update accuracy evaluation scripts (#184)

* update accuracy evaluation scripts

* remove labels of image classification

tools/eval/README.md

@@ -5,7 +5,7 @@ Make sure you have the following packages installed:
 ```shell
 pip install tqdm
 pip install scikit-learn
-pip install scipy
+pip install scipy==1.8.1
 ```
 
 Generally speaking, evaluation can be done with the following command:
@@ -27,7 +27,7 @@ Supported datasets:
 
 ### Prepare data
 
-Please visit https://image-net.org/ to download the ImageNet dataset and [the labels from caffe](http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz). Organize files as follow:
+Please visit https://image-net.org/ to download the ImageNet dataset (only need images in `ILSVRC/Data/CLS-LOC/val`) and [the labels from caffe](http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz). Organize files as follow:
 
 ```shell
 $ tree -L 2 /path/to/imagenet

tools/eval/eval.py

@@ -22,24 +22,24 @@ args = parser.parse_args()
 
 models = dict(
     mobilenetv1=dict(
-        name="MobileNetV1",
+        name="MobileNet",
         topic="image_classification",
         modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr.onnx"),
         topK=5),
     mobilenetv1_q=dict(
-        name="MobileNetV1",
+        name="MobileNet",
         topic="image_classification",
-        modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr-int8-quantized.onnx"),
+        modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr_int8.onnx"),
         topK=5),
     mobilenetv2=dict(
-        name="MobileNetV2",
+        name="MobileNet",
         topic="image_classification",
         modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr.onnx"),
         topK=5),
     mobilenetv2_q=dict(
-        name="MobileNetV2",
+        name="MobileNet",
         topic="image_classification",
-        modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr-int8-quantized.onnx"),
+        modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr_int8.onnx"),
         topK=5),
     ppresnet=dict(
         name="PPResNet",
@@ -49,7 +49,7 @@ models = dict(
     ppresnet_q=dict(
         name="PPResNet",
         topic="image_classification",
-        modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan-act_int8-wt_int8-quantized.onnx"),
+        modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan_int8.onnx"),
         topK=5),
     yunet=dict(
         name="YuNet",
@@ -72,19 +72,23 @@ models = dict(
     sface_q=dict(
         name="SFace",
         topic="face_recognition",
-        modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec-act_int8-wt_int8-quantized.onnx")),
-    crnn=dict(
+        modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec_int8.onnx")),
+    crnn_en=dict(
         name="CRNN",
         topic="text_recognition",
         modelPath=os.path.join(root_dir, "models/text_recognition_crnn/text_recognition_CRNN_EN_2021sep.onnx")),
+    crnn_en_q=dict(
+        name="CRNN",
+        topic="text_recognition",
+        modelPath=os.path.join(root_dir, "models/text_recognition_crnn/text_recognition_CRNN_EN_2022oct_int8.onnx")),
     pphumanseg=dict(
         name="PPHumanSeg",
         topic="human_segmentation",
-        modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2021oct.onnx")),
+        modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar.onnx")),
     pphumanseg_q=dict(
         name="PPHumanSeg",
         topic="human_segmentation",
-        modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2021oct-act_int8-wt_int8-quantized.onnx")),
+        modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar_int8.onnx")),
 )
 
 datasets = dict(