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AlicIA
::
Usage: alicia [OPTIONS] COMMAND [ARGS]...
A CLI to download, create, modify, train, test, predict and compare an image classifiers.
Supporting mostly all torch-vision neural networks and datasets.
This will also identify cute 🐱 or a fierce 🐶, also flowers or what type of
🏘️ you should be.
Options:
-v, --verbose
-g, --gpu
--version Show the version and exit.
--help Show this message and exit.
Commands:
compare Compare the info, accuracy, and step speed two (or more by...
create Creates a new model for a given architecture.
download Download a MNIST dataset with PyTorch and split it into...
info Display information about a model architecture.
modify Changes the hyper parameters of a model.
predict Predict images using a pre trained model, for a given folder...
test Test a pre trained model.
train Train a given architecture with a data directory containing a...
View a FashionMNIST demo
.. image:: https://asciinema.org/a/561138.png
:target: https://asciinema.org/a/561138?autoplay=1"
Install and usage
::
pip install alicia
alicia --help
If you just want to see a quick showcase of the tool, download and run showcase.sh
https://github.com/aemonge/alicia/raw/main/docs/showcase.sh
Features
To see the full list of features, and option please refer to alicia --help
- Download common torchvision datasets (tested with the following):
- MNIST
- FashionMNIST
- Flowers102
- EMNIST
- StanfordCars
- KMNIST and CIFAR10
- Select different transforms to train.
- Train, test and predict using different custom-made and torch-vision models:
- Get information about each model.
- Compare models training speed, accuracy, and meta information.
- View test prediction results in the console, or with matplotlib.
- Adds the network training history log, to the model. To enhance the info and compare.
- Supports pre-trained models, with weights settings.
- Automatically set the input size based on the image resolution.
References
Useful links found and used while developing this