml
ResNet50_ML	83.50	-	-	108.9 M	ResNet50_ML.yaml

Note: The above accuracy metrics are mAP for the multi-label classification task on [COCO2017](https://cocodataset.org/#home). ## [Pedestrian Attribute Module](../module_usage/tutorials/cv_modules/pedestrian_attribute_recognition.en.md)

Model Name	mA (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-LCNet_x1_0_pedestrian_attribute	92.2	3.84845	9.23735	6.7 M	PP-LCNet_x1_0_pedestrian_attribute.yaml

Note: The above accuracy metrics are mA on PaddleX's internal self-built dataset. ## [Vehicle Attribute Module](../module_usage/tutorials/cv_modules/vehicle_attribute_recognition.en.md)

Model Name	mA (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-LCNet_x1_0_vehicle_attribute	91.7	3.84845	9.23735	6.7 M	PP-LCNet_x1_0_vehicle_attribute.yaml

Note: The above accuracy metrics are mA on the VeRi dataset. ## [Image Feature Module](../module_usage/tutorials/cv_modules/image_feature.en.md)

Model Name	recall@1 (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-ShiTuV2_rec	84.2	5.23428	19.6005	16.3 M	PP-ShiTuV2_rec.yaml
PP-ShiTuV2_rec_CLIP_vit_base	88.69	13.1957	285.493	306.6 M	PP-ShiTuV2_rec_CLIP_vit_base.yaml
PP-ShiTuV2_rec_CLIP_vit_large	91.03	51.1284	1131.28	1.05 G	PP-ShiTuV2_rec_CLIP_vit_large.yaml

Note: The above accuracy metrics are recall@1 on AliProducts. ## [Document Orientation Classification Module](../module_usage/tutorials/ocr_modules/doc_img_orientation_classification.en.md)

Model Name	Top-1 Acc (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-LCNet_x1_0_doc_ori	99.26	3.84845	9.23735	7.1 M	PP-LCNet_x1_0_doc_ori.yaml

Note: The above accuracy metrics are Top-1 Acc on PaddleX's internal self-built dataset. ## [Face Feature Module](../module_usage/tutorials/cv_modules/face_feature.en.md)

Model Name	Output Feature Dimension	Acc (%) AgeDB-30/CFP-FP/LFW	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size (M)	YAML File
MobileFaceNet	128	96.28/96.71/99.58			4.1	MobileFaceNet.yaml
ResNet50_face	512	98.12/98.56/99.77			87.2	ResNet50_face.yaml

Note: The above accuracy metrics are Accuracy scores measured on the AgeDB-30, CFP-FP, and LFW datasets, respectively. ## [Main Body Detection Module](../module_usage/tutorials/cv_modules/mainbody_detection.en.md)

Model Name	mAP (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-ShiTuV2_det	41.5	33.7426	537.003	27.6 M	PP-ShiTuV2_det.yaml

Note: The above accuracy metrics are mAP(0.5:0.95) on the [PaddleClas main body detection dataset](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/zh_CN/training/PP-ShiTu/mainbody_detection.md). ## [Object Detection Module](../module_usage/tutorials/cv_modules/object_detection.en.md)

