Rspress

# #S600 模型性能Benchmark

- 测试开发板：S600

- 模型来源：OE包内 `samples/ucp_tutorial/dnn/ai_benchmark` 路径下的模型

- 运行环境：Linux

## #模型精度

MODEL NAME | INPUT SIZE | ACCURACY | Dataset
--- | --- | --- | ---
ResNet50 | 1x3x224x224 | Top1: 0.7704(FLOAT)/0.7661(INT8) | ImageNet
GoogleNet | 1x3x224x224 | Top1: 0.7018(FLOAT)/0.6995(INT8) | ImageNet
EfficientNet_Lite1 | 1x240x240x3 | Top1: 0.7652(FLOAT)/0.7602(INT8) | ImageNet
EfficientNet_Lite2 | 1x260x260x3 | Top1: 0.7734(FLOAT)/0.7696(INT8) | ImageNet
EfficientNet_Lite3 | 1x280x280x3 | Top1: 0.7917(FLOAT)/0.7885(INT8) | ImageNet
EfficientNet_Lite4 | 1x300x300x3 | Top1: 0.8063(FLOAT)/0.8041(INT8) | ImageNet
Vargconvnet | 1x3x224x224 | Top1: 0.7793(FLOAT)/0.7765(INT8) | ImageNet
Efficientnasnet_m | 1x3x300x300 | Top1: 0.7935(FLOAT)/0.7927(INT8) | ImageNet
Efficientnasnet_s | 1x3x280x280 | Top1: 0.7441(FLOAT)/0.7512(INT8) | ImageNet
ResNet18 | 1x3x224x224 | Top1: 0.7170(FLOAT)/0.7159(INT8) | ImageNet
YOLOv2_Darknet19 | 1x3x608x608 | [IoU=0.50:0.95]= 0.2760(FLOAT)/0.2707(INT8) | COCO
YOLOv3_Darknet53 | 1x3x416x416 | [IoU=0.50:0.95]= 0.3370(FLOAT)/0.3370(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)/0.3260(INT8) | COCO
Deeplabv3plus_efficientnetb0 | 1x3x1024x2048 | mIoU: 0.7630(FLOAT)/0.7569(INT8) | Cityscapes
Fastscnn_efficientnetb0 | 1x3x1024x2048 | mIoU: 0.6997(FLOAT)/0.6909(INT8) | Cityscapes
Deeplabv3plus_efficientnetm1 | 1x3x1024x2048 | mIoU: 0.7794(FLOAT)/0.7756(INT8) | Cityscapes
Deeplabv3plus_efficientnetm2 | 1x3x1024x2048 | mIoU: 0.7882(FLOAT)/0.7854(INT8) | Cityscapes
Bev_gkt_mixvargenet_multitask | image: 6x3x512x960 points(0-8): 6x64x64x2 | NDS: 0.2810(FLOAT)/0.2788(INT8) MeanIOU: 0.4852(FLOAT)/0.4839(INT8) mAP: 0.1990(FLOAT)/0.1992(INT8) | Nuscenes
Bev_ipm_4d_efficientnetb0_multitask | image: 6x3x512x960 points: 6x128x128x2 prev_feat: 1x164x28x128 prev_point: 1x128x128x2 | NDS: 0.3722(FLOAT)/0.3723(INT8) MeanIOU: 0.5287(FLOAT)/0.5389(INT8) mAP: 0.2201(FLOAT)/0.2217(INT8) | Nuscenes
Bev_ipm_efficientnetb0_multitask | image: 6x3x512x960 points: 6x128x128x2 | NDS: 0.3055(FLOAT)/0.3041(INT8) MeanIOU: 0.5145(FLOAT)/0.5103(INT8) mAP: 0.2170(FLOAT)/0.2166(INT8) | Nuscenes
Bev_lss_efficientnetb0_multitask | image: 6x3x256x704 points(0&1): 10x128x128x2 | NDS: 0.3006(FLOAT)/0.3008(INT8) MeanIOU: 0.5180(FLOAT)/0.5172(INT8) mAP: 0.2061(FLOAT)/0.2043(INT8) | Nuscenes
