Computer vision data¶

In [1]:

%matplotlib inline
from fastai.gen_doc.nbdoc import *
from fastai import * 
from fastai.vision import * 

This module contains the classes that define datasets handling Image objects and their tranformations. As usual, we'll start with a quick overview, before we get in to the detailed API docs.

Quickly get your data ready for training¶

To get you started as easily as possible, the fastai provides two helper functions to create a DataBunch object that you can directly use for training a classifier. To demonstrate them you'll first need to download and untar the file by executing the following cell. This will create a data folder containing an MNIST subset in data/mnist_sample.

In [2]:

path = untar_data(URLs.MNIST_SAMPLE); path

Out[2]:

PosixPath('/home/jhoward/.fastai/data/mnist_sample')

There are a number of ways to create an ImageDataBunch. One common approach is to use Imagenet-style folders (see a ways down the page below for details) with ImageDataBunch.from_folder:

In [3]:

tfms = get_transforms(do_flip=False)
data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=24)

Here the datasets will be automatically created in the structure of Imagenet-style folders. The parameters specified:

the transforms to apply to the images in ds_tfms (here with do_flip=False because we don't want to flip numbers),
the target size of our pictures (here 24).

As with all DataBunch usage, a train_dl and a valid_dl are created that are of the type PyTorch DataLoader.

If you want to have a look at a few images inside a batch, you can use ImageDataBunch.show_batch. The rows argument is the number of rows and columns to display.

In [ ]:

data.show_batch(rows=3, figsize=(5,5))

The second way to define the data for a classifier requires a structure like this:

path\
  train\
  test\
  labels.csv

where the labels.csv file defines the label(s) of each image in the training set. This is the format you will need to use when each image can have multiple labels. It also works with single labels:

In [ ]:

pd.read_csv(path/'labels.csv').head()

Out[ ]:

	name	label
0	train/3/7463.png	0
1	train/3/21102.png	0
2	train/3/31559.png	0
3	train/3/46882.png	0
4	train/3/26209.png	0

You can then use ImageDataBunch.from_csv:

In [ ]:

data = ImageDataBunch.from_csv(path, ds_tfms=tfms, size=28)

In [ ]:

data.show_batch(rows=3, figsize=(5,5))

An example of multiclassification can be downloaded with the following cell. It's a sample of the planet dataset.

In [ ]:

planet = untar_data(URLs.PLANET_SAMPLE)

If we open the labels files, we seach that each image has one or more tags, separated by a space.

In [ ]:

df =pd.read_csv(planet/'labels.csv')
df.head()

Out[ ]:

	image_name	tags
0	train_21983	partly_cloudy primary
1	train_9516	clear cultivation primary water
2	train_12664	haze primary
3	train_36960	clear primary
4	train_5302	haze primary road

In [ ]:

data = ImageDataBunch.from_csv(planet, folder='train', size=128, suffix='.jpg',
    ds_tfms=get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.))

The show_batchmethod will then print all the labels that correspond to each image.

In [ ]:

data.show_batch(rows=3, figsize=(10,8), ds_type=DatasetType.Valid)

You can find more ways to build an ImageDataBunch without the factory methods in data_block.

In [2]:

show_doc(ImageDataBunch, doc_string=False)

`class` `ImageDataBunch`[source]

ImageDataBunch(train_dl:DataLoader, valid_dl:DataLoader, test_dl:Optional[DataLoader]=None, device:device=None, tfms:Optional[Collection[Callable]]=None, path:PathOrStr='.', collate_fn:Callable='data_collate') :: DataBunch

Factory methods¶

In [3]:

show_doc(ImageDataBunch.create, arg_comments={
    'bs': 'Desired batchsize for the dataloaders',
    'num_workers': 'The number of process to launch for data collection',
    'ds_tfms': 'Tuple of two lists of transforms (first for training and second for validation and test set)',
    'size': 'Target size for those transforms',
    'tfms': 'List of transforms to be applied at a batch level (like normalization)',
    'device': 'The device on which to put the batches'
})

`create`[source]

create(train_ds, valid_ds, test_ds=None, path:PathOrStr='.', bs:int=64, ds_tfms:Union[Transform, Collection[Transform], NoneType]=None, num_workers:int=16, tfms:Optional[Collection[Callable]]=None, device:device=None, collate_fn:Callable='data_collate', size:int=None, kwargs) → ImageDataBunch

Factory method. bs batch size, ds_tfms for Dataset, tfms for DataLoader.

bs: Desired batchsize for the dataloaders
num_workers: The number of process to launch for data collection
ds_tfms: Tuple of two lists of transforms (first for training and second for validation and test set)
size: Target size for those transforms
tfms: List of transforms to be applied at a batch level (like normalization)
device: The device on which to put the batches

You don't often need to call this directly yourself; instead, you'll normally use one of the convenience wrappers below. However, these wrappers all accept a kwargs that is passed to this method, so you can pass any of the above parameters as well.