Model Name	mAP (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
Cascade-FasterRCNN-ResNet50-FPN	41.1	-	-	245.4 M	Cascade-FasterRCNN-ResNet50-FPN.yaml
Cascade-FasterRCNN-ResNet50-vd-SSLDv2-FPN	45.0	-	-	246.2 M	Cascade-FasterRCNN-ResNet50-vd-SSLDv2-FPN.yaml
CenterNet-DLA-34	37.6	-	-	75.4 M	CenterNet-DLA-34.yaml
CenterNet-ResNet50	38.9	-	-	319.7 M	CenterNet-ResNet50.yaml
DETR-R50	42.3	59.2132	5334.52	159.3 M	DETR-R50.yaml
FasterRCNN-ResNet34-FPN	37.8	-	-	137.5 M	FasterRCNN-ResNet34-FPN.yaml
FasterRCNN-ResNet50-FPN	38.4	-	-	148.1 M	FasterRCNN-ResNet50-FPN.yaml
FasterRCNN-ResNet50-vd-FPN	39.5	-	-	148.1 M	FasterRCNN-ResNet50-vd-FPN.yaml
FasterRCNN-ResNet50-vd-SSLDv2-FPN	41.4	-	-	148.1 M	FasterRCNN-ResNet50-vd-SSLDv2-FPN.yaml
FasterRCNN-ResNet50	36.7	-	-	120.2 M	FasterRCNN-ResNet50.yaml
FasterRCNN-ResNet101-FPN	41.4	-	-	216.3 M	FasterRCNN-ResNet101-FPN.yaml
FasterRCNN-ResNet101	39.0	-	-	188.1 M	FasterRCNN-ResNet101.yaml
FasterRCNN-ResNeXt101-vd-FPN	43.4	-	-	360.6 M	FasterRCNN-ResNeXt101-vd-FPN.yaml
FasterRCNN-Swin-Tiny-FPN	42.6	-	-	159.8 M	FasterRCNN-Swin-Tiny-FPN.yaml
FCOS-ResNet50	39.6	103.367	3424.91	124.2 M	FCOS-ResNet50.yaml
PicoDet-L	42.6	16.6715	169.904	20.9 M	PicoDet-L.yaml
PicoDet-M	37.5	16.2311	71.7257	16.8 M	PicoDet-M.yaml
PicoDet-S	29.1	14.097	37.6563	4.4 M	PicoDet-S.yaml
PicoDet-XS	26.2	13.8102	48.3139	5.7M	PicoDet-XS.yaml
PP-YOLOE_plus-L	52.9	33.5644	814.825	185.3 M	PP-YOLOE_plus-L.yaml
PP-YOLOE_plus-M	49.8	19.843	449.261	83.2 M	PP-YOLOE_plus-M.yaml
PP-YOLOE_plus-S	43.7	16.8884	223.059	28.3 M	PP-YOLOE_plus-S.yaml
PP-YOLOE_plus-X	54.7	57.8995	1439.93	349.4 M	PP-YOLOE_plus-X.yaml
RT-DETR-H	56.3	114.814	3933.39	435.8 M	RT-DETR-H.yaml
RT-DETR-L	53.0	34.5252	1454.27	113.7 M	RT-DETR-L.yaml
RT-DETR-R18	46.5	19.89	784.824	70.7 M	RT-DETR-R18.yaml
RT-DETR-R50	53.1	41.9327	1625.95	149.1 M	RT-DETR-R50.yaml
RT-DETR-X	54.8	61.8042	2246.64	232.9 M	RT-DETR-X.yaml
YOLOv3-DarkNet53	39.1	40.1055	883.041	219.7 M	YOLOv3-DarkNet53.yaml
YOLOv3-MobileNetV3	31.4	18.6692	267.214	83.8 M	YOLOv3-MobileNetV3.yaml
YOLOv3-ResNet50_vd_DCN	40.6	31.6276	856.047	163.0 M	YOLOv3-ResNet50_vd_DCN.yaml
YOLOX-L	50.1	185.691	1250.58	192.5 M	YOLOX-L.yaml
YOLOX-M	46.9	123.324	688.071	90.0 M	YOLOX-M.yaml
YOLOX-N	26.1	79.1665	155.59	3.4 M	YOLOX-N.yaml
YOLOX-S	40.4	184.828	474.446	32.0 M	YOLOX-S.yaml
YOLOX-T	32.9	102.748	212.52	18.1 M	YOLOX-T.yaml
YOLOX-X	51.8	227.361	2067.84	351.5 M	YOLOX-X.yaml

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 (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-YOLOE_plus_SOD-S	25.1	65.4608	324.37	77.3 M	PP-YOLOE_plus_SOD-S.yaml
PP-YOLOE_plus_SOD-L	31.9	57.1448	1006.98	325.0 M	PP-YOLOE_plus_SOD-L.yaml
PP-YOLOE_plus_SOD-largesize-L	42.7	458.521	11172.7	340.5 M	PP-YOLOE_plus_SOD-largesize-L.yaml

Note: The above accuracy metrics are mAP(0.5:0.95) on the [VisDrone-DET](https://github.com/VisDrone/VisDrone-Dataset) validation set. ## [Pedestrian Detection Module](../module_usage/tutorials/cv_modules/human_detection.en.md)

Model Name	mAP (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-YOLOE-L_human	48.0	32.7754	777.691	196.1 M	PP-YOLOE-L_human.yaml
PP-YOLOE-S_human	42.5	15.0118	179.317	28.8 M	PP-YOLOE-S_human.yaml

