Skip to content

Concepts - `vision` module of FastAI

List of relevant functions and classes

Getting sample data

  • fastai.datasets contains a set of curated datasets that sits on S3. You can get the list from fastai.datasets.URLs
  • URLs.PETS for example will return the download URL.
  • fastai.datasets.untar_data() will download to a .fastai/data folder under local user data and will return that path as a Pathlib.Path object

Loading image data

  • will scan a directory of image files and return a list of Path objects.
  • The next step is to create an ImageDataBunch instance. In FastAI, DataBunch objects form the main way to represent and hold training and test datasets.
  • is a static, factory method allows you to construct an ImageDataBunch and while doing that, it can extract labels from file names. It accepts a Python Regular Expression syntax for this. You also feed it with transformations, size to resize and batch size the GPU can handle. See example below:
pat = r'/([^/]+)_\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat, ds_tfms=get_transforms(), size=224, bs=bs)
data = data.normalize(imagenet_stats)
  • is a utility func that is used to specify and get back a list of transformation that need to applied on the DataBunch object.
  • The from_name_re() will split the data into training and validation sets. These can be accessed via data.valid_ds and data.train_ds where, data is instance of ImageDataBunch.
  • data.show_batch() can be used to display training data in a notebook.
  • data.classes will return the label classes it has parsed using the regular expression earlier.
  • data.batch_size shows the batch size configured

Different ways of loading data into DataBunch objects

  • data = ImageDataBunch.from_folder(path, ds_tfms, size) can create it from folder, sub-folder structure
  • data = ImageDataBunch.from_csv(path, ds_tfms, size) can load it from a CSV containing file names and class values
  • data = ImageDataBunch.from_df(path, df, ds_tfms, size) can load data from a df
  • data = ImageDataBunch.from_name_func(path, fn_paths, ds_tfms, size, label_func= lambda x:'3' if '/3/' in str(x) else '7') to create from an anonymous function
  • data = ImageDataBunch.from_lists(path, fn_paths, labels, ds_tfms, size) to create from a list of class values.


  • module can list all models that are supported. For instance, [mdl for mdl in dir( if '__' not in mdl] list comp will return 40 such models as of 2021.
from pprint import pprint
pprint([mdl for mdl in dir( if '__' not in mdl], compact=True)

>>> ['BasicBlock', 'Darknet', 'DynamicUnet', 'ResLayer', 'ResNet', 'SqueezeNet',
 'UnetBlock', 'WideResNet', 'XResNet', 'alexnet', 'darknet', 'densenet121',
 'densenet161', 'densenet169', 'densenet201', 'mobilenet_v2', 'resnet101',
 'resnet152', 'resnet18', 'resnet34', 'resnet50', 'squeezenet1_0',
 'squeezenet1_1', 'unet', 'vgg11_bn', 'vgg13_bn', 'vgg16_bn', 'vgg19_bn', 'wrn',
 'wrn_22', 'xception', 'xresnet', 'xresnet101', 'xresnet152', 'xresnet18',
 'xresnet18_deep', 'xresnet34', 'xresnet34_deep', 'xresnet50',
  • fastai.metrics.error_rate() is a type of loss function, we use in training on images
  • is a static, factory method that creates a convolutional neural network based on the backbone and loss function specified. For instance, learn = cnn_learner(data, models.resnet34, metrics=error_rate).
    • Note, when creating the learner, you pass the whole data bunch - including both training and test data.
    • The error_rate function will help in evaluating the performance on both the training data as well as the validation data.
  • learn.fit_one_cycle(cyc_len=4) is used to train the restnet34 model. The cycle length parameter determines how many times to repeat the one cycle learning. The output of this cell shows the following:
epoch   train_loss  valid_loss  error_rate  time
0       1.361700    0.337071    0.104195    02:24
1       0.601790    0.297722    0.089310    02:07
2       0.380089    0.282888    0.079838    02:26
3       0.271350    0.246164    0.071719    02:07

Wall time: 9min 6s
  • The output above shows at end of epoch 4, we have an error rate of 0.071 which means about 92.9% accuracy.
  • Calling learn.summary() returns you a high level info on each of the layers in the DL model along with summary info at the end.
  • Finally, save the model by calling'pets-lesson01-stage1', return_path=True) which will return the path to the model file on disk, such as: PosixPath('/Users/atma6951/.fastai/data/oxford-iiit-pet/images/models/pets-lesson01-stage-1.pth) and weighs about 90 MB in size. By default, Fastai tries to keep the models in the same location as the data bunch.

