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-0.3.0b20181008.tar.gz (147.1 kB view details)

Uploaded Source

Built Distribution

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

gluoncv-0.3.0b20181008-py2.py3-none-any.whl (212.2 kB view details)

Uploaded Python 2Python 3

File details

Details for the file gluoncv-0.3.0b20181008.tar.gz.

File metadata

  • Download URL: gluoncv-0.3.0b20181008.tar.gz
  • Upload date:
  • Size: 147.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.5.4

File hashes

Hashes for gluoncv-0.3.0b20181008.tar.gz
Algorithm Hash digest
SHA256 cc42c95f187ad407076eea3c23c671f07ebc6e9cb7edcae46096f78676627220
MD5 a610ddc26b48c4c27ed58e4fcb3f0b45
BLAKE2b-256 396621d4dda0a6564cdaa82e4bb6b5d4f2fc0c9beb41552a27e9ec7030038fc7

See more details on using hashes here.

File details

Details for the file gluoncv-0.3.0b20181008-py2.py3-none-any.whl.

File metadata

  • Download URL: gluoncv-0.3.0b20181008-py2.py3-none-any.whl
  • Upload date:
  • Size: 212.2 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.5.4

File hashes

Hashes for gluoncv-0.3.0b20181008-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 bff2dd00c20e94b5eecd234dd931a0c9a998128dfcb352c07d00965af6b3db15
MD5 76b090017fd8c2a4a770bfc3d1b645c8
BLAKE2b-256 363261a675b48403562d1df51bad1c1b3dde3b4def7957e3dca474d6fc415210

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