目前，EdgeBoard主要支持视觉类模型，包含分类、检测、分割以及特定场景模型。

分类模型

模型为PaddleClas release/static的ModelZoo，github地址：https://github.com/PaddlePaddle/PaddleClas/tree/release/static

以下数据的测试环境：

模型训练环境：PaddlePaddle = 1.8.5、PaddleClas = release/static

模型部署环境：EdgeBoard 软核1.8.1、系统Ubuntu18.04

ResNet and Vd series

ResNet model	Top-1 Acc by PaddleClas	Top-5 Acc by PaddleClas	Top-1 Acc by 3A	Top-5 Acc by 3A	time by 3A(ms)	Top-1 Acc by 3B	Top-5 Acc by 3B	time by 3B(ms)	Top-1 Acc by 5D	Top-5 Acc by 5D	time by 5D(ms)	Top-1 Acc by 9D	Top-5 Acc by 9D	time by 9D(ms)
ResNet18	0.7098	0.8992	0.7096	0.8991	14.71548588	0.733	0.914	8.95363345	0.7097	0.8988	8.95363345	0.7097	0.8988	6.40590735
ResNet18_vd	0.7226	0.908	0.723	0.908	16.02208967	0.7223	0.9081	9.83267931	0.7223	0.9081	17.8796712	0.7221	0.908	7.09548706
ResNet34	0.7457	0.9214	0.7441	0.9211	26.98735892	0.7441	0.921	15.6827087	0.7441	0.921	15.6827087	0.7437	0.9211	10.6187022
ResNet34_vd	0.7598	0.9298	0.7586	0.9303	28.36009001	0.7582	0.93	16.5780194	0.7582	0.93	16.5780194	0.7587	0.9298	11.3229563
ResNet50	0.765	0.93	0.7637	0.9294	34.05404204	0.7641	0.9294	21.732892	0.7641	0.9294	21.732892	0.7639	0.9296	16.6979658
ResNet50_vc	0.7835	0.9403	0.7827	0.9398	35.21767119	0.7903	0.9446	22.9020555	0.7903	0.9446	22.9020555	0.7823	0.9398	17.2306128
ResNet50_vd	0.7912	0.9444	0.7904	0.9446	35.71911547	0.7741	0.9365	37.4155384	0.7741	0.9365	37.4155384	0.7903	0.9444	17.6102096
ResNet101	0.7756	0.9364	0.7742	0.9366	59.52945458	0.8013	0.9493	38.5317895	0.8013	0.9493	38.5317895	0.7742	0.9361	27.3622326
ResNet101_vd	0.8017	0.9497	0.8014	0.9495	61.17122603	0.7823	0.939	53.0845316	0.7823	0.939	53.0845316	0.8014	0.9491	28.3110359
ResNet152	0.7826	0.9396	0.7827	0.9393	85.05916077	0.8041	0.9523	54.2026246	0.8041	0.9523	54.2026246	0.782	0.939	37.6998016
ResNet152_vd	0.8059	0.953	0.8041	0.9523	86.59876956	0.8075	0.9534	68.9850622	0.8075	0.9534	68.9850622	0.8043	0.9522	38.5713139
ResNet200_vd	0.8093	0.9533	0.807	0.9537	110.5979351	0.8069	0.9538	49.9698168	0.8069	0.9538	49.9698168	0.8069	0.9538	49.9698168