If you quickly want to get a ImageDataBunch and train a model, you should process your data to have it in one of the formats the following functions handle.

In [4]:

show_doc(ImageDataBunch.from_folder)

`from_folder`[source]

from_folder(path:PathOrStr, train:PathOrStr='train', valid:PathOrStr='valid', test:Union[Path, str, NoneType]=None, valid_pct=None, kwargs:Any) → ImageDataBunch

Create from imagenet style dataset in path with train,valid,test subfolders (or provide valid_pct).

"Imagenet-style" datasets look something like this (note that the test folder is optional):

path\
  train\
    clas1\
    clas2\
    ...
  valid\
    clas1\
    clas2\
    ...
  test\

For example:

In [ ]:

data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=24)

Note that this (and all factory methods in this section) pass any kwargs to ImageDataBunch.create.

In [5]:

show_doc(ImageDataBunch.from_csv)

`from_csv`[source]

from_csv(path:PathOrStr, folder:PathOrStr='.', sep=None, csv_labels:PathOrStr='labels.csv', valid_pct:float=0.2, fn_col:int=0, label_col:int=1, test:Union[Path, str, NoneType]=None, suffix:str=None, header:Union[int, str, NoneType]='infer', kwargs:Any) → ImageDataBunch

Create from a csv file.

Create ImageDataBunch from path by splitting the data in folder and labelled in a file csv_labels between a training and validation set. Use valid_pct to indicate the percentage of the total images for the validation set. An optional test folder contains unlabelled data and suffix contains an optional suffix to add to the filenames in csv_labels (such as '.jpg'). For example:

In [ ]:

data = ImageDataBunch.from_csv(path, ds_tfms=tfms, size=24);

In [6]:

show_doc(ImageDataBunch.from_df)

`from_df`[source]

from_df(path:PathOrStr, df:DataFrame, folder:PathOrStr='.', sep=None, valid_pct:float=0.2, fn_col:int=0, label_col:int=1, test:Union[Path, str, NoneType]=None, suffix:str=None, kwargs:Any) → ImageDataBunch

Create from a DataFrame.

Same as ImageDataBunch.from_csv, but passing in a DataFrame instead of a csv file. E.gL

In [ ]:

df = pd.read_csv(path/'labels.csv', header='infer')
df.head()

Out[ ]:

	name	label
0	train/3/7463.png	0
1	train/3/21102.png	0
2	train/3/31559.png	0
3	train/3/46882.png	0
4	train/3/26209.png	0

In [ ]:

data = ImageDataBunch.from_df(path, df, ds_tfms=tfms, size=24)

Different datasets are labeled in many different ways. The following methods can help extract the labels from the dataset in a wide variety of situations. The way they are built in fastai is constructive: there are methods which do a lot for you but apply in specific circumstances and there are methods which do less for you but give you more flexibility.

In this case the hierachy is:

ImageDataBunch.from_name_re: Gets the labels from the filenames using a regular expression
ImageDataBunch.from_name_func: Gets the labels from the filenames using any function
ImageDataBunch.from_lists: Labels need to be provided as an input in a list

In [7]:

show_doc(ImageDataBunch.from_name_re)

`from_name_re`[source]

from_name_re(path:PathOrStr, fnames:FilePathList, pat:str, valid_pct:float=0.2, test:str=None, kwargs)

Creates an ImageDataBunch from fnames, calling a regular expression (containing one re group) on the file names to get the labels, putting aside valid_pct for the validation. In the same way as ImageDataBunch.from_csv, an optional test folder contains unlabelled data.

Our previously created dataframe contains the labels in the filenames so we can leverage it to test this new method. ImageDataBunch.from_name_re needs the exact path of each file so we will append the data path to each filename before creating our ImageDataBunch object.

