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PaddleClas

Introduction

PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.

Recent update

• 2021.04.15 Add MixNet and ReXNet pretrained models, MixNet_L's Top-1 Acc on ImageNet-1k reaches 78.6% and ReXNet_3_0 reaches 82.09%.
• 2021.03.02 Add support for model quantization.
• 2021.02.01 Add RepVGG pretrained models, whose Top-1 Acc on ImageNet-1k dataset reaches 79.65%.
• 2021.01.27 Add ViT and DeiT pretrained models, ViT's Top-1 Acc on ImageNet-1k dataset reaches 85.13%, and DeiT reaches 85.1%.
• 2021.01.08 Add support for whl package and its usage, Model inference can be done by simply install paddleclas using pip.
• 2020.12.16 Add support for TensorRT when using cpp inference to obain more obvious acceleration.
• more

Features

• Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 29 series of classification network structures and training configurations, 134 models' pretrained weights and their evaluation metrics.

• SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.

• Data augmentation: PaddleClas provides detailed introduction of 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, code reproduction and effect evaluation in a unified experimental environment.

• Pretrained model with 100,000 categories: Based on ResNet50_vd model, Baidu open sourced the ResNet50_vd pretrained model trained on a 100,000-category dataset. In some practical scenarios, the accuracy based on the pretrained weights can be increased by up to 30%.

• A variety of training modes, including multi-machine training, mixed precision training, etc.

• A variety of inference and deployment solutions, including TensorRT inference, Paddle-Lite inference, model service deployment, model quantification, Paddle Hub, etc.

• Support Linux, Windows, macOS and other systems.

Community

• Scan the QR code below with your Wechat and send the message 分类 out, then you will be invited into the official technical exchange group.

• You can also scan the QQ group QR code to enter the PaddleClas QQ group. Look forward to your participation.

Tutorials

• Installation
• Quick start PaddleClas in 30 minutes
• Model introduction and model zoo
  • Model zoo overview
  • SSLD pretrained models
  • ResNet and Vd series
  • Mobile series
  • SEResNeXt and Res2Net series
  • DPN and DenseNet series
  • HRNet series
  • Inception series
  • EfficientNet and ResNeXt101_wsl series
  • ResNeSt and RegNet series
  • Transformer series
  • RepVGG series
  • MixNet series
  • ReXNet series
  • Others
  • HS-ResNet: arxiv link: https://arxiv.org/pdf/2010.07621.pdf. Code and models are coming soon!
• Model training/evaluation
  • Data preparation
  • Model training and finetuning
  • Model evaluation
  • Configuration details
• Model prediction/inference
  • Prediction based on training engine
  • Python inference
  • C++ inference
  • Serving deployment
  • Mobile
  • Inference using whl
  • Model Quantization and Compression
• Advanced tutorials
  • Knowledge distillation
  • Data augmentation
  • Multilabel classification
• Applications
  • Transfer learning
  • Pretrained model with 100,000 categories
  • Generic object detection
• FAQ
  • General image classification problems
  • PaddleClas FAQ
• Competition support
• License
• Contribution

Model zoo overview

Based on the ImageNet-1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The evaluation environment is as follows.

• CPU evaluation environment is based on Snapdragon 855 (SD855).
• The GPU evaluation speed is measured by running 500 times under the FP32+TensorRT configuration (excluding the warmup time of the first 10 times).

Curves of accuracy to the inference time of common server-side models are shown as follows.

Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.

SSLD pretrained models

Accuracy and inference time of the prtrained models based on SSLD distillation are as follows. More detailed information can be refered to SSLD distillation tutorial.

