diff --git a/timm/models/_efficientnet_blocks.py b/timm/models/_efficientnet_blocks.py
index 92b849e461..a5a6f30b79 100644
--- a/timm/models/_efficientnet_blocks.py
+++ b/timm/models/_efficientnet_blocks.py
@@ -75,7 +75,7 @@ def feature_info(self, location):
         if location == 'expansion':  # output of conv after act, same as block coutput
             return dict(module='bn1', hook_type='forward', num_chs=self.conv.out_channels)
         else:  # location == 'bottleneck', block output
-            return dict(module='', hook_type='', num_chs=self.conv.out_channels)
+            return dict(module='', num_chs=self.conv.out_channels)
 
     def forward(self, x):
         shortcut = x
@@ -116,7 +116,7 @@ def feature_info(self, location):
         if location == 'expansion':  # after SE, input to PW
             return dict(module='conv_pw', hook_type='forward_pre', num_chs=self.conv_pw.in_channels)
         else:  # location == 'bottleneck', block output
-            return dict(module='', hook_type='', num_chs=self.conv_pw.out_channels)
+            return dict(module='', num_chs=self.conv_pw.out_channels)
 
     def forward(self, x):
         shortcut = x
@@ -173,7 +173,7 @@ def feature_info(self, location):
         if location == 'expansion':  # after SE, input to PWL
             return dict(module='conv_pwl', hook_type='forward_pre', num_chs=self.conv_pwl.in_channels)
         else:  # location == 'bottleneck', block output
-            return dict(module='', hook_type='', num_chs=self.conv_pwl.out_channels)
+            return dict(module='', num_chs=self.conv_pwl.out_channels)
 
     def forward(self, x):
         shortcut = x
@@ -266,7 +266,7 @@ def feature_info(self, location):
         if location == 'expansion':  # after SE, before PWL
             return dict(module='conv_pwl', hook_type='forward_pre', num_chs=self.conv_pwl.in_channels)
         else:  # location == 'bottleneck', block output
-            return dict(module='', hook_type='', num_chs=self.conv_pwl.out_channels)
+            return dict(module='', num_chs=self.conv_pwl.out_channels)
 
     def forward(self, x):
         shortcut = x
diff --git a/timm/models/_efficientnet_builder.py b/timm/models/_efficientnet_builder.py
index e6cd05ae2e..b5dbeaae3a 100644
--- a/timm/models/_efficientnet_builder.py
+++ b/timm/models/_efficientnet_builder.py
@@ -370,9 +370,7 @@ def __call__(self, in_chs, model_block_args):
         stages = []
         if model_block_args[0][0]['stride'] > 1:
             # if the first block starts with a stride, we need to extract first level feat from stem
-            feature_info = dict(
-                module='act1', num_chs=in_chs, stage=0, reduction=current_stride,
-                hook_type='forward' if self.feature_location != 'bottleneck' else '')
+            feature_info = dict(module='bn1', num_chs=in_chs, stage=0, reduction=current_stride)
             self.features.append(feature_info)
 
         # outer list of block_args defines the stacks
@@ -418,10 +416,16 @@ def __call__(self, in_chs, model_block_args):
                 # stash feature module name and channel info for model feature extraction
                 if extract_features:
                     feature_info = dict(
-                        stage=stack_idx + 1, reduction=current_stride, **block.feature_info(self.feature_location))
-                    module_name = f'blocks.{stack_idx}.{block_idx}'
+                        stage=stack_idx + 1,
+                        reduction=current_stride,
+                        **block.feature_info(self.feature_location),
+                    )
                     leaf_name = feature_info.get('module', '')
-                    feature_info['module'] = '.'.join([module_name, leaf_name]) if leaf_name else module_name
+                    if leaf_name:
+                        feature_info['module'] = '.'.join([f'blocks.{stack_idx}.{block_idx}', leaf_name])
+                    else:
+                        assert last_block
+                        feature_info['module'] = f'blocks.{stack_idx}'
                     self.features.append(feature_info)
 
                 total_block_idx += 1  # incr global block idx (across all stacks)
diff --git a/timm/models/_features.py b/timm/models/_features.py
index 7924453638..7ef51809bc 100644
--- a/timm/models/_features.py
+++ b/timm/models/_features.py
@@ -27,12 +27,13 @@ class FeatureInfo:
 
     def __init__(self, feature_info: List[Dict], out_indices: Tuple[int]):
         prev_reduction = 1
-        for fi in feature_info:
+        for i, fi in enumerate(feature_info):
             # sanity check the mandatory fields, there may be additional fields depending on the model
             assert 'num_chs' in fi and fi['num_chs'] > 0
             assert 'reduction' in fi and fi['reduction'] >= prev_reduction
             prev_reduction = fi['reduction']
             assert 'module' in fi
+            fi.setdefault('index', i)
         self.out_indices = out_indices
         self.info = feature_info
 
diff --git a/timm/models/_features_fx.py b/timm/models/_features_fx.py
index c894d66c85..b84312a739 100644
--- a/timm/models/_features_fx.py
+++ b/timm/models/_features_fx.py
@@ -6,7 +6,7 @@
 import torch
 from torch import nn
 
