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-0.3.0b20180709.tar.gz (98.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gluoncv-0.3.0b20180709-py2.py3-none-any.whl (137.0 kB view details)

Uploaded Python 2Python 3

File details

Details for the file gluoncv-0.3.0b20180709.tar.gz.

File metadata

File hashes

Hashes for gluoncv-0.3.0b20180709.tar.gz
Algorithm Hash digest
SHA256 41d1f3ada2c70974f48bcfe91143b955f4912af60d1bc24ed0684b3083c3605b
MD5 c52d09c436a61c6565de443650f95261
BLAKE2b-256 42ebab109dfcdcbcedd7d17b96120e456d3b95e17c2a5af3260e1017d6042630

See more details on using hashes here.

File details

Details for the file gluoncv-0.3.0b20180709-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for gluoncv-0.3.0b20180709-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 67f548f4192d7c138b794ff207e4ddd7b0d26f5799e379f86fdcee0814eae244
MD5 c15fd7952a9fc5c615af2c983ef32c93
BLAKE2b-256 bf3db6a634ba5b08aef0d9c72f2d25981b1ba8cdd95aabefaaab646dd103b4eb

See more details on using hashes here.

Supported by

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