| # 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 | |
|  | |
| Hardware Setup: | |
| x86-64: | |
| - [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. | |
| ARM: | |
| - [Khadas VIM3](https://www.khadas.com/vim3): Amlogic A311D SoC with a 2.2GHz Quad core ARM Cortex-A73 + 1.8GHz dual core Cortex-A53 ARM CPU, and a 5 TOPS NPU. Benchmarks are done using **per-tensor quantized** models. Follow [this guide](https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU) to build OpenCV with TIM-VX backend enabled. | |
| - [Khadas VIM4](https://www.khadas.com/vim4): Amlogic A311D2 SoC with 2.2GHz Quad core ARM Cortex-A73 and 2.0GHz Quad core Cortex-A53 CPU, and 3.2 TOPS Build-in NPU. | |
| - [Khadas Edge 2](https://www.khadas.com/edge2): Rockchip RK3588S SoC with a CPU of 2.25 GHz Quad Core ARM Cortex-A76 + 1.8 GHz Quad Core Cortex-A55, and a 6 TOPS NPU. | |
| - [Atlas 200 DK](https://e.huawei.com/en/products/computing/ascend/atlas-200): Ascend 310 NPU with 22 TOPS @ INT8. Follow [this guide](https://github.com/opencv/opencv/wiki/Huawei-CANN-Backend) to build OpenCV with CANN backend enabled. | |
| - [Atlas 200I DK A2](https://www.hiascend.com/hardware/developer-kit-a2): SoC with 1.0GHz Quad-core CPU and Ascend 310B NPU with 8 TOPS @ INT8. | |
| - [NVIDIA Jetson Nano B01](https://developer.nvidia.com/embedded/jetson-nano-developer-kit): a Quad-core ARM A57 @ 1.43 GHz CPU, and a 128-core NVIDIA Maxwell GPU. | |
| - [NVIDIA Jetson Nano Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/): a 6-core Arm® Cortex®-A78AE v8.2 64-bit CPU, and a 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores (max freq 625MHz). | |
| - [Raspberry Pi 4B](https://www.raspberrypi.com/products/raspberry-pi-4-model-b/specifications/): Broadcom BCM2711 SoC with a Quad core Cortex-A72 (ARM v8) 64-bit @ 1.5 GHz. | |
| - [Horizon Sunrise X3](https://developer.horizon.ai/sunrise): an SoC from Horizon Robotics with a quad-core ARM Cortex-A53 1.2 GHz CPU and a 5 TOPS BPU (a.k.a NPU). | |
| - [MAIX-III AXera-Pi](https://wiki.sipeed.com/hardware/en/maixIII/ax-pi/axpi.html#Hardware): Axera AX620A SoC with a quad-core ARM Cortex-A7 CPU and a 3.6 TOPS @ int8 NPU. | |
| - [Toybrick RV1126](https://t.rock-chips.com/en/portal.php?mod=view&aid=26): Rockchip RV1126 SoC with a quard-core ARM Cortex-A7 CPU and a 2.0 TOPs NPU. | |
| RISC-V: | |
| - [StarFive VisionFive 2](https://doc-en.rvspace.org/VisionFive2/Product_Brief/VisionFive_2/specification_pb.html): `StarFive JH7110` SoC with a RISC-V quad-core CPU, which can turbo up to 1.5GHz, and an GPU of model `IMG BXE-4-32 MC1` from Imagination, which has a work freq up to 600MHz. | |
| - [Allwinner Nezha D1](https://d1.docs.aw-ol.com/en): Allwinner D1 SoC with a 1.0 GHz single-core RISC-V [Xuantie C906 CPU](https://www.t-head.cn/product/C906?spm=a2ouz.12986968.0.0.7bfc1384auGNPZ) with RVV 0.7.1 support. YuNet is tested 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/) | |
|  | |
| ### Face Recognition with [SFace](./models/face_recognition_sface/) | |
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| ### Facial Expression Recognition with [Progressive Teacher](./models/facial_expression_recognition/) | |
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| ### Human Segmentation with [PP-HumanSeg](./models/human_segmentation_pphumanseg/) | |
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| ### Image Segmentation with [EfficientSAM](./models/image_segmentation_efficientsam/) | |
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| ### License Plate Detection with [LPD_YuNet](./models/license_plate_detection_yunet/) | |
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| ### Object Detection with [NanoDet](./models/object_detection_nanodet/) & [YOLOX](./models/object_detection_yolox/) | |
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| ### Object Tracking with [VitTrack](./models/object_tracking_vittrack/) | |
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| ### Palm Detection with [MP-PalmDet](./models/palm_detection_mediapipe/) | |
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| ### Hand Pose Estimation with [MP-HandPose](models/handpose_estimation_mediapipe/) | |
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| ### Person Detection with [MP-PersonDet](./models/person_detection_mediapipe) | |
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| ### Pose Estimation with [MP-Pose](models/pose_estimation_mediapipe) | |
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| ### QR Code Detection and Parsing with [WeChatQRCode](./models/qrcode_wechatqrcode/) | |
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| ### Chinese Text detection [PPOCR-Det](./models/text_detection_ppocr/) | |
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| ### English Text detection [PPOCR-Det](./models/text_detection_ppocr/) | |
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| ### Text Detection with [CRNN](./models/text_recognition_crnn/) | |
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| ## License | |
| OpenCV Zoo is licensed under the [Apache 2.0 license](./LICENSE). Please refer to licenses of different models. | |