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Add hardware: Allwinner D1 (Xuantie C906 CPU, RISC-V) (#29)

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* add benchmark results of YuNet on D1

* add a link for tutorial of benchmarking on D1-CPU

* improve description

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  1. README.md +14 -13
README.md CHANGED
@@ -14,24 +14,25 @@ Guidelines:
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  ## Models & Benchmark Results
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- | Model | Input Size | INTEL-CPU | RPI-CPU | JETSON-GPU |
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- |-------|------------|-----------|---------|------------|
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- | [YuNet](./models/face_detection_yunet) | 160x120 | 1.45 | 6.22 | 12.18 |
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- | [DB-IC15](./models/text_detection_db) | 640x480 | 142.91 | 2835.91 | 208.41 |
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- | [DB-TD500](./models/text_detection_db) | 640x480 | 142.91 | 2841.71 | 210.51 |
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- | [CRNN-EN](./models/text_recognition_crnn) | 100x32 | 50.21 | 234.32 | 196.15 |
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- | [CRNN-CN](./models/text_recognition_crnn) | 100x32 | 73.52 | 322.16 | 239.76 |
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- | [SFace](./models/face_recognition_sface) | 112x112 | 8.65 | 99.20 | 24.88 |
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- | [PP-ResNet](./models/image_classification_ppresnet) | 224x224 | 56.05 | 602.58 | 98.64 |
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- | [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192 | 19.92 | 105.32 | 67.97 |
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- | [WeChatQRCode](./models/qrcode_wechatqrcode) | 100x100 | 7.04 | 37.68 | --- |
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- | [DaSiamRPN](./models/object_tracking_dasiamrpn) | 1280x720 | 36.15 | 705.48 | 76.82 |
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- | [YoutuReID](./models/person_reid_youtureid) | 128x256 | 35.81 | 521.98 | 90.07 |
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  Hardware Setup:
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  - `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.
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  - `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.
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  - `JETSON-GPU`: [NVIDIA Jetson Nano B01](https://developer.nvidia.com/embedded/jetson-nano-developer-kit), 128-core NVIDIA Maxwell GPU.
 
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  ***Important Notes***:
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  - The data under each column of hardware setups on the above table represents the elapsed time of an inference (preprocess, forward and postprocess).
 
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  ## Models & Benchmark Results
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+ | Model | Input Size | INTEL-CPU | RPI-CPU | JETSON-GPU | D1-CPU |
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+ |-------|------------|-----------|---------|------------|--------|
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+ | [YuNet](./models/face_detection_yunet) | 160x120 | 1.45 | 6.22 | 12.18 | 86.69 |
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+ | [DB-IC15](./models/text_detection_db) | 640x480 | 142.91 | 2835.91 | 208.41 | --- |
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+ | [DB-TD500](./models/text_detection_db) | 640x480 | 142.91 | 2841.71 | 210.51 | --- |
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+ | [CRNN-EN](./models/text_recognition_crnn) | 100x32 | 50.21 | 234.32 | 196.15 | --- |
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+ | [CRNN-CN](./models/text_recognition_crnn) | 100x32 | 73.52 | 322.16 | 239.76 | --- |
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+ | [SFace](./models/face_recognition_sface) | 112x112 | 8.65 | 99.20 | 24.88 | --- |
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+ | [PP-ResNet](./models/image_classification_ppresnet) | 224x224 | 56.05 | 602.58 | 98.64 | --- |
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+ | [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192 | 19.92 | 105.32 | 67.97 | --- |
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+ | [WeChatQRCode](./models/qrcode_wechatqrcode) | 100x100 | 7.04 | 37.68 | --- | --- |
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+ | [DaSiamRPN](./models/object_tracking_dasiamrpn) | 1280x720 | 36.15 | 705.48 | 76.82 | --- |
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+ | [YoutuReID](./models/person_reid_youtureid) | 128x256 | 35.81 | 521.98 | 90.07 | --- |
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  Hardware Setup:
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  - `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.
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  - `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.
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  - `JETSON-GPU`: [NVIDIA Jetson Nano B01](https://developer.nvidia.com/embedded/jetson-nano-developer-kit), 128-core NVIDIA Maxwell GPU.
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+ - `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.
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  ***Important Notes***:
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  - The data under each column of hardware setups on the above table represents the elapsed time of an inference (preprocess, forward and postprocess).