Detr3d_efficientnetb3 | coords(0-3): 6x4x256x2 image: 6x3x512x1408 masks: 1x4x256x24 | NDS: 0.3304(FLOAT)/0.3306(INT8) mAP: 0.2753(FLOAT)/0.2742(INT8) | Nuscenes
Petr_efficientnetb3 | image: 6x3x512x1408 pos_embed: 1x96x44x256 | NDS: 0.3765(FLOAT)/0.3748(INT8) mAP: 0.3038(FLOAT)/0.2942(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)/0.3700(INT8) mAP: 0.2673(FLOAT)/0.2644(INT8) | Nuscenes
Flashocc_henet_lss_occ3d_nuscenes | img: 6x3x512x960 points: 10x128x128x2 points_depth: 10x128x128x2 | mIoU: 0.3675(FLOAT)/0.3685(INT8) | Nuscenes
Horizon_swin_transformer | 1x3x224x224 | Top1: 0.8024(FLOAT)/0.7982(INT8) | ImageNet
Vargnetv2 | 1x3x224x224 | Top1: 0.7342(FLOAT)/0.7332(INT8) | ImageNet
Vit_small | 1x3x224x224 | Top1: 0.7950(FLOAT)/0.7924(INT8) | ImageNet
Centerpoint_pointpillar | points: 300000x5 voxel_feature: 1x5x20x40000 coors: 40000x4 | NDS: 0.5832(FLOAT)/0.5820(INT8) mAP: 0.4804(FLOAT)/0.4783(INT8) | Nuscenes
Detr_efficientnetb3 | 1x3x800x1333 | [IoU=0.50:0.95]= 0.3720(FLOAT)/0.3584(INT8) | MS COCO
Detr_resnet50 | 1x3x800x1333 | [IoU=0.50:0.95]= 0.3569(FLOAT)/0.3168(INT8) | MS COCO
FCOS3D_efficientnetb0 | 1x3x512x896 | NDS: 0.3061(FLOAT)/0.3022(INT8) mAP: 0.2133(FLOAT)/0.2067(INT8) | nuscenes
Fcos_efficientnetb0 | 1x3x512x512 | [IoU=0.50:0.95]= 0.3626(FLOAT)/0.3562(INT8) | MS COCO
Ganet_mixvargenet | 1x3x320x800 | F1Score: 0.7949(FLOAT)/0.7884(INT8) | CuLane
Deformable_detr_resnet50 | 1x3x800x1333 | [IoU=0.50:0.95]= 0.4413(FLOAT)/0.4499(INT8) | MS COCO
Stereonetplus_mixvargenet | 2x3x544x960 | EPE: 1.1270(FLOAT)/1.1336(INT8) | SceneFlow
Centerpoint_mixvargnet_multitask | points: 300000x5 voxel_feature: 1x5x20x40000 coors: 40000x4 | NDS: 0.5809(FLOAT)/0.5766(INT8) MeanIOU: 0.9128(FLOAT)/0.9126(INT8) mAP: 0.4727(FLOAT)/0.4649(INT8) | Nuscenes
Unet_mobilenetv1 | 1x3x1024x2048 | mIoU: 0.6801(FLOAT)/0.6764(INT8) | Cityscapes
Densetnt_vectornet | goals_2d: 30x1x2048x2 goals_2d_mask: 30x1x2048x1 instance_mask: 30x1x96x1 lane_feat: 30x9x64x11 traj_feat: 30x19x32x9 | minFDA: 1.2975(FLOAT)/1.2989(INT8) | Argoverse 1
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)/0.6569(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)/0.7979(INT8) | Argoverse 2