Mobile series

Model	Top-1 Acc by PaddleClas	Top-5 Acc by PaddleClas	Top-1 Acc by 3A	Top-5 Acc by 3B	time by 3B(ms)	Top-1 Acc by 3B	Top-5 Acc by 3B	time by 3B(ms)	Top1 Acc by 5C	Top-5 Acc by 5C	time by 5C(ms)	Top-1 Acc by 5D	Top-5 Acc by 5D	time by 5D(ms)	Top-1 Acc by 9D	Top-5 Acc by 9D	time by 9D(ms)
MobileNetV1_x0_25	0.5143	0.7546	0.5025	0.7443	3.68112385	0.5025	0.7443	3.47995238	0.5025	0.7443	4.05508755	0.5025	0.7443	4.05508755	0.5025	0.7443	3.47995238
MobileNetV1_x0_5	0.6352	0.8473	0.6325	0.8469	4.97116533	0.6325	0.8469	4.35903775	0.6325	0.8469	5.05141552	0.6325	0.8469	5.05141552	0.6325	0.8469	4.35903774
MobileNetV1_x0_75	0.6881	0.8823	0.6838	0.8803	7.60554075	0.6843	0.8802	6.29608946	0.6843	0.8802	7.24652382	0.6843	0.8802	7.24652382	0.6843	0.8802	6.29608946
MobileNetV1	0.7099	0.8968	0.7079	0.8956	9.34210897	0.708	0.8954	7.22738171	0.708	0.8954	8.4207836	0.708	0.8954	8.4207836	0.708	0.8954	7.05951087
MobileNetV2_x0_25	0.5321	0.7652	0.5273	0.7616	7.74662709	0.5269	0.7616	7.09189643	0.5273	0.7617	8.24141499	0.5273	0.7617	8.24141499	0.5269	0.7616	6.96900913
MobileNetV2_x0_5	0.6503	0.8572	0.6453	0.8541	7.4191766	0.5135	0.6808	6.86391964	0.6451	0.8539	8.13312565	0.6451	0.8539	8.13312565	0.5936	0.7877	6.75820331
MobileNetV2_x0_75	0.6983	0.8901	0.6959	0.8879	12.7507112	0.6959	0.8878	11.5829853	0.696	0.8878	13.0029739	0.696	0.8878	13.0029739	0.6959	0.8878	11.3544805
MobileNetV2	0.7215	0.9065	0.7186	0.9052	11.0632651	0.7188	0.9051	9.86122213	0.7188	0.9052	11.3809789	0.7188	0.9052	11.3809789	0.7188	0.9051	9.66140689
MobileNetV2_x1_5	0.7412	0.9167	0.7385	0.9155	17.1437208	0.7345	0.9133	14.5713013	0.7345	0.9133	16.5544266	0.7345	0.9133	16.5544266	0.8014	0.9491	14.2934955
MobileNetV2_x2_0	0.7523	0.9258	0.7386	0.9154	22.0435947	0.3747	0.6042	17.6118443	0.7384	0.9153	20.1993121	0.7384	0.9153	20.1993121	0.7826	0.9397	17.1324282
GhostNet_x1_0	0.7402	0.9165	0.4417	0.677	34.1087561	0.5221	0.7592	30.9889381	0.3239	0.5406	33.5847206	0.3239	0.5406	33.5847206	0.5221	0.7592	30.9889381