Note: The above accuracy metrics are mAP(0.5:0.95) on the [CrowdHuman](https://bj.bcebos.com/v1/paddledet/data/crowdhuman.zip) validation set. ## [Vehicle Detection Module](../module_usage/tutorials/cv_modules/vehicle_detection.en.md)

Model Name	mAP (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-YOLOE-L_vehicle	63.9	32.5619	775.633	196.1 M	PP-YOLOE-L_vehicle.yaml
PP-YOLOE-S_vehicle	61.3	15.3787	178.441	28.8 M	PP-YOLOE-S_vehicle.yaml

Note: The above accuracy metrics are mAP(0.5:0.95) on the [PPVehicle](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppvehicle) validation set. ## [Face Detection Module](../module_usage/tutorials/cv_modules/face_detection.en.md)

Model	AP (%) Easy/Medium/Hard	Model Size (M)	YAML File
BlazeFace	77.7/73.4/49.5	0.447	BlazeFace.yaml
BlazeFace-FPN-SSH	83.2/80.5/60.5	0.606	BlazeFace-FPN-SSH.yaml
PicoDet_LCNet_x2_5_face	93.7/90.7/68.1	28.9	PicoDet_LCNet_x2_5_face.yaml
PP-YOLOE_plus-S_face	93.9/91.8/79.8	26.5	PP-YOLOE_plus-S_face

Note: The above accuracy metrics are evaluated on the WIDER-FACE validation set with an input size of 640*640. ## [Abnormality Detection Module](../module_usage/tutorials/cv_modules/anomaly_detection.en.md)

Model Name	Avg (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
STFPM	96.2	-	-	21.5 M	STFPM.yaml

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. ## [Semantic Segmentation Module](../module_usage/tutorials/cv_modules/semantic_segmentation.en.md)

Model Name	mIoU (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
Deeplabv3_Plus-R50	80.36	61.0531	1513.58	94.9 M	Deeplabv3_Plus-R50.yaml
Deeplabv3_Plus-R101	81.10	100.026	2460.71	162.5 M	Deeplabv3_Plus-R101.yaml
Deeplabv3-R50	79.90	82.2631	1735.83	138.3 M	Deeplabv3-R50.yaml
Deeplabv3-R101	80.85	121.492	2685.51	205.9 M	Deeplabv3-R101.yaml
OCRNet_HRNet-W18	80.67	48.2335	906.385	43.1 M	OCRNet_HRNet-W18.yaml
OCRNet_HRNet-W48	82.15	78.9976	2226.95	249.8 M	OCRNet_HRNet-W48.yaml
PP-LiteSeg-T	73.10	7.6827	138.683	28.5 M	PP-LiteSeg-T.yaml
PP-LiteSeg-B	75.25	10.9935	194.727	47.0 M	PP-LiteSeg-B.yaml
SegFormer-B0 (slice)	76.73	11.1946	268.929	13.2 M	SegFormer-B0.yaml
SegFormer-B1 (slice)	78.35	17.9998	403.393	48.5 M	SegFormer-B1.yaml
SegFormer-B2 (slice)	81.60	48.0371	1248.52	96.9 M	SegFormer-B2.yaml
SegFormer-B3 (slice)	82.47	64.341	1666.35	167.3 M	SegFormer-B3.yaml
SegFormer-B4 (slice)	82.38	82.4336	1995.42	226.7 M	SegFormer-B4.yaml
SegFormer-B5 (slice)	82.58	97.3717	2420.19	229.7 M	SegFormer-B5.yaml

Note: The above accuracy metrics are evaluated on the [Cityscapes](https://www.cityscapes-dataset.com/) dataset using mIoU.

Model Name	mIoU (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
SeaFormer_base(slice)	40.92	24.4073	397.574	30.8 M	SeaFormer_base.yaml
SeaFormer_large (slice)	43.66	27.8123	550.464	49.8 M	SeaFormer_large.yaml
SeaFormer_small (slice)	38.73	19.2295	358.343	14.3 M	SeaFormer_small.yaml
SeaFormer_tiny (slice)	34.58	13.9496	330.132	6.1 M	SeaFormer_tiny.yaml

Note: The above accuracy metrics are evaluated on the [ADE20k](https://groups.csail.mit.edu/vision/datasets/ADE20K/) dataset. "slice" indicates that the input image has been cropped. ## [Instance Segmentation Module](../module_usage/tutorials/cv_modules/instance_segmentation.en.md)

