| # OpenCV Zoo Benchmark | |
| Benchmarking the speed of OpenCV DNN inferring different models in the zoo. Result of each model includes the time of its preprocessing, inference and postprocessing stages. | |
| Data for benchmarking will be downloaded and loaded in [data](./data) based on given config. | |
| ## Preparation | |
| 1. Install `python >= 3.6`. | |
| 2. Install dependencies: `pip install -r requirements.txt`. | |
| 3. Download data for benchmarking. | |
| 1. Download all data: `python download_data.py` | |
| 2. Download one or more specified data: `python download_data.py face text`. Available names can be found in `download_data.py`. | |
| 3. You can also download all data from https://pan.baidu.com/s/18sV8D4vXUb2xC9EG45k7bg (code: pvrw). Please place and extract data packages under [./data](./data). | |
| ## Benchmarking | |
| **Linux**: | |
| ```shell | |
| export PYTHONPATH=$PYTHONPATH:.. | |
| # Single config | |
| python benchmark.py --cfg ./config/face_detection_yunet.yaml | |
| # All configs | |
| python benchmark.py --all | |
| # All configs but only fp32 models (--fp32, --fp16, --int8 --int8bq are available for now) | |
| python benchmark.py --all --fp32 | |
| # All configs but exclude some of them (fill with config name keywords, not sensitive to upper/lower case, seperate with colons) | |
| python benchmark.py --all --cfg_exclude wechat | |
| python benchmark.py --all --cfg_exclude wechat:crnn | |
| # All configs but exclude some of the models (fill with exact model names, sensitive to upper/lower case, seperate with colons) | |
| python benchmark.py --all --model_exclude license_plate_detection_lpd_yunet_2023mar_int8.onnx:human_segmentation_pphumanseg_2023mar_int8.onnx | |
| # All configs with overwritten backend and target (run with --help to get available combinations) | |
| python benchmark.py --all --cfg_overwrite_backend_target 1 | |
| ``` | |
| **Windows**: | |
| - CMD | |
| ```shell | |
| set PYTHONPATH=%PYTHONPATH%;.. | |
| python benchmark.py --cfg ./config/face_detection_yunet.yaml | |
| ``` | |
| - PowerShell | |
| ```shell | |
| $env:PYTHONPATH=$env:PYTHONPATH+";.." | |
| python benchmark.py --cfg ./config/face_detection_yunet.yaml | |
| ``` | |
| ## Detailed Results | |
| Benchmark is done with latest opencv-python & opencv-contrib-python (current 4.10.0) on the following platforms. Some models are excluded because of support issues. | |
| ### Intel 12700K | |
| Specs: [details](https://www.intel.com/content/www/us/en/products/sku/134594/intel-core-i712700k-processor-25m-cache-up-to-5-00-ghz/specifications.html) | |
| - CPU: 8 Performance-cores, 4 Efficient-cores, 20 threads | |
| - Performance-core: 3.60 GHz base freq, turbo up to 4.90 GHz | |
| - Efficient-core: 2.70 GHz base freq, turbo up to 3.80 GHz | |
| CPU: | |
| ``` | |
| $ python3 benchmark.py --all | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 0.69 0.70 0.68 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 0.79 0.80 0.68 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 5.09 5.13 4.96 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 6.50 6.79 4.96 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 1.79 1.76 1.75 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 2.92 3.11 1.75 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 2.40 2.43 2.37 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 3.11 3.15 2.37 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 5.59 5.56 5.28 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 6.07 6.22 5.28 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 3.13 3.14 3.05 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 3.04 3.02 2.92 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 3.46 3.03 2.92 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 3.84 3.77 2.92 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 19.47 19.47 19.08 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 21.52 21.86 19.08 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 5.68 5.66 5.51 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 7.41 7.36 5.51 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 41.02 40.99 40.86 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 42.23 42.30 40.86 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 78.77 79.76 77.16 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 75.69 75.58 72.57 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 4.01 3.84 3.79 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 5.35 5.41 5.22 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 6.73 6.85 5.22 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 7.65 7.65 7.55 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 15.56 15.57 15.10 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 16.67 16.57 15.10 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 6.33 6.63 6.14 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 1.19 1.30 1.07 [100, 100] WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel'] | |
| 18.76 19.59 18.48 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 18.59 19.33 18.12 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 22.05 18.60 18.12 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 24.47 25.06 18.12 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 10.61 10.66 10.50 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 11.03 11.23 10.50 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 9.85 11.62 7.74 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 10.02 9.71 7.74 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 9.53 7.83 7.74 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 9.68 9.21 7.74 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 9.85 10.63 7.74 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 9.63 9.28 7.74 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### Raspberry Pi 4B | |
| Specs: [details](https://www.raspberrypi.com/products/raspberry-pi-4-model-b/specifications/) | |
| - CPU: Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5 GHz. | |
| CPU: | |
| ``` | |
| $ python3 benchmark.py --all | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 6.23 6.27 6.18 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 6.68 6.73 6.18 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 68.82 69.06 68.45 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 87.42 89.84 68.45 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 27.81 27.77 27.67 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 35.71 36.67 27.67 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 42.58 42.41 42.25 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 46.49 46.95 42.25 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 71.35 71.62 70.78 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 73.81 74.23 70.78 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 64.20 64.30 63.98 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 57.91 58.41 52.53 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 61.35 52.83 52.53 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 61.49 61.28 52.53 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 420.93 420.73 419.04 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 410.96 395.74 364.68 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 153.87 152.71 140.85 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 157.86 145.90 140.85 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 214.59 211.95 210.98 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 215.09 238.39 208.18 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 1614.13 1639.80 1476.58 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 1597.92 1599.12 1476.58 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 48.55 46.87 41.75 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 97.05 95.40 80.93 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 112.39 116.22 80.93 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 105.60 113.27 88.55 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 478.89 498.05 444.14 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 442.56 477.87 369.59 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 116.15 120.13 106.81 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 5.90 5.90 5.81 [100, 100] WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel'] | |
