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64 changes: 51 additions & 13 deletions README.md
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Expand Up @@ -14,24 +14,31 @@ Guidelines:

## Models & Benchmark Results

| Model | Input Size | INTEL-CPU | RPI-CPU | JETSON-GPU | D1-CPU |
|-------|------------|-----------|---------|------------|--------|
| [YuNet](./models/face_detection_yunet) | 160x120 | 1.45 | 6.22 | 12.18 | 86.69 |
| [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 | --- |
| [CRNN-CN](./models/text_recognition_crnn) | 100x32 | 73.52 | 322.16 | 239.76 | --- |
| [SFace](./models/face_recognition_sface) | 112x112 | 8.65 | 99.20 | 24.88 | --- |
| [PP-ResNet](./models/image_classification_ppresnet) | 224x224 | 56.05 | 602.58 | 98.64 | --- |
| [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192 | 19.92 | 105.32 | 67.97 | --- |
| [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 | --- |
| 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 | --- |
| [LPD-YuNet](./models/license_plate_detection_yunet/) | 320x240 | --- | 168.03 | 56.12 | 154.20\* | |
| [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 | --- |
| [MobileNet-V1](./models/image_classification_mobilenet)| 224x224 | 9.04 | 92.25 | 33.18 | 145.66\* | --- |
| [MobileNet-V2](./models/image_classification_mobilenet)| 224x224 | 8.86 | 74.03 | 31.92 | 146.31\* | --- |
| [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 | --- |
| [MPPalmDet](./models/palm_detection_mediapipe) | 256x256 | 15.57 | 89.41 | 50.64 | 145.56\* | --- |

\*: 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***:
Expand All @@ -41,6 +48,37 @@ Hardware Setup:
- `---` 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)

### 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 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)

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

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

### 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.