SEResNeXt and Res2Net series

Model	Top-1 Acc by PaddleClas	Top-5 Acc by PaddleClas	Top-1 Acc by 5D	Top-5 Acc by 5D	time by 5D(ms)	Top-1 Acc by 9D	Top-5 Acc by 9D	time by 9D(ms)
Res2Net50_26w_4s	0.7933	0.9457	0.7923	0.945	46.9715829	0.7923	0.945	41.3515309
Res2Net50_vd_26w_4s	0.7975	0.9491	0.797	0.9484	48.1105378	0.7969	0.9485	42.0706068
Res2Net50_14w_8s	0.7946	0.947	0.7875	0.9414	64.7512679	0.7873	0.9412	57.5119234
Res2Net101_vd_26w_4s	0.8064	0.9522	0.7938	0.9393	74.3632303	0.7933	0.9394	62.2968839
Res2Net200_vd_26w_4s	0.8121	0.9571	0.7967	0.9418	132.182714	0.7967	0.9413	108.708479
ResNeXt50_32x4d	0.7775	0.9382	0.7757	0.937	27.6730268	0.7675	0.9281	22.2482438
ResNeXt50_vd_32x4d	0.7956	0.9462	0.7757	0.937	27.6730268	0.74	0.96	23.0841588
ResNeXt50_64x4d	0.7843	0.9413	0.7836	0.9403	44.1621114	0.7843	0.9413	52.8567324
ResNeXt50_vd_64x4d	0.8012	0.9486	0.8002	0.9483	45.2178389	0.8012	0.9486	54.1873319
ResNeXt101_32x4d	0.7865	0.9419	0.7854	0.9415	44.4215912	0.7865	0.9419	33.8506747
ResNeXt101_vd_32x4d	0.8033	0.9512	0.8013	0.9501	45.5457859	0.8033	0.9512	34.8075458
ResNeXt101_64x4d	0.7835	0.9452	0.7925	0.9452	72.0490677	0.792	0.9449	82.8039959
ResNeXt101_vd_64x4d	0.8078	0.952	0.8027	0.9472	73.287074	0.8078	0.952	81.9382522
ResNeXt152_32x4d	0.7898	0.9433	0.7888	0.943	62.0476544	0.7889	0.9432	48.0175154
ResNeXt152_vd_32x4d	0.8072	0.952	0.8063	0.9519	63.1244978	0.8072	0.952	47.7385326
ResNeXt152_64x4d	0.7951	0.9471	0.7937	0.946	101.227566	0.7951	0.9471	113.319645
ResNeXt152_vd_64x4d	0.8108	0.9534	0.8102	0.9532	102.414722	0.8107	0.9533	113.768651
SE_ResNet18_vd	0.7333	0.9138	0.733	0.9142	12.8133037	0.7334	0.913	9.29966265
SE_ResNet34_vd	0.7651	0.932	0.7641	0.9313	22.3687792	0.7651	0.932	15.6554105
SE_ResNet50_vd	0.7952	0.9475	0.7946	0.9473	34.68155	0.7952	0.9475	27.4156064
SE_ResNeXt50_32x4d	0.7844	0.9396	0.7832	0.9396	39.3880242	0.7844	0.9396	32.3298221
SE_ResNeXt50_vd_32x4d	0.8024	0.9489	0.8024	0.9489	42.8019707	0.7922	0.9386	34.4062971
SE_ResNeXt101_32x4d	0.7912	0.942	0.7924	0.9438	66.9515808	0.7834	0.942	53.2186031
SENet154_vd	0.814	0.9548	0.8127	0.9542	138.551309	0.814	0.9548	143.699729

DPN and DenseNet series

Model	Top-1 Acc by PaddleClas	Top-5 Acc by PaddleClas	Top-1 Acc by 3A	Top-5 Acc by 3A	time by 3A(ms)	Top-1 Acc by 3B	Top-5 Acc by 3B	time by 3B(ms)	Top-1 Acc by 5D	Top-5 Acc by 5D	time by 5D(ms)	Top-1 Acc by 9D	Top-5 Acc by 9D	time by 9D(ms)
DenseNet121	0.7566	0.9258	0.7558	0.9258	53.6027625	0.7558	0.9258	53.6027625	0.7558	0.9259	44.3492407	0.7566	0.9258	39/3477994
DenseNet161	0.7857	0.9414	0.7844	0.9406	115.46785	0.7844	0.9406	115.46785	0.7845	0.9411	91.7863596	0.7857	0.9414	80.0224323
DenseNet169	0.7681	0.9331	0.768	0.9328	68.6313306	0.768	0.9328	68.6313306	0.7675	0.9328	57.4114082	0.7681	0.9331	50.6865443
DenseNet201	0.7763	0.9366	0.7755	0.9364	90.441223	0.7755	0.9364	90.441223	0.7757	0.9366	75.0129829	0.7763	0.9366	66.8523895
DenseNet264	0.7796	0.9385	0.7793	0.9388	129.401795	0.7793	0.9388	129.401795	0.779	0.9389	105.48438	0.7796	0.9385	93.7701779
DPN68	0.7678	0.9343	0.724	0.9092	55.3380497	0.724	0.9092	55.3380497	0.7243	0.9093	44.8887849	0.7678	0.9343	40.6067176
DPN92	0.7985	0.948	0.7979	0.9478	132.163013	0.7979	0.9478	132.163013	0.7983	0.9478	96.3646919	0.7985	0.948	85.7311024
DPN98	0.8059	0.951	0.803	0.9496	166.167311	0.803	0.9496	166.167311	0.8035	0.9501	116.329713	0.8059	0.951	121.238888
DPN107	0.8089	0.9532	0.7019	0.8887	254.276414	0.7019	0.8887	254.276414	0.7015	0.8878	174.463986	0.8089	0.9532	181.168191
DPN131	0.807	0.9514	0.8034	0.9489	223.894086	0.8034	0.9489	223.894086	0.8036	0.949	157.157379	0.807	0.9514	163.428369

