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docs/api.rst

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.. autoclass:: cellpose.models.Cellpose
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:members:
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.. _cpmclass:
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CellposeModel
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~~~~~~~~~~~~~~~~~~
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docs/benchmark.rst

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------------------------------------
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The algorithm runtime and memory usage increases with the data size. The runtimes
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shown below are for a single image run for the first time on an A100 with a batch_size of 32
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- this timing includes warm-up of GPU, thus runtimes
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will be faster for subsequent images. It will also be faster if you run many images of the same size
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shown below are for a single image run once on an A100 with a batch_size of 32.
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This timing includes warm-up of GPU, thus runtimes will be faster for subsequent images. It will also be faster if you run many images of the same size
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input as an array into Cellpose with a large batch_size. The runtimes will also be
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slightly faster if you have fewer cells/cell pixels.
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Table for 2D:
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.. image:: https://www.cellpose.org/static/images/benchmark_2d.png
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:width: 400
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:width: 800
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Table for 3D:
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.. image:: https://www.cellpose.org/static/images/benchmark_3d.png
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:width: 400
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:width: 800
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If you are running out of GPU memory for your images, you can reduce the
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``batch_size`` parameter in the ``model.eval`` function or in the CLI (default is 8).

docs/settings.rst

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--------------------------
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The important settings are described on this page.
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See the :ref:`cpclass` for all run options.
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See the :ref:`cpmclass` for all run options.
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Here is an example of calling the Cellpose class and
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running a list of images for reference:
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::code-block::
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::
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from cellpose import models
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from cellpose.io import imread
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