README.md

# OpenCV Zoo and Benchmark

A zoo for models tuned for OpenCV DNN with benchmarks on different platforms.

Guidelines:
- 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 | Input Size | INTEL-CPU (ms) | RPI-CPU (ms) | JETSON-GPU (ms) | KV3-NPU (ms) | D1-CPU (ms) |
|-------|------------|----------------|--------------|-----------------|--------------|-------------|
| [YuNet](./models/face_detection_yunet)                | 160x120  | 1.45   | 6.22    | 12.18  | 4.04   | 86.69 |
| [SFace](./models/face_recognition_sface)              | 112x112  | 8.65   | 99.20   | 24.88  | 46.25  | ---   |
| [DB-IC15](./models/text_detection_db)                 | 640x480  | 142.91 | 2835.91 | 208.41 | ---    | ---   |
| [DB-TD500](./models/text_detection_db)                | 640x480  | 142.91 | 2841.71 | 210.51 | ---    | ---   |
| [CRNN-EN](./models/text_recognition_crnn)             | 100x32   | 50.21  | 234.32  | 196.15 | 125.30 | ---   |
| [CRNN-CN](./models/text_recognition_crnn)             | 100x32   | 73.52  | 322.16  | 239.76 | 166.79 | ---   |
| [PP-ResNet](./models/image_classification_ppresnet)   | 224x224  | 56.05  | 602.58  | 98.64  | 75.45  | ---   |
| [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192  | 19.92  | 105.32  | 67.97  | 74.77  | ---   |
| [WeChatQRCode](./models/qrcode_wechatqrcode)          | 100x100  | 7.04   | 37.68   | ---    | ---    | ---   |
| [DaSiamRPN](./models/object_tracking_dasiamrpn)       | 1280x720 | 36.15  | 705.48  | 76.82  | ---    | ---   |
| [YoutuReID](./models/person_reid_youtureid)           | 128x256  | 35.81  | 521.98  | 90.07  | 44.61  | ---   |

Hardware Setup:
- `INTEL-CPU`: [Intel Core i7-5930K](https://www.intel.com/content/www/us/en/products/sku/82931/intel-core-i75930k-processor-15m-cache-up-to-3-70-ghz/specifications.html) @ 3.50GHz, 6 cores, 12 threads.
- `RPI-CPU`: [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 @ 1.5GHz.
- `JETSON-GPU`: [NVIDIA Jetson Nano B01](https://developer.nvidia.com/embedded/jetson-nano-developer-kit), 128-core NVIDIA Maxwell GPU.
- `KV3-NPU`: [Khadas VIM3](https://www.khadas.com/vim3), 5TOPS Performance. Benchmarks are done using **quantized** models. [TIM-VX backend and NPU target support for OpenCV](https://github.com/opencv/opencv/pull/21036) is under reivew. You will need to compile OpenCV with TIM-VX following [this guide](https://gist.github.com/zihaomu/f040be4901d92e423f227c10dfa37650) to run benchmarks.
- `D1-CPU`: [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) @ 1.0GHz, 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 median 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.

## License

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