| 325.02 325.88 303.55 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 323.54 332.45 303.55 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 372.32 328.56 303.55 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 407.90 411.97 303.55 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 235.70 236.07 234.87 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 240.95 241.14 234.87 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 226.09 247.02 200.44 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 229.25 224.63 200.44 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 224.10 201.29 200.44 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 223.58 219.82 200.44 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 225.60 243.89 200.44 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 220.97 223.16 193.91 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### Jetson Nano B01 | |
| Specs: [details](https://developer.nvidia.com/embedded/jetson-nano-developer-kit) | |
| - CPU: Quad-core ARM A57 @ 1.43 GHz | |
| - GPU: 128-core NVIDIA Maxwell | |
| CPU: | |
| ``` | |
| $ python3 benchmark.py --all | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 5.62 5.54 5.52 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 6.14 6.24 5.52 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 64.80 64.95 64.60 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 78.31 79.85 64.60 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 26.54 26.61 26.37 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 33.96 34.85 26.37 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 38.45 41.45 38.20 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 42.62 43.20 38.20 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 64.95 64.85 64.73 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 72.39 73.16 64.73 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 65.72 65.98 65.59 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 56.66 57.56 49.10 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 62.09 49.27 49.10 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 62.17 62.02 49.10 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 346.78 348.06 345.53 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 371.11 373.54 345.53 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 134.36 134.33 133.45 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 140.62 140.94 133.45 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 215.67 216.76 214.69 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 216.58 216.78 214.69 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 1209.12 1213.05 1201.68 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 1240.02 1249.95 1201.68 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 48.39 47.38 45.00 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 75.30 75.25 74.96 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 83.83 84.99 74.96 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 87.65 87.59 87.37 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 356.78 357.77 355.69 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 346.84 351.10 335.96 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 75.20 79.36 73.71 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 5.56 5.56 5.48 [100, 100] WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel'] | |
| 209.80 210.04 208.84 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 209.60 212.74 208.49 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 254.56 211.17 208.49 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 286.57 296.56 208.49 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 252.60 252.48 252.21 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 259.28 261.38 252.21 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 245.18 266.94 220.49 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 247.72 244.25 220.49 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 241.63 221.43 219.06 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 243.46 238.98 219.06 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 246.87 256.05 219.06 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 243.37 238.90 219.06 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| GPU (CUDA-FP32): | |
| <!-- config wechat is excluded due to its api does not support setting backend and target --> | |
| ``` | |
| $ python3 benchmark.py --all --fp32 --cfg_exclude wechat --cfg_overwrite_backend_target 1 | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_CUDA | |
| target=cv.dnn.DNN_TARGET_CUDA | |
| mean median min input size model | |
| 10.99 10.71 9.64 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 25.25 25.81 24.54 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 13.97 14.01 13.72 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 24.47 24.36 23.69 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 67.25 67.99 64.90 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 28.96 28.92 28.85 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 28.61 28.45 27.92 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 98.80 100.11 94.57 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 54.88 56.51 52.78 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 63.86 63.59 63.35 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 371.32 374.79 367.78 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 47.26 45.56 44.69 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 37.61 37.61 33.64 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 37.39 37.71 37.03 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 90.84 91.34 85.77 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 76.44 78.00 74.90 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 112.68 112.21 110.42 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 112.48 111.86 110.04 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 43.99 43.33 41.68 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 44.97 44.42 41.68 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 36.77 46.38 21.77 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| ``` | |
| GPU (CUDA-FP16): | |
| <!-- config wechat is excluded due to its api does not support setting backend and target --> | |
