Speeding Up Image and Video Labeling with Annotation Tools
Accessing precisely labeled training data at scale is a key challenge for computer vision based AI companies. The process of labeling images and video can be extremely time consuming and labour intensive. As a consequence commercial annotation tool providers, like Keylabs, are constantly seeking to speed up annotation tasks.
However, rushed labeling practices have the potential to impact quality. Smart annotation tools must find the optimum balance of speed and accuracy by leveraging automatic annotation features and quality control systems.
This blog will detail the innovations that are facilitating fast labeling as well as the quality control procedures that are being implemented in annotation tools.
Commercial annotation tools are finding innovative ways to streamline and accelerate labeling. These features help managers complete project goals on time and reduce the volume of tedious tasks that annotators must perform.
- Automatic Annotation Features: Annotation efficiency can be greatly improved by automated labeling features. Shape interpolation tracks objects through multiple frames of video. Annotators locate the object with a bounding box in the the first key frame and then again in a later key frame.
An algorithm then places a box over the object in the intervening frames. This means that annotators do not have to find a specific object in each frame of a video and can instead simply verify the results of the interpolation.
Automated labeling tool can be deployed to speed up the outlining of objects with polygon annotation. Keylabs automatic annotation feature uses a neural network to annotate images. An annotator draws a bounding box around an object, the program then automatically creations polygon lines around the object. This functionality removes a time consuming aspect of annotation.
- Image and Video Classification: Classification allows for direct control over how annotators label images. Multiple labels can be applied to images and videos increasing the granularity of data. Finally auto-switching between images whilst annotating greatly improves the workflow for annotators and speeds up labeling tasks as a result.
Speed of annotation is important for completing machine learning projects in a timely manner. However, if this produces inaccurate labels and poor quality data then it is counterproductive. Annotation tools can ensure this is not the case with management and quality control features.
- Real-time management: Detailed analytics allow managers to assess performance and control for annotation errors. Managers can see every action taken by an individual annotator. This information can then be deployed for the purposes of training or improving productivity.
- Quality control: Annotation tools, like Keylabs, are integrating quality control processes into their platforms. Annotations are reviewed by the tool and then graded for accuracy. Errors can then be flagged and work can be assigned based on performance. This system of feedback and troubleshooting can scale through the tool as a project expands.
Speed and Quality with Keylabs
In the competitive and fast moving world of computer vision AI, fast annotation can provide a critical edge for any project. This increase in productivity can be accomplished without compromising on quality. The way to achieve this is by taking advantage of well designed, commercially available annotation tools.
Keylabs is an image and video annotation tool designed and built by annotation specialists. This market leading platform combines annotation accelerating features with rigorous management and quality control capabilities.