-from ._features import _get_feature_info
+from ._features import _get_feature_info, _get_return_layers
 
 try:
     from torchvision.models.feature_extraction import create_feature_extractor as _create_feature_extractor
@@ -93,9 +93,7 @@ def __init__(self, model, out_indices, out_map=None):
         self.feature_info = _get_feature_info(model, out_indices)
         if out_map is not None:
             assert len(out_map) == len(out_indices)
-        return_nodes = {
-            info['module']: out_map[i] if out_map is not None else info['module']
-            for i, info in enumerate(self.feature_info) if i in out_indices}
+        return_nodes = _get_return_layers(self.feature_info, out_map)
         self.graph_module = create_feature_extractor(model, return_nodes)
 
     def forward(self, x):
diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py
index 1854bc3058..905f8b7596 100644
--- a/timm/models/efficientnet.py
+++ b/timm/models/efficientnet.py
@@ -232,7 +232,7 @@ def __init__(
         )
         self.blocks = nn.Sequential(*builder(stem_size, block_args))
         self.feature_info = FeatureInfo(builder.features, out_indices)
-        self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
+        self._stage_out_idx = {f['stage']: f['index'] for f in self.feature_info.get_dicts()}
 
         efficientnet_init_weights(self)
 
@@ -268,20 +268,28 @@ def forward(self, x) -> List[torch.Tensor]:
 
 
 def _create_effnet(variant, pretrained=False, **kwargs):
-    features_only = False
+    features_mode = ''
     model_cls = EfficientNet
     kwargs_filter = None
     if kwargs.pop('features_only', False):
-        features_only = True
-        kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'global_pool')
-        model_cls = EfficientNetFeatures
+        if 'feature_cfg' in kwargs:
+            features_mode = 'cfg'
+        else:
+            kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'global_pool')
+            model_cls = EfficientNetFeatures
+            features_mode = 'cls'
+
     model = build_model_with_cfg(
-        model_cls, variant, pretrained,
-        pretrained_strict=not features_only,
+        model_cls,
+        variant,
+        pretrained,
+        features_only=features_mode == 'cfg',
+        pretrained_strict=features_mode != 'cls',
         kwargs_filter=kwargs_filter,
-        **kwargs)
-    if features_only:
-        model.default_cfg = pretrained_cfg_for_features(model.default_cfg)
+        **kwargs,
+    )
+    if features_mode == 'cls':
+        model.pretrained_cfg = model.default_cfg = pretrained_cfg_for_features(model.pretrained_cfg)
     return model
 
 
diff --git a/timm/models/hrnet.py b/timm/models/hrnet.py
index 4a6fd80eaa..e56a550936 100644
--- a/timm/models/hrnet.py
+++ b/timm/models/hrnet.py
@@ -829,7 +829,7 @@ def __init__(
             **kwargs,
         )
         self.feature_info = FeatureInfo(self.feature_info, out_indices)
-        self._out_idx = {i for i in out_indices}
+        self._out_idx = {f['index'] for f in self.feature_info.get_dicts()}
 
     def forward_features(self, x):
         assert False, 'Not supported'
diff --git a/timm/models/mobilenetv3.py b/timm/models/mobilenetv3.py
index 70087b62fa..8de94f7e26 100644
--- a/timm/models/mobilenetv3.py
+++ b/timm/models/mobilenetv3.py
@@ -210,7 +210,7 @@ def __init__(
         )
         self.blocks = nn.Sequential(*builder(stem_size, block_args))
         self.feature_info = FeatureInfo(builder.features, out_indices)
-        self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
+        self._stage_out_idx = {f['stage']: f['index'] for f in self.feature_info.get_dicts()}
 
         efficientnet_init_weights(self)
 
@@ -247,21 +247,27 @@ def forward(self, x) -> List[torch.Tensor]:
 
 
 def _create_mnv3(variant, pretrained=False, **kwargs):
-    features_only = False
+    features_mode = ''
     model_cls = MobileNetV3
     kwargs_filter = None
     if kwargs.pop('features_only', False):
-        features_only = True
-        kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'head_bias', 'global_pool')
-        model_cls = MobileNetV3Features
+        if 'feature_cfg' in kwargs:
+            features_mode = 'cfg'
+        else:
+            kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'head_bias', 'global_pool')
+            model_cls = MobileNetV3Features
+            features_mode = 'cls'
+
     model = build_model_with_cfg(
         model_cls,
         variant,
         pretrained,
-        pretrained_strict=not features_only,
+        features_only=features_mode == 'cfg',
+        pretrained_strict=features_mode != 'cls',
         kwargs_filter=kwargs_filter,
-        **kwargs)
-    if features_only:
+        **kwargs,
+    )
+    if features_mode == 'cls':
         model.default_cfg = pretrained_cfg_for_features(model.default_cfg)
     return model