• Server-side distillation pretrained models

Model	Top-1 Acc	Reference Top-1 Acc	Acc gain	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
ResNet34_vd_ssld	0.797	0.760	0.037	2.434	6.222	7.39	21.82	Download link
ResNet50_vd_ ssld	0.824	0.791	0.033	3.531	8.090	8.67	25.58	Download link
ResNet50_vd_ ssld_v2	0.830	0.792	0.039	3.531	8.090	8.67	25.58	Download link
ResNet101_vd_ ssld	0.837	0.802	0.035	6.117	13.762	16.1	44.57	Download link
Res2Net50_vd_ 26w_4s_ssld	0.831	0.798	0.033	4.527	9.657	8.37	25.06	Download link
Res2Net101_vd_ 26w_4s_ssld	0.839	0.806	0.033	8.087	17.312	16.67	45.22	Download link
Res2Net200_vd_ 26w_4s_ssld	0.851	0.812	0.049	14.678	32.350	31.49	76.21	Download link
HRNet_W18_C_ssld	0.812	0.769	0.043	7.406	13.297	4.14	21.29	Download link
HRNet_W48_C_ssld	0.836	0.790	0.046	13.707	34.435	34.58	77.47	Download link
SE_HRNet_W64_C_ssld	0.848	-	-	31.697	94.995	57.83	128.97	Download link

• Mobile-side distillation pretrained models

Model	Top-1 Acc	Reference Top-1 Acc	Acc gain	SD855 time(ms) bs=1	Flops(G)	Params(M)	Model size(M)	Download Address
MobileNetV1_ ssld	0.779	0.710	0.069	32.523	1.11	4.19	16	Download link
MobileNetV2_ ssld	0.767	0.722	0.045	23.318	0.6	3.44	14	Download link
MobileNetV3_ small_x0_35_ssld	0.556	0.530	0.026	2.635	0.026	1.66	6.9	Download link
MobileNetV3_ large_x1_0_ssld	0.790	0.753	0.036	19.308	0.45	5.47	21	Download link
MobileNetV3_small_ x1_0_ssld	0.713	0.682	0.031	6.546	0.123	2.94	12	Download link
GhostNet_ x1_3_ssld	0.794	0.757	0.037	19.983	0.44	7.3	29	Download link

• Note: Reference Top-1 Acc means accuracy of pretrained models which are trained on ImageNet1k dataset.

ResNet and Vd series

Accuracy and inference time metrics of ResNet and Vd series models are shown as follows. More detailed information can be refered to ResNet and Vd series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
ResNet18	0.7098	0.8992	1.45606	3.56305	3.66	11.69	Download link
ResNet18_vd	0.7226	0.9080	1.54557	3.85363	4.14	11.71	Download link
ResNet34	0.7457	0.9214	2.34957	5.89821	7.36	21.8	Download link
ResNet34_vd	0.7598	0.9298	2.43427	6.22257	7.39	21.82	Download link
ResNet34_vd_ssld	0.7972	0.9490	2.43427	6.22257	7.39	21.82	Download link
ResNet50	0.7650	0.9300	3.47712	7.84421	8.19	25.56	Download link
ResNet50_vc	0.7835	0.9403	3.52346	8.10725	8.67	25.58	Download link
ResNet50_vd	0.7912	0.9444	3.53131	8.09057	8.67	25.58	Download link
ResNet50_vd_v2	0.7984	0.9493	3.53131	8.09057	8.67	25.58	Download link
ResNet101	0.7756	0.9364	6.07125	13.40573	15.52	44.55	Download link
ResNet101_vd	0.8017	0.9497	6.11704	13.76222	16.1	44.57	Download link
ResNet152	0.7826	0.9396	8.50198	19.17073	23.05	60.19	Download link
ResNet152_vd	0.8059	0.9530	8.54376	19.52157	23.53	60.21	Download link
ResNet200_vd	0.8093	0.9533	10.80619	25.01731	30.53	74.74	Download link
ResNet50_vd_ ssld	0.8239	0.9610	3.53131	8.09057	8.67	25.58	Download link
ResNet50_vd_ ssld_v2	0.8300	0.9640	3.53131	8.09057	8.67	25.58	Download link
ResNet101_vd_ ssld	0.8373	0.9669	6.11704	13.76222	16.1	44.57	Download link

Mobile series

Accuracy and inference time metrics of Mobile series models are shown as follows. More detailed information can be refered to Mobile series tutorial.

