# load_dataset

### deepinv.utils.load_dataset(dataset_name, transform, data_dir=None, download=True, url=None, file_type='zip')

Loads an ImageFolder dataset from DeepInverse HuggingFace repository.

* **Parameters:**
  * **dataset_name** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str) *,* [*pathlib.Path*](https://docs.python.org/3.12/library/pathlib.html#pathlib.Path)) – dataset name without file extension.
  * **transform** (*Callable*) – optional transform to pass to torchvision dataset.
  * **data_dir** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str) *,* [*pathlib.Path*](https://docs.python.org/3.12/library/pathlib.html#pathlib.Path)) – dataset root directory, defaults to None
  * **download** ([*bool*](https://docs.python.org/3.12/library/functions.html#bool)) – whether to download, defaults to True
  * **url** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – download URL, if `None`, gets URL using [`deepinv.utils.get_image_url()`](https://deepinv.org/api/stubs/deepinv.utils.get_image_url.html.md#deepinv.utils.get_image_url)
  * **file_type** ([*str*](https://docs.python.org/3.12/library/stdtypes.html#str)) – file extension, defaults to “zip”
* **Returns:**
  [`deepinv.datasets.ImageFolder`](https://deepinv.org/api/stubs/deepinv.datasets.ImageFolder.html.md#deepinv.datasets.ImageFolder) dataset.
* **Return type:**
  [ImageFolder](https://deepinv.org/api/stubs/deepinv.datasets.ImageFolder.html.md#deepinv.datasets.ImageFolder)

## Examples using `load_dataset`:

<div class="sphx-glr-thumbnails">
<!-- thumbnail-parent-div-open --><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use a standard TV prior for image deblurring. The problem writes as y = Ax + \\epsilon where A is a convolutional operator and \\epsilon is the realization of some Gaussian noise. The goal is to recover the original image x from the blurred and noisy image y. The TV prior is used to regularize the problem.">![](auto_examples/optimization/images/thumb/sphx_glr_demo_TV_minimisation_thumb.png)

[Image deblurring with Total-Variation (TV) prior](https://deepinv.org/auto_examples/optimization/demo_TV_minimisation.html.md)

  <div class="sphx-glr-thumbnail-title">Image deblurring with Total-Variation (TV) prior</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to solve a deblurring inverse problem using an explicit prior.">![](auto_examples/optimization/images/thumb/sphx_glr_demo_custom_prior_thumb.png)

[Image deblurring with custom deep explicit prior.](https://deepinv.org/auto_examples/optimization/demo_custom_prior.html.md)

  <div class="sphx-glr-thumbnail-title">Image deblurring with custom deep explicit prior.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to solve Poisson inverse problems using the Maximum-Likelihood Expectation-Maximization (MLEM) algorithm sheppMaximumLikelihoodReconstruction1982, also known as the Richardson-Lucy algorithm in the deconvolution setting richardsonBayesianBasedIterativeMethod1972,lucyIterativeTechniqueRectification1974.">![](auto_examples/optimization/images/thumb/sphx_glr_demo_poisson_mlem_thumb.png)

[Poisson Inverse Problems with Maximum-Likelihood Expectation-Maximization (MLEM)](https://deepinv.org/auto_examples/optimization/demo_poisson_mlem.html.md)

  <div class="sphx-glr-thumbnail-title">Poisson Inverse Problems with Maximum-Likelihood Expectation-Maximization (MLEM)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use a standard wavelet prior for image inpainting. The problem writes as y = Ax + \\epsilon where A is a mask and \\epsilon is the realization of some Gaussian noise. The goal is to recover the original image x from the blurred and noisy image y. The wavelet prior is used to regularize the problem.">![](auto_examples/optimization/images/thumb/sphx_glr_demo_wavelet_prior_thumb.png)

[Image inpainting with wavelet prior](https://deepinv.org/auto_examples/optimization/demo_wavelet_prior.html.md)

  <div class="sphx-glr-thumbnail-title">Image inpainting with wavelet prior</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the DPIR method to solve a PnP image deblurring problem. The DPIR method is described in zhang2021plug. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3929-3938).">![](auto_examples/plug-and-play/images/thumb/sphx_glr_demo_PnP_DPIR_deblur_thumb.png)

[DPIR method for PnP image deblurring.](https://deepinv.org/auto_examples/plug-and-play/demo_PnP_DPIR_deblur.html.md)

  <div class="sphx-glr-thumbnail-title">DPIR method for PnP image deblurring.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plug-and-Play (PnP) is known to be challenging to apply to certain inverse problems like inpainting. One way to overcome this is to use a multi-scale approach instead of the standard single-scale approach.">![](auto_examples/plug-and-play/images/thumb/sphx_glr_demo_PnP_multiscale_thumb.png)

[Multi-scale Plug-and-Play for Inpainting](https://deepinv.org/auto_examples/plug-and-play/demo_PnP_multiscale.html.md)

  <div class="sphx-glr-thumbnail-title">Multi-scale Plug-and-Play for Inpainting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Implementation of romano2017little using as plug-in denoiser the Gradient-Step Denoiser (GSPnP) hurault2021gradient which provides an explicit prior.">![](auto_examples/plug-and-play/images/thumb/sphx_glr_demo_RED_GSPnP_SR_thumb.png)

[Regularization by Denoising (RED) for Super-Resolution.](https://deepinv.org/auto_examples/plug-and-play/demo_RED_GSPnP_SR.html.md)

  <div class="sphx-glr-thumbnail-title">Regularization by Denoising (RED) for Super-Resolution.</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This a toy example to show you how to use DEQ to solve a deblurring problem. Note that this is a small dataset for training. For optimal results, use a larger dataset.">![](auto_examples/unfolded/images/thumb/sphx_glr_demo_DEQ_thumb.png)

[Deep Equilibrium (DEQ) algorithms for image deblurring](https://deepinv.org/auto_examples/unfolded/demo_DEQ.html.md)

  <div class="sphx-glr-thumbnail-title">Deep Equilibrium (DEQ) algorithms for image deblurring</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Some unfolded architectures rely on a least-squares solver to compute the proximal step w.r.t. the data-fidelity term (e.g., ADMM or HQS):  ">![](auto_examples/unfolded/images/thumb/sphx_glr_demo_unfolded_constant_memory_thumb.png)

[Reducing the memory and computational complexity of unfolded network training](https://deepinv.org/auto_examples/unfolded/demo_unfolded_constant_memory.html.md)

  <div class="sphx-glr-thumbnail-title">Reducing the memory and computational complexity of unfolded network training</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Image inpainting consists in solving y = Ax where A is a mask operator. This problem can be reformulated as the following minimization problem:">![](auto_examples/unfolded/images/thumb/sphx_glr_demo_unfolded_constrained_LISTA_thumb.png)

[Unfolded Chambolle-Pock for constrained image inpainting](https://deepinv.org/auto_examples/unfolded/demo_unfolded_constrained_LISTA.html.md)

  <div class="sphx-glr-thumbnail-title">Unfolded Chambolle-Pock for constrained image inpainting</div>
</div>
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