| ``` | |
| $ python3 benchmark.py --all --fp32 --cfg_exclude wechat --cfg_overwrite_backend_target 2 | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_CUDA | |
| target=cv.dnn.DNN_TARGET_CUDA_FP16 | |
| mean median min input size model | |
| 25.05 25.05 24.95 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 117.82 126.96 113.17 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 88.54 88.33 88.04 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 97.43 97.38 96.98 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 69.40 68.28 66.36 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 120.92 131.57 119.37 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 128.43 128.08 119.37 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 64.90 63.88 62.81 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 370.21 371.97 366.38 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 164.28 164.75 162.94 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 299.22 300.54 295.64 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 49.61 47.58 47.14 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 149.50 151.12 147.24 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 156.59 154.01 153.92 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 43.66 43.64 43.31 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 75.87 77.33 74.38 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 428.97 428.99 426.11 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 428.66 427.46 425.66 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 32.41 31.90 31.68 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 33.42 35.75 31.68 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 29.34 36.44 21.27 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| ``` | |
| ### Khadas VIM3 | |
| Specs: [details](https://www.khadas.com/vim3) | |
| - (SoC) CPU: Amlogic A311D, 2.2 GHz Quad core ARM Cortex-A73 and 1.8 GHz dual core Cortex-A53 | |
| - NPU: 5 TOPS Performance NPU INT8 inference up to 1536 MAC Supports all major deep learning frameworks including TensorFlow and Caffe | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 4.62 4.62 4.53 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 5.24 5.29 4.53 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 55.04 54.55 53.54 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 67.34 67.96 53.54 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 29.50 45.62 26.14 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 35.59 36.22 26.14 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 35.80 35.08 34.76 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 40.32 45.32 34.76 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 71.92 66.92 62.98 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 70.68 72.31 62.98 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 59.27 53.91 52.09 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 52.17 67.58 41.23 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 55.44 47.28 41.23 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 55.83 56.80 41.23 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 335.75 329.39 325.42 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 340.42 335.78 325.42 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 128.58 127.15 124.03 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 125.85 126.47 110.14 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 179.93 170.66 166.76 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 178.61 213.72 164.61 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 1108.12 1100.93 1072.45 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 1100.58 1121.31 982.74 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 32.20 32.84 30.99 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 78.26 78.96 75.60 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 87.18 88.22 75.60 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 83.22 84.20 80.07 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 327.07 339.80 321.98 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 316.56 302.60 269.10 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 75.38 73.67 70.15 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 211.02 213.14 199.28 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 210.19 217.15 199.28 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 242.34 225.59 199.28 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 265.33 271.87 199.28 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 194.77 195.13 192.69 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 197.16 200.94 192.69 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 185.45 199.47 161.37 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 187.64 180.57 161.37 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 182.53 166.96 161.37 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 182.90 178.97 161.37 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 184.26 194.43 161.37 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 180.65 180.59 155.36 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| NPU (TIMVX): | |
| ``` | |
| $ python3 benchmark.py --all --int8 --cfg_overwrite_backend_target 3 | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_TIMVX | |
| target=cv.dnn.DNN_TARGET_NPU | |
| mean median min input size model | |
| 5.24 7.45 4.77 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 45.96 46.10 43.21 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 30.25 30.30 28.68 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 19.75 20.18 18.19 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 28.75 28.85 28.47 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 148.80 148.85 143.45 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 143.17 141.11 136.58 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 73.19 78.57 62.89 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 32.11 30.50 29.97 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 116.32 120.72 99.40 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 408.18 418.89 374.12 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 37.34 38.57 32.03 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 41.82 39.84 37.63 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 160.70 160.90 153.15 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 160.47 160.48 151.88 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 239.38 237.47 231.95 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 197.61 201.16 162.69 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 196.69 164.78 162.69 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### Atlas 200 DK | |
| Specs: [details_en](https://e.huawei.com/uk/products/cloud-computing-dc/atlas/atlas-200), [details_cn](https://www.hiascend.com/zh/hardware/developer-kit) | |
| - (SoC) CPU: 8-core Coretext-A55 @ 1.6 GHz (max) | |
