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cellfinder
v1.3.0 blog post
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V1 cellfinder 1_3 blogpost
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blogpost: true | ||
date: June 3, 2024 | ||
author: Igor Tatarnikov | ||
location: London, England | ||
category: brainglobe | ||
language: English | ||
--- | ||
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# Cellfinder version 1.3.0 is released! | ||
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We are excited to announce that a new version of `cellfinder` has been released. | ||
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## Main updates | ||
* This update brings a significant change to the backend of `cellfinder`, as we have switched from TensorFlow to PyTorch. This change allows `cellfinder` to support python versions 3.11+, and simplifies the installation process. The new `cellfinder` version maintains the same classification accuracy. Models trained using previous versions of `cellfinder` will continue to work with the new version. | ||
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* The default batch size used for detection has been increased to 64, which improves classification speed by approximately 40% on most systems. The batch size used for detection can now also be adjusted in the `napari` plugin. | ||
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## What do I need to do? | ||
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We recommend using a fresh conda environment to simplify the update. | ||
For GPU support, please follow the installation instructions in the [documentation](../documentation/setting-up/gpu.md). | ||
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```bash | ||
conda create -n cellfinder -c conda-forge python=3.11 | ||
conda activate cellfinder | ||
pip install cellfinder | ||
``` | ||
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You can also update an existing installation of `cellfinder` using pip: | ||
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```bash | ||
pip install --upgrade cellfinder | ||
``` | ||
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## Classification performance | ||
The classification performance between the two versions is comparable. Below is a comparison of the performance between the two versions using data from the [`cellfinder` paper](https://doi.org/10.1371/journal.pcbi.1009074). Running `cellfinder` with a PyTorch backend results in a comparable Pearson correlation and slightly improved linear best-fit slope (labelled as "coeff" in the plot) when comparing to manual cell counts. For more details on how the plots were generated, see the [`cellfinder` paper](https://doi.org/10.1371/journal.pcbi.1009074). | ||
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### TensorFlow backend | ||
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<p align="center"> | ||
<img src="../_static/comparison_tensorflow.png" width="800px"> | ||
</p> | ||
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### PyTorch backend | ||
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<p align="center"> | ||
<img src="../_static/comparison_torch.png" width="800px-"> | ||
</p> |
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Once everything is merged, this will point to new, pytorch-based instructions right?
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If I understand Sphinx correctly then yes, this should point to the instructions as updated in #189!