![]() ![]() 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. It also accepts and produces the same data format.'Hotdog' or 'not hotdog'? That could be the question - at least when performing an image classification task. Under the hood, the POS tagging interface uses the same UI component as the This live demo requires JavaScript to be enabled. Review and merge existing annotations by multiple annotators.Ĭombine different interfaces and custom content. Annotated tasks contain an additional "answer" key mapping to eitherĪnnotate and correct potentially overlapping spans.Īnnotate and correct part-of-speech tags.Īnnotate syntactic dependencies and semantic relations.Ĭreate bounding boxes and image segments.Īnnotate and correct regions in audio and video files. This format is also used to communicate tasks between REST API and webĪpplication, and will be used when exporting annotations via the db-outĬommand. Prodigy expects annotation tasks to follow a simple JSON-style format. Text, named entities, categorization, images and A/B comparisons, as well as raw The web app lets you annotate a variety of different formats, including plain ![]()
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