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-0.3.0b20180725.tar.gz (100.9 kB view details)

Uploaded Source

Built Distribution

gluoncv-0.3.0b20180725-py2.py3-none-any.whl (139.3 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file gluoncv-0.3.0b20180725.tar.gz.

File metadata

File hashes

Hashes for gluoncv-0.3.0b20180725.tar.gz
Algorithm Hash digest
SHA256 d44a1fcd0257b4a17aa7f371071c35a236844770d5f86019caab0b115ef0d5e6
MD5 60c553cb5fc64067c9d698ead8d4711a
BLAKE2b-256 9adc6379440f0af85d15fc9b7104f7d1a27fe2f0700879fbb087f65e7c7cb854

See more details on using hashes here.

File details

Details for the file gluoncv-0.3.0b20180725-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for gluoncv-0.3.0b20180725-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 5f6ddfcbc9d720c2a5d9e4dd6bc387b24408da1be806af1355ab2a04b276f07e
MD5 2ec66fd9b24526928cd09a8fdefafacf
BLAKE2b-256 c30b4dc550f85fd7abbbe0e0bde93cced99390e12db9b9536181238bf0781630

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