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+---
+blogpost: true
+date: June 3, 2024
+author: Igor Tatarnikov
+location: London, England
+category: brainglobe
+language: English
+---
+
+# Cellfinder version 1.3.0 is released!
+
+We are excited to announce that a new version of `cellfinder` has been released.
+
+## 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.
+
+
+ * 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.
+
+## What do I need to do?
+
+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).
+
+```bash
+conda create -n cellfinder -c conda-forge python=3.11
+conda activate cellfinder
+pip install cellfinder
+```
+
+You can also update an existing installation of `cellfinder` using pip:
+
+```bash
+pip install --upgrade cellfinder
+```
+
+
+## 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).
+
+### TensorFlow backend
+
+
+ 
+
+
+### PyTorch backend
+
+
+ 
+