Skip to main content

Quickvision

  • Faster Computer Vision.

GitHub issues GitHub forks GitHub stars GitHub license codecov

PEP8 CI Tests Docs PyPi Release

Slack Downloads Downloads Downloads

demo

Install Quickvision

  • Install from PyPi.

  • Current stable release 0.1.1 needs PyTorch 1.7.1 and torchvision 0.8.2.

    pip install quickvision
    

What is Quickvision?

  • Quickvision makes Computer Vision tasks much faster and easier with PyTorch.

    It provides: -

    1. Easy to use PyTorch native API, for fit(), train_step(), val_step() of models.
    2. Easily customizable and configurable models with various backbones.
    3. A complete PyTorch native interface. All models are nn.Module, all the training APIs are optional and not binded to models.
    4. A lightning API which helps to accelerate training over multiple GPUs, TPUs.
    5. A datasets API to convert common data formats very easily and quickly to PyTorch formats.
    6. A minimal package, with very low dependencies.
  • Train your models faster. Quickvision has already implemented the long learning in PyTorch.

Quickvision is just PyTorch!!

  • Quickvision does not make you learn a new library. If you know PyTorch, you are good to go!!!
  • Quickvision does not abstract any code from PyTorch, nor implements any custom classes over it.
  • It keeps the data format in Tensor so that you don't need to convert it.

Do you want just a model with some backbone configuration?

  • Use model made by us. It's just a nn.Module which has Tensors only Input and Output format.
  • Quickvision provides reference scripts too for training it!

Do you want to train your model but not write lengthy loops?

  • Just use our training methods such as fit(), train_step(), val_step().

Do you want multi GPU training but worried about model configuration?

  • Just subclass the PyTorch Lightning model!
  • Implement the train_step(), val_step().

Metadata

Release files for quickvision 0.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for quickvision 0.2.1
File Size Uploaded
quickvision-0.2.1.tar.gz 35.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for quickvision 0.2.1
File Interpreter ABI Platform
quickvision-0.2.1-py3-none-any.whl Python 3 none any Details

Total release size: 87.4 kB

Release files / quickvision-0.2.1.tar.gz

Download URL quickvision-0.2.1.tar.gz
Size 35.3 kB
Tags Source
SHA-256 checksum
How to use checksums
0e460201dd514b8df4b5085f12fb1c697a45b0813fea5dcffdec96493550a464
BLAKE2b-256 checksum
How to use checksums
4467d379949820d97655cd9e2c08184368985bb18c6b9ebea33a89a229ed3254
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

Release files / quickvision-0.2.1-py3-none-any.whl

Download URL quickvision-0.2.1-py3-none-any.whl
Size 52.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
adc10b339afa3e272ae1ffcdb9be50ef99bb84c8c28ea9fa92a4975e96e2d1a5
BLAKE2b-256 checksum
How to use checksums
69ec94263107146bd6928ba8d0a3509bdf4763829fe65257dffd7008a5ae74e5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page