ipywidget's Output widget offers a neat way to leverage IPython’s display capabilities, while the Button widget lets you add interactive buttons to the tool. Second, we would like to display one image at a time together with interactive buttons to the person doing the labeling. So let us have a look at the modules and widgets we are going to use to create our simple labeling tool.įirst, we import python's built-in pathlib to properly work with the image file paths. That is, it lets you write code in pure python and generates all the necessary JavaScript underneath to create interactive HTML widgets that can run in your browser. Among other things, it offers the possibility to add common widgets such as sliders, dropdowns, or buttons to apps and dashboards. Ipywidgets offers a handy solution to turn Jupyter notebooks into interactive applications. For example, we want the displayed contents to be dynamically updated whenever the user clicks on a button - in other words: we require JavaScript. However, to implement an intuitive labeling tool we require interactivity beyond the ability to execute code. As an interactive interpreter with graphical capabilities, Jupyter allows users to run short code snippets and perform basic data analysis. Jupyter notebooks have become an essential piece in the toolbox of many data scientists and researchers. Additionally, it would be convenient if there was a way to navigate between images.įor example, a simple tool could look more or less like this: Then all you want your labeling tool to do is show you one image at a time, provide buttons to assign the class labels to the image at display. Let us suppose you need to label a dataset that contains images of various foods, and you are supposed to put them into the categories 'hotdog' and 'not hotdog'. Making a Jupyter notebook interactive with ipywidgets Therefore, to fill this tooling gap, we have created a simple labeling tool ourselves in the context of one of our projects, which provides all the functionality required for image class labeling - and just that. However, for simple image classification, a full-fledged labeling solution can add substantial complexity to the workflow without bringing substantial benefits. Hence, the labeling tool should be as intuitive as possible while abstracting away from functionalities needed only for more nuanced computer vision tasks such as image segmentation or object detection. So why do we need an additional tool if there are plenty of solutions out there already? Here, it is important to keep in mind that labeling is oftentimes carried out by an expert in the problem domain who is not very familiar with the intricacies of machine learning. As discussed in earlier articles on labeling tools for computer vision and NLP, there are many commercial or open-source solutions available for the labeling process, all with their advantages and disadvantages. Now, if your use case does not entail a conveniently labeled dataset such as the ubiquitous imagenet, manual labeling work is necessary before we can even start to train a machine learning model. As with all supervised learning techniques, it is necessary to feed the training loop with labeled data for the algorithm to be able to pick up patterns in the images. Real contexts in which we have encountered image classification problems are, for instance, the extraction of information from technical drawings, the automatic attribution of detected defects, or the visual inspection of the success of a manufacturing process in one of our ongoing projects. Of course, applications will not always be as mundane as telling hotdogs from other foods, but this simple case makes a good graphic example for illustrating what we are after. Image classification is one of the most fundamental applications of machine learning to computer vision. Image classification and the need for yet another labeling tool That is exactly right and also what we set out to do in this article: Create a simple annotation tool to easily assign class labels to a set of images. Wouldn't it be great if there was a streamlined solution that makes this labeling process more efficient, even fun? However, such a heavy-handed approach sounds rather tedious and is likely prone to fat-fingering errors. One way to do that would be to open up one image at a time and keep track of image classes in another file, e.g., a spreadsheet. That is, we sometimes have to manually look at hundreds or even thousands of images that do or do not contain hotdogs, and decide if they do. To be able to address this or a similarly important question by means of a machine learning model, we first need to come up with a labeled dataset for training. 'Hotdog' or 'not hotdog'? That could be the question - at least when performing an image classification task.
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