Add `list_benchmarks` to compare models easily

This is a simple function for listing ImageNet benchmarks found [here](https://github.com/rwightman/pytorch-image-models/blob/master/results/results-imagenet.csv) for users to use

timm/models/registry.py

@@ -66,8 +66,8 @@ def list_models(filter='', module='', pretrained=False, exclude_filters='', name
         name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
 
     Example:
-        model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
-        model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
+        list_models('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
+        list_models('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
     """
     if module:
         all_models = list(_module_to_models[module])
@@ -96,6 +96,25 @@ def list_models(filter='', module='', pretrained=False, exclude_filters='', name
     return list(sorted(models, key=_natural_key))
 
 
+def list_benchmarks(filter='', module='', pretrained=False, exclude_filters='', name_matches_cfg=False):
+    """ Return list of available benchmarks on imagenet, sorted alphabetically
+
+    Args:
+        filter (str) - Wildcard filter string that works with fnmatch
+        module (str) - Limit model selection to a specific sub-module (ie 'gen_efficientnet')
+        pretrained (bool) - Include only models with pretrained weights if True
+        exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
+        name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
+
+    Example:
+        list_benchmarks('gluon_resnet*') -- returns a pandas dataframe with all the benchmarks starting with 'gluon_resnet'
+    """
+    models = list_models(filter=filter, module=module, pretrained=pretrained, exclude_filters=exclude_filters, name_matches_cfg=name_matches_cfg)
+    df = pd.read_csv("https://raw.githubusercontent.com/rwightman/pytorch-image-models/master/results/results-imagenet.csv")    
+    
+    return df[df["model"].isin(models)]
+
+
 def is_model(model_name):
     """ Check if a model name exists
     """
@@ -156,4 +175,4 @@ def get_pretrained_cfg_value(model_name, cfg_key):
     """
     if model_name in _model_pretrained_cfgs:
         return _model_pretrained_cfgs[model_name].get(cfg_key, None)
-    return None
+    return None