Metadata-Version: 2.1
Name: pathpretrain
Version: 0.1.2
Summary: Simple setup train image models.
Home-page: https://github.com/jlevy44/PathPretrain
Author: Joshua Levy
Author-email: joshualevy44@berkeley.edu
License: MIT
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: dask (==2023.2.0)
Requires-Dist: fire (==0.5.0)
Requires-Dist: kornia (==0.7.0)
Requires-Dist: matplotlib (==3.7.1)
Requires-Dist: networkx (==3.1)
Requires-Dist: numpy (==1.24.1)
Requires-Dist: opencv-python (==4.8.0.74)
Requires-Dist: pandas (==1.5.3)
Requires-Dist: Pillow (==10.0.0)
Requires-Dist: pytorchcv (==0.0.67)
Requires-Dist: scikit-learn (==1.2.2)
Requires-Dist: scipy (==1.11.2)
Requires-Dist: seaborn (==0.12.2)
Requires-Dist: segmentation-models-pytorch (==0.3.3)
Requires-Dist: scikit-image (==0.18.3)
Requires-Dist: torch (==2.0.1)
Requires-Dist: torchvision (==0.15.2)
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Requires-Dist: zarr (==2.16.1)
Requires-Dist: tifffile (==2021.11.2)
Requires-Dist: imagecodecs (==2023.9.4)

# PathPretrain
 
## Installation
Run the ```install.sh```       
Alternatively, you can use the ```spec-list.txt``` to install the conda libraries and run everything but the first line of the ```install.sh```
Then run ```pip install git+https://github.com/jlevy44/PathPretrain```