Model accuracy

Once training is complete, you can use the following tools to evaluate the accuracy.

  • Use interp = fastai.train.ClassificationInterpretation.from_learner(learn) to create an instance of ClassificationInterpretation class. Running this takes a while as fastai will compute the accuracy of each of the result in the validation dataset.
  • interp.top_losses() will return a tuple of losses and indices which correspond to loss value and index of that dataset in the data.valid_ds bunch. Since the function sorts the data by descending loss value, it supplies the index to match with original dataset.
  • interp.plot_top_losses(k=9, figsize=(7,7)) will plot the top losses in a matrix along with the predicted / actual / loss / probability values as annotations.
  • interp.plot_confusion_matrix(figsize=(16,16)) will plot the seaborn style confusion matrix with heatmap. For a 37 class problem like the pets, this matrix gets hard to read. When num classes is high and accuracy is also generally high, use,
  • interp.most_confused(min_val=2) will return a list of tuples - containing prediction, actual, num_confusions. The min_val=2 tells the API to ignore cases where just 1 file is misclassified. It is essentially, the descending order of non-diagonal cells in the confusion matrix.

Model fine-tuning

So far, the fit_one_cycle() method was used on 4 epochs and the training went fairly quickly (10 mins). This is because, the cnn_learner() produced a model that is based on resnet32 and added a few layers to the end. The fit_one_cycle() trained only those last few layers and left most of the earlier ones intact.

This (transfer learning) principle works fairly well and gets us about 92% accuracy. To improve this further, we need to unfreeze all the layers in the models and adjust their weights during the training.

  • learn.unfreeze() will unfreeze the whole model - backbone and the additional layers
  • learn.fit_one_cycle(1) thereafter will try to teach / change weights on the whole model. Often this results in lower accuracy because the initial layers of the model need not be changed as much since they often do preliminary work compared to the later layers which do the actual classification.

To resolve the lower accuracy issue, we need to introduce learning rate and modify the weights of the initial layers much less frequently than those of the later layers. Learning rate is usually specified as a list of floats to match each layer in the model. In Fastai, we use the slice(low, high) Python function to evenly distribute LR between the first and last layers of the model.

  • Run learn.lr_find() where the API will evaluate various learning rates and find the loss for each.
  • Run learn.recorder.plot(suggestion=True, show_grid=True) to view the learning rate plot. It looks like below:

In general, we are not looking for learning at lowest loss, but for rate at the steepest segment of the loss curve. Using this suggestion, you can run

learn.fit_one_cycle(2, max_lr=slice(1e-6,1e-5))  # LR chosen from the suggestion in the image

epoch   train_loss  valid_loss  error_rate  time
    0   0.230512    0.231129    0.067659    02:13
    1   0.246171    0.228836    0.064276    02:15

Wall time: 4min 28s

With an additional 4.5 minutes, we improved the accuracy to 93%. You can then save that model as'pets-lesson01-stage-2', return_path=True) for later.

Predict on real world data

To predict on any given image, use the function to load an image. You get back an object that can be passed to learn.predict() function.

The prediction is a tuple of (Category, category index, probabilities for each class).

Hyper-parameter tuning

Learning rate too high

When the rate it too high, the validation loss gets obscenely high - like an impossible number. The default max_lr is 0.003.

Learning rate too low

When the rate is too small, the model's validation drops, but very very slowly. The command learn.recorder.plot_losses() will plot the validation and training loss for visual interpretation. You can bump the rate by a factor of 10 or 100 and try again.

Training loss > Validation loss

When a model is properly trained, the training loss is typically lower than validation loss. If the training loss is greater, it means the model is not trained enough - try increasing number of epochs or increase the learning rate.

Too few epochs

Too few epochs and too low LR look alike. For instance when you train for just 1 epoch, the training loss might be greater than validation loss. Or, the difference between training and validation might be too high. Try increasing epochs or the LR.

Too many epochs

Too many epochs is too much training and can lead to overfitting. However, it is quite difficult to overfit in deep learning in practice. A sign of overfitting is when the model error starts increasing after a few epochs.