Are you currently researching an image annotation service to train your AI model? We highly recommend reading this resource to understand how image annotation services work. In addition to that, we outlined a few important factors to consider before outsourcing your image annotation project.
What is image annotation?
As the name suggestions, image annotation is the task of labeling an image to let computers understand what it is. Image annotation plays crucial role in providing computer vision to your AI models. Without labeling, computers will neither ‘see’ images nor work the way you train them. In simple words, image annotation is the human process of training AI to recognize different images.
How does image annotation work?
Image annotation or image labeling is not as complex as it sounds. On a basic level, all it needs is:
- Images
- A human (annotator)
- A tool or a platform to label images
The difficult side of image annotation is finding thousands of human resources to do the job.
This is the reason why image annotation outsourcing companies exist. An image annotation service like ScaleOps eventually takes the annotation burden off your shoulders and allows you to focus on the core project.
Important Things to Consider Before Outsourcing Image Annotation
- Types of Image Annotation
Image annotation is an umbrella term for different types of labeling techniques. Depending upon the type of the AI project, you may be requiring bounding box object detection, polygon annotation, semantic segregation, or a few other types. Keep in mind that not all image annotation service providers offer all the different types of annotation. The following are the some of the widely used image annotation methods.
2D Bounding Box Detection
Rectangular 2D bounding boxes are drawn around specific objects of interest across a large image set. A PASCAL VOC formatted file, containing the XY coordinates of the bounding box, is the human intelligence data that is used to enrich the original dataset. The goal is typically to train an AI Model for Object Detection in future images.
3D Bounding Box Detection
3D Bounding Boxes, or Cuboids, are an advanced form of object detection. They identify not just the rectangular space that an object of interest occupies, but also provide the Deep Machine Learning algorithm a sense of the height of the object in 3-Dimensions.
Polygon Annotation
Object detection does not always happen in neat rectangles, and some deep learning applications require a precise outline of objects. Polygon Annotation is for applications that require not just object detection, but a clear outline of the object.
Landmark Points
Landmark points or Landmark key points annotation service is used by Deep Learning models to closely understand, and identify the internal shape of objects. Key use cases include facial recognition, gesture and sentiment analysis, directional movement prediction, for sports and for autonomous driving.
Polylines and Splines
Smoothly drawn, fitted, Lines Polylines and Splines enable AI and ML systems to detect boundaries. Autonomous self-driving cars use this technology for lane detection systems; As do Warehousing robots and security cameras in stores.
Image Categorization
Some applications require Images to be categorized or tagged. Image Tags can be of people in the photo, emotions, scenes, or activities.
Semantic Segregations
Semantic Segregation or Semantic Segmentation marks each pixel in the Image to semantically separate all the entities that appear in an image. Semantic Segmentation applications are critical to Autonomous Self-Driving Car technology.
Even though you may require a certain type of object detection, it is always good idea to choose a full-service image annotation service that can meet your future needs as well.
- Data Security
Depending upon where you live, you may need to choose companies that are certified to handle your data properly. GDPR compliance, SOC 2 compliance, etc. are some of the aspects you need to look consider while choosing an image annotation agency.
Companies with high-value or mission-critical AI projects are highly advised not to choose a company that uses crowdsourcing over in-house/full-time employees for annotation.
- Budget
Please keep in mind that image annotation is a small part of your ambitious AI/ML project. You should reserve your best resources for building, scaling, and marketing the application rather than spending it all on image or data annotation.
Companies like ScaleOps provide top-notch data labelling services in a budget, thus playing its part transiting the world into a better place in terms of technology.
Avoid choosing cheap image annotation services that neither offers quality nor data security.
- Turn-Around-Time TAT
As said earlier, your primary focus should be on building your application. As a startup or a well-established technology company, you cannot afford to wait for an eternity to complete the image annotation task.
Always prefer a company that talks about turnaround time as one of their USPs. No matter how huge your data is, a good image annotation company like ScaleOps should be able to deliver it quickly.
- Quality
While data labelling is mere data entry and certainly does not need computer engineers, you must also keep in mind that certain level of skill and expertise is needed for better output.
If the annotations are not accurate, your AI//ML model cannot be trained properly.
Companies like ScaleOps hire qualified professionals and train them properly on what they need to do to achieve the needed results.
Image annotation crowdsourcing websites or companies that rely on crowdsourcing/contractors/freelances often will not deliver the needed perfection and results.
