模型仓库modelzoo

Classification

network	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
mobilenetv1_imagenet	74.12	73.92	73.61	ImageNet	1x3x224x224	0.42	5069.83
mobilenetv2_imagenet	72.65	72.51	72.11	ImageNet	1x3x224x224	0.45	4541.88
resnet18_imagenet	72.04	72.03	72.03	ImageNet	1x3x224x224	0.65	2346.22
resnet50_imagenet	77.37	76.99	76.94	ImageNet	1x3x224x224	1.12	1117.93
vargnetv2_imagenet	73.94	73.56	73.64	ImageNet	1x3x224x224	0.50	3763.68
efficientnet_imagenet	74.31	74.23	74.18	ImageNet	1x3x224x224	0.49	3887.20
horizon_swin_transformer_imagenet	80.24	80.15	80.05	ImageNet	1x3x224x224	4.19	257.265367
mixvargenet_imagenet	71.33	71.23	71.04	ImageNet	1x3x224x224	0.44	4728.13
efficientnasnetm_imagenet	80.24	79.99	79.94	ImageNet	1x3x280x280	0.99	1305.20
efficientnasnets_imagenet	76.63	76.23	76.03	ImageNet	1x3x300x300	0.56	2943.99
vit_small_imagenet	79.50	79.40	77.86	ImageNet	1x3x224x224	2.35	472.41
henet_tinye_imagenet	77.68	77.22	76.92	ImageNet	1x3x224x224	0.60	2713.99
henet_tinym_imagenet	78.38	77.95	77.62	ImageNet	1x3x224x224	0.48(J6M)	3451.43(J6M)

Detection

FCOS

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
fcos_efficientnetb0_mscoco	efficientnetb0	36.26	35.79	35.59	MS COCO	1x3x512x512	-	-
fcos_efficientnetb1_mscoco	efficientnetb1	41.37	41.21	40.71	MS COCO	1x3x640x640	2.44	487.89
fcos_efficientnetb2_mscoco	efficientnetb2	45.35	45.10	45.00	MS COCO	1x3x768x768	3.46	325.84
fcos_efficientnetb3_mscoco	efficientnetb3	48.03	47.65	47.58	MS COCO	1x3x896x896	5.75	187.30

DETR

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
detr_resnet50_mscoco	resnet50	35.70	31.42	31.31	MS COCO	1x3x800x1333	28.63	35.27
detr_efficientnetb3_mscoco	efficientnetb3	37.21	35.95	35.99	MS COCO	1x3x800x1333	22.20	45.64

Deform DETR

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
deform_detr_resnet50_mscoco	resnet50	44.34	44.65	44.80	MS COCO	1x3x800x1333	222.42	4.51

FCOS3D

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
fcos3d_efficientnetb0_nuscenes	efficientnetb0	30.60	30.27	30.31	nuscenes	1x3x512x896	3.03	409.89

Segmentation

UNet

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
unet_mobilenetv1_cityscapes	MobileNetV1	68.02	67.56	67.53	Cityscapes	1x3x1024x2048	1.64	783.06

Deeplab

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
deeplabv3plus_efficientnetm0_cityscapes	EfficientNet-M0	76.30	76.22	76.12	Cityscapes	1x3x1024x2048	4.83	218.18
deeplabv3plus_efficientnetm1_cityscapes	EfficientNet-M1	77.94	77.64	77.65	Cityscapes	1x3x1024x2048	9.21	111.76
deeplabv3plus_efficientnetm2_cityscapes	EfficientNet-M2	78.82	78.65	78.63	Cityscapes	1x3x1024x2048	13.77	74.04

FastScnn

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
fastscnn_efficientnetb0tiny_cityscapes	EfficientNet-B0lite	69.97	69.90	69.88	Cityscapes	1x3x1024x2048	2.27	493.85

Lidar

PointPillars

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
pointpillars_kitti_car	SequentialBottleNeck	77.31	76.86	76.76	KITTI3D	150000x4	185.42	28.12

CenterPoint

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
centerpoint_pointpillar_nuscenes	SequentialBottleNeck	58.32(NDS) 48.04(MAP)	58.11(NDS) 47.85(MAP)	58.14(NDS) 47.81(MAP)	nuscenes det	1x5x20x40000, 40000x4	55.26	100.62

