New YAPiC Release (1.2): A Simple Command Line Tool For Deep Learning Based Image Segmentation
We are happy to announce new YAPiC version 1.2! Check out the YAPiC website for tutorials and installation instructions.
With YAPiC you can train your own customized neural network (u-net) to detect a certain structure of your choice with a simple python based command line interface. You can label your training data with Ilastik.
$ yapic train unet_2d "path/to/my/images/*.tif" path/to/my/ilastik_labels.ilp
$ yapic predict my_trained_model.h5 path/to/my/images path/to/results/
- Installation with
pip install yapic
- Support of sparse labels, training data can be imported from Ilastik projects.
- NEW: Trainied models can be used in ImageJ/Fiji
- To quickly get started check out the new tutorials at the YAPiC website and train your first model.
Trained classifiers can be applied in ImageJ, using DeepImageJ Plugin
With the new release we aim to build a direct brigde to the ImageJ/Fiji ecosystem. Models trained with YAPiC can now be simply converted into DeepImageJ bundled models with the new
$ yapic deploy my_trained_model.h5 path/to/example_image.tif path/to/Fiji.app/models/my_deepimagejmodel
All necessary metadata, example images and conversion scripts included in the bundled model and can be directly opened in DeepImageJ and are ready to use.
Tensorflow and Keras dependencies were removed
In previous YAPiC versions, Tensorflow version 2.1. was automatically installed. We removed the Tensorflow dependecy to allow the user to install the Tensorflow version that fits best to the specifications of their GPU hardware and CUDA driver setup. Keras functions are now imported from the Tensorflow backend, preventing potential version conflicts between Keras and Tensorflow.
All release notes here.
Happy model training!
All the best