| - NPU: Ascend 310, dual DaVinci AI cores, 22/16/8 TOPS INT8. | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 7.82 7.82 7.77 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 8.57 8.77 7.77 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 92.21 92.11 91.87 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 122.07 126.02 91.87 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 42.93 43.26 42.75 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 55.91 57.40 42.75 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 67.85 67.91 67.47 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 70.06 70.21 67.47 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 102.49 102.65 102.10 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 114.02 116.16 102.10 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 92.66 92.49 92.36 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 79.39 80.75 68.47 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 89.66 68.66 68.47 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 90.59 92.13 68.47 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 499.55 500.15 498.36 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 571.85 580.88 498.36 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 201.99 201.55 200.62 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 216.72 217.34 200.62 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 313.66 313.85 312.13 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 322.98 323.45 312.13 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 1875.33 1877.53 1871.26 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 1989.04 2005.25 1871.26 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 143.62 143.19 137.16 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 159.80 159.62 159.40 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 152.18 152.86 145.56 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 145.83 145.77 145.45 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 521.46 521.66 520.28 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 541.50 544.02 520.28 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 134.02 136.01 132.06 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 360.26 360.82 359.13 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 361.22 361.51 359.13 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 427.85 362.87 359.13 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 475.44 490.06 359.13 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 285.19 284.91 284.69 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 318.96 323.30 284.69 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 289.82 360.87 244.07 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 285.40 303.13 244.07 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 274.67 244.47 243.87 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 277.84 262.99 243.87 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 283.02 280.77 243.87 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 279.21 262.55 243.87 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| NPU (CANN): | |
| <!-- vittrack is excluded due to HardSwish is not supported by CANN backend yet --> | |
| ``` | |
| $ python3 benchmark.py --all --fp32 --cfg_exclude wechat:crnn:vittrack --model_exclude pose_estimation_mediapipe_2023mar.onnx --cfg_overwrite_backend_target 4 | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_CANN | |
| target=cv.dnn.DNN_TARGET_NPU | |
| mean median min input size model | |
| 2.24 2.21 2.19 [160, 120] YuNet with ['face_detection_yunet_2022mar.onnx'] | |
| 2.66 2.66 2.64 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 2.19 2.19 2.16 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 6.27 6.22 6.17 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 6.94 6.94 6.85 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 5.15 5.13 5.10 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 5.41 5.42 5.10 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 6.99 6.99 6.95 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 7.63 7.64 7.43 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 20.62 22.09 19.16 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 28.59 28.60 27.91 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 5.17 5.26 5.09 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 16.45 16.44 16.31 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 5.58 5.57 5.54 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| ``` | |
| ### Toybrick RV1126 | |
| Specs: [details](https://t.rock-chips.com/en/portal.php?mod=view&aid=26) | |
| - CPU: Quard core ARM Cortex-A7, up to 1.5GHz | |
| - NPU (Not supported by OpenCV): 2.0TOPS, support 8bit / 16bit | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 56.78 56.74 56.46 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 51.16 51.41 45.18 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 1737.74 1733.23 1723.65 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 1298.48 1336.02 920.44 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 609.51 611.79 584.89 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 500.21 517.38 399.97 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 465.12 471.89 445.36 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 389.95 385.01 318.29 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 10.16.66.1781623.94 1607.90 1595.09 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 1109.61 1186.03 671.15 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 1567.09 1578.61 1542.75 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 1188.83 1219.46 850.92 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 996.30 884.80 689.11 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 849.51 805.93 507.78 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 11855.64 11836.80 11750.10 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 7752.60 8149.00 4429.83 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 3260.22 3251.14 3204.85 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 2287.10 2400.53 1482.04 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 2335.89 2335.93 2313.63 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 1899.16 1945.72 1529.46 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 37600.81 37558.85 37414.98 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 24185.35 25519.27 13395.47 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 411.41 448.29 397.86 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 905.77 890.22 866.06 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 780.94 817.69 653.26 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 1315.48 1321.44 1299.68 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 11143.23 11155.05 11105.11 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 7056.60 7457.76 3753.42 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 736.02 732.90 701.14 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 4267.03 4288.42 4229.69 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 4265.58 4276.54 4222.22 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 3678.65 4265.95 2636.57 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 3383.73 3490.66 2636.57 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 2180.44 2197.45 2152.67 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 2217.08 2241.77 2152.67 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 2217.15 2251.65 2152.67 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 2206.73 2219.60 2152.63 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 2208.84 2219.14 2152.63 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 2035.98 2185.05 1268.94 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 1927.93 2178.84 1268.94 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 1822.23 2213.30 1183.93 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### Khadas Edge2 (with RK3588) | |
