If you’re hoping to streamline your company’s image annotation process, there are a variety of image labeling tools you can use to make creating annotations quicker and easier. Considering the plethora of image annotation tools out there, how do you choose the tools that satisfy your needs?
Choosing the best image labeling tool involves understanding the different image labeling techniques and their use cases, as well as considering your own needs and means. Let’s explore how the different image annotation techniques are used, and then we’ll take a look at some considerations for choosing the right tool for your image annotation needs.
There are numerous image annotation labeling techniques, and each one has its own use cases. Choosing a good image annotation tool means understanding the different annotation techniques and knowing which techniques you’ll need to prepare your data with.
The various image annotation techniques you can apply to your image of data set include: bounding boxes, line annotation, point annotation, semantic segmentation and instance segmentation.
Each of these annotation techniques have their own best use cases. Bounding boxes are probably the most commonly used form of image annotation. Bounding boxes are simply boxes created around relevant objects in an image, with the box defining where the image classifier should look for an object. A label is also applied to the object within the bounding box. Bounding boxes are commonly used because of their versatility, working in the most scenario was where pixel-level accuracy isn't needed.
Line annotation is typically used in situations where a linear region has to be highlighted and bounding boxes won’t suffice. Bounding boxes may be too large to properly annotate linear portions of an image, and in this case line annotation can label thin, small sections of an image.
Point annotation is a technique that uses a series of interconnected points to annotate the outline or skeleton of an object. Point annotation is useful when maintaining a representation of an object’s shape is important.
Semantic segmentation is the process of assigning every pixel in a semantically defined region a class label. Any portion of an image that is described with a word can have all the pixels within that portion labeled, and the use of this pixel-perfect detail can dramatically enhance the performance of an image classifier. Instance segmentation takes the concept of semantic segmentation and extrapolates it to individual instances of an object or region. To be concrete, while in semantic segmentation every apple in an image would be part of a single “apples” instance, instance segmentation would apply the apple class to every individual apple.
So how do you choose the best labeling tool for your AI company? You should choose a labeling tool that lets you use the appropriate annotation techniques, but you should also consider other variables like model integration, label correction, project management, and export options.
One of the primary considerations when choosing an image labeling tool is what kinds of annotation techniques the tool supports. For instance, if the image classifier you are creating requires instance segmentation training, it would waste your time to invest in a labelling tool that doesn't support instance segmentation. Many labeling tools only offer support for bounding boxes and semantic segmentation, and if point annotation or line annotation is needed it becomes important to select an annotation platform that enables them.
Other considerations for selecting an image labeling tool are model integration and deployment. After images have been annotated, they must be passed into the machine learning model, which trains upon them. After the model is trained, it has to be deployed. Needing to set up training and deployment of a model after image annotation can be a time-consuming process. For this reason, tools that allow easy model training and deployment should be prioritized. Ideally, an image labeling platform will allow you to label your images then train and deploy your model with just a few more button clicks.
You'll also want to consider how easily the tool lets you correct improper labels or add a new label classes. Changes to annotation requirements come up all the time when conducting image labeling of a dataset, so it’s important to determine how difficult it is to make these changes with the labeling tool. Good labeling tools should make correcting mislabeled data simple and intuitive.
Project management options are important to assess as well. How easily does the labeling tool let you create, manage, and organize projects? Some labeling tools have almost nothing in terms of project management, only letting you label images. Ideally, a labeling tool will enable you to define images as belonging to a certain project, which assists in organization. Having a clear project structure will help you discern which images need to be annotated with what labels, as well as train your model on the images within that project.
Interface options and export options should also be investigated. Can the labeling tool be interacted with online and offline? Some annotation platforms have an online interface and the annotations can be done directly in a web browser. Labeling tools with both online and offline capabilities will expand your options. It's also important to consider export options for your annotations. JSON, CML, and CSV formats are common export options, and the platform you choose should be influenced by which export file format best suits your needs.
Finally, all the considerations listed above should be balanced against consideration of the annotation tool’s price point.
Whether your field of work is agriculture, space tech, fashion, or retail, you want to choose the best labeling tool for the job. To select the right image labeling tool you’ll want to give due consideration to all the important variables. Start by considering which annotation techniques you need to use, and find out which tools support them. You can then consider other variables like model integration and project management.
You’re unlikely to find a labeling tool that perfectly fits all your needs, so your decision will ultimately be the one that best balances your competing requirements. However, by making informed decisions about your needs you can choose the labeling tool that works best for you.