5 Reasons to Outsource Your Data Annotation Projects

5 Reasons to Outsource Your Data Annotation Projects

When training an image classifier, it is important to use data that contains high-quality annotations. Even the most sophisticated computer vision algorithms will be rendered useless when they are trained on data that is of poor quality. Outsourcing the data annotation tasks helps to ensure that your datasets are of the highest quality. Data annotators working at annotation companies are trained professionals providing the skills and knowledge that are necessary for the creation of high-quality annotations.

Professional image annotators have access to tools that were built especially for data annotation tasks, and they know how to create annotations in a timely manner while still preserving accuracy. Professional Imaging annotators know how to take edge cases into account. First-time annotators, for example, in-house staff that is temporarily assigned to this task, may not know how to handle and to communicate such problems.

Photo: geralt via Pixabay

These are the five main reasons why you should outsource the annotation of your image data:

1. Quality

The number one reason to outsource your data annotation projects is also the most obvious one: professional image annotators are able to produce higher quality annotations than first-time in-house annotators. The quality of annotations is of crucial importance to the success of a project.

Professional annotators understand annotation needs and are able to consider an image classifier’s perspective in this process. For example, when creating bounding boxes, the boxes must neither be too loose nor too tight around the object to be labeled. A Bounding box is too tight whenever it cuts away parts of the object, and it is too loose when it captures unnecessary parts of the image surrounding the object. For a high-quality annotation, the object of interest must fit perfectly within the box. Moreover, it is important to know when to use different kinds of annotation tools. Knowing when to use bounding boxes and when to work with semantic segmentation is important for a project’s success. Such considerations are second nature to trained annotators.

One reason that prevents companies from outsourcing the annotation of their data is the fear that the annotation company won’t understand the requirements of the project. Professional image annotation teams will work in close collaboration with the client to make sure that the annotations are appropriate for the planned project and all client needs are met.

2. Scale

Creating a quality image classifier requires large-scale datasets that may be comprised of thousands or even millions of images. Every image in such a dataset has to be annotated. Most companies simply lack the number of staff that is needed to annotate such amounts of data. Large additional costs may be associated with re-assigning staff from other teams for the task of data labeling.

Outsourcing image annotation tasks saves time and effort that is better spent dedicated to tasks your employees are experienced in, instead of asking them to learn the ins-and-outs of image annotation.  Various challenges emerge during an image annotation process. Image annotators thus need to be able to quickly adjust to new demands. In-house image annotation teams may lack the necessary resources and experience to quickly adapt to changes in the project requirements.

3. Speed

The in-house staff that is re-assigned to image annotation tasks often takes much longer than trained professionals to complete the annotation of a dataset. In-house annotators need to learn the art of annotation from scratch, or need to receive additional training in order to properly annotate images. If the image annotation project must be completed in a timely fashion, the annotation team may feel tempted to sacrifice accuracy for speed. In the worst-case, a data set may be annotated in such poor quality that it cannot be used and needs to be annotated once more from scratch. Thus, doubling the time needed for the annotation task. Choosing to outsource the annotation of images can save weeks or even months of time spent on this task.

Professional image imitators are trained in producing high-quality annotations at a high speed.In case annotators with additional skills, such as native speakers of a specific language, are needed, an annotation company can more easily recruit and train data annotators that meet these requirements. Moreover, such an annotation service is able to quickly adapt to new project requirements and to adjust the production speed as needed.

4. Data Security

For many computer vision projects, data security is of utmost importance. Many companies are hesitant to invest in professional image annotation as they fear that the security of their data might be compromised. However, image annotation companies are aware of potential data breaches and have multiple data security measures in place. Image annotation companies will provide their clients with data confidentiality agreements, thus ensuring that their clients’ data is handled with care and any misuse is prevented. 

5. Reducing Internal Bias

In-house annotation may introduce various biases into a dataset. Thus, another major benefit when outsourcing the annotation of images is the mitigation of internal bias. Machine learning bias stems from faulty assumptions about the nature of a dataset or sample. When such assumptions are introduced as part of the annotations, the entire system will suffer. Some of the most common types of bias in machine learning are internal bias, sample bias, and prejudicial bias. 

Internal bias occurs when members of a development team expect a model to behave in a certain way and don’t account for other possibilities. They may thus provide the model with data that targets this specific outcome.
Prejudicial bias occurs when training data reflects stereotypes found in society. These might, for example, be cultural stereotypes that manifest themselves during the annotation process. The best way to account for this form of bias is to employ a highly diverse team of annotators. Annotation companies have this in mind when hiring new team members and when assigning project teams.
Sample bias is introduced when the data that is used to train the machine learning model is not representative of the conditions the model will encounter within the actual application. Professional image annotation teams can help in the creation of datasets that will better reflect the environment the model will be used in.