In week 7, we covered the second half of lesson 5 and started with lesson 6 on Other Computer Vision problems like Multi-Label classification. We have covered so far almost 25% of the FastBook aka. Deep Learning for Coders.

Last week, I wrote about what is covered in rest of lesson 5. Some of the new things which Aman introduced was:

  • The valley function in fastai, which is available in latest version to get the exact learning rate to be passed.


  • More scheduling algorithms were mentioned like Torch optimizers.

  • I tried working on these applying techniques in a different dataset consisting of images of various painters in Kaggle. The notebook used for training can be found here:

The chapter 6 consists of discussion other Computer Vision problems like Multi-label classification, Regression. During the session we covered more about loading dataset for multi-label classification.

We used PASCAL 2007 dataset for this task, which is consisting of images and tabular data with the labels, filename and whether itโ€™s part of validation datset.


Aman during the lesson explained the difference between lambda functions and normal funcitons with a simple example. To check out more about lambda functions checkout this article from RealPython.


  • Next we can create a Datablock, based on the input dataset. With training file path as independendent variable, and labelled list as dependent variable.

When looking into Pytorch and, there are two classes for representing datasets:

  1. Datasets - a collection that returns a tuple of independent and dependent variables for a single item. In fastai it returns an iterator for bringing your training and validation dataset.

  2. DataLoaders - a iterator which provides stream of mini-batches where each mini-batches is a couple of batch of independent variables and dependent variables.


This week I tried more about learning Pytorch Datasets and Dataloaders based on this tutorial. The details can be found in week 6 training notebook.

We used a Mulicategory block this time and used one hot encoding technique to identify where is the correct category for each labelled image. Using a splitter for training and validation splitting, the dataset was finally loaded as follows:


The full notebook of week 6 can be found here

The full session recording can be viewed in the below link and thanks for reading ๐Ÿ™.


Interesting Article Links