Pre-release
This release is a pre-release and may not be stable for production use.
GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.
It is designed for engineers, researchers, and students to fast prototype products and research ideas based on these models.
Installation
To install, use:
pip install gluoncv mxnet>=1.3.0 --upgradeTo enable different hardware supports such as GPUs, check out mxnet variants.
For example, you can install cuda-9.0 supported mxnet alongside gluoncv:
pip install gluoncv mxnet-cu90>=1.3.0 --upgrade1 maintainer
Metadata
1 maintainer
Release files for gluoncv 0.4.0b20190130
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gluoncv-0.4.0b20190130.tar.gz | 176.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gluoncv-0.4.0b20190130-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 420.0 kB
Release files / gluoncv-0.4.0b20190130.tar.gz
| Download URL | gluoncv-0.4.0b20190130.tar.gz |
|---|---|
| Size | 176.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.7.1 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.5.4
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Release files / gluoncv-0.4.0b20190130-py2.py3-none-any.whl
| Download URL | gluoncv-0.4.0b20190130-py2.py3-none-any.whl |
|---|---|
| Size | 243.2 kB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.7.1 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.5.4
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