Computer Vision models and training
Project description
Quickvision
- Faster Computer Vision.
Install Quickvision
-
Install from PyPi.
-
Current stable
release 0.1.1needsPyTorch 1.7.1andtorchvision 0.8.2.pip install quickvision
What is Quickvision?
-
Quickvision makes Computer Vision tasks much faster and easier with PyTorch.
It provides: -
- Easy to use PyTorch native API, for
fit(),train_step(),val_step()of models. - Easily customizable and configurable models with various backbones.
- A complete PyTorch native interface. All models are
nn.Module, all the training APIs are optional and not binded to models. - A lightning API which helps to accelerate training over multiple GPUs, TPUs.
- A datasets API to convert common data formats very easily and quickly to PyTorch formats.
- A minimal package, with very low dependencies.
- Easy to use PyTorch native API, for
-
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
Tensorso 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.Modulewhich 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().
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
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file quickvision-0.2.1.tar.gz.
File metadata
- Download URL: quickvision-0.2.1.tar.gz
- Upload date:
- Size: 35.3 kB
- Tags: Source
- Uploaded using 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
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0e460201dd514b8df4b5085f12fb1c697a45b0813fea5dcffdec96493550a464
|
|
| MD5 |
fac375f3396cffb972f13551fd0b44dd
|
|
| BLAKE2b-256 |
4467d379949820d97655cd9e2c08184368985bb18c6b9ebea33a89a229ed3254
|
File details
Details for the file quickvision-0.2.1-py3-none-any.whl.
File metadata
- Download URL: quickvision-0.2.1-py3-none-any.whl
- Upload date:
- Size: 52.1 kB
- Tags: Python 3
- Uploaded using 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
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
adc10b339afa3e272ae1ffcdb9be50ef99bb84c8c28ea9fa92a4975e96e2d1a5
|
|
| MD5 |
f28a4dac82ef9ce0f9e2df324f01cfab
|
|
| BLAKE2b-256 |
69ec94263107146bd6928ba8d0a3509bdf4763829fe65257dffd7008a5ae74e5
|