| Board specs: [details](https://www.khadas.com/edge2) | |
| SoC specs: [details](https://www.rock-chips.com/a/en/products/RK35_Series/2022/0926/1660.html) | |
| - CPU: 2.25GHz Quad Core ARM Cortex-A76 + 1.8GHz Quad Core Cortex-A55 | |
| - NPU (Not supported by OpenCV): Build-in 6 TOPS Performance NPU, triple core, support int4 / int8 / int16 / fp16 / bf16 / tf32 | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 2.30 2.29 2.26 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 2.70 2.73 2.26 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 28.94 29.00 28.60 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 37.46 38.85 28.60 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 12.44 12.40 12.36 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 17.14 17.64 12.36 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 20.22 20.36 20.08 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 23.11 23.50 20.08 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 29.63 29.78 28.61 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 35.57 35.61 28.61 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 27.45 27.46 27.25 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 22.95 23.37 19.13 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 27.50 19.40 19.13 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 28.46 29.33 19.13 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 151.10 151.79 146.96 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 181.69 184.19 146.96 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 53.83 52.64 50.24 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 60.95 60.06 50.24 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 98.03 104.53 83.47 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 106.91 110.68 83.47 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 554.30 550.32 538.99 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 591.95 599.62 538.99 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 14.02 13.89 13.56 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 45.03 44.65 43.28 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 50.87 52.24 43.28 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 42.90 42.68 42.40 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 148.01 146.42 139.56 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 159.16 155.98 139.56 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 37.06 37.43 36.39 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 103.42 104.24 101.26 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 103.41 104.41 100.08 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 126.21 103.90 100.08 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 142.53 147.66 100.08 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 69.49 69.52 69.17 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 70.63 70.69 69.17 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 67.15 72.03 61.13 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 67.74 66.72 61.13 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 66.26 61.46 61.13 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 67.36 65.65 61.13 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 68.52 69.93 61.13 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 68.36 65.65 61.13 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### Horizon Sunrise X3 PI | |
| Specs: [details_cn](https://developer.horizon.ai/sunrise) | |
| - CPU: ARM Cortex-A53，4xCore, 1.2G | |
| - BPU (aka NPU, not supported by OpenCV): (Bernoulli Arch) 2×Core，up to 1.0G, ~5Tops | |
| CPU: | |
| ``` | |
| $ python3 benchmark.py --all | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 10.56 10.69 10.46 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 12.45 12.60 10.46 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 124.80 127.36 124.45 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 168.67 174.03 124.45 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 55.12 55.38 54.91 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 76.31 79.00 54.91 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 77.44 77.53 77.07 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 89.22 90.40 77.07 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 132.95 133.21 132.35 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 147.40 149.99 132.35 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 119.71 120.69 119.32 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 102.57 104.40 88.49 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 114.56 88.81 88.49 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 117.12 116.07 88.49 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 653.39 653.85 651.99 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 706.43 712.61 651.99 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 252.05 252.16 250.98 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 273.03 274.27 250.98 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 399.35 405.40 390.82 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 413.37 410.75 390.82 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 2516.91 2516.82 2506.54 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 2544.65 2551.55 2506.54 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 84.15 85.18 77.31 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 168.54 169.05 168.15 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 196.46 199.81 168.15 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 172.55 172.83 171.85 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 678.74 678.04 677.44 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 653.71 655.74 631.68 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 162.87 165.82 160.04 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 9.93 9.97 9.82 [100, 100] WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel'] | |
| 475.98 475.34 472.72 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 475.90 477.57 472.44 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 585.72 475.98 472.44 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 663.34 687.10 472.44 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 446.82 445.92 444.32 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 453.60 456.07 444.32 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 427.47 463.88 381.10 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 432.15 421.18 381.10 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 420.61 386.28 380.35 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 425.24 426.69 380.35 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 431.14 447.85 380.35 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 424.77 417.01 380.35 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### MAIX-III AX-PI | |
| Specs: [details_en](https://wiki.sipeed.com/hardware/en/maixIII/ax-pi/axpi.html#Hardware), [details_cn](https://wiki.sipeed.com/hardware/zh/maixIII/ax-pi/axpi.html#%E7%A1%AC%E4%BB%B6%E5%8F%82%E6%95%B0) | |
