Cellpose

Latest version: v3.1.1.1

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0.6.1

fixes bugs with

* 2D resizing of flows
* training with CUDA in torch
* `__main__.py` relative imports -> absolute imports

0.6

Pytorch is now the default deep neural network software for cellpose. Mxnet will still be supported. To install mxnet (CPU), run `pip install mxnet-mkl`. To use mxnet in a notebook, declare `torch=False` when creating a model, e.g. `model = models.Cellpose(torch=False)`. To use mxnet on the command line, add the flag `--mxnet`, e.g. `python -m cellpose --dir ~/images/ --mxnet`. The pytorch implementation is 20% faster than the mxnet implementation when running on the GPU and 20% slower when running on the CPU.

Dynamics are computed using bilinear interpolation by default instead of nearest neighbor interpolation. Set `interp=False` in `model.eval` to turn off. The bilinear interpolation will be slightly slower on the CPU, but it is faster than nearest neighbor if using torch and the GPU is enabled.

0.5

* sped up 3D segmentation by reducing padding
* tile_overlap as a parameter
* fixed bug with batch_size in CLI

0.1.0.1

* automated testing implemented
* dynamics are run at rescaled size (will be faster for images with cells larger than 30 pixels in diameter)
* pyinstaller binaries created from this release

0.0.3.1

removed matplotlib dependencies and added fast_mode that does not do test-time augmentations or average over 4 networks' outputs

0.0.1.25

- 3D segmentation enabled
- run and train at command line options debugged
- docs added

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