.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/package/tutorial_timm_backbone.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_tutorials_package_tutorial_timm_backbone.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials_package_tutorial_timm_backbone.py:


.. _lightly-timm-backbone-tutorial-8:

Tutorial 8: Using timm Models as Backbones
===========================================

You can use LightlySSL to pre-train any timm model using self-supervised learning since
most methods share a similar workflow. All methods have a base model (the backbone), which
can be any fundamental model such as ResNet or MobileNet.

In this tutorial, we will learn how to use a model architecture from the timm library
as a backbone in a self-supervised learning workflow.

.. GENERATED FROM PYTHON SOURCE LINES 16-25

Imports
-------

Import the Python frameworks we need for this tutorial.
Make sure you have the necessary packages installed.

.. code-block:: console

  pip install lightly"[timm]"

.. GENERATED FROM PYTHON SOURCE LINES 25-31

.. code-block:: Python



    import timm
    import torch
    import torch.nn as nn








.. GENERATED FROM PYTHON SOURCE LINES 32-36

timm comes packaged with >700 pre-trained models designed to be flexible and easy to use.
These pre-trained models can be loaded using the
`create_model() <https://huggingface.co/docs/timm/v1.0.8/en/reference/models#timm.create_model>`_
function. For example, we can use the following snippet to create an efficient model.

.. GENERATED FROM PYTHON SOURCE LINES 36-40

.. code-block:: Python


    backbone = timm.create_model(model_name="efficientnet_b0")









.. GENERATED FROM PYTHON SOURCE LINES 41-46

Using a timm Model as a Backbone
---------------------------------

We can now use this model as a backbone for training. Let's see how we can
create a torch module for the `SimCLR <https://arxiv.org/abs/2002.05709>`_ method.

.. GENERATED FROM PYTHON SOURCE LINES 46-71

.. code-block:: Python


    from lightly.models.modules.heads import SimCLRProjectionHead


    class SimCLR(torch.nn.Module):
        def __init__(self, backbone, feature_dim, out_dim):
            super().__init__()
            self.backbone = backbone
            self.projection_head = SimCLRProjectionHead(feature_dim, feature_dim, out_dim)

        def forward(self, x):
            features = self.backbone.forward_features(x)
            h = self.backbone.global_pool(features).flatten(start_dim=1)
            z = self.projection_head(h)
            return z


    simclr = SimCLR(backbone, feature_dim=1280, out_dim=128)

    # check if it works
    input_a = torch.randn((2, 3, 224, 224))
    input_b = torch.randn((2, 3, 224, 224))
    out_a = simclr(input_a)
    out_b = simclr(input_b)








.. GENERATED FROM PYTHON SOURCE LINES 72-86

Next Steps
------------

For an indepth tutorial on fine-tuning a model using `SimCLR <https://arxiv.org/abs/2002.05709>`_
you can refer to our fine-tuning :ref:`lightly-checkpoint-finetuning-tutorial-7`.
Interested in pre-training your own self-supervised models? Check out our other
tutorials:

- :ref:`input-structure-label`
- :ref:`lightly-moco-tutorial-2`
- :ref:`lightly-simsiam-tutorial-4`
- :ref:`lightly-custom-augmentation-5`
- :ref:`lightly-detectron-tutorial-6`



.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.277 seconds)


.. _sphx_glr_download_tutorials_package_tutorial_timm_backbone.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: tutorial_timm_backbone.ipynb <tutorial_timm_backbone.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: tutorial_timm_backbone.py <tutorial_timm_backbone.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: tutorial_timm_backbone.zip <tutorial_timm_backbone.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_