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· 9 min read
Nischay Dhankhar

Finding Popularity of Pets

Introduction

In this competition, we will be predicting engagement with a shelter pet's profile based on the photograph for that profile. Along with the image of each pet, we are also provided Metadata for them that consists of different features like focus, eyes, etc. We aim to somehow utilize both images as well as tabular data in the best possible way to minimize the error rate.

· 5 min read
Nischay Dhankhar

understanding question answering

Introduction

In this competition, we will be predicting answers to questions in Hindi and Tamil. The answers are drawn directly from a limited context given to us for each sample. The competition is diverse and unique compared to other competitions currently held on Kaggle focusing on Multilingual Natural Language Understanding (NLU), which makes it difficult and exciting to work with. Hence, the task of this competition is to build a robust model in which you have to generate answers to the questions about some Hindi/Tamil Wikipedia articles provided.

· 5 min read
Vishnu Subramanian

Resnet Strikes back

Introduction

Most of the modern architectures proposed in Computer Vision use ResNet architecture to benchmark their results. These novel architectures train with improved training strategy, data augmentation, and optimizers. In the ResNet Strikes Back paper, the authors retrain a ResNet50 model with modern best practices and achieve a top-1 accuracy of 80.4% from the standard 75.3% top-1 accuracy.

· 3 min read
Vishnu Subramanian

understanding multi-GPU training

Fastai makes training deep learning models on multiple GPUs a lot easier. In this blog, let's look at different approaches to train a model using multiple GPUs.

In PyTorch, you can achieve Multi-GPU training using 2 different approaches.

  • nn.DataParallel
  • torch.distributed

· 7 min read
Poonam Ligade

understanding resnet architecture

Why it is important to understand ResNet?

ResNets are the backbone behind most of the modern computer vision architectures. For a lot of common problems in computer vision, the go-to architecture is resnet 34. Most of the modern CNN architectures like ResNext, DenseNet are different variants to original resnet architecture. In different subfields of computer vision like object detection, image segmentation resnet plays an important role as a pre-trained backbone.

· 8 min read
Vishnu Subramanian

teddy with a mask

In the last several weeks I saw a lot of posts showcasing demos/products on how to use AI algorithms for recognizing people who are not wearing masks. In this post, I will take you through a very simple approach to show how can you build one yourself and also end by asking few questions that can help in building a product that can be used for production. We will be using PyTorch for this task, but the steps would remain almost the same if you are trying to achieve it using another framework like TensorFlow, Keras, or MxNet.

To build any AI algorithm, the most common approach is to

  1. Prepare a labeled dataset.
  2. Choose an architecture that suits your needs. Preferably pre-trained based on your use-case.
  3. Train the model and test the model.

· 12 min read
Vishnu Subramanian

View on Github

Transfer learning has become a key component of modern deep learning, both in the fields of CV and NLP. In this post, we will look at how to apply transfer learning for a Computer Vision classification problem. Along the way I will be showing you how to tweak your neural network to achieve better results. We are using PyTorch for this, but the techniques that we learn can be applied across other frameworks too.

· 12 min read
Vishnu Subramanian

View on Github  

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.