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2 posts tagged with "Segmentation"

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· 12 min read
Vishnu Subramanian

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Building image segmentation model using fastai and PyTorch | Part-2

In the first part, we looked at how we built a data pipeline required for creating data loaders using fastai. In this part, we will create different segmentation models that help us rank in the top 4% of the Kaggle Leader board.

clay modelling

It is often said the modeling is the easy part, but it is rarely true. Building models for any kind of problems have too many levers that can give you great results or bad results. Anyone who has participated in a Kaggle competition or built a real-world deep learning model can tell us more stories of how a particular technique that worked for a certain problem did not work on a new dataset. We are going to take a step by step approach to build a model and improve it.

· 13 min read
Vishnu Subramanian

When working on Deep learning projects getting the right pipeline starting from data processing to creating predictions is a nontrivial task. In the last few years, several frameworks were built on top of popular deep learning frameworks like TensorFlow and PyTorch to accelerate building these pipelines. In this blog, we will explore how we can use one of the popular frameworks fastai2 which is currently in the early release but the high-level API is stable for us to use.

pot making

About fastai2

One of the best places to know about the library is to go through the paper published by the authors of the library. Find the abstract from the paper

fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes: a new type dispatch system for Python along with a semantic type hierarchy for tensors; a GPU-optimized computer vision library which can be extended in pure Python; an optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4-5 lines of code; a novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training; a new data block API; and much more. We have used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching. NB: This paper covers fastai v2, which is currently in pre-release at dev.fast.ai