update yunet to v2 (#151)

models/face_detection_yunet/README.md

@@ -13,8 +13,8 @@ Results of accuracy evaluation with [tools/eval](../../tools/eval).
 
 | Models      | Easy AP | Medium AP | Hard AP |
 | ----------- | ------- | --------- | ------- |
-| YuNet       | 0.8498  | 0.8384    | 0.7357  |
-| YuNet quant | 0.7751  | 0.8145    | 0.7312  |
+| YuNet       | 0.8871  | 0.8710    | 0.7681  |
+| YuNet quant | 0.8838  | 0.8683    | 0.7676  |
 
 \*: 'quant' stands for 'quantized'.
 

models/face_detection_yunet/demo.cpp

@@ -112,7 +112,7 @@ int main(int argc, char** argv)
     cv::CommandLineParser parser(argc, argv,
         "{help  h           |                                   | Print this message}"
         "{input i           |                                   | Set input to a certain image, omit if using camera}"
-        "{model m           | face_detection_yunet_2022mar.onnx | Set path to the model}"
+        "{model m           | face_detection_yunet_2023mar.onnx  | Set path to the model}"
         "{backend b         | opencv                            | Set DNN backend}"
         "{target t          | cpu                               | Set DNN target}"
         "{save s            | false                             | Whether to save result image or not}"

models/face_detection_yunet/demo.py

@@ -27,8 +27,8 @@ backend_target_pairs = [
 parser = argparse.ArgumentParser(description='YuNet: A Fast and Accurate CNN-based Face Detector (https://github.com/ShiqiYu/libfacedetection).')
 parser.add_argument('--input', '-i', type=str,
                     help='Usage: Set input to a certain image, omit if using camera.')
-parser.add_argument('--model', '-m', type=str, default='face_detection_yunet_2022mar.onnx',
-                    help="Usage: Set model type, defaults to 'face_detection_yunet_2022mar.onnx'.")
+parser.add_argument('--model', '-m', type=str, default='face_detection_yunet_2023mar.onnx',
+                    help="Usage: Set model type, defaults to 'face_detection_yunet_2023mar.onnx'.")
 parser.add_argument('--backend_target', '-bt', type=int, default=0,
                     help='''Choose one of the backend-target pair to run this demo:
                         {:d}: (default) OpenCV implementation + CPU,

models/face_recognition_sface/demo.py

@@ -57,7 +57,7 @@ if __name__ == '__main__':
                        backendId=backend_id,
                        targetId=target_id)
     # Instantiate YuNet for face detection
-    detector = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2022mar.onnx',
+    detector = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2023mar.onnx',
                      inputSize=[320, 320],
                      confThreshold=0.9,
                      nmsThreshold=0.3,

models/facial_expression_recognition/demo.py

@@ -86,7 +86,7 @@ if __name__ == '__main__':
     backend_id = backend_target_pairs[args.backend_target][0]
     target_id = backend_target_pairs[args.backend_target][1]
 
-    detect_model = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2022mar.onnx')
+    detect_model = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2023mar.onnx')
 
     fer_model = FacialExpressionRecog(modelPath=args.model,
                                       backendId=backend_id,

tools/eval/eval.py

@@ -54,14 +54,14 @@ models = dict(
     yunet=dict(
         name="YuNet",
         topic="face_detection",
-        modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2022mar.onnx"),
+        modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar.onnx"),
         topK=5000,
         confThreshold=0.3,
         nmsThreshold=0.45),
     yunet_q=dict(
         name="YuNet",
         topic="face_detection",
-        modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2022mar-act_int8-wt_int8-quantized.onnx"),
+        modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx"),
         topK=5000,
         confThreshold=0.3,
         nmsThreshold=0.45),