Fixes on 2021-12-15

README.md

@@ -14,7 +14,7 @@ Guidelines:
 
 ## Models & Benchmark Results
 
-| Model | Input Size | INTEL-CPU | RPI-CPU | JETSON-GPU | D1-CPU |
+| Model | Input Size | INTEL-CPU (ms) | RPI-CPU (ms) | JETSON-GPU (ms) | D1-CPU (ms) |
 |-------|------------|-----------|---------|------------|--------|
 | [YuNet](./models/face_detection_yunet)   | 160x120 | 1.45   | 6.22    | 12.18 | 86.69 |
 | [SFace](./models/face_recognition_sface) | 112x112 | 8.65 | 99.20 | 24.88 | --- |

benchmark/config/face_detection_yunet.yaml

@@ -2,7 +2,7 @@ Benchmark:
   name: "Face Detection Benchmark"
   type: "Detection"
   data:
-    path: "benchmark/data/face/detection"
+    path: "benchmark/data/face_detection"
     files: ["group.jpg", "concerts.jpg", "dance.jpg"]
     sizes: # [[w1, h1], ...], Omit to run at original scale
       - [160, 120]

benchmark/config/face_recognition_sface.yaml

@@ -2,7 +2,7 @@ Benchmark:
   name: "Face Recognition Benchmark"
   type: "Recognition"
   data:
-    path: "benchmark/data/face/recognition"
+    path: "benchmark/data/face_recognition"
     files: ["Aaron_Tippin_0001.jpg", "Alvaro_Uribe_0028.jpg", "Alvaro_Uribe_0029.jpg", "Jose_Luis_Rodriguez_Zapatero_0001.jpg"]
   metric: # 'sizes' is omitted since this model requires input of fixed size
     warmup: 30

benchmark/download_data.py

@@ -165,10 +165,14 @@ def get_confirm_token(response):  # in case of large files
 
 # Data will be downloaded and extracted to ./data by default
 data_downloaders = dict(
-    face=Downloader(name='face',
+    face_detection=Downloader(name='face_detection',
         url='https://drive.google.com/u/0/uc?id=1lOAliAIeOv4olM65YDzE55kn6XjiX2l6&export=download',
-        sha='8397f115c0d4447e55ea05488579e71a813e2691',
-        filename='face.zip'),
+        sha='0ba67a9cfd60f7fdb65cdb7c55a1ce76c1193df1',
+        filename='face_detection.zip'),
+    face_recognition=Downloader(name='face_recognition',
+        url='https://drive.google.com/u/0/uc?id=1BRIozREIzqkm_aMQ581j93oWoS-6TLST&export=download',
+        sha='03892b9036c58d9400255ff73858caeec1f46609',
+        filename='face_recognition.zip'),
     text=Downloader(name='text',
         url='https://drive.google.com/u/0/uc?id=1lTQdZUau7ujHBqp0P6M1kccnnJgO-dRj&export=download',
         sha='a40cf095ceb77159ddd2a5902f3b4329696dd866',
@@ -192,7 +196,7 @@ def get_confirm_token(response):  # in case of large files
     person_reid=Downloader(name='person_reid',
         url='https://drive.google.com/u/0/uc?id=1G8FkfVo5qcuyMkjSs4EA6J5e16SWDGI2&export=download',
         sha='5b741fbf34c1fbcf59cad8f2a65327a5899e66f1',
-        filename='person_reid')
+        filename='person_reid.zip')
 )
 
 if __name__ == '__main__':

models/image_classification_ppresnet/demo.py

@@ -22,11 +22,12 @@ def str2bool(v):
 parser = argparse.ArgumentParser(description='Deep Residual Learning for Image Recognition (https://arxiv.org/abs/1512.03385, https://github.com/PaddlePaddle/PaddleHub)')
 parser.add_argument('--input', '-i', type=str, help='Path to the input image.')
 parser.add_argument('--model', '-m', type=str, default='image_classification_ppresnet50_2021oct.onnx', help='Path to the model.')
+parser.add_argument('--label', '-l', type=str, default='./imagenet_labels.txt', help='Path to the dataset labels.')
 args = parser.parse_args()
 
 if __name__ == '__main__':
     # Instantiate ResNet
-    model = PPResNet(modelPath=args.model)
+    model = PPResNet(modelPath=args.model, labelPath=args.label)
 
     # Read image and get a 224x224 crop from a 256x256 resized
     image = cv.imread(args.input)