| # OpenCV Zoo and Benchmark | |
| A zoo for models tuned for OpenCV DNN with benchmarks on different platforms. | |
| Guidelines: | |
| - Install latest `opencv-python`: | |
| ```shell | |
| python3 -m pip install opencv-python | |
| # Or upgrade to latest version | |
| python3 -m pip install --upgrade opencv-python | |
| ``` | |
| - Clone this repo to download all models and demo scripts: | |
| ```shell | |
| # Install git-lfs from https://git-lfs.github.com/ | |
| git clone https://github.com/opencv/opencv_zoo && cd opencv_zoo | |
| git lfs install | |
| git lfs pull | |
| ``` | |
| - To run benchmarks on your hardware settings, please refer to [benchmark/README](./benchmark/README.md). | |
| ## Models & Benchmark Results | |
| | Model | Task | Input Size | CPU-INTEL (ms) | CPU-RPI (ms) | GPU-JETSON (ms) | NPU-KV3 (ms) | NPU-Ascend310 (ms) | CPU-D1 (ms) | | |
| | ------------------------------------------------------- | ----------------------------- | ---------- | -------------- | ------------ | --------------- | ------------ | ------------------ | ----------- | | |
| | [YuNet](./models/face_detection_yunet) | Face Detection | 160x120 | 0.72 | 5.43 | 12.18 | 4.04 | 2.24 | 86.69 | | |
| | [SFace](./models/face_recognition_sface) | Face Recognition | 112x112 | 6.04 | 78.83 | 24.88 | 46.25 | 2.66 | --- | | |
| | [FER](./models/facial_expression_recognition/) | Facial Expression Recognition | 112x112 | 3.16 | 32.53 | 31.07 | 29.80 | 2.19 | --- | | |
| | [LPD-YuNet](./models/license_plate_detection_yunet/) | License Plate Detection | 320x240 | 8.63 | 167.70 | 56.12 | 29.53 | 7.63 | --- | | |
| | [YOLOX](./models/object_detection_yolox/) | Object Detection | 640x640 | 141.20 | 1805.87 | 388.95 | 420.98 | 28.59 | --- | | |
| | [NanoDet](./models/object_detection_nanodet/) | Object Detection | 416x416 | 66.03 | 225.10 | 64.94 | 116.64 | 20.62 | --- | | |
| | [DB-IC15](./models/text_detection_db) (EN) | Text Detection | 640x480 | 71.03 | 1862.75 | 208.41 | --- | 17.15 | --- | | |
| | [DB-TD500](./models/text_detection_db) (EN&CN) | Text Detection | 640x480 | 72.31 | 1878.45 | 210.51 | --- | 17.95 | --- | | |
| | [CRNN-EN](./models/text_recognition_crnn) | Text Recognition | 100x32 | 20.16 | 278.11 | 196.15 | 125.30 | --- | --- | | |
| | [CRNN-CN](./models/text_recognition_crnn) | Text Recognition | 100x32 | 23.07 | 297.48 | 239.76 | 166.79 | --- | --- | | |
| | [PP-ResNet](./models/image_classification_ppresnet) | Image Classification | 224x224 | 34.71 | 463.93 | 98.64 | 75.45 | 6.99 | --- | | |
| | [MobileNet-V1](./models/image_classification_mobilenet) | Image Classification | 224x224 | 5.90 | 72.33 | 33.18 | 145.66\* | 5.15 | --- | | |
| | [MobileNet-V2](./models/image_classification_mobilenet) | Image Classification | 224x224 | 5.97 | 66.56 | 31.92 | 146.31\* | 5.41 | --- | | |
| | [PP-HumanSeg](./models/human_segmentation_pphumanseg) | Human Segmentation | 192x192 | 8.81 | 73.13 | 67.97 | 74.77 | 6.94 | --- | | |
| | [WeChatQRCode](./models/qrcode_wechatqrcode) | QR Code Detection and Parsing | 100x100 | 1.29 | 5.71 | --- | --- | --- | --- | | |
| | [DaSiamRPN](./models/object_tracking_dasiamrpn) | Object Tracking | 1280x720 | 29.05 | 712.94 | 76.82 | --- | --- | --- | | |
| | [YoutuReID](./models/person_reid_youtureid) | Person Re-Identification | 128x256 | 30.39 | 625.56 | 90.07 | 44.61 | 5.58 | --- | | |
| | [MP-PalmDet](./models/palm_detection_mediapipe) | Palm Detection | 192x192 | 6.29 | 86.83 | 83.20 | 33.81 | 5.17 | --- | | |
| | [MP-HandPose](./models/handpose_estimation_mediapipe) | Hand Pose Estimation | 224x224 | 4.68 | 43.57 | 40.10 | 19.47 | 6.27 | --- | | |
| | [MP-PersonDet](./models/person_detection_mediapipe) | Person Detection | 224x224 | 13.88 | 98.52 | 56.69 | --- | 16.45 | --- | | |
