Implementation of Random subwindows and Extra-Trees algorithm.
Project description
pyxit
This code implements the core algorithms for Random subwindows extraction and Extra-Trees classifiers. It is used by Cytomine DataMining applications: classification_validation, classification_model_builder, classification_prediction, segmentation_model_builder and segmentation_prediction. But it can be run without Cytomine on local data (using dir_ls and dir_ts arguments).
It is based on the following paper:
-
For image/object classification: "Towards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study". Raphael Maree, Pierre Geurts, Louis Wehenkel. Pattern Recognition Letters, DOI: 10.1016/j.patrec.2016.01.006, 2016.
-
For image semantic segmentation: Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees Dumont et al., 2009 http://orbi.ulg.ac.be/handle/2268/12205
Install
Simply:
pip install pyxit
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file pyxit-1.1.5.tar.gz
.
File metadata
- Download URL: pyxit-1.1.5.tar.gz
- Upload date:
- Size: 8.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200529 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.8.3
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | a3efce8ad047034502b65c799b7c955ed01dd4aaf4e297fc5fa439a48045ffdc |
|
MD5 | 3b9ebfa5ffb7b871e721cb04d009b3ce |
|
BLAKE2b-256 | 5ab13c4ab967a22c33878d6418dda0d0f6e9d2b0fdf21bb16d0bd4817fcfda6a |