crnn demo

Author: Charles-258

README.md

@@ -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***:
@@ -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.