HRNet series

Model	Top-1 Acc by PaddleClas	Top-5 Acc by PaddleClas	Top-1 Acc by 3A	Top-5 Acc by 3A	time by 3A(ms)	Top-1 Acc by 3B	Top-5 Acc by 3B	time by 3B(ms)	Top-1 Acc by 5D	Top-5 Acc by 5D	time by 5D(ms)	Top-1 Acc by 9D	Top-5 Acc by 9D	time by 9D(ms)
HRNet_W18_C	0.7692	0.9339	0.5661	0.8185	60.9819089	0.5661	0.8185	60.9819089	0.5659	0.8186	55.4794437	0.7873	0.9412	39.3477994
HRNet_W30_C	0.7804	0.9402	0.657	0.8775	83.3555966	0.657	0.8775	83.3555966	0.6571	0.8782	65.7723425	0.7969	0.9485	44.0211977
HRNet_W32_C	0.7828	0.9424	0.7821	0.9425	82.9289218	0.7821	0.9425	82.9289218	0.7821	0.9423	64.4770416	0.7923	0.945	50.0433493
HRNet_W40_C	0.7877	0.9447	0.7839	0.9428	111.186797	0.7839	0.9428	111.186797	0.7835	0.9432	80.2612852	0.7933	0.9394	60.8345537
HRNet_W44_C	0.79	0.9451	0.766	0.9349	126.927966	0.766	0.9349	126.927966	0.7663	0.9349	88.4218833	0.7967	0.9413	65.3390154
HRNet_W48_C	0.7895	0.9442	0.7892	0.9443	135.097451	0.7892	0.9443	135.097451	0.7894	0.9441	90.5619071	0.7936	0.9677	65.9286927
HRNet_W64_C	0.793	0.9461	0.7926	0.9462	206.795211	0.7926	0.9462	206.795211	0.7929	0.946	122.473623	0.7959	0.9478	83.8883701

Inception series

Model	Top-1 Acc by PaddleClas	Top-5 Acc by PaddleClas	Top-1 Acc by 3A	Top-5 Acc by 3A	time by 3A(ms)	Top-1 Acc by 3B	Top-5 Acc by 3B	time by 3B(ms)	Top-1 Acc by 5D	Top-5 Acc by 5D	time by 5D(ms)	Top-1 Acc by 9D	Top-5 Acc by 9D	time by 9D(ms)
Xception41_deeplab	0.7955	0.9438	0.7593	0.9214	68.4773261	0.7593	0.9214	68.4773261	0.7593	0.9215	47.2026896	0.7923	0.945	38.5318629
Xception65_deeplab	0.8032	0.9449	0.7735	0.929	103.461153	0.7735	0.929	103.461153	0.7743	0.9287	69.1011319	0.7967	0.9413	55.9068845
InceptionV3	0.7914	0.9459	0.7348	0.9108	27.4721269	0.7348	0.9108	27.4721269	0.735	0.9104	21.0058066	0.6959	0.8878	16.2281211
InceptionV4	0.8077	0.9526	0.7591	0.9218	54.4613488	0.7591	0.9218	54.4613488	0.7591	0.9214	38.6780565	0.7188	0.9051	29.8487604