LidarMultiTask

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
centerpoint_mixvargnet_multitask_nuscenes	MixVarGENet	58.09(NDS) 47.27(MAP) 91.28(MeanIOU)	57.72(NDS) 46.76(MAP) 91.18(MeanIOU)	57.53(NDS) 46.28(MAP) 91.21(MeanIOU)	nuscenes det && seg	1x5x20x40000, 40000x4	53.56	125.52

注解

PointPillars 的指标是 Box3d Moderate 这项。

Lane Detection

GaNet

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
ganet_mixvargenet_culane	MixVarGENet	79.49	78.72	78.72	CuLane	1x3x320x800	0.94	1445.91

Multiple Object Track

Motr

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
motr_efficientnetb3_mot17	efficientnetb3	58.02	57.62	57.76	Mot17	1x3x800x1422, 1x256x2x128, 1x1x1x256, 1x4x2x128	14.99	68.31

Binocular depth estimation

StereoNet

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
stereonetplus_mixvargenet_sceneflow	MixVarGENet	1.1270	1.1329	1.1351	SceneFlow	2x3x544x960	5.12	208.22

Bev

Bev

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
bev_ipm_efficientnetb0_multitask_nuscenes	efficientnetb0	30.54(NDS) 21.70(MAP) 51.45(MeanIOU)	30.80(NDS) 21.66(MAP) 51.47(MeanIOU)	30.26(NDS) 21.56(MAP) 50.99(MeanIOU)	nuscenes det && seg	6x3x512x960, 6x128x128x2	9.52	112.59
bev_lss_efficientnetb0_multitask_nuscenes	efficientnetb0	30.06(NDS) 20.62(MAP) 51.80(MeanIOU)	30.10(NDS) 20.51(MAP) 51.78(MeanIOU)	30.08(NDS) 20.46(MAP) 51.47(MeanIOU)	nuscenes det && seg	6x3x256x704, 10x128x128x2, 10x128x128x2	6.90	160.88
bev_gkt_mixvargenet_multitask_nuscenes	MixVarGENet	28.10(NDS) 19.91(MAP) 48.52(MeanIOU)	27.98(NDS) 19.99(MAP) 48.42(MeanIOU)	27.90(NDS) 20.00(MAP) 48.35(MeanIOU)	nuscenes det && seg	6x3x512x960, 6x64x64x2, 6x64x64x2, 6x64x64x2, 6x64x64x2, 6x64x64x2, 6x64x64x2, 6x64x64x2, 6x64x64x2, 6x64x64x2	16.31	64.30
bev_ipm_4d_efficientnetb0_multitask_nuscenes	efficientnetb0	37.21(NDS) 22.00(MAP) 52.87(MeanIOU)	37.32(NDS) 22.21(MAP) 53.83(MeanIOU)	37.34(NDS) 22.15(MAP) 53.87(MeanIOU)	nuscenes det && seg	6x3x512x960, 6x128x128x2, 1x64x128x128, 1x128x128x2	9.83	108.87
detr3d_efficientnetb3_nuscenes	efficientnetb3	33.04(NDS) 27.52(MAP)	32.84(NDS) 27.14(MAP)	32.81(NDS) 27.06(MAP)	nuscenes det	6x3x512x1408	37.63	26.92
petr_efficientnetb3_nuscenes	efficientnetb3	37.65(NDS) 30.38(MAP)	37.26(NDS) 29.29(MAP)	37.40(NDS) 29.33(MAP)	nuscenes det	6x3x512x1408	77.10	13.05
bevformer_tiny_resnet50_detection_nuscenes	resnet50	37.12(NDS) 26.79(MAP)	37.16(NDS) 26.50(MAP)	37.15(NDS) 26.59(MAP)	nuscenes det	6x3x480x800, 1x2500x256, 1x50x50x2, 6x20x32x2, 1x100x50x2, 6x640x4x2, 1x2500x1	28.29(J6M)	35.90(J6M)
bev_cft_efficientnetb3_nuscenes	efficientnetb3	32.79(NDS) 24.79(MAP)	32.50(NDS) 24.47(MAP)	32.42(NDS) 24.46(MAP)	nuscenes det	6x3x512x1408	36.50	27.75
bev_sparse_henet_tinym_nuscenes	henet_tinym	54.19(NDS) 43.38(MAP)	52.23(NDS) 42.17(MAP)	-	nuscenes det	6x3x256x704, 6x4x4, 1x384x11, 1x384x256	12.92(J6M)	79.68(J6M)

