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>=1.6.0 --upgrade
# for installing gluoncv with all dependencies
pip install gluoncv[full] mxnet>=1.6.0 --upgradeTo enable different hardware supports such as GPUs, check out mxnet variants.
For example, you can install cuda-11.0 supported mxnet alongside gluoncv:
pip install gluoncv mxnet-cu110>=1.6.0 --upgrade1 maintainer
Metadata
1 maintainer
Release files for gluoncv 0.10.5.post0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| gluoncv-0.10.5.post0.tar.gz | 1.0 MB | Without provenance | Details for gluoncv-0.10.5.post0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| gluoncv-0.10.5.post0-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details for gluoncv-0.10.5.post0-py2.py3-none-any.whl |
Total release size: 2.3 MB
Release files / gluoncv-0.10.5.post0.tar.gz
| Download URL | gluoncv-0.10.5.post0.tar.gz |
|---|---|
| Size | 1.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / gluoncv-0.10.5.post0-py2.py3-none-any.whl
| Download URL | gluoncv-0.10.5.post0-py2.py3-none-any.whl |
|---|---|
| Size | 1.3 MB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
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twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.64.0 importlib-metadata/4.8.3 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.6.8
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Security for gluoncv 0.10.5.post0
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