Recent Releases of dash_doodler
dash_doodler - Journal paper software archive for Zenodo
A release that contains an image of the code on github, 01/13/2022, for journal paper publishing
- Python
Published by dbuscombe-usgs over 4 years ago
dash_doodler - v1.2.5 doodler paper
Version of Doodler used for the Doodler paper (forthcoming)
- Python
Published by dbuscombe-usgs almost 5 years ago
dash_doodler - example results from the Doodler program
- Python
Published by dbuscombe-usgs almost 5 years ago
dash_doodler - Sample data for the Doodler program
Sample data for the Doodler program: 15 jpeg images of shoreline environments
- Python
Published by dbuscombe-usgs almost 5 years ago
dash_doodler - v1.2.1
See https://dbuscombe-usgs.github.io/dash_doodler/blog/2021/05/16/blog-post for details
Version of the code to be used for the forthcoming Doodler manuscript
- Python
Published by dbuscombe-usgs about 5 years ago
dash_doodler - Doodler v1.1.1, March 17 2021
First release to enforce the use of relative pixel location for both RF and CRF inference. Results in improved task-specific estimates and predictions
- Python
Published by dbuscombe-usgs about 5 years ago
dash_doodler - v1.0.0
This is the first release of dash-doodler, to coincide with the first version that appears to work well for multiple data sets and class sets, using both versions (appCRF.py and appRF.py).
This serves to snapshot this version of the program ahead of major planned changes, involving a database backend, user ID, and web serving.
Subsequent updates will follow major.minor.build convention
the CRF implementation is now faster and better. The RF implementation works well on all tested data, too. the defaults for CRF parameters have changed. Also, the image data representation it is using has changed, and the segmentation now uses nearest-neighbour rather than linear interpolation, which makes more sense for these label images with discrete classes. New sliders are now available to control the data density (increase the downsample factor for larger images). Note the new behavior of the median filter slider – median filtering still occurs if the value > 1, but won’t automatically redo the segmentation when its value is changed (to do that, you should recheck the compute/show segmentation box). The program now automatically selects a new colormap if you have more than 10 classes.
- Python
Published by dbuscombe-usgs over 5 years ago