In this article, we will learn how to build state-of-the-art models in computer vision and natural language processing within a couple of minutes using H2O Hydrogen Torch.
H2O Hydrogen Torch (HT) aims to simplify building and deploying deep learning models for a wide range of tasks in computer vision (CV) and natural language processing (NLP). HT is a no-code graphical user interface (GUI) to build and train state-of-the-art (SOTA) deep learning models easily. The framework is developed by Kaggle Grandmasters and renowned data scientists across the world. HT allows both experienced and novice data scientists to build deep learning models efficiently without any prior knowledge of deep learning frameworks. All you need is H2O AI Cloud .
Let’s look at some HT use cases for computer vision and natural language processing.
Classifying an image into a set of classes:
Predicting a continuous value from an image:
Classifying an object in an image along with its position:
Identifying the exact shape of an object in an image along with its class:
The objective of instance segmentation is almost similar to semantic segmentation. The only difference between Semantic and Instance Segmentation is that “semantic segmentation treats multiple objects of the same class as one but instance segmentation treats them as different”. For example:
Identifying the similarity between the images:
Classifying text into different classes:
Predicting a continuous value from text:
Text Sequence to Sequence refers to solving Sequence to Sequence problems. These problems deal with sequences i.e. both input and output consist of a text. For example:
Classifying each word in a text:
Identifying the similarity between the text:
Okay, it is time to try out HT. You can follow this tutorial and build your first HT deep learning model.
Now let’s go through different options for uploading datasets to HT.
By default, HT provides access to different datasets stored on a public S3 bucket called
hydrogen-torch-external. In this tutorial, let’s work on a multi-label image classification problem based on the Amazon image dataset. The dataset consists of satellite images of size 256×256. Each image is tagged into multiple classes like rain forest, agriculture, rivers, towns/cities and so on. The dataset is very noisy and ambiguous. Satellite Imagery Analysis helps in understanding Deforestation and Human Encroachment in the forest. The ultimate objective of the problem is to build an image classifier for tracking the human footprint and thereby reduce deforestation with immediate action.
In order to import the Amazon dataset, select
amazon_multilabel_image_classification.zip and click
continue as shown below:
amazon_multilabel_image_classicationand explore the dataset
We can now build a deep learning model with the Amazon dataset. First, we need to create an experiment.
Any new experiment in HT is populated with some default hyperparameters based on the problem type. These are good default values that should work across a variety of different datasets and are handy for new users.
You can do A LOT MORE than relying on the default hyperparameters in HT. By selecting different
Experience Level settings, you can get access to more settings and have full control of your deep learning model configuration WITHOUT writing any code. Below are a few examples:
Once you are happy with the experiment settings, click
Run experiment. You will be able to find the new experiment from the
List experiments tab. You can click
Refresh to look at the latest status of the experiment.
There are a few tabs that you can explore during and after the experiment. Simply click the name of the experiment and you will see different tabs of results as shown below.
If you have followed all the above steps, you should have trained an image classification deep learning model by now. Let’s use this model to make some predictions.
Note: In theory, you should have uploaded a new, unseen dataset for making new predictions. For demonstration purposes, let’s use the same Amazon dataset for now. The main goal here is to walk you through the steps to make new predictions. But do remember that the predictions from the same Amazon datasets are going to be very optimistic as the dataset was used for training.
That’s it. I hope you enjoyed this end-to-end, no-code walkthrough of image classification example using H2O Hydrogen Torch.
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