98.16 98.99 97.73 [160, 120] YuNet with ['face_detection_yunet_2022mar.onnx']
93.21 93.81 89.15 [160, 120] YuNet with ['face_detection_yunet_2022mar_int8.onnx']
2093.12 2093.02 2092.54 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx']
1845.87 1871.17 1646.65 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx']
811.32 811.47 810.80 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx']
743.24 750.04 688.44 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx']
636.22 635.89 635.43 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx']
588.83 594.01 550.49 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx']
2157.86 2157.82 2156.99 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx']
2091.13 2091.61 2090.72 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx']
1583.25 1634.14 1176.19 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx']
1450.55 1177.07 1176.19 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx']
1272.81 1226.00 873.94 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx']
15753.56 15751.29 15748.97 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx']
11610.11 12023.99 8290.04 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx']
4300.13 4301.43 4298.29 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx']
3360.20 3357.84 3356.70 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx']
2961.58 3005.40 2641.27 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx']
49994.75 49968.90 49958.48 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx']
35966.66 37391.40 24670.30 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx']
19800.14 19816.02 19754.69 [1280, 720] DaSiamRPN with ['object_tracking_dasiamrpn_kernel_cls1_2021nov.onnx', 'object_tracking_dasiamrpn_kernel_r1_2021nov.onnx', 'object_tracking_dasiamrpn_model_2021nov.onnx']
1191.81 1192.42 1191.40 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx']
1162.64 1165.77 1138.35 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx']
1835.97 1836.24 1835.34 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx']
14886.02 14884.48 14881.73 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx']
10491.63 10930.80 6975.34 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx']
65681.91 65674.89 65612.09 [640, 480] DB with ['text_detection_DB_IC15_resnet18_2021sep.onnx']
65630.56 65652.90 65531.21 [640, 480] DB with ['text_detection_DB_TD500_resnet18_2021sep.onnx']
3248.11 3242.59 3241.18 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx']
3330.69 3350.38 3241.18 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx']
3277.07 3427.65 3195.84 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx']
[ WARN:0@17240.397] global onnx_graph_simplifier.cpp:804 getMatFromTensor DNN: load FP16 model as FP32 model, and it takes twice the FP16 RAM requirement.
3263.48 3319.83 3195.84 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx']
3258.78 3196.90 3195.84 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx']
3090.12 3224.64 2353.81 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx']
3001.31 3237.93 2353.81 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx']
2887.05 3224.12 2206.89 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx']