In [ ]:

fn_paths = [path/name for name in df['name']]; fn_paths[:2]

Out[ ]:

[PosixPath('/home/ubuntu/.fastai/data/mnist_sample/train/3/7463.png'),
 PosixPath('/home/ubuntu/.fastai/data/mnist_sample/train/3/21102.png')]

In [ ]:

pat = r"/(\d)/\d+\.png$"
data = ImageDataBunch.from_name_re(path, fn_paths, pat=pat, ds_tfms=tfms, size=24)

In [ ]:

data.classes

Out[ ]:

['3', '7']

In [8]:

show_doc(ImageDataBunch.from_name_func)

`from_name_func`[source]

from_name_func(path:PathOrStr, fnames:FilePathList, label_func:Callable, valid_pct:float=0.2, test:str=None, kwargs)

Works in the same way as ImageDataBunch.from_name_re, but instead of a regular expression it expects a function that will determine how to extract the labels from the filenames. (Note that from_name_re uses this function in its implementation).

To test it we could build a function with our previous regex. Let's try another, similar approach to show that the labels can be obtained in a different way.

In [ ]:

def get_labels(file_path): return '3' if '/3/' in str(file_path) else '7'
data = ImageDataBunch.from_name_func(path, fn_paths, label_func=get_labels, ds_tfms=tfms, size=24)
data.classes

Out[ ]:

['3', '7']

In [9]:

show_doc(ImageDataBunch.from_lists)

`from_lists`[source]

from_lists(path:PathOrStr, fnames:FilePathList, labels:StrList, valid_pct:float=0.2, test:str=None, kwargs)

The most flexible factory function; pass in a list of labels that correspond to each of the filenames in fnames.

To show an example we have to build the labels list outside our ImageDataBunch object and give it as an argument when we call from_lists. Let's use our previously created function to create our labels list.

In [ ]:

labels_ls = list(map(get_labels, fn_paths))
data = ImageDataBunch.from_lists(path, fn_paths, labels=labels_ls, ds_tfms=tfms, size=24)
data.classes

Out[ ]:

['3', '7']

Methods¶

In [10]:

show_doc(ImageDataBunch.show_batch)

`show_batch`[source]

show_batch(rows:int=None, figsize:Tuple[int, int]=(9, 10), ds_type:DatasetType=<DatasetType.Valid: 2>)

Create a rows by rows grid of images from dataset ds_type for a figsize figure. This function works for all type of computer vision data (see data_block for more examples).

Once you have your ImageDataBunch, you can have a quick look at your data by using this:

In [ ]:

data.show_batch(rows=3, figsize=(6,6))

In [11]:

show_doc(ImageDataBunch.labels_to_csv)

`labels_to_csv`[source]

labels_to_csv(dest:str)

Save file names and labels in data as CSV to file name dest.

This is a functional version of ImageDataBunch.show_batch.

In the next two methods we will use a new dataset, CIFAR. This is because the second method will get the statistics for our dataset and we want to be able to show different statistics per channel. If we were to use MNIST, these statistics would be the same for every channel. White pixels are [255,255,255] and black pixels are [0,0,0] (or in normalized form [1,1,1] and [0,0,0]) so there is no variance between channels.

In [ ]:

path = untar_data(URLs.CIFAR); path

Out[ ]:

PosixPath('/home/ubuntu/.fastai/data/cifar10')

In [12]:

show_doc(channel_view)

`channel_view`[source]

channel_view(x:Tensor) → Tensor

Make channel the first axis of x and flatten remaining axes

In [ ]:

data = ImageDataBunch.from_folder(path, ds_tfms=tfms, valid='test', size=24)

In [ ]:

def channel_view(x:Tensor)->Tensor:
    "Make channel the first axis of `x` and flatten remaining axes"
    return x.transpose(0,1).contiguous().view(x.shape[1],-1) 

This function takes a tensor and flattens all dimensions except the channels, which it keeps as the first axis. This function is used to feed ImageDataBunch.batch_stats so that it can get the pixel statistics of a whole batch.

Let's take as an example the dimensions our MNIST batches: 128, 3, 24, 24.

In [ ]:

t = torch.Tensor(128, 3, 24, 24)

In [ ]:

t.size()

Out[ ]:

torch.Size([128, 3, 24, 24])

In [ ]:

tensor = channel_view(t)

In [ ]:

tensor.size()

Out[ ]:

torch.Size([3, 73728])

In [13]:

show_doc(ImageDataBunch.batch_stats)

`batch_stats`[source]

batch_stats(funcs:Collection[Callable]=None) → Tensor

Grab a batch of data and call reduction function func per channel

Gets the statistics of each channel of a batch of data. If no functions are specified, default statistics are mean and standard deviation.

