# 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

![](benchmark/color_table.svg?raw=true)

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.
- `CPU-RV1126`: [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 2.0TOPs NPU.
- `CPU-KVE2`: [Khadas Edge 2](https://www.khadas.com/edge2), Rockchip RK3588S SoC with a CPU of 2.25GHz Quad Core ARM Cortex-A76 + 1.8GHz Quad Core Cortex-A55, and a 6TOPS NPU.
- `CPU-HSX3`: [Horizon Sunrise X3](https://developer.horizon.ai/sunrise), an SoC from Horizon Robotics with a quad-core ARM Cortex-A53 1.2GHz CPU and a 5TOPS BPU (a.k.a NPU).
- `CPU-AXP`: [MAIX-III AXera-Pi](https://wiki.sipeed.com/hardware/en/maixIII/ax-pi/axpi.html#Hardware), Axera AX620A with a quad-core ARM Cortex-A7 CPU and a 3.6TOPS@int8 NPU.
- `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/)

![largest selfie](./models/face_detection_yunet/examples/largest_selfie.jpg)

### Facial Expression Recognition with [Progressive Teacher](./models/facial_expression_recognition/)

![fer demo](./models/facial_expression_recognition/examples/selfie.jpg)

### Human Segmentation with [PP-HumanSeg](./models/human_segmentation_pphumanseg/)

![messi](./models/human_segmentation_pphumanseg/examples/messi.jpg)

### License Plate Detection with [LPD_YuNet](./models/license_plate_detection_yunet/)

![license plate detection](./models/license_plate_detection_yunet/examples/lpd_yunet_demo.gif)

### Object Detection with [NanoDet](./models/object_detection_nanodet/) & [YOLOX](./models/object_detection_yolox/)

![nanodet demo](./models/object_detection_nanodet/samples/1_res.jpg)

![yolox demo](./models/object_detection_yolox/samples/3_res.jpg)

### Object Tracking with [DaSiamRPN](./models/object_tracking_dasiamrpn/)

![webcam demo](./models/object_tracking_dasiamrpn/examples/dasiamrpn_demo.gif)

### Palm Detection with [MP-PalmDet](./models/palm_detection_mediapipe/)

![palm det](./models/palm_detection_mediapipe/examples/mppalmdet_demo.gif)

### Hand Pose Estimation with [MP-HandPose](models/handpose_estimation_mediapipe/)

![handpose estimation](models/handpose_estimation_mediapipe/examples/mphandpose_demo.webp)

### Person Detection with [MP-PersonDet](./models/person_detection_mediapipe)

![person det](./models/person_detection_mediapipe/examples/mppersondet_demo.webp)

### Pose Estimation with [MP-Pose](models/pose_estimation_mediapipe)

![pose_estimation](models/pose_estimation_mediapipe/examples/mpposeest_demo.webp)

### QR Code Detection and Parsing with [WeChatQRCode](./models/qrcode_wechatqrcode/)

![qrcode](./models/qrcode_wechatqrcode/examples/wechat_qrcode_demo.gif)

### Chinese Text detection [DB](./models/text_detection_db/)

![mask](./models/text_detection_db/examples/mask.jpg)

### English Text detection [DB](./models/text_detection_db/)

![gsoc](./models/text_detection_db/examples/gsoc.jpg)

### Text Detection with [CRNN](./models/text_recognition_crnn/)

![crnn_demo](./models/text_recognition_crnn/examples/CRNNCTC.gif)

## License

OpenCV Zoo is licensed under the [Apache 2.0 license](./LICENSE). Please refer to licenses of different models.