Model	Top-1 Acc	Top-5 Acc	SD855 time(ms) bs=1	Flops(G)	Params(M)	Model storage size(M)	Download Address
MobileNetV1_ x0_25	0.5143	0.7546	3.21985	0.07	0.46	1.9	Download link
MobileNetV1_ x0_5	0.6352	0.8473	9.579599	0.28	1.31	5.2	Download link
MobileNetV1_ x0_75	0.6881	0.8823	19.436399	0.63	2.55	10	Download link
MobileNetV1	0.7099	0.8968	32.523048	1.11	4.19	16	Download link
MobileNetV1_ ssld	0.7789	0.9394	32.523048	1.11	4.19	16	Download link
MobileNetV2_ x0_25	0.5321	0.7652	3.79925	0.05	1.5	6.1	Download link
MobileNetV2_ x0_5	0.6503	0.8572	8.7021	0.17	1.93	7.8	Download link
MobileNetV2_ x0_75	0.6983	0.8901	15.531351	0.35	2.58	10	Download link
MobileNetV2	0.7215	0.9065	23.317699	0.6	3.44	14	Download link
MobileNetV2_ x1_5	0.7412	0.9167	45.623848	1.32	6.76	26	Download link
MobileNetV2_ x2_0	0.7523	0.9258	74.291649	2.32	11.13	43	Download link
MobileNetV2_ ssld	0.7674	0.9339	23.317699	0.6	3.44	14	Download link
MobileNetV3_ large_x1_25	0.7641	0.9295	28.217701	0.714	7.44	29	Download link
MobileNetV3_ large_x1_0	0.7532	0.9231	19.30835	0.45	5.47	21	Download link
MobileNetV3_ large_x0_75	0.7314	0.9108	13.5646	0.296	3.91	16	Download link
MobileNetV3_ large_x0_5	0.6924	0.8852	7.49315	0.138	2.67	11	Download link
MobileNetV3_ large_x0_35	0.6432	0.8546	5.13695	0.077	2.1	8.6	Download link
MobileNetV3_ small_x1_25	0.7067	0.8951	9.2745	0.195	3.62	14	Download link
MobileNetV3_ small_x1_0	0.6824	0.8806	6.5463	0.123	2.94	12	Download link
MobileNetV3_ small_x0_75	0.6602	0.8633	5.28435	0.088	2.37	9.6	Download link
MobileNetV3_ small_x0_5	0.5921	0.8152	3.35165	0.043	1.9	7.8	Download link
MobileNetV3_ small_x0_35	0.5303	0.7637	2.6352	0.026	1.66	6.9	Download link
MobileNetV3_ small_x0_35_ssld	0.5555	0.7771	2.6352	0.026	1.66	6.9	Download link
MobileNetV3_ large_x1_0_ssld	0.7896	0.9448	19.30835	0.45	5.47	21	Download link
MobileNetV3_small_ x1_0_ssld	0.7129	0.9010	6.5463	0.123	2.94	12	Download link
ShuffleNetV2	0.6880	0.8845	10.941	0.28	2.26	9	Download link
ShuffleNetV2_ x0_25	0.4990	0.7379	2.329	0.03	0.6	2.7	Download link
ShuffleNetV2_ x0_33	0.5373	0.7705	2.64335	0.04	0.64	2.8	Download link
ShuffleNetV2_ x0_5	0.6032	0.8226	4.2613	0.08	1.36	5.6	Download link
ShuffleNetV2_ x1_5	0.7163	0.9015	19.3522	0.58	3.47	14	Download link
ShuffleNetV2_ x2_0	0.7315	0.9120	34.770149	1.12	7.32	28	Download link
ShuffleNetV2_ swish	0.7003	0.8917	16.023151	0.29	2.26	9.1	Download link
GhostNet_ x0_5	0.6688	0.8695	5.7143	0.082	2.6	10	Download link
GhostNet_ x1_0	0.7402	0.9165	13.5587	0.294	5.2	20	Download link
GhostNet_ x1_3	0.7579	0.9254	19.9825	0.44	7.3	29	Download link
GhostNet_ x1_3_ssld	0.7938	0.9449	19.9825	0.44	7.3	29	Download link

