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# 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 | Task | Input Size | INTEL-CPU (ms) | RPI-CPU (ms) | JETSON-GPU (ms) | KV3-NPU (ms) | D1-CPU (ms) |
|---------------------------------------------------------|-------------------------------|------------|----------------|--------------|-----------------|--------------|-------------|
| [YuNet](./models/face_detection_yunet) | Face Detection | 160x120 | 1.45 | 6.22 | 12.18 | 4.04 | 86.69 |
| [SFace](./models/face_recognition_sface) | Face Recognition | 112x112 | 8.65 | 99.20 | 24.88 | 46.25 | --- |
| [LPD-YuNet](./models/license_plate_detection_yunet/) | License Plate Detection | 320x240 | --- | 168.03 | 56.12 | 154.20\* | |
| [DB-IC15](./models/text_detection_db) | Text Detection | 640x480 | 142.91 | 2835.91 | 208.41 | --- | --- |
| [DB-TD500](./models/text_detection_db) | Text Detection | 640x480 | 142.91 | 2841.71 | 210.51 | --- | --- |
| [CRNN-EN](./models/text_recognition_crnn) | Text Recognition | 100x32 | 50.21 | 234.32 | 196.15 | 125.30 | --- |
| [CRNN-CN](./models/text_recognition_crnn) | Text Recognition | 100x32 | 73.52 | 322.16 | 239.76 | 166.79 | --- |
| [PP-ResNet](./models/image_classification_ppresnet) | Image Classification | 224x224 | 56.05 | 602.58 | 98.64 | 75.45 | --- |
| [MobileNet-V1](./models/image_classification_mobilenet) | Image Classification | 224x224 | 9.04 | 92.25 | 33.18 | 145.66\* | --- |
| [MobileNet-V2](./models/image_classification_mobilenet) | Image Classification | 224x224 | 8.86 | 74.03 | 31.92 | 146.31\* | --- |
| [PP-HumanSeg](./models/human_segmentation_pphumanseg) | Human Segmentation | 192x192 | 19.92 | 105.32 | 67.97 | 74.77 | --- |
| [WeChatQRCode](./models/qrcode_wechatqrcode) | QR Code Detection and Parsing | 100x100 | 7.04 | 37.68 | --- | --- | --- |
| [DaSiamRPN](./models/object_tracking_dasiamrpn) | Object Tracking | 1280x720 | 36.15 | 705.48 | 76.82 | --- | --- |
| [YoutuReID](./models/person_reid_youtureid) | Person Re-Identification | 128x256 | 35.81 | 521.98 | 90.07 | 44.61 | --- |
| [MP-PalmDet](./models/palm_detection_mediapipe) | Palm Detection | 256x256 | 15.57 | 168.37 | 50.64 | 145.56\* | --- |
| [MP-HandPose](./models/handpose_estimation_mediapipe) | Hand Pose Estimation | 256x256 | 20.16 | 148.24 | 156.30 | 663.77\* | --- |
\*: Models are quantized in per-channel mode, which run slower than per-tensor quantized models on NPU.
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. 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.
- `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.
## 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/)
### Human Segmentation with [PP-HumanSeg](./models/human_segmentation_pphumanseg/)
### License Plate Detection with [LPD_YuNet](./models/license_plate_detection_yunet/)
### 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.
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