Keypoint Detection

HeatmapKeypointModel

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
keypoint_efficientnetb0_carfusion	efficientnetb0	94.33	94.30	94.31	carfusion	1x3x128x128	0.45	4550.72

Trajectory Prediction

DenseTNT

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
densetnt_vectornet_argoverse1	vectornet	1.2974	1.2989	1.3038	argoverse 1	30x9x19x32, 30x11x9x64, 30x1x1x96, 30x2x1x2048, 30x1x1x2048	11.30	144.725382

QCNet

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
qcnet_oe_argoverse2	-	80.09	79.54	78.21	argoverse 2	输入见下方list	3.62(J6M)	247.74

注解

qcnet_oe_argoverse2 的指标是 HitRate 这项。

qcnet_oe_argoverse2 模型输入shape为:

1x30x10, 1x10x30x30, 1x30x1, 1x1x30x1, 1x1x30x1, 1x1x30x1, 1x1x30x1, 1x1x30x80, 1x1x30x80, 1x1x30x80, 1x1x30x6, 1x1x30x6, 1x1x30x6, 1x1x30x6, 1x1x30x30, 1x1x30x30, 1x1x30x30, 1x30x2x128, 1x30x6x128, 1x80, 1x1x80x80, 1x1x80x80, 1x1x80x80, 1x1x80x50, 1x1x80x50, 1x1x80x50, 1x80x80, 1x80x50, 1x30x30, 1x30x1, 1x80x80

Occupancy Prediction

FlashOcc

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
flashocc_henet_lss_occ3d_nuscenes	henet_tinym_imagenet	0.3674	0.3657	0.3693	occ3d_nuscenes	6x3x512x960, 10x128x128x2, 10x128x128x2	8.39(J6M)	119.23(J6M)

Online Map Construction

MapTROE

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
maptroe_henet_tinym_bevformer_nuscenes	henet_tinym_imagenet	0.6632	0.6577	0.6567	nuscenes	6x3x480x800, 1x1x50x100, 6x20x100x2, 1x100x100x2, 6x2000x4x2, 1x5000x1	11.03(J6M)	93.77(J6M)
maptroe_sparse_henet_tinym_nuscenes	henet_tinym_imagenet	0.5982	0.5999	0.5992	nuscenes	6x3x256x704, 6x4x4	13.22(J6M)	77.14(J6M)

Lidar Fusion

LidarFusion

network	backbone	float	qat	quantization	dataset	input shape	bpu latency (ms)	FPS
bevfusion_pointpillar_henet_multisensor_multitask_nuscenes	henet_tinym_imagenet	64.28(NDS) 58.09(MAP) 51.77(MIOU)	62.91(NDS) 57.48(MAP) 52.51(MIOU)	-	nuscenes det && occ3d	1x5x20x40000, 40000x4, 6x3x512x960, 1x256x128x2, 6x5120x2x2, 1x16384x1	24.87(J6M)	41.21(J6M)
bev_sparse_lidar_fusion_henet_tinym_nuscenes	henet_tinym_imagenet	66.64(NDS) 61.16(MAP)	66.31(NDS), 60.70(mAP)	65.96(NDS) 60.35(mAP)	nuscenes det	6x3x256x704, 6x4x4, 1x384x11, 1x384x256, 1x5x20x40000, 40000x4	19.16(J6M)	52.20(J6M)

#模型仓库modelzoo

#Classification

#Detection

#Segmentation

#Lidar

#Lane Detection

#Multiple Object Track

#Binocular depth estimation

#Bev

#Keypoint Detection

#Trajectory Prediction

#Occupancy Prediction

#Online Map Construction

#Lidar Fusion