Docs for AutoBackbone & Backbone (#27456)

* Initial commit for AutoBackbone & Backbone

* Added timm and clarified out_indices

* Swapped the example to out_indices

* fix toctree

* Update autoclass_tutorial.md

* Update backbones.md

* Update autoclass_tutorial.md

* Add dummy torch input instead

* Add dummy torch input

* Update autoclass_tutorial.md

* Update backbones.md

* minor fix

* Update docs/source/en/main_classes/backbones.md
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* Update docs/source/en/autoclass_tutorial.md
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* Added illustrations and explained backbone & neck

* Update docs/source/en/main_classes/backbones.md
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

* Update backbones.md

---------
Co-authored-by: Maria Khalusova <kafooster@gmail.com>

docs/source/en/_toctree.yml

@@ -218,6 +218,8 @@
       title: Agents and Tools
     - local: model_doc/auto
       title: Auto Classes
+    - local: main_classes/backbones
+      title: Backbones
     - local: main_classes/callback
       title: Callbacks
     - local: main_classes/configuration

docs/source/en/autoclass_tutorial.md

@@ -31,6 +31,7 @@ In this tutorial, learn to:
 * Load a pretrained feature extractor.
 * Load a pretrained processor.
 * Load a pretrained model.
+* Load a model as a backbone.
 
 ## AutoTokenizer
 
@@ -95,7 +96,7 @@ Load a processor with [`AutoProcessor.from_pretrained`]:
 
 <frameworkcontent>
 <pt>
-Finally, the `AutoModelFor` classes let you load a pretrained model for a given task (see [here](model_doc/auto) for a complete list of available tasks). For example, load a model for sequence classification with [`AutoModelForSequenceClassification.from_pretrained`]:
+The `AutoModelFor` classes let you load a pretrained model for a given task (see [here](model_doc/auto) for a complete list of available tasks). For example, load a model for sequence classification with [`AutoModelForSequenceClassification.from_pretrained`]:
 
 ```py
 >>> from transformers import AutoModelForSequenceClassification
@@ -141,3 +142,24 @@ Easily reuse the same checkpoint to load an architecture for a different task:
 Generally, we recommend using the `AutoTokenizer` class and the `TFAutoModelFor` class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next [tutorial](preprocessing), learn how to use your newly loaded tokenizer, image processor, feature extractor and processor to preprocess a dataset for fine-tuning.
 </tf>
 </frameworkcontent>
+
+## AutoBackbone
+
+`AutoBackbone` lets you use pretrained models as backbones and get feature maps as outputs from different stages of the models. Below you can see how to get feature maps from a [Swin](model_doc/swin) checkpoint.
+
+```py
+>>> from transformers import AutoImageProcessor, AutoBackbone
+>>> import torch
+>>> from PIL import Image
+>>> import requests
+>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+>>> processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
+>>> model = AutoBackbone.from_pretrained("microsoft/swin-tiny-patch4-window7-224", out_indices=(0,))
+
+>>> inputs = processor(image, return_tensors="pt")
+>>> outputs = model(**inputs)
+>>> feature_maps = outputs.feature_maps
+>>> list(feature_maps[-1].shape)
+[1, 96, 56, 56]
+```

docs/source/en/main_classes/backbones.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
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+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Backbones
+
+Backbones are models used for feature extraction for computer vision tasks. One can use a model as backbone in two ways:
+
+* initializing `AutoBackbone` class with a pretrained model,
+* initializing a supported backbone configuration and passing it to the model architecture. 
+
+## Using AutoBackbone 
+
+You can use `AutoBackbone` class to initialize a model as a backbone and get the feature maps for any stage. You can define `out_indices` to indicate the index of the layers which you would like to get the feature maps from. You can also use `out_features` if you know the name of the layers. You can use them interchangeably. If you are using both `out_indices` and `out_features`, ensure they are consistent. Not passing any of the feature map arguments will make the backbone yield the feature maps of the last layer.
+To visualize how stages look like, let's take the Swin model. Each stage is responsible from feature extraction, outputting feature maps.
+<div style="text-align: center">
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Swin%20Stages.png">
+</div>
+
+Illustrating feature maps of the first stage looks like below.
+<div style="text-align: center">
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Swin%20Stage%201.png">
+</div>
+
+Let's see with an example. Note that `out_indices=(0,)` results in yielding the stem of the model. Stem refers to the stage before the first feature extraction stage. In above diagram, it refers to patch partition. We would like to have the feature maps from stem, first, and second stage of the model.
+```py
+>>> from transformers import AutoImageProcessor, AutoBackbone
+>>> import torch
+>>> from PIL import Image
+>>> import requests
+
+>>> processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
+>>> model = AutoBackbone.from_pretrained("microsoft/swin-tiny-patch4-window7-224", out_indices=(0,1,2))
+>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> inputs = processor(image, return_tensors="pt")
+>>> outputs = model(**inputs)
+>>> feature_maps = outputs.feature_maps
+```
+`feature_maps` object now has three feature maps, each can be accessed like below. Say we would like to get the feature map of the stem.
+```python
+>>> list(feature_maps[0].shape)
+[1, 96, 56, 56]
+```
+
+We can get the feature maps of first and second stages like below.
+```python
+>>> list(feature_maps[1].shape)
+[1, 96, 56, 56]
+>>> list(feature_maps[2].shape)
+[1, 192, 28, 28]
+```
+
+## Initializing Backbone Configuration
+
+In computer vision, models consist of backbone, neck, and a head. Backbone extracts the features, neck transforms the output of the backbone and head is used for the main task (e.g. object detection). You can initialize neck and head with model backbones by passing a model configuration to `backbone_config`. For example, below you can see how to initialize the [MaskFormer](../model_doc/maskformer) model with instance segmentation head with [ResNet](../model_doc/resnet) backbone.
+
+```py
+from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, ResNetConfig
+
+backbone_config = ResNetConfig.from_pretrained("microsoft/resnet-50")
+config = MaskFormerConfig(backbone_config=backbone_config)
+model = MaskFormerForInstanceSegmentation(config)
+```
+You can also initialize a backbone with random weights to initialize the model neck with it. 
+
+```py
+backbone_config = ResNetConfig()
+config = MaskFormerConfig(backbone_config=backbone_config)
+model = MaskFormerForInstanceSegmentation(config)
+```
+
+`timm` models are also supported in transformers through `TimmBackbone` and `TimmBackboneConfig`.
+
+```python
+from transformers import TimmBackboneConfig, TimmBackbone
+
+backbone_config = TimmBackboneConfig("resnet50")
+model = TimmBackbone(config=backbone_config)
+```