.. _sphx_glr_auto_examples_models:

Models
-------------------------------




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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to perform inference on and fine-tune the Reconstruct Anything Model (RAM) foundation model :footciteterris2025reconstruct to solve inverse problems.">

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  .. image:: /auto_examples/models/images/thumb/sphx_glr_demo_foundation_model_thumb.png
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  :ref:`sphx_glr_auto_examples_models_demo_foundation_model.py`

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      <div class="sphx-glr-thumbnail-title">Inference and fine-tune a foundation model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example provides a very simple quick start introduction to training reconstruction networks with DeepInverse for solving imaging inverse problems.">

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  .. image:: /auto_examples/models/images/thumb/sphx_glr_demo_training_thumb.png
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  :ref:`sphx_glr_auto_examples_models_demo_training.py`

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      <div class="sphx-glr-thumbnail-title">Training a reconstruction model</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example provides a tour of the denoisers in DeepInverse. A denoiser is a model that takes in a noisy image and outputs a denoised version of it. Basically, it solves the following problem:">

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  .. image:: /auto_examples/models/images/thumb/sphx_glr_demo_denoiser_tour_thumb.png
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  :ref:`sphx_glr_auto_examples_models_demo_denoiser_tour.py`

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      <div class="sphx-glr-thumbnail-title">Benchmarking pretrained denoisers</div>
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    </div>


.. toctree::
   :hidden:

   /auto_examples/models/demo_foundation_model
   /auto_examples/models/demo_training
   /auto_examples/models/demo_denoiser_tour