Skip to main content

MXNet Gluon CV Toolkit

Project description

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>=1.2.0

To 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>=1.2.0

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gluoncv-0.2.0b20180623.tar.gz (98.0 kB view details)

Uploaded Source

Built Distribution

gluoncv-0.2.0b20180623-py2.py3-none-any.whl (136.6 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file gluoncv-0.2.0b20180623.tar.gz.

File metadata

File hashes

Hashes for gluoncv-0.2.0b20180623.tar.gz
Algorithm Hash digest
SHA256 784bc2c3eb690a271a418308e76390aac6e03270b4271d2925dc7f79a7087bcd
MD5 0b2da5890174b1e32bf6214af065a332
BLAKE2b-256 997bd5f948d1a88fa0e181e8686558611cecb1ae6a1a963f3993585df7691811

See more details on using hashes here.

File details

Details for the file gluoncv-0.2.0b20180623-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for gluoncv-0.2.0b20180623-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 ffc3493724be4e1ccca64a5830769582538affc5afac35b4445234d58cb916b9
MD5 8a990016085b8789c0efa761acb31d94
BLAKE2b-256 04d96d4495c95dd22b4604754b67e2229d84e9fbd099308d8b033546a7e14989

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page