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  • 3.8.1

    • J6E/M Benchmark of Model Performance

    • Test Board: J6E

    • Range: Models under the samples/ucp_tutorial/dnn/ai_benchmark/j6 path in the OE package

    • Runtime Environment: Linux

    MODEL ACCURACY

    MODEL NAMEINPUT SIZEACCURACYDataset
    ResNet50

    1x3x224x224

    Top1:

    0.7704(FLOAT)/0.7665(INT8)

    		ImageNet
    GoogleNet

    1x3x224x224

    Top1:

    0.7018(FLOAT)/0.6998(INT8)

    		ImageNet
    EfficientNet_Lite1

    1x240x240x3

    Top1:

    0.7652(FLOAT)/0.7614(INT8)

    		ImageNet
    EfficientNet_Lite2

    1x260x260x3

    Top1:

    0.7734(FLOAT)/0.7697(INT8)

    		ImageNet
    EfficientNet_Lite3

    1x280x280x3

    Top1:

    0.7917(FLOAT)/0.7896(INT8)

    		ImageNet
    EfficientNet_Lite4

    1x300x300x3

    Top1:

    0.8063(FLOAT)/0.8043(INT8)

    		ImageNet
    Vargconvnet

    1x3x224x224

    Top1:

    0.7793(FLOAT)/0.7770(INT8)

    		ImageNet
    Efficientnasnet_m

    1x3x300x300

    Top1:

    0.7935(FLOAT)/0.7923(INT8)

    		ImageNet
    Efficientnasnet_s

    1x3x280x280

    Top1:

    0.7441(FLOAT)/0.7524(INT8)

    		ImageNet
    ResNet18

    1x3x224x224

    Top1:

    0.6976(FLOAT)/0.6938(INT8)

    		ImageNet
    YOLOv2_Darknet19

    1x3x608x608

    [IoU=0.50:0.95]=

    0.2760(FLOAT)/0.2700(INT8)

    		COCO
    YOLOv3_Darknet53

    1x3x416x416

    [IoU=0.50:0.95]=

    0.3370(FLOAT)/0.3360(INT8)

    		COCO
    YOLOv5x_v2.0

    1x3x672x672

    [IoU=0.50:0.95]=

    0.4810(FLOAT)/0.4670(INT8)

    		COCO
    Centernet_resnet101

    1x3x512x512

    [IoU=0.50:0.95]=

    0.3420(FLOAT)/0.3270(INT8)

    		COCO
    YOLOv3_VargDarknet

    1x3x416x416

    [IoU=0.50:0.95]=

    0.3280(FLOAT)/0.3270(INT8)

    		COCO
    Deeplabv3plus_efficientnetb0

    1x3x1024x2048

    mIoU:

    0.7630(FLOAT)/0.7571(INT8)

    		Cityscapes
    Fastscnn_efficientnetb0

    1x3x1024x2048

    mIoU:

    0.6997(FLOAT)/0.6914(INT8)

    		Cityscapes
    Deeplabv3plus_efficientnetm1

    1x3x1024x2048

    mIoU:

    0.7794(FLOAT)/0.7754(INT8)

    		Cityscapes
    Deeplabv3plus_efficientnetm2

    1x3x1024x2048

    mIoU:

    0.7882(FLOAT)/0.7854(INT8)

    		Cityscapes
    Bev_lss_efficientnetb0_multitask

    image:

    6x3x256x704

    points(0&1):

    10x128x128x2

    NDS:

    0.3006(FLOAT)/0.3003(INT8)

    MeanIOU:

    0.5180(FLOAT)/0.5171(INT8)

    mAP:

    0.2061(FLOAT)/0.2046(INT8)

    		Nuscenes
    Detr3d_efficientnetb3

    coords(0-3):

    6x4x256x2

    image:

    6x3x512x1408

    masks:

    1x4x256x24

    NDS:

    0.3304(FLOAT)/0.3309(INT8)

    mAP:

    0.2753(FLOAT)/0.2743(INT8)

    		Nuscenes
    Petr_efficientnetb3

    image:

    6x3x512x1408

    pos_embed:

    1x96x44x256

    NDS:

    0.3765(FLOAT)/0.3745(INT8)

    mAP:

    0.3038(FLOAT)/0.2937(INT8)

