Do you know it is super easy to deploy a Gradio APP on Jarvislabs.ai 🤔 In this post we will explore how to deploy a Marvel character classifier app on Jarvislabs.ai.
In this post, we shall look at the task of metric learning, and implement the paper Classification is a strong baseline for deep metric learning on the Inshop dataset
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.
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 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.
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.