SEResNeXt and Res2Net series

Accuracy and inference time metrics of SEResNeXt and Res2Net series models are shown as follows. More detailed information can be refered to SEResNext and_Res2Net series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
Res2Net50_ 26w_4s	0.7933	0.9457	4.47188	9.65722	8.52	25.7	Download link
Res2Net50_vd_ 26w_4s	0.7975	0.9491	4.52712	9.93247	8.37	25.06	Download link
Res2Net50_ 14w_8s	0.7946	0.9470	5.4026	10.60273	9.01	25.72	Download link
Res2Net101_vd_ 26w_4s	0.8064	0.9522	8.08729	17.31208	16.67	45.22	Download link
Res2Net200_vd_ 26w_4s	0.8121	0.9571	14.67806	32.35032	31.49	76.21	Download link
Res2Net200_vd_ 26w_4s_ssld	0.8513	0.9742	14.67806	32.35032	31.49	76.21	Download link
ResNeXt50_ 32x4d	0.7775	0.9382	7.56327	10.6134	8.02	23.64	Download link
ResNeXt50_vd_ 32x4d	0.7956	0.9462	7.62044	11.03385	8.5	23.66	Download link
ResNeXt50_ 64x4d	0.7843	0.9413	13.80962	18.4712	15.06	42.36	Download link
ResNeXt50_vd_ 64x4d	0.8012	0.9486	13.94449	18.88759	15.54	42.38	Download link
ResNeXt101_ 32x4d	0.7865	0.9419	16.21503	19.96568	15.01	41.54	Download link
ResNeXt101_vd_ 32x4d	0.8033	0.9512	16.28103	20.25611	15.49	41.56	Download link
ResNeXt101_ 64x4d	0.7835	0.9452	30.4788	36.29801	29.05	78.12	Download link
ResNeXt101_vd_ 64x4d	0.8078	0.9520	30.40456	36.77324	29.53	78.14	Download link
ResNeXt152_ 32x4d	0.7898	0.9433	24.86299	29.36764	22.01	56.28	Download link
ResNeXt152_vd_ 32x4d	0.8072	0.9520	25.03258	30.08987	22.49	56.3	Download link
ResNeXt152_ 64x4d	0.7951	0.9471	46.7564	56.34108	43.03	107.57	Download link
ResNeXt152_vd_ 64x4d	0.8108	0.9534	47.18638	57.16257	43.52	107.59	Download link
SE_ResNet18_vd	0.7333	0.9138	1.7691	4.19877	4.14	11.8	Download link
SE_ResNet34_vd	0.7651	0.9320	2.88559	7.03291	7.84	21.98	Download link
SE_ResNet50_vd	0.7952	0.9475	4.28393	10.38846	8.67	28.09	Download link
SE_ResNeXt50_ 32x4d	0.7844	0.9396	8.74121	13.563	8.02	26.16	Download link
SE_ResNeXt50_vd_ 32x4d	0.8024	0.9489	9.17134	14.76192	10.76	26.28	Download link
SE_ResNeXt101_ 32x4d	0.7939	0.9443	18.82604	25.31814	15.02	46.28	Download link
SENet154_vd	0.8140	0.9548	53.79794	66.31684	45.83	114.29	Download link

DPN and DenseNet series

Accuracy and inference time metrics of DPN and DenseNet series models are shown as follows. More detailed information can be refered to DPN and DenseNet series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
DenseNet121	0.7566	0.9258	4.40447	9.32623	5.69	7.98	Download link
DenseNet161	0.7857	0.9414	10.39152	22.15555	15.49	28.68	Download link
DenseNet169	0.7681	0.9331	6.43598	12.98832	6.74	14.15	Download link
DenseNet201	0.7763	0.9366	8.20652	17.45838	8.61	20.01	Download link
DenseNet264	0.7796	0.9385	12.14722	26.27707	11.54	33.37	Download link
DPN68	0.7678	0.9343	11.64915	12.82807	4.03	10.78	Download link
DPN92	0.7985	0.9480	18.15746	23.87545	12.54	36.29	Download link
DPN98	0.8059	0.9510	21.18196	33.23925	22.22	58.46	Download link
DPN107	0.8089	0.9532	27.62046	52.65353	35.06	82.97	Download link
DPN131	0.8070	0.9514	28.33119	46.19439	30.51	75.36	Download link