    		Nuscenes
    Bevformer_tiny_resnet50_detection

    img:

    6x3x480x800

    prev_bev:

    1x2500x256

    prev_bev_ref:

    1x50x50x2

    queries_rebatch_grid:

    6x20x32x2

    restore_bev_grid:

    1x100x50x2

    reference_points_rebatch:

    6x640x4x2

    bev_pillar_counts:

    1x2500x1

    NDS:

    0.3713(FLOAT)/0.3695(INT8)

    mAP:

    0.2673(FLOAT)/0.2645(INT8)

    		Nuscenes
    Flashocc_henet_lss_occ3d_nuscenes

    img:

    6x3x512x960

    points:

    10x128x128x2

    points_depth:

    10x128x128x2

    mIoU:

    0.3675(FLOAT)/0.3687(INT8)

    		Nuscenes
    Centerpoint_pointpillar

    points:

    300000x5

    voxel_feature:

    1x5x20x40000

    coors:

    40000x4

    NDS:

    0.5832(FLOAT)/0.5816(INT8)

    mAP:

    0.4804(FLOAT)/0.4784(INT8)

    		Nuscenes
    FCOS3D_efficientnetb0

    1x3x512x896

    NDS:

    0.3061(FLOAT)/0.3026(INT8)

    mAP:

    0.2133(FLOAT)/0.2094(INT8)

    		nuscenes
    Ganet_mixvargenet

    1x3x320x800

    F1Score:

    0.7949(FLOAT)/0.7883(INT8)

    		CuLane
    Deformable_detr_resnet50

    1x3x800x1333

    [IoU=0.50:0.95]=

    0.4413(FLOAT)/0.4497(INT8)

    		MS COCO
    Unet_mobilenetv1

    1x3x1024x2048

    mIoU:

    0.6801(FLOAT)/0.6767(INT8)

    		Cityscapes
    Maptroe_henet_tinym_bevformer

    img:

    6x3x480x800

    osm_mask:

    1x1x50x100

    queries_rebatch_grid:

    6x20x100x2

    restore_bev_grid:

    1x100x100x2

    reference_points_rebatch:

    6x2000x4x2

    bev_pillar_counts:

    1x5000x1

    mAP:

    0.6633(FLOAT)/0.6568(INT8)

    		Nuscenes
    Qcnet_oe

    valid_mask:

    1x30x10

    valid_mask_a2a:

    1x10x30x30

    agent_type:

    1x30x1

    x_a_cur:

    1x1x30x1,1x1x30x1,1x1x30x1,1x1x30x1

    r_pl2a_cur:

    1x1x30x80,1x1x30x80,1x1x30x80

    r_t_cur:

    1x1x30x6,1x1x30x6,1x1x30x6,1x1x30x6

    r_a2a_cur:

    1x1x30x30,1x1x30x30,1x1x30x30

    x_a_mid_emb:

    1x30x2x128

    x_a:

    1x30x6x128

    pl_type,is_intersection:

    1x80

    r_pl2pl:

    1x1x80x80,1x1x80x80,1x1x80x80

    r_pt2pl:

    1x1x80x50,1x1x80x50,1x1x80x50

    mask_pl2pl:

    1x80x80

    magnitude,pt_type,side,mask:

    1x80x50

    mask_a2m:

    1x30x30

    mask_dst:

    1x30x1

    type_pl2pl:

    1x80x80

    hitrate:

    0.8026(FLOAT)/0.7982(INT8)