## #模型性能

- FPS = 每秒帧率。此数据使用 `hrt_model_exec` 工具多线程运行获取，详细使用方法请参考 hrt_model_exec工具介绍-模型性能分析 章节的介绍。

- Latency = 推理耗时，单位为ms（毫秒）。此数据使用 `hrt_model_exec` 工具单线程运行获取，详细使用方法请参考 hrt_model_exec工具介绍-模型性能分析 章节的介绍。

注解

在进行性能评测前，请参考如下方式，通过环境变量设置模型推理中CPU算子推理线程池的线程数量为12：

`export HB_UCP_ENABLE_CPU_BACKEND_CORE_NUM=12`

MODEL NAME | INPUT SIZE | Latency(ms) | FPS | FPS Configuration
--- | --- | --- | --- | ---
ResNet50 | 1x3x224x224 | 0.587 | 6519.27 | thread_num:10
GoogleNet | 1x3x224x224 | 0.379 | 18480.92 | thread_num:12
EfficientNet_Lite1 | 1x240x240x3 | 0.382 | 17998.63 | thread_num:12
EfficientNet_Lite2 | 1x260x260x3 | 0.447 | 14056.08 | thread_num:10
EfficientNet_Lite3 | 1x280x280x3 | 0.512 | 11572.69 | thread_num:10
EfficientNet_Lite4 | 1x300x300x3 | 0.656 | 8327.86 | thread_num:10
Vargconvnet | 1x3x224x224 | 0.523 | 12423.31 | thread_num:12
Efficientnasnet_m | 1x3x300x300 | 0.576 | 9260.61 | thread_num:10
Efficientnasnet_s | 1x3x280x280 | 0.376 | 19348.62 | thread_num:12
ResNet18 | 1x3x224x224 | 0.384 | 14316.09 | thread_num:12
YOLOv2_Darknet19 | 1x3x608x608 | 2.229 | 1987.00 | thread_num:8
YOLOv3_Darknet53 | 1x3x416x416 | 2.453 | 1752.26 | thread_num:8
YOLOv5x_v2.0 | 1x3x672x672 | 8.202 | 494.50 | thread_num:8
Centernet_resnet101 | 1x3x512x512 | 2.446 | 1786.98 | thread_num:8
YOLOv3_VargDarknet | 1x3x416x416 | 1.628 | 2673.33 | thread_num:8
Deeplabv3plus_efficientnetb0 | 1x3x1024x2048 | 2.952 | 1468.62 | thread_num:8
Fastscnn_efficientnetb0 | 1x3x1024x2048 | 1.741 | 2647.19 | thread_num:8
Deeplabv3plus_efficientnetm1 | 1x3x1024x2048 | 5.126 | 818.14 | thread_num:8
Deeplabv3plus_efficientnetm2 | 1x3x1024x2048 | 7.330 | 561.92 | thread_num:8
Bev_gkt_mixvargenet_multitask | image: 6x3x512x960 points(0-8): 6x64x64x2 | 6.911 | 605.09 | thread_num:8
Bev_ipm_4d_efficientnetb0_multitask | image: 6x3x512x960 points: 6x128x128x2 prev_feat: 1x164x28x128 prev_point: 1x128x128x2 | 4.615 | 940.42 | thread_num:8
Bev_ipm_efficientnetb0_multitask | image: 6x3x512x960 points: 6x128x128x2 | 4.499 | 967.65 | thread_num:8
Bev_lss_efficientnetb0_multitask | image: 6x3x256x704 points(0&1): 10x128x128x2 | 3.075 | 1454.60 | thread_num:8
Detr3d_efficientnetb3 | coords(0-3): 6x4x256x2 image: 6x3x512x1408 masks: 1x4x256x24 | 15.168 | 258.88 | thread_num:8
Petr_efficientnetb3 | image: 6x3x512x1408 pos_embed: 1x96x44x256 | 21.444 | 187.70 | thread_num:8
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 | 14.164 | 276.44 | thread_num:8
Flashocc_henet_lss_occ3d_nuscenes | img: 6x3x512x960 points: 10x128x128x2 points_depth: 10x128x128x2 | 5.519 | 742.29 | thread_num:8
Horizon_swin_transformer | 1x3x224x224 | 1.694 | 2556.99 | thread_num:8
Vargnetv2 | 1x3x224x224 | 0.332 | 19838.28 | thread_num:12
Vit_small | 1x3x224x224 | 1.092 | 4245.19 | thread_num:8
Centerpoint_pointpillar | points: 300000x5 voxel_feature: 1x5x20x40000 coors: 40000x4 | 7.530 | 1067.16 | thread_num:16
Detr_efficientnetb3 | 1x3x800x1333 | 8.499 | 390.55 | thread_num:8
Detr_resnet50 | 1x3x800x1333 | 10.795 | 317.51 | thread_num:8
FCOS3D_efficientnetb0 | 1x3x512x896 | 1.475 | 3567.10 | thread_num:10
Fcos_efficientnetb0 | 1x3x512x512 | 0.887 | 7227.14 | thread_num:10
Ganet_mixvargenet | 1x3x320x800 | 0.550 | 12439.15 | thread_num:12
Deformable_detr_resnet50 | 1x3x800x1333 | 77.201 | 29.28 | thread_num:8
Stereonetplus_mixvargenet | 2x3x544x960 | 2.022 | 2147.39 | thread_num:8
Centerpoint_mixvargnet_multitask | points: 300000x5 voxel_feature: 1x5x20x40000 coors: 40000x4 | 6.263 | 1638.08 | thread_num:18
Unet_mobilenetv1 | 1x3x1024x2048 | 0.881 | 6401.99 | thread_num:10
Densetnt_vectornet | goals_2d: 30x1x2048x2 goals_2d_mask: 30x1x2048x1 instance_mask: 30x1x96x1 lane_feat: 30x9x64x11 traj_feat: 30x19x32x9 | 3.560 | 1108.38 | thread_num:12
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 | 6.439 | 625.40 | thread_num:8
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 | 2.611 | 1723.45 | thread_num:8

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