I can do this all day — Steve Rogers
The topics covered in this post are as follows
- Introduction
- The Task
- The fastai DataBlock API
- Training A Classification Model
- Interpreting A Classification Model
- References
You can click on any topic above to navigate to the respective section
Introduction
Image Classification has been a very common task since time immemorial however it wasn’t until Deep Learning that computers were proficient at doing this task. With the advent of Convolutional Neural Networks this task has become so good that in recent years computers have also beat humans in few classification applications. Building a model to do image classification (MNIST digit recognition) marks the start of the deep learning journey for many beginners. Let’s therefore do the same only let’s make it even more exciting by using a dataset curated on Kaggle called LEGO Minifigures classification.
fastai developed by Jeremy Howard and Sylvain Gugger is a library built on top of PyTorch for deep learning practitioners to ease the process of building/training and inferring from DL models. With relatively a very short code, you can build state of the art models for almost all tasks (classification, regression (on both structured & unstructured data), collaborative filtering etc.)using this library; that’s the amount of effort that has gone into making it. So let’s leverage it to our benefit and start with this task of image classification.
#fastai #deep-learning #python #image-classification #pytorch