In [ ]:

data.batch_stats()

Out[ ]:

[tensor([0.5047, 0.5339, 0.5599]), tensor([0.2412, 0.2319, 0.2493])]

In [14]:

show_doc(ImageDataBunch.normalize)

`normalize`[source]

normalize(stats:Collection[Tensor]=None)

Add normalize transform using stats (defaults to DataBunch.batch_stats)

Adds the normalize transform to the set of transforms associated with the data. In the fast.ai library we have imagenet_stats, cifar_stats and mnist_stats so we can add normalization easily with any of these datasets. Let's see an example with our dataset of choice: MNIST.

In [ ]:

data.normalize(cifar_stats)

In [ ]:

data.batch_stats()

Out[ ]:

[tensor([0.0556, 0.2135, 0.4325]), tensor([0.9765, 0.9543, 0.9552])]

Other functions¶

In [15]:

show_doc(show_image_batch, arg_comments={
    'dl': 'A dataloader from which to show a sample',
    'classes': 'List of classes (for the labels)',
    'rows': 'Will make a square of `rows` by `rows` images',
    'figsize': 'Size of the graph shown'
})

`show_image_batch`[source]

show_image_batch(dl:DataLoader, classes:StrList, rows:int=None, figsize:Tuple[int, int]=(9, 10))

Show a few images from a batch.

dl: A dataloader from which to show a sample
classes: List of classes (for the labels)
rows: Will make a square of rows by rows images
figsize: Size of the graph shown

This is a functional version of ImageDataBunch.show_batch.

Data normalization¶

You may also want to normalize your data, which can be done by using the following functions.

In [16]:

show_doc(normalize)

`normalize`[source]

normalize(x:Tensor, mean:FloatTensor, std:FloatTensor) → Tensor

Normalize x with mean and std.

In [17]:

show_doc(denormalize)

`denormalize`[source]

denormalize(x:Tensor, mean:FloatTensor, std:FloatTensor) → Tensor

Denormalize x with mean and std.

In [18]:

show_doc(normalize_funcs, doc_string=False)

`normalize_funcs`[source]

normalize_funcs(mean:FloatTensor, std:FloatTensor) → Tuple[Callable, Callable]

Create normalize and denormalize functions using mean and std. device will store them on the device specified. do_y determines if the target should also be normaized or not.

On MNIST the mean and std are 0.1307 and 0.3081 respectively (looked on Google). If you're using a pretrained model, you'll need to use the normalization that was used to train the model. The imagenet norm and denorm functions are stored as constants inside the library named imagenet_norm and imagenet_denorm. If you're training a model on CIFAR-10, you can also use cifar_norm and cifar_denorm.

You may sometimes see warnings about clipping input data when plotting normalized data. That's because even although it's denormalized when plotting automatically, sometimes floating point errors may make some values slightly out or the correct range. You can safely ignore these warnings in this case.

In [ ]:

data = ImageDataBunch.from_folder(untar_data(URLs.MNIST_SAMPLE),
                                  ds_tfms=tfms, size=24)
data.normalize()
data.show_batch(rows=3, figsize=(6,6))

Datasets¶

Depending on the task you are tackling, you'll need one of the following fastai datasets.

In [19]:

show_doc(ImageClassificationDataset, title_level=3)

`class` `ImageClassificationDataset`[source]

ImageClassificationDataset(fns:FilePathList, labels:StrList, classes:Optional[ArgStar]=None) :: ImageClassificationBase

Dataset for folders of images in style {folder}/{class}/{images}.

This is the basic dataset for image classification: fns are the filenames of the images and labels the corresponding labels. Optionally, classes contains a name for each possible label.

In [20]:

show_doc(ImageClassificationDataset.from_folder)

`from_folder`[source]

from_folder(folder:Path, classes:Optional[ArgStar]=None, valid_pct:float=0.0, extensions:StrList={'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'}) → Union[ImageClassificationDataset, List[ImageClassificationDataset]]

Dataset of classes labeled images in folder. Optional valid_pct split validation set.

Create an ImageClassificationDataset automatically from a folder. If classes is None, it will be set to the names of the directories in folder. check_ext forces the function to only keep filenames with image extensions.

In [21]:

show_doc(ImageClassificationDataset.from_single_folder, doc_string=False)

`from_single_folder`[source]

from_single_folder(folder:PathOrStr, classes:ArgStar, extensions:StrList={'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'})

Typically used for define a test set. Label all images in folder with classes[0]. check_ext forces the function to only keep filenems with image extensions.