Model Name	Mask AP	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
Mask-RT-DETR-H	50.6	132.693	4896.17	449.9 M	Mask-RT-DETR-H.yaml
Mask-RT-DETR-L	45.7	46.5059	2575.92	113.6 M	Mask-RT-DETR-L.yaml
Mask-RT-DETR-M	42.7	36.8329	-	66.6 M	Mask-RT-DETR-M.yaml
Mask-RT-DETR-S	41.0	33.5007	-	51.8 M	Mask-RT-DETR-S.yaml
Mask-RT-DETR-X	47.5	75.755	3358.04	237.5 M	Mask-RT-DETR-X.yaml
Cascade-MaskRCNN-ResNet50-FPN	36.3	-	-	254.8 M	Cascade-MaskRCNN-ResNet50-FPN.yaml
Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN	39.1	-	-	254.7 M	Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN.yaml
MaskRCNN-ResNet50-FPN	35.6	-	-	157.5 M	MaskRCNN-ResNet50-FPN.yaml
MaskRCNN-ResNet50-vd-FPN	36.4	-	-	157.5 M	MaskRCNN-ResNet50-vd-FPN.yaml
MaskRCNN-ResNet50	32.8	-	-	127.8 M	MaskRCNN-ResNet50.yaml
MaskRCNN-ResNet101-FPN	36.6	-	-	225.4 M	MaskRCNN-ResNet101-FPN.yaml
MaskRCNN-ResNet101-vd-FPN	38.1	-	-	225.1 M	MaskRCNN-ResNet101-vd-FPN.yaml
MaskRCNN-ResNeXt101-vd-FPN	39.5	-	-	370.0 M	MaskRCNN-ResNeXt101-vd-FPN.yaml
PP-YOLOE_seg-S	32.5	-	-	31.5 M	PP-YOLOE_seg-S.yaml

|SOLOv2| 35.5|-|-|179.1 M|[SOLOv2.yaml](https://github.com/PaddlePaddle/PaddleX/blob/develop/paddlex/configs/instance_segmentation/SOLOv2.yaml) Note: The above accuracy metrics are evaluated on the [COCO2017](https://cocodataset.org/#home) validation set using Mask AP(0.5:0.95). ## [Text Detection Module](../module_usage/tutorials/ocr_modules/text_detection.en.md)

Model Name	Detection Hmean (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-OCRv4_mobile_det	77.79	10.6923	120.177	4.2 M	PP-OCRv4_mobile_det.yaml
PP-OCRv4_server_det	82.69	83.3501	2434.01	100.1M	PP-OCRv4_server_det.yaml

Note: The above accuracy metrics are evaluated on a self-built Chinese dataset by PaddleOCR, covering street scenes, web images, documents, and handwritten texts, with 500 images for detection. ## [Seal Text Detection Module](../module_usage/tutorials/ocr_modules/seal_text_detection.en.md)

Model Name	Detection Hmean (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-OCRv4_mobile_seal_det	96.47	10.5878	131.813	4.7 M	PP-OCRv4_mobile_seal_det.yaml
PP-OCRv4_server_seal_det	98.21	84.341	2425.06	108.3 M	PP-OCRv4_server_seal_det.yaml

Note: The above accuracy metrics are evaluated on a self-built seal dataset by PaddleX, containing 500 seal images. ## [Text Recognition Module](../module_usage/tutorials/ocr_modules/text_recognition.en.md)

Model Name	Recognition Avg Accuracy (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PP-OCRv4_mobile_rec	78.20	7.95018	46.7868	10.6 M	PP-OCRv4_mobile_rec.yaml
PP-OCRv4_server_rec	79.20	7.19439	140.179	71.2 M	PP-OCRv4_server_rec.yaml

Note: The above accuracy metrics are evaluated on a self-built Chinese dataset by PaddleOCR, covering street scenes, web images, documents, and handwritten texts, with 11,000 images for text recognition.