| \*: Models are quantized in per-channel mode, which run slower than per-tensor quantized models on NPU. | |
| Hardware Setup: | |
| - `CPU-INTEL`: [Intel Core i7-12700K](https://www.intel.com/content/www/us/en/products/sku/134594/intel-core-i712700k-processor-25m-cache-up-to-5-00-ghz/specifications.html), 8 Performance-cores (3.60 GHz, turbo up to 4.90 GHz), 4 Efficient-cores (2.70 GHz, turbo up to 3.80 GHz), 20 threads. | |
| - `CPU-RPI`: [Raspberry Pi 4B](https://www.raspberrypi.com/products/raspberry-pi-4-model-b/specifications/), Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5 GHz. | |
| - `GPU-JETSON`: [NVIDIA Jetson Nano B01](https://developer.nvidia.com/embedded/jetson-nano-developer-kit), 128-core NVIDIA Maxwell GPU. | |
| - `NPU-KV3`: [Khadas VIM3](https://www.khadas.com/vim3), 5TOPS Performance. Benchmarks are done using **quantized** models. You will need to compile OpenCV with TIM-VX following [this guide](https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU) to run benchmarks. The test results use the `per-tensor` quantization model by default. | |
| - `NPU-Ascend310`: [Ascend 310](https://e.huawei.com/uk/products/cloud-computing-dc/atlas/atlas-200), 22 TOPS @ INT8. Benchmarks are done on [Atlas 200 DK AI Developer Kit](https://e.huawei.com/in/products/cloud-computing-dc/atlas/atlas-200). Get the latest OpenCV source code and build following [this guide](https://github.com/opencv/opencv/wiki/Huawei-CANN-Backend) to enable CANN backend. | |
| - `CPU-D1`: [Allwinner D1](https://d1.docs.aw-ol.com/en), [Xuantie C906 CPU](https://www.t-head.cn/product/C906?spm=a2ouz.12986968.0.0.7bfc1384auGNPZ) (RISC-V, RVV 0.7.1) @ 1.0 GHz, 1 core. YuNet is supported for now. Visit [here](https://github.com/fengyuentau/opencv_zoo_cpp) for more details. | |
| ***Important Notes***: | |
| - The data under each column of hardware setups on the above table represents the elapsed time of an inference (preprocess, forward and postprocess). | |
| - The time data is the mean of 10 runs after some warmup runs. Different metrics may be applied to some specific models. | |
| - Batch size is 1 for all benchmark results. | |
| - `---` represents the model is not availble to run on the device. | |
| - View [benchmark/config](./benchmark/config) for more details on benchmarking different models. | |
| ## Some Examples | |
| Some examples are listed below. You can find more in the directory of each model! | |
| ### Face Detection with [YuNet](./models/face_detection_yunet/) | |
|  | |
| ### Facial Expression Recognition with [Progressive Teacher](./models/facial_expression_recognition/) | |
|  | |
| ### Human Segmentation with [PP-HumanSeg](./models/human_segmentation_pphumanseg/) | |
|  | |
| ### License Plate Detection with [LPD_YuNet](./models/license_plate_detection_yunet/) | |
|  | |
| ### Object Detection with [NanoDet](./models/object_detection_nanodet/) & [YOLOX](./models/object_detection_yolox/) | |
|  | |
|  | |
| ### Object Tracking with [DaSiamRPN](./models/object_tracking_dasiamrpn/) | |
|  | |
| ### Palm Detection with [MP-PalmDet](./models/palm_detection_mediapipe/) | |
|  | |
| ### Hand Pose Estimation with [MP-HandPose](models/handpose_estimation_mediapipe/) | |
|  | |
| ### QR Code Detection and Parsing with [WeChatQRCode](./models/qrcode_wechatqrcode/) | |
|  | |
| ### Chinese Text detection [DB](./models/text_detection_db/) | |
|  | |
| ### English Text detection [DB](./models/text_detection_db/) | |
|  | |
| ### Text Detection with [CRNN](./models/text_recognition_crnn/) | |
|  | |
| ## License | |
| OpenCV Zoo is licensed under the [Apache 2.0 license](./LICENSE). Please refer to licenses of different models. | |