| SoC specs: [details_cn](https://axera-tech.com/product/T7297367876123493768) | |
| - CPU: Quad cores ARM Cortex-A7 | |
| - NPU (Not supported by OpenCV): 14.4Tops@int4，3.6Tops@int8 | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 83.95 83.76 83.62 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 79.35 79.92 75.47 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 2326.96 2326.49 2326.08 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 1950.83 1988.86 1648.47 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 823.42 823.35 822.50 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 750.31 757.91 691.41 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 664.73 664.61 663.84 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 596.29 603.96 540.72 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 2175.34 2173.62 2172.91 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 1655.11 1705.43 1236.22 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 2123.08 2122.92 2122.18 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 1619.08 1672.32 1215.05 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 1470.74 1216.86 1215.05 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 1287.09 1242.01 873.92 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 15841.89 15841.20 15828.32 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 11652.03 12079.50 8299.15 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 4371.75 4396.81 4370.29 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 3428.89 3521.87 2670.46 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 3421.19 3412.22 3411.20 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 2990.22 3034.11 2645.09 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 50633.38 50617.44 50614.78 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 36260.23 37731.28 24683.40 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 548.36 551.97 537.90 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 1285.54 1285.40 1284.43 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 1204.04 1211.89 1137.65 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 1849.87 1848.78 1847.80 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 14895.99 14894.27 14884.17 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 10496.44 10931.97 6976.60 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 1045.98 1052.05 1040.56 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 5899.23 5900.08 5896.73 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 5889.39 5890.58 5878.81 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 5436.61 5884.03 4665.77 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 5185.53 5273.76 4539.47 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 3230.95 3226.14 3225.53 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 3281.31 3295.46 3225.53 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 3247.56 3337.52 3196.25 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 3243.20 3276.35 3196.25 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 3230.49 3196.80 3195.02 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 3065.33 3217.99 2348.42 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 2976.24 3244.75 2348.42 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 2864.72 3219.46 2208.44 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### StarFive VisionFive 2 | |
| Specs: [details_cn](https://doc.rvspace.org/VisionFive2/PB/VisionFive_2/specification_pb.html), [details_en](https://doc-en.rvspace.org/VisionFive2/Product_Brief/VisionFive_2/specification_pb.html) | |
| - CPU: StarFive JH7110 with RISC-V quad-core CPU with 2 MB L2 cache and a monitor core, supporting RV64GC ISA, working up to 1.5 GHz | |
| - GPU: IMG BXE-4-32 MC1 with work frequency up to 600 MHz | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 41.13 41.07 41.06 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 37.43 37.83 34.35 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 1169.96 1169.72 1168.74 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 887.13 987.00 659.71 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 423.91 423.98 423.62 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 350.89 358.26 292.27 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 319.69 319.26 318.76 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 278.74 282.75 245.22 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 1127.61 1127.36 1127.17 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 785.44 819.07 510.77 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 1079.69 1079.66 1079.31 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 820.15 845.54 611.26 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 698.13 612.64 516.41 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 600.12 564.13 382.59 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 8116.21 8127.96 8113.70 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 5408.02 5677.71 3240.16 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 2267.96 2268.26 2266.59 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 1605.80 1671.91 1073.50 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 1731.61 1733.17 1730.54 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 1435.43 1477.52 1196.01 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 26185.41 26190.85 26168.68 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 17019.14 17923.20 9673.68 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 288.95 290.28 260.40 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 628.64 628.47 628.27 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 562.90 569.91 509.93 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 910.38 910.94 909.64 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 7613.64 7626.26 7606.07 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 4895.28 5166.85 2716.65 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 524.52 526.33 522.71 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 2988.22 2996.51 2980.17 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 2981.84 2979.74 2975.80 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 2610.78 2979.14 1979.37 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 2425.29 2478.92 1979.37 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 1404.01 1415.46 1401.36 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 1425.42 1426.51 1401.36 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 1432.21 1450.47 1401.36 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 1425.24 1448.27 1401.36 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 1428.84 1446.76 1401.36 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 1313.68 1427.46 808.70 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 1242.07 1408.93 808.70 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 1174.32 1426.07 774.78 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### Khadas VIM4 | |
| Board specs: https://www.khadas.com/vim4, https://dl.khadas.com/products/vim4/specs/vim4-specs.pdf | |
| SoC specs: | |
