In this article, we will walk through a baseline model for the Jigsaw Rate Severity of Toxic Comments Competition on Kaggle. The goal of the competition is to rank relative ratings of toxicity between comments.
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.
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.
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.
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.DataParalleltorch.distributed
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.
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
In the last few years, computer vision algorithms have been able to do many things. One amazing and dangerous thing it can do also is, generate new images, faces, voices, etc. The evolution of what these algorithms can do and if it is good is for a separate debate.
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.
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.
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.
