Metadata-Version: 2.1
Name: fastai-datasets
Version: 0.0.5
Summary: Leveraging fastai to easily load and handle datasets
Home-page: https://github.com/Irad-Zehavi/fastai-datasets
Author: iradz
Author-email: irad.zehavi@outlook.com
License: Apache Software License 2.0
Description: fastai-datasets
        ================
        
        <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
        
        ## Docs
        
        See https://irad-zehavi.github.io/fastai-datasets/
        
        ## Install
        
        ``` sh
        pip install fastai_datasets
        ```
        
        ## How to use
        
        As an nbdev library, `fatai_datasets` supports `import *` (without
        importing unwanted symbols):
        
        ``` python
        from fastai_datasets.all import *
        ```
        
        Here are a few usage examles:
        
        ### Easily load a dataset
        
        ``` python
        mnist = MNIST()
        mnist.dls().show_batch()
        ```
        
        ![](index_files/figure-commonmark/cell-3-output-1.png)
        
        ### Show the class distribution
        
        ``` python
        mnist.plot_class_distribution()
        ```
        
            <div>
              <progress value='10' class='' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>
              100.00% [10/10 00:00&lt;00:00 Class map: partitioning]
            </div>
            
        
        ![](index_files/figure-commonmark/cell-4-output-2.png)
        
        ### Sample a subset
        
        Whole datasets:
        
        ``` python
        mnist
        ```
        
            [(#60000) [(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7))...]
            (#10000) [(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7)),(PILImage mode=RGB size=28x28, TensorCategory(7))...]]
        
        Subset:
        
        ``` python
        mnist.random_sub_dsets(1000)
        ```
        
            [(#865) [(PILImage mode=RGB size=28x28, TensorCategory(3)),(PILImage mode=RGB size=28x28, TensorCategory(1)),(PILImage mode=RGB size=28x28, TensorCategory(3)),(PILImage mode=RGB size=28x28, TensorCategory(0)),(PILImage mode=RGB size=28x28, TensorCategory(9)),(PILImage mode=RGB size=28x28, TensorCategory(8)),(PILImage mode=RGB size=28x28, TensorCategory(9)),(PILImage mode=RGB size=28x28, TensorCategory(1)),(PILImage mode=RGB size=28x28, TensorCategory(8)),(PILImage mode=RGB size=28x28, TensorCategory(1))...]
            (#135) [(PILImage mode=RGB size=28x28, TensorCategory(3)),(PILImage mode=RGB size=28x28, TensorCategory(9)),(PILImage mode=RGB size=28x28, TensorCategory(4)),(PILImage mode=RGB size=28x28, TensorCategory(1)),(PILImage mode=RGB size=28x28, TensorCategory(4)),(PILImage mode=RGB size=28x28, TensorCategory(5)),(PILImage mode=RGB size=28x28, TensorCategory(0)),(PILImage mode=RGB size=28x28, TensorCategory(4)),(PILImage mode=RGB size=28x28, TensorCategory(1)),(PILImage mode=RGB size=28x28, TensorCategory(9))...]]
        
        ### Construct a subset based on classes
        
        ``` python
        cifar10 = CIFAR10()
        dig_frog_bird = cifar10.by_target['dog'] + cifar10.by_target['frog'] + cifar10.by_target['bird']
        dig_frog_bird.dls().show_batch()
        ```
        
            <div>
              <progress value='10' class='' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>
              100.00% [10/10 00:00&lt;00:00 Class map: partitioning]
            </div>
            
        
        ![](index_files/figure-commonmark/cell-7-output-2.png)
        
        ### Construct a dataset of similarity pairs
        
        ``` python
        Pairs(cifar10, .01).dls().show_batch()
        ```
        
            <div>
              <progress value='50' class='' max='50' style='width:300px; height:20px; vertical-align: middle;'></progress>
              100.00% [50/50 00:00&lt;00:00 Generating negative pairs]
            </div>
            
        
        ![](index_files/figure-commonmark/cell-8-output-2.png)
        
Keywords: nbdev jupyter notebook python
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