EfficientNet and ResNeXt101_wsl series

Model	Top-1 Acc by PaddleClas	Top-5 Acc by PaddleClas	Top-1 Acc by 3A	Top-5 Acc by 3A	time by 3A(ms)	Top-1 Acc by 3B	Top-5 Acc by 3B	time by 3B(ms)	Top-1 Acc by 5D	Top-5 Acc by 5D	time by 5D(ms)	Top-1 Acc by 9D	Top-5 Acc by 9D	time by 9D(ms)
ResNeXt101_32x8d_wsl	0.8255	0.9674	0.8223	0.9659	133.606047	0.8223	0.9659	133.606047	0.8225	0.9656	67.4824983	0.82	0.95	47.6001574
ResNeXt101_32x16d_wsl	0.8424	0.9726	0.8385	0.9712	83.7354768	0.8385	0.9712	83.7354768	0.8385	0.9712	151.637222	0.8312	0.9714	82.075878
ResNeSt50_fast_1s1x64d	0.8035	0.9528	0.8022	0.9521	40.8778803	0.8022	0.9521	40.8778803	0.802	0.952	28.9425293	0.7973	0.9612	21.9779988

检测模型

模型为paddledetection release/0.5的ModelZoo，github地址：https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.5/docs/MODEL_ZOO_cn.md

以下数据的测试环境：

模型训练环境：PaddlePaddle = 1.8.5、PaddleDetection = release/0.5

模型部署环境：EdgeBoard 软核1.8.1、系统Ubuntu

Yolov3 on pascal voc

骨架网络	输入尺寸	mAP by paddledetection	mAP on FZ9D
DarkNet53	608	83.5	81.6
DarkNet53	416	83.6	80.9
DarkNet53	320	82.2	81.2
DarkNet53 Diou-Loss	608	83.5	83.3
MobileNet-V1	608	76.2	75.6
MobileNet-V1	416	76.7	74.3
MobileNet-V1	320	75.3	73.1
ResNet34	608	82.6	80.2
ResNet34	416	81.9	81.1
ResNet34	320	80.1	78.2

Yolov3 on COCO

骨架网络	输入尺寸	加入deformable卷积	mAP by paddledetection	mAP by FZ9D	mAP by FZ5C	FZ5C time cost
DarkNet53	608	否	38.9	38.7	38.5	248.9
DarkNet53	416	否	37.5	37.2	34.5	83
DarkNet53	320	否	34.8	33.5	34.5	82.95
MobileNet-V1	608	否	29.3	28.7	28.9	155
MobileNet-V1	416	否	29.3	28.8	26.7	50
MobileNet-V1	320	否	27.1	26.9	27.6	50
ResNet34	608	否	36.2	36	35.86	177.4
ResNet34	416	否	34.3	33.9	31	59.3
ResNet34	320	否	31.4	30.5	31.1	59

SSD on pascal voc

骨架网络	输入尺寸	mAP by paddledetection	mAP on EdgeBoard
MobileNet v1	300	73.2	暂无数据

其他支持的模型

分类	模型	支持平台
分割模型	deeplabv3P	FZ3、FZ5、FZ9
关键点	HRnet	FZ3、FZ5、FZ9
特定场景	人体检测	场景化软硬一体设备
特定场景	人体关键点检测	场景化软硬一体设备
特定场景	人脸识别	场景化软硬一体设备
特定场景	安全帽佩戴合规检测	场景化软硬一体设备
特定场景	烟火检测	场景化软硬一体设备
特定场景	车辆检测	场景化软硬一体设备
特定场景	车型识别	场景化软硬一体设备
特定场景	动态车流	场景化软硬一体设备
特定场景	车辆属性	场景化软硬一体设备
特定场景	车牌识别	场景化软硬一体设备

模型库持续更新中...