Model Name	Recognition Avg Accuracy (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
ch_SVTRv2_rec	68.81	8.36801	165.706	73.9 M	ch_SVTRv2_rec.yaml

Model Name	Recognition Avg Accuracy (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
ch_RepSVTR_rec	65.07	10.5047	51.5647	22.1 M	ch_RepSVTR_rec.yaml

Note: The above accuracy metrics are evaluated on [PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition](https://aistudio.baidu.com/competition/detail/1131/0/introduction) B-Rank. ## [Formula Recognition Module](../module_usage/tutorials/ocr_modules/formula_recognition.en.md)

Model Name	BLEU Score	Normed Edit Distance	ExpRate (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
LaTeX_OCR_rec	0.8821	0.0823	40.01	-	-	89.7 M	LaTeX_OCR_rec.yaml

Note: The above accuracy metrics are measured on the [LaTeX-OCR formula recognition test set](https://drive.google.com/drive/folders/13CA4vAmOmD_I_dSbvLp-Lf0s6KiaNfuO). ## [Table Structure Recognition Module](../module_usage/tutorials/ocr_modules/table_structure_recognition.en.md)

Model Name	Accuracy (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
SLANet	59.52	522.536	1845.37	6.9 M	SLANet.yaml
SLANet_plus	63.69	522.536	1845.37	6.9 M	SLANet_plus.yaml

Note: The above accuracy metrics are evaluated on a self-built English table recognition dataset by PaddleX. ## [Image Rectification Module](../module_usage/tutorials/ocr_modules/text_image_unwarping.en.md)

Model Name	MS-SSIM (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
UVDoc	54.40	-	-	30.3 M	UVDoc.yaml

Note: The above accuracy metrics are measured on a self-built image rectification dataset by PaddleX. ## [Layout Detection Module](../module_usage/tutorials/ocr_modules/layout_detection.en.md)

Model Name	mAP (%)	GPU Inference Time (ms)	CPU Inference Time (ms)	Model Size	YAML File
PicoDet_layout_1x	86.8	13.036	91.2634	7.4 M	PicoDet_layout_1x.yaml
PicoDet-S_layout_3cls	87.1	13.521	45.7633	4.8 M	PicoDet-S_layout_3cls.yaml
PicoDet-S_layout_17cls	70.3	13.5632	46.2059	4.8 M	PicoDet-S_layout_17cls.yaml
PicoDet-L_layout_3cls	89.3	15.7425	159.771	22.6 M	PicoDet-L_layout_3cls.yaml
PicoDet-L_layout_17cls	79.9	17.1901	160.262	22.6 M	PicoDet-L_layout_17cls.yaml
RT-DETR-H_layout_3cls	95.9	114.644	3832.62	470.1 M	RT-DETR-H_layout_3cls.yaml
RT-DETR-H_layout_17cls	92.6	115.126	3827.25	470.2 M	RT-DETR-H_layout_17cls.yaml

Note: The evaluation set for the above accuracy metrics is the PaddleX self-built Layout Detection Dataset, containing 10,000 images. ## [Time Series Forecasting Module](../module_usage/tutorials/time_series_modules/time_series_forecasting.en.md)

Model Name	mse	mae	Model Size	YAML File
DLinear	0.382	0.394	72 K	DLinear.yaml
NLinear	0.386	0.392	40 K	NLinear.yaml
Nonstationary	0.600	0.515	55.5 M	Nonstationary.yaml
PatchTST	0.385	0.397	2.0 M	PatchTST.yaml
RLinear	0.384	0.392	40 K	RLinear.yaml
TiDE	0.405	0.412	31.7 M	TiDE.yaml
TimesNet	0.417	0.431	4.9 M	TimesNet.yaml

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](../module_usage/tutorials/time_series_modules/time_series_anomaly_detection.en.md)

Model Name	Precision	Recall	f1_score	Model Size	YAML File
AutoEncoder_ad	99.36	84.36	91.25	52 K	AutoEncoder_ad.yaml
DLinear_ad	98.98	93.96	96.41	112 K	DLinear_ad.yaml
Nonstationary_ad	98.55	88.95	93.51	1.8 M	Nonstationary_ad.yaml
PatchTST_ad	98.78	90.70	94.57	320 K	PatchTST_ad.yaml
TimesNet_ad	98.37	94.80	96.56	1.3 M	TimesNet_ad.yaml

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](../module_usage/tutorials/time_series_modules/time_series_classification.en.md)

Model Name	acc (%)	Model Size	YAML File
TimesNet_cls	87.5	792 K	TimesNet_cls.yaml

Note: The above accuracy metrics are measured on the [UWaveGestureLibrary](https://paddlets.bj.bcebos.com/classification/UWaveGestureLibrary_TEST.csv) dataset. >Note: All GPU inference times for the above models are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.