Update vi_backbones.py

vi_backbones.py

@@ -19,6 +19,7 @@ class vi_backbones(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "ver": datasets.Value("string"),
+                    "name": datasets.Value("string"),
                     "type": datasets.Value("string"),
                     "input_size": datasets.Value("int16"),
                     "output_size": datasets.Value("int64"),
@@ -58,25 +59,31 @@ class vi_backbones(datasets.GeneratorBasedBuilder):
         return BeautifulSoup(html, 'html.parser')
 
     def _special_type(self, m_type):
+        m_name = m_type
         if m_type == 'wide' or m_type == 'resnext':
-            return 'resnet'
+            m_type = 'resnet'
 
-        if m_type == 'swin':
-            return 'swin_transformer'
+        elif m_type == 'swin':
+            m_type = 'swin_transformer'
 
-        if m_type == 'inception':
-            return 'googlenet'
+        elif m_type == 'inception':
+            m_type = 'googlenet'
 
-        return m_type
+        pattern = r'_v\d+_'
+        if re.search(pattern, m_name):
+            m_name = re.sub(pattern, '_', m_name)
+
+        return m_type, m_name
 
     def _info_on_dataset(self, m_ver, m_type, in1k_span):
         url_span = in1k_span.find_next_sibling('span', {'class': 's2'})
         size_span = url_span.find_next_sibling('span', {'class': 'mi'})
-        m_type = self._special_type(m_type)
+        m_type, m_name = self._special_type(m_type)
         m_url = str(url_span.text[1:-1])
         input_size = int(size_span.text)
         m_dict = {
             'ver': m_ver,
+            'name': m_name,
             'type': m_type,
             'input_size': input_size,
             'url': m_url
@@ -166,6 +173,7 @@ class vi_backbones(datasets.GeneratorBasedBuilder):
         for i, model in enumerate(subset):
             yield i, {
                 "ver": model['ver'],
+                "name": model['name'],
                 "type": model['type'],
                 "input_size": model['input_size'],
                 "output_size": 1234,