Skip to main content

Image classification models for PyTorch

Project description

Large-scale image classification networks

Collection of large-scale image classification models on PyTorch, pretrained on the ImageNet-1k dataset.

Installation

To install, use:

pip install pytorchcv torch>=0.4.1

To enable/disable different hardware supports such as GPUs, check out PyTorch installation instructions.

Usage

Example of using the pretrained ResNet-18 model:

from pytorchcv.model_provider import get_model as ptcv_get_model
net = ptcv_get_model("resnet18", pretrained=True)

Project details


Download files

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

Source Distribution

pytorchcv-0.0.18.tar.gz (52.8 kB view details)

Uploaded Source

Built Distribution

pytorchcv-0.0.18-py2.py3-none-any.whl (105.9 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file pytorchcv-0.0.18.tar.gz.

File metadata

  • Download URL: pytorchcv-0.0.18.tar.gz
  • Upload date:
  • Size: 52.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/39.1.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.5

File hashes

Hashes for pytorchcv-0.0.18.tar.gz
Algorithm Hash digest
SHA256 992e1f25fac8056d8d939e797547ceda14876ba6f7eca7ae32601bc1748cf682
MD5 97972f464e6100822842a66a96274938
BLAKE2b-256 335737978716010c2aa27001dbec288edbd0c7771389078a227ab0434ab0d2ab

See more details on using hashes here.

File details

Details for the file pytorchcv-0.0.18-py2.py3-none-any.whl.

File metadata

  • Download URL: pytorchcv-0.0.18-py2.py3-none-any.whl
  • Upload date:
  • Size: 105.9 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/39.1.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.5

File hashes

Hashes for pytorchcv-0.0.18-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 4e5b2514b453948a1ed932f943fe51466a6193ae3541559c21b039bae056f39c
MD5 fcb373f9479a288c3390d5d75d57ff69
BLAKE2b-256 b02f2c8893b70f97014acd7730a4cfbe0d905c33ce286d9a92688cec144025d6

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