HRNet series

Accuracy and inference time metrics of HRNet series models are shown as follows. More detailed information can be refered to Mobile series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
HRNet_W18_C	0.7692	0.9339	7.40636	13.29752	4.14	21.29	Download link
HRNet_W18_C_ssld	0.81162	0.95804	7.40636	13.29752	4.14	21.29	Download link
HRNet_W30_C	0.7804	0.9402	9.57594	17.35485	16.23	37.71	Download link
HRNet_W32_C	0.7828	0.9424	9.49807	17.72921	17.86	41.23	Download link
HRNet_W40_C	0.7877	0.9447	12.12202	25.68184	25.41	57.55	Download link
HRNet_W44_C	0.7900	0.9451	13.19858	32.25202	29.79	67.06	Download link
HRNet_W48_C	0.7895	0.9442	13.70761	34.43572	34.58	77.47	Download link
HRNet_W48_C_ssld	0.8363	0.9682	13.70761	34.43572	34.58	77.47	Download link
HRNet_W64_C	0.7930	0.9461	17.57527	47.9533	57.83	128.06	Download link
SE_HRNet_W64_C_ssld	0.8475	0.9726	31.69770	94.99546	57.83	128.97	Download link

Inception series

Accuracy and inference time metrics of Inception series models are shown as follows. More detailed information can be refered to Inception series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
GoogLeNet	0.7070	0.8966	1.88038	4.48882	2.88	8.46	Download link
Xception41	0.7930	0.9453	4.96939	17.01361	16.74	22.69	Download link
Xception41_deeplab	0.7955	0.9438	5.33541	17.55938	18.16	26.73	Download link
Xception65	0.8100	0.9549	7.26158	25.88778	25.95	35.48	Download link
Xception65_deeplab	0.8032	0.9449	7.60208	26.03699	27.37	39.52	Download link
Xception71	0.8111	0.9545	8.72457	31.55549	31.77	37.28	Download link
InceptionV3	0.7914	0.9459	6.64054	13.53630	11.46	23.83	Download link
InceptionV4	0.8077	0.9526	12.99342	25.23416	24.57	42.68	Download link

EfficientNet and ResNeXt101_wsl series

Accuracy and inference time metrics of EfficientNet and ResNeXt101_wsl series models are shown as follows. More detailed information can be refered to EfficientNet and ResNeXt101_wsl series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
ResNeXt101_ 32x8d_wsl	0.8255	0.9674	18.52528	34.25319	29.14	78.44	Download link
ResNeXt101_ 32x16d_wsl	0.8424	0.9726	25.60395	71.88384	57.55	152.66	Download link
ResNeXt101_ 32x32d_wsl	0.8497	0.9759	54.87396	160.04337	115.17	303.11	Download link
ResNeXt101_ 32x48d_wsl	0.8537	0.9769	99.01698256	315.91261	173.58	456.2	Download link
Fix_ResNeXt101_ 32x48d_wsl	0.8626	0.9797	160.0838242	595.99296	354.23	456.2	Download link
EfficientNetB0	0.7738	0.9331	3.442	6.11476	0.72	5.1	Download link
EfficientNetB1	0.7915	0.9441	5.3322	9.41795	1.27	7.52	Download link
EfficientNetB2	0.7985	0.9474	6.29351	10.95702	1.85	8.81	Download link
EfficientNetB3	0.8115	0.9541	7.67749	16.53288	3.43	11.84	Download link
EfficientNetB4	0.8285	0.9623	12.15894	30.94567	8.29	18.76	Download link
EfficientNetB5	0.8362	0.9672	20.48571	61.60252	19.51	29.61	Download link
EfficientNetB6	0.8400	0.9688	32.62402	-	36.27	42	Download link
EfficientNetB7	0.8430	0.9689	53.93823	-	72.35	64.92	Download link
EfficientNetB0_ small	0.7580	0.9258	2.3076	4.71886	0.72	4.65	Download link

ResNeSt and RegNet series

Accuracy and inference time metrics of ResNeSt and RegNet series models are shown as follows. More detailed information can be refered to ResNeSt and RegNet series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
ResNeSt50_ fast_1s1x64d	0.8035	0.9528	3.45405	8.72680	8.68	26.3	Download link
ResNeSt50	0.8083	0.9542	6.69042	8.01664	10.78	27.5	Download link
RegNetX_4GF	0.785	0.9416	6.46478	11.19862	8	22.1	Download link