    		Argoverse 2

    MODEL PERFORMANCE

    MODEL NAMEINPUT SIZELatency(ms)FPSFPS Configuration
    ResNet50

    1x3x224x224

    		1.1481156.52thread_num:4
    GoogleNet

    1x3x224x224

    		0.6262849.10thread_num:4
    EfficientNet_Lite1

    1x240x240x3

    		0.6103239.57thread_num:4
    EfficientNet_Lite2

    1x260x260x3

    		0.7072477.68thread_num:4
    EfficientNet_Lite3

    1x280x280x3

    		0.8331900.66thread_num:4
    EfficientNet_Lite4

    1x300x300x3

    		1.0701303.55thread_num:4
    Vargconvnet

    1x3x224x224

    		0.9691455.76thread_num:4
    Efficientnasnet_m

    1x3x300x300

    		0.9661455.76thread_num:4
    Efficientnasnet_s

    1x3x280x280

    		0.5903207.72thread_num:4
    ResNet18

    1x3x224x224

    		0.6882386.00thread_num:4
    YOLOv2_Darknet19

    1x3x608x608

    		4.671227.67thread_num:4
    YOLOv3_Darknet53

    1x3x416x416

    		5.049209.86thread_num:4
    YOLOv5x_v2.0

    1x3x672x672

    		16.29062.36thread_num:4
    Centernet_resnet101

    1x3x512x512

    		5.638186.73thread_num:4
    YOLOv3_VargDarknet

    1x3x416x416

    		3.561306.42thread_num:4
    Deeplabv3plus_efficientnetb0

    1x3x1024x2048

    		6.899150.81thread_num:4
    Fastscnn_efficientnetb0

    1x3x1024x2048

    		4.368244.50thread_num:4
    Deeplabv3plus_efficientnetm1

    1x3x1024x2048

    		11.19391.46thread_num:4
    Deeplabv3plus_efficientnetm2

    1x3x1024x2048

    		15.77564.40thread_num:4
    Bev_lss_efficientnetb0_multitask

    image:

    6x3x256x704

    points(0&1):

    10x128x128x2

    		5.825186.28thread_num:4
    Detr3d_efficientnetb3

    coords(0-3):

    6x4x256x2

    image:

    6x3x512x1408

    masks:

    1x4x256x24

    		30.99432.50thread_num:4
    Petr_efficientnetb3

    image:

    6x3x512x1408

    pos_embed:

    1x96x44x256

    		48.20320.85thread_num:4
    Bevformer_tiny_resnet50_detection

    img:

    6x3x480x800

    prev_bev:

    1x2500x256

    prev_bev_ref:

    1x50x50x2

    queries_rebatch_grid:

    6x20x32x2

    restore_bev_grid:

    1x100x50x2

    reference_points_rebatch:

    6x640x4x2

    bev_pillar_counts:

    1x2500x1

    		31.95731.53thread_num:4
    Flashocc_henet_lss_occ3d_nuscenes

    img:

    6x3x512x960

    points:

    10x128x128x2

    points_depth:

    10x128x128x2

    		10.97093.61thread_num:6
    Centerpoint_pointpillar

    points:

    300000x5

    voxel_feature:

    1x5x20x40000

    coors:

    40000x4

    		12.824122.84thread_num:4
    FCOS3D_efficientnetb0

    1x3x512x896

    		2.663449.64thread_num:4
    Ganet_mixvargenet

    1x3x320x800

    		0.9271578.03thread_num:4
    Deformable_detr_resnet50

    1x3x800x1333

    		200.0365.01thread_num:6
    Unet_mobilenetv1

    1x3x1024x2048

    		1.611811.54thread_num:4
    Maptroe_henet_tinym_bevformer

    img:

    6x3x480x800

    osm_mask:

    1x1x50x100

    queries_rebatch_grid:

    6x20x100x2

    restore_bev_grid:

    1x100x100x2

    reference_points_rebatch:

    6x2000x4x2

    bev_pillar_counts:

    1x5000x1

    		14.48170.53thread_num:4
    Qcnet_oe

    valid_mask:

    1x30x10

    valid_mask_a2a:

    1x10x30x30

    agent_type:

    1x30x1

    x_a_cur:

    1x1x30x1,1x1x30x1,1x1x30x1,1x1x30x1

    r_pl2a_cur:

    1x1x30x80,1x1x30x80,1x1x30x80

    r_t_cur:

    1x1x30x6,1x1x30x6,1x1x30x6,1x1x30x6

    r_a2a_cur:

    1x1x30x30,1x1x30x30,1x1x30x30

    x_a_mid_emb:

    1x30x2x128

    x_a:

    1x30x6x128

    pl_type,is_intersection:

    1x80

    r_pl2pl:

    1x1x80x80,1x1x80x80,1x1x80x80

    r_pt2pl:

    1x1x80x50,1x1x80x50,1x1x80x50

    mask_pl2pl:

    1x80x80

    magnitude,pt_type,side,mask:

    1x80x50

    mask_a2m:

    1x30x30

    mask_dst:

    1x30x1

    type_pl2pl:

    1x80x80

    		4.757235.41thread_num:4