| - CPU: Amlogic A311D2, 2.2GHz Quad core ARM Cortex-A73 and 2.0GHz Quad core Cortex-A53 CPU, with 32-bit STM32G031K6 microprocessor. | |
| - GPU: Mali G52MP8(8EE) 800Mhz GPU. | |
| - NPU: 3.2 TOPS Build-in NPU (Not supported by dnn yet) | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 4.27 4.33 4.17 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 4.58 4.58 4.17 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 39.94 39.98 39.42 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 49.33 50.59 39.42 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 17.28 17.63 16.93 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 22.78 23.27 16.93 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 25.83 25.46 25.30 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 28.23 28.87 25.30 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 47.68 47.72 45.65 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 49.25 49.45 45.65 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 38.73 38.18 37.89 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 33.68 33.99 29.16 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 36.22 29.50 29.16 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 36.12 35.69 29.16 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 219.81 220.21 215.97 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 224.03 222.27 215.97 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 81.46 84.07 77.95 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 81.46 83.07 77.95 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 136.14 136.12 128.61 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 136.57 136.30 128.61 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 805.54 805.23 795.82 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 768.87 766.00 727.12 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 29.47 29.39 28.49 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 54.45 54.76 53.45 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 60.84 61.07 53.45 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 57.22 57.22 56.14 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 218.22 224.50 215.54 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 199.53 203.24 179.85 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 53.06 54.61 51.82 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 148.82 149.62 146.73 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 148.91 148.99 146.59 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 175.33 150.60 146.59 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 194.12 201.48 146.59 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 133.27 132.90 132.54 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 135.27 135.12 132.54 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 127.49 137.43 113.82 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 129.18 125.95 113.82 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 125.82 114.44 113.82 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 127.63 124.81 113.82 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 129.24 134.50 113.82 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 126.64 125.09 110.45 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| ### Jetson Nano Orin | |
| Specs: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ | |
| - CPU: 6-core Arm® Cortex®-A78AE v8.2 64-bit CPU, 1.5MB L2 + 4MB L3 | |
| - GPU: 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores, max freq 625MHz | |
| CPU: | |
| ``` | |
| $ python3 benchmark.py --all | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 2.59 2.62 2.50 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 2.98 2.97 2.50 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 20.05 24.76 19.75 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 31.84 32.72 19.75 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 9.15 9.22 9.04 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 14.33 15.35 9.04 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 15.00 15.17 14.80 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 18.37 18.63 14.80 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 24.86 25.09 24.12 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 30.17 34.51 24.12 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 18.47 18.55 18.23 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 17.08 17.30 15.80 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 21.26 15.89 15.80 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 23.19 24.15 15.80 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 102.30 101.90 101.44 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 142.33 146.24 101.44 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 39.91 39.01 38.46 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 51.35 50.70 38.46 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 125.31 126.50 121.92 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 132.95 133.67 121.92 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 400.91 430.48 384.87 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 476.63 509.48 384.87 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 19.16 19.91 18.04 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 27.73 26.93 26.72 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 35.16 41.14 26.72 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 33.05 33.18 32.67 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 93.58 94.02 92.36 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 119.80 153.20 92.36 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 31.51 32.19 30.69 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 3.53 3.53 3.51 [100, 100] WeChatQRCode with ['detect_2021nov.prototxt', 'detect_2021nov.caffemodel', 'sr_2021nov.prototxt', 'sr_2021nov.caffemodel'] | |
| 78.10 77.77 77.17 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 78.03 78.38 77.17 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 99.09 79.42 77.17 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 112.82 116.06 77.17 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 142.97 142.84 135.56 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 144.53 148.52 135.56 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 134.47 146.62 112.91 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 136.37 131.39 112.91 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 132.08 117.15 109.24 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 135.17 130.23 109.24 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 138.38 143.25 109.24 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 137.08 134.22 109.24 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |
| GPU (CUDA-FP32): | |
| ``` | |
| $ python3 benchmark.py --all --fp32 --cfg_exclude wechat --cfg_overwrite_backend_target 1 | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_CUDA | |
| target=cv.dnn.DNN_TARGET_CUDA | |
| mean median min input size model | |