Transformer series

Accuracy and inference time metrics of ViT and DeiT series models are shown as follows. More detailed information can be refered to Transformer series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
ViT_small_ patch16_224	0.7769	0.9342	-	-			Download link
ViT_base_ patch16_224	0.8195	0.9617	-	-		86	Download link
ViT_base_ patch16_384	0.8414	0.9717	-	-			Download link
ViT_base_ patch32_384	0.8176	0.9613	-	-			Download link
ViT_large_ patch16_224	0.8323	0.9650	-	-		307	Download link
ViT_large_ patch16_384	0.8513	0.9736	-	-			Download link
ViT_large_ patch32_384	0.8153	0.9608	-	-			Download link

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
DeiT_tiny_ patch16_224	0.718	0.910	-	-		5	Download link
DeiT_small_ patch16_224	0.796	0.949	-	-		22	Download link
DeiT_base_ patch16_224	0.817	0.957	-	-		86	Download link
DeiT_base_ patch16_384	0.830	0.962	-	-		87	Download link
DeiT_tiny_ distilled_patch16_224	0.741	0.918	-	-		6	Download link
DeiT_small_ distilled_patch16_224	0.809	0.953	-	-		22	Download link
DeiT_base_ distilled_patch16_224	0.831	0.964	-	-		87	Download link
DeiT_base_ distilled_patch16_384	0.851	0.973	-	-		88	Download link

RepVGG

Accuracy and inference time metrics of RepVGG series models are shown as follows. More detailed information can be refered to RepVGG series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
RepVGG_A0	0.7131	0.9016					Download link
RepVGG_A1	0.7380	0.9146					Download link
RepVGG_A2	0.7571	0.9264					Download link
RepVGG_B0	0.7450	0.9213					Download link
RepVGG_B1	0.7773	0.9385					Download link
RepVGG_B2	0.7813	0.9410					Download link
RepVGG_B1g2	0.7732	0.9359					Download link
RepVGG_B1g4	0.7675	0.9335					Download link
RepVGG_B2g4	0.7881	0.9448					Download link
RepVGG_B3g4	0.7965	0.9485					Download link

MixNet

Accuracy and inference time metrics of MixNet series models are shown as follows. More detailed information can be refered to MixNet series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(M)	Params(M)	Download Address
MixNet_S	0.7628	0.9299			252.977	4.167	Download link
MixNet_M	0.7767	0.9364			357.119	5.065	Download link
MixNet_L	0.7860	0.9437			579.017	7.384	Download link

ReXNet

Accuracy and inference time metrics of ReXNet series models are shown as follows. More detailed information can be refered to ReXNet series tutorial.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
ReXNet_1_0	0.7746	0.9370			0.415	4.838	Download link
ReXNet_1_3	0.7913	0.9464			0.683	7.611	Download link
ReXNet_1_5	0.8006	0.9512			0.900	9.791	Download link
ReXNet_2_0	0.8122	0.9536			1.561	16.449	Download link
ReXNet_3_0	0.8209	0.9612			3.445	34.833	Download link

Others

Accuracy and inference time metrics of AlexNet, SqueezeNet series, VGG series and DarkNet53 models are shown as follows. More detailed information can be refered to Others.

Model	Top-1 Acc	Top-5 Acc	time(ms) bs=1	time(ms) bs=4	Flops(G)	Params(M)	Download Address
AlexNet	0.567	0.792	1.44993	2.46696	1.370	61.090	Download link
SqueezeNet1_0	0.596	0.817	0.96736	2.53221	1.550	1.240	Download link
SqueezeNet1_1	0.601	0.819	0.76032	1.877	0.690	1.230	Download link
VGG11	0.693	0.891	3.90412	9.51147	15.090	132.850	Download link
VGG13	0.700	0.894	4.64684	12.61558	22.480	133.030	Download link
VGG16	0.720	0.907	5.61769	16.40064	30.810	138.340	Download link
VGG19	0.726	0.909	6.65221	20.4334	39.130	143.650	Download link
DarkNet53	0.780	0.941	4.10829	12.1714	18.580	41.600	Download link

License

PaddleClas is released under the Apache 2.0 license

Contribution

Contributions are highly welcomed and we would really appreciate your feedback!!

• Thank nblib to fix bug of RandErasing.
• Thank chenpy228 to fix some typos PaddleClas.
• Thank jm12138 to add ViT, DeiT models and RepVGG models into PaddleClas.