oclrfc
To see OpenCL-based Random Forest Classifiers in action, check out the demo-notebook. For optimal performance and classification quality, it is recommended to generate feature stacks that fit well to the the image data you would like to process.
Installation
You can install oclrfc via [pip]. Note: you also need pyopencl.
conda install pyopencl
pip install oclrfc
Contributing
Contributions are very welcome. Tests can be run with [tox], please ensure the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license, "oclrfc" is free and open source software
Issues
If you encounter any problems, please open a thread on image.sc along with a detailed description and tag @haesleinhuepf.
1 maintainer
Metadata
1 maintainer
Release files for oclrfc 0.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| oclrfc-0.4.1.tar.gz | 9.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| oclrfc-0.4.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 19.8 kB
Release files / oclrfc-0.4.1.tar.gz
| Download URL | oclrfc-0.4.1.tar.gz |
|---|---|
| Size | 9.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.5
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Release files / oclrfc-0.4.1-py3-none-any.whl
| Download URL | oclrfc-0.4.1-py3-none-any.whl |
|---|---|
| Size | 10.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
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twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.5
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