| 5.23 5.27 5.17 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 7.59 7.62 7.55 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 8.48 8.46 8.37 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 12.29 13.04 11.11 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 12.91 13.28 12.79 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 8.41 8.42 8.35 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 9.36 9.43 8.35 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 32.58 32.71 31.11 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 16.33 16.08 16.04 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 24.46 24.35 24.01 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 103.28 103.41 102.37 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 19.75 19.78 19.10 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 10.84 10.76 10.75 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 14.50 14.50 14.36 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 23.53 23.36 23.16 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 26.54 27.22 25.99 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 27.49 27.80 26.97 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 27.53 27.75 26.95 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 15.66 16.30 15.41 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 15.91 15.80 15.41 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 13.58 16.70 9.48 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| ``` | |
| GPU (CUDA-FP16): | |
| ``` | |
| $ python3 benchmark.py --all --fp32 --cfg_exclude wechat --cfg_overwrite_backend_target 2 | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_CUDA | |
| target=cv.dnn.DNN_TARGET_CUDA_FP16 | |
| mean median min input size model | |
| 5.00 5.04 4.92 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 5.09 5.08 5.05 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 6.81 6.86 6.66 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 9.19 10.18 9.06 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 16.20 16.62 15.93 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 6.84 6.82 6.80 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 7.46 7.87 6.80 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 14.18 14.16 14.03 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 13.35 13.10 13.04 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 19.94 19.95 19.50 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 72.25 72.91 70.99 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 22.37 22.44 21.60 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 8.92 8.92 8.84 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 11.11 11.13 10.98 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 13.22 13.23 13.12 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 26.79 27.04 26.24 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 19.71 19.75 19.47 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 19.76 19.93 19.47 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 16.30 15.88 15.80 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 16.36 16.51 15.80 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 13.64 16.27 8.90 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| ``` | |
| ### Atlas 200I DK A2 | |
| Specs: https://www.hiascend.com/hardware/developer-kit-a2 (cn) | |
| - CPU: 4 core * 1.0 GHz | |
| - NPU: Ascend 310B, 8 TOPS INT8, 4 TFLOPS FP16 (Benchmark results are coming later) | |
| CPU: | |
| <!-- config wechat is excluded due to it needs building with opencv_contrib --> | |
| ``` | |
| $ python3 benchmark.py --all --cfg_exclude wechat | |
| Benchmarking ... | |
| backend=cv.dnn.DNN_BACKEND_OPENCV | |
| target=cv.dnn.DNN_TARGET_CPU | |
| mean median min input size model | |
| 6.67 6.80 5.17 [160, 120] YuNet with ['face_detection_yunet_2023mar.onnx'] | |
| 8.70 9.22 5.17 [160, 120] YuNet with ['face_detection_yunet_2023mar_int8.onnx'] | |
| 78.90 81.48 74.18 [150, 150] SFace with ['face_recognition_sface_2021dec.onnx'] | |
| 113.79 115.49 74.18 [150, 150] SFace with ['face_recognition_sface_2021dec_int8.onnx'] | |
| 36.94 38.64 33.23 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july.onnx'] | |
| 55.14 60.34 33.23 [112, 112] FacialExpressionRecog with ['facial_expression_recognition_mobilefacenet_2022july_int8.onnx'] | |
| 56.00 55.56 51.99 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb.onnx'] | |
| 71.09 72.20 51.99 [224, 224] MPHandPose with ['handpose_estimation_mediapipe_2023feb_int8.onnx'] | |
| 78.01 80.36 73.97 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar.onnx'] | |
| 111.56 113.84 73.97 [192, 192] PPHumanSeg with ['human_segmentation_pphumanseg_2023mar_int8.onnx'] | |
| 70.20 68.69 65.12 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr.onnx'] | |
| 61.72 63.39 48.28 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr.onnx'] | |
| 80.12 54.37 48.28 [224, 224] MobileNet with ['image_classification_mobilenetv1_2022apr_int8.onnx'] | |
| 87.42 96.71 48.28 [224, 224] MobileNet with ['image_classification_mobilenetv2_2022apr_int8.onnx'] | |
| 417.31 417.30 406.17 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan.onnx'] | |
| 597.15 619.24 406.17 [224, 224] PPResNet with ['image_classification_ppresnet50_2022jan_int8.onnx'] | |
| 155.73 153.40 145.10 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar.onnx'] | |
| 200.41 200.24 145.10 [320, 240] LPD_YuNet with ['license_plate_detection_lpd_yunet_2023mar_int8.onnx'] | |
| 253.05 252.73 245.91 [416, 416] NanoDet with ['object_detection_nanodet_2022nov.onnx'] | |
| 274.44 269.76 245.91 [416, 416] NanoDet with ['object_detection_nanodet_2022nov_int8.onnx'] | |
| 1407.75 1416.44 1357.23 [640, 640] YoloX with ['object_detection_yolox_2022nov.onnx'] | |
| 1716.25 1709.35 1357.23 [640, 640] YoloX with ['object_detection_yolox_2022nov_int8.onnx'] | |
| 37.02 37.66 32.50 [1280, 720] VitTrack with ['object_tracking_vittrack_2023sep.onnx'] | |
| 92.56 97.78 87.87 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb.onnx'] | |
| 119.29 123.56 87.87 [192, 192] MPPalmDet with ['palm_detection_mediapipe_2023feb_int8.onnx'] | |
| 90.13 90.75 87.78 [224, 224] MPPersonDet with ['person_detection_mediapipe_2023mar.onnx'] | |
| 285.75 284.54 278.06 [128, 256] YoutuReID with ['person_reid_youtu_2021nov.onnx'] | |
| 389.02 405.12 278.06 [128, 256] YoutuReID with ['person_reid_youtu_2021nov_int8.onnx'] | |
| 83.16 85.91 77.83 [256, 256] MPPose with ['pose_estimation_mediapipe_2023mar.onnx'] | |
| 219.28 220.74 214.53 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may.onnx'] | |
| 217.18 227.44 207.15 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may.onnx'] | |
| 319.73 210.22 207.15 [640, 480] PPOCRDet with ['text_detection_cn_ppocrv3_2023may_int8.onnx'] | |
| 396.47 399.45 207.15 [640, 480] PPOCRDet with ['text_detection_en_ppocrv3_2023may_int8.onnx'] | |
| 165.34 172.10 156.36 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2021sep.onnx'] | |
| 169.22 174.21 156.36 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov.onnx'] | |
| 158.82 172.23 135.52 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2021sep.onnx'] | |
| 159.39 156.42 135.52 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2023feb_fp16.onnx'] | |
| 155.87 146.82 135.52 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2023feb_fp16.onnx'] | |
| 163.43 152.16 135.52 [1280, 720] CRNN with ['text_recognition_CRNN_CH_2022oct_int8.onnx'] | |
| 173.46 162.85 135.52 [1280, 720] CRNN with ['text_recognition_CRNN_CN_2021nov_int8.onnx'] | |
| 175.28 145.22 135.52 [1280, 720] CRNN with ['text_recognition_CRNN_EN_2022oct_int8.onnx'] | |
| ``` | |