In [22]:

show_doc(ImageMultiDataset, doc_string=False, title_level=3)

`class` `ImageMultiDataset`[source]

ImageMultiDataset(fns:FilePathList, labels:StrList, classes:Optional[ArgStar]=None) :: ImageClassificationBase

This is the basic dataset for image classification with multiple labels: fns are the filenames of the images and labels the corresponding labels (may be more than one for each image). Optionally, classes contains a name for each possible label.

In [23]:

show_doc(ImageMultiDataset.from_folder, doc_string=False)

`from_folder`[source]

from_folder(path:PathOrStr, folder:PathOrStr, fns:Series, labels:StrList, valid_pct:float=0.2, classes:Optional[ArgStar]=None)

To create an ImageMultiDataset automatically in path from a folder and fns. If classes is None, it will be set to the names of the different labels seen. You can split the images in this folder in a train/valid dataset if valid_pct is non-zero. check_ext forces the function to only keep filenems with image extensions.

In [24]:

show_doc(ImageMultiDataset.from_single_folder, doc_string=False)

`from_single_folder`[source]

from_single_folder(folder:PathOrStr, classes:ArgStar, extensions={'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'})

Typically used for define a test set. Label all images in folder with classes[0]. check_ext forces the function to only keep filenems with image extensions.

To help scan a folder for these Dataset, we use the following helper function:

In [25]:

show_doc(get_image_files, doc_string=False)

`get_image_files`[source]

get_image_files(c:PathOrStr, check_ext:bool=True, recurse=False) → FilePathList

Return list of files in c that are images. check_ext will filter to keep only the files with image extensions.

In [26]:

show_doc(ImageMultiDataset.get_labels)

`get_labels`[source]

get_labels(idx:int) → StrList

Gets the labels of a batch of images of choice. Pass in the batch number/index and will return the labels for each of the examples in that batch.

In [27]:

show_doc(ImageMultiDataset.encode)

`encode`[source]

encode(x:Collection[int])

One-hot encode the target.

In [28]:

show_doc(SegmentationDataset, doc_string=False, title_level=3)

`class` `SegmentationDataset`[source]

SegmentationDataset(x:FilePathList, y:FilePathList, classes:ArgStar) :: ImageClassificationBase

This is the basic dataset for image sementation: x contains the filenames of the images and y the ones of the masks.

In [29]:

show_doc(ObjectDetectDataset, doc_string=False, title_level=3)

`class` `ObjectDetectDataset`[source]

ObjectDetectDataset(x_fns:FilePathList, labelled_bbs:Collection[Tuple[Collection[int], str]], classes:StrList=None) :: ImageClassificationBase

This is the basic dataset for object detection: x_fns contains the filenames of the images, labelled_bbs the corresponding bounding boxes with their corresponding labels. classes contains the list of classes.

In [30]:

show_doc(ObjectDetectDataset.from_json)

`from_json`[source]

from_json(folder, fname, valid_pct=None, classes=None)

Create an ObjectDetectDataset by looking at the images in folder according to annotations in the json fname. If valid_pct is passed, split a training and validation set. classes is the list of classes.

This factory method uses the following helper function.

In [31]:

show_doc(get_annotations)

`get_annotations`[source]

get_annotations(fname, prefix=None)

Open a COCO style json in fname and returns the lists of filenames (with maybe prefix) and labelled bboxes.

Finally, to apply transformations to Image in a Dataset, we use this last class.

In [32]:

show_doc(DatasetTfm, doc_string=False, title_level=3)

`class` `DatasetTfm`[source]

DatasetTfm(ds:Dataset, tfms:Union[Transform, Collection[Transform]]=None, tfm_y:bool=False, kwargs:Any) :: Dataset

Dataset that applies the list of transforms tfms to every item drawn. If tfms should be applied to the targets as well, tfm_y should be True. kwargs will be passed to apply_tfms internally.

Then this last function automatizes the process of creating DatasetTfm:

In [33]:

show_doc(transform_datasets, doc_string=False)

`transform_datasets`[source]

transform_datasets(train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None, tfms:Optional[Tuple[Union[Transform, Collection[Transform]], Union[Transform, Collection[Transform]]]]=None, resize_method:ResizeMethod=None, kwargs:Any)

Create train, valid and maybe test DatasetTfm from train_ds, valid_ds and maybe test_ds using tfms. It should be a tuple containing the transforms for the training set, then for the validation and test set.

