🚨 Delete duplicate code in backbone utils

docs/source/en/backbones.md

@@ -36,8 +36,8 @@ This guide describes the backbone class, backbones from the [timm](https://hf.co
 
 There are two backbone classes.
 
-- [`~transformers.utils.BackboneMixin`] allows you to load a backbone and includes functions for extracting the feature maps and indices.
-- [`~transformers.utils.BackboneConfigMixin`] allows you to set the feature map and indices of a backbone configuration.
+- [`~transformers.utils.BackboneMixin`] allows you to load a backbone and includes functions for extracting the feature maps and indices from config.
+- [`~transformers.utils.BackboneConfigMixin`] allows you to set, align and verify the feature map and indices of a backbone configuration.
 
 Refer to the [Backbone](./main_classes/backbones) API documentation to check which models support a backbone.
 
@@ -69,12 +69,12 @@ When you know a model supports a backbone, you can load the backbone and neck di
 
 The example below loads a [ResNet](./model_doc/resnet) backbone and neck for use in a [MaskFormer](./model_doc/maskformer) instance segmentation head.
 
-Set `backbone` to a pretrained model and  `use_pretrained_backbone=True` to use pretrained weights instead of randomly initialized weights.
+Note that initializing from config will create the model with random weights. If you want to load a pretrained model, use `from_pretrained` API.
 
 ```py
 from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation
 
-config = MaskFormerConfig(backbone="microsoft/resnet-50", use_pretrained_backbone=True)
+config = MaskFormerConfig(backbone="microsoft/resnet-50")
 model = MaskFormerForInstanceSegmentation(config)
 ```
 
@@ -96,14 +96,12 @@ model = MaskFormerForInstanceSegmentation(config)
 
 ## timm backbones
 
-[timm](https://hf.co/docs/timm/index) is a collection of vision models for training and inference. Transformers supports timm models as backbones with the [`TimmBackbone`] and [`TimmBackboneConfig`] classes.
-
-Set `use_timm_backbone=True` to load pretrained timm weights, and `use_pretrained_backbone` to use pretrained or randomly initialized weights.
+[timm](https://hf.co/docs/timm/index) is a collection of vision models for training and inference. Transformers supports timm models as backbones with the [`TimmBackbone`] and [`TimmBackboneConfig`] classes. Set the neccessary backbone checkpoint in `backbone` to create a model with Timm backbone with randomly initialized weights.
 
 ```py
 from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation
 
-config = MaskFormerConfig(backbone="resnet50", use_timm_backbone=True, use_pretrained_backbone=True)
+config = MaskFormerConfig(backbone="resnet50")
 model = MaskFormerForInstanceSegmentation(config)
 ```
 
@@ -112,7 +110,7 @@ You could also explicitly call the [`TimmBackboneConfig`] class to load and crea
 ```py
 from transformers import TimmBackboneConfig
 
-backbone_config = TimmBackboneConfig("resnet50", use_pretrained_backbone=True)
+backbone_config = TimmBackboneConfig("resnet50")
 ```
 
 Pass the backbone configuration to the model configuration and instantiate the model head, [`MaskFormerForInstanceSegmentation`], with the backbone.

docs/source/en/main_classes/backbones.md

@@ -18,8 +18,8 @@ rendered properly in your Markdown viewer.
 
 A backbone is a model used for feature extraction for higher level computer vision tasks such as object detection and image classification. Transformers provides an [`AutoBackbone`] class for initializing a Transformers backbone from pretrained model weights, and two utility classes:
 
-* [`~utils.BackboneMixin`] enables initializing a backbone from Transformers or [timm](https://hf.co/docs/timm/index) and includes functions for returning the output features and indices.
-* [`~utils.BackboneConfigMixin`] sets the output features and indices of the backbone configuration.
+* [`~modeling_backbone_utils.BackboneMixin`] enables initializing a backbone from Transformers or [timm](https://hf.co/docs/timm/index) and includes functions for returning the output features and indices.
+* [`~modeling_backbone_utils.BackboneConfigMixin`] sets the output features and indices of the backbone configuration.
 
 [timm](https://hf.co/docs/timm/index) models are loaded with the [`TimmBackbone`] and [`TimmBackboneConfig`] classes.
 
@@ -45,11 +45,11 @@ Backbones are supported for the following models:
 
 ## BackboneMixin
 
-[[autodoc]] utils.BackboneMixin
+[[autodoc]] modeling_backbone_utils.BackboneMixin
 
 ## BackboneConfigMixin
 
-[[autodoc]] utils.BackboneConfigMixin
+[[autodoc]] modeling_backbone_utils.BackboneConfigMixin
 
 ## TimmBackbone