In [34]:

show_doc(ImageClassificationBase, title_level=3, doc_string=False)

`class` `ImageClassificationBase`[source]

ImageClassificationBase(fns:FilePathList, classes:Optional[ArgStar]=None) :: LabelDataset

Base class for computer vision datasets. Maps classes to indexes via class2idx.

Links with the data block API¶

The vision application adds a few methods to implement data augmentation in the data block API or create an ImageDataBunch.

In [35]:

show_doc(SplitDatasetsImage, title_level=3)

`class` `SplitDatasetsImage`[source]

SplitDatasetsImage(path:PathOrStr, train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None) :: SplitDatasets

A class regrouping train_ds, a valid_ds and maybe a train_ds dataset, inside a path.

In [36]:

show_doc(SplitDatasetsImage.transform)

`transform`[source]

transform(tfms:Union[Transform, Collection[Transform]], kwargs) → SplitDatasets

Apply tfms to the underlying datasets, kwargs are passed to DatasetTfm.

In [37]:

show_doc(SplitDatasetsImage.databunch)

`databunch`[source]

databunch(path:PathOrStr=None, kwargs) → ImageDataBunch

Create an ImageDataBunch from self, path will override self.path, kwargs are passed to ImageDataBunch.create.

To change the default extensions in InputList.from_folder to image extensions, we subclass it to the following:

In [38]:

show_doc(ImageFileList, title_level=3, doc_string=False)

`class` `ImageFileList`[source]

ImageFileList(items:Iterator, path:PathOrStr='.') :: InputList

In [39]:

show_doc(ImageFileList.from_folder)

`from_folder`[source]

from_folder(path:PathOrStr='.', extensions:StrList={'.jpg', '.jpe', '.jpeg', '.gif', '.ico', '.rgb', '.xbm', '.ppm', '.xwd', '.svg', '.xpm', '.pgm', '.png', '.bmp', '.tiff', '.ras', '.tif', '.pbm', '.ief', '.pnm'}, recurse=True) → ImageFileList

Get the list of files in path that have a suffix in extensions. recurse determines if we search subfolders.

Computer vision data¶

Quickly get your data ready for training¶

class ImageDataBunch[source]

Factory methods¶

create[source]

from_folder[source]

from_csv[source]

from_df[source]

from_name_re[source]

from_name_func[source]

from_lists[source]

Methods¶

show_batch[source]

labels_to_csv[source]

channel_view[source]

batch_stats[source]

normalize[source]

Other functions¶

show_image_batch[source]

Data normalization¶

normalize[source]

denormalize[source]

normalize_funcs[source]

Datasets¶

class ImageClassificationDataset[source]

from_folder[source]

from_single_folder[source]

class ImageMultiDataset[source]

from_folder[source]

from_single_folder[source]

get_image_files[source]

get_labels[source]

encode[source]

class SegmentationDataset[source]

class ObjectDetectDataset[source]

from_json[source]

get_annotations[source]

class DatasetTfm[source]

transform_datasets[source]

class ImageClassificationBase[source]

Links with the data block API¶

class SplitDatasetsImage[source]

transform[source]

databunch[source]

class ImageFileList[source]

from_folder[source]

Undocumented Methods - Methods moved below this line will intentionally be hidden¶

New Methods - Please document or move to the undocumented section¶

`class` `ImageDataBunch`[source]

`create`[source]

`from_folder`[source]

`from_csv`[source]

`from_df`[source]

`from_name_re`[source]

`from_name_func`[source]

`from_lists`[source]

`show_batch`[source]

`labels_to_csv`[source]

`channel_view`[source]

`batch_stats`[source]

`normalize`[source]

`show_image_batch`[source]

`normalize`[source]

`denormalize`[source]

`normalize_funcs`[source]

`class` `ImageClassificationDataset`[source]

`from_folder`[source]

`from_single_folder`[source]

`class` `ImageMultiDataset`[source]

`from_folder`[source]

`from_single_folder`[source]

`get_image_files`[source]

`get_labels`[source]

`encode`[source]

`class` `SegmentationDataset`[source]

`class` `ObjectDetectDataset`[source]

`from_json`[source]

`get_annotations`[source]

`class` `DatasetTfm`[source]

`transform_datasets`[source]

`class` `ImageClassificationBase`[source]

`class` `SplitDatasetsImage`[source]

`transform`[source]

`databunch`[source]

`class` `ImageFileList`[source]

`from_folder`[source]