AI training data: a key factor to increase the performance and reliability of the model.
Hello, I’m Evelyn, the marketing manager of the AI data company AIMMO.
Training data plays an important role in developing AI models. With the training data, the AI models learn and analyze the patterns, which allows the AI models to draw responses to various situations. Therefore, the training data’s amount and accuracy are the key factors to increase the performance and reliability of the AI model.
To achieve this, the labeling process is necessary, assigning the desired output or tag to the data. For example, to train AI models for autonomous driving, lanes, signs, and pedestrians on roads should be accurately labeled to enable AI models to make accurate decisions.
However, labeling work is not the only factor to achieve this; enough datasets for each case and accurate inspection processes with real-time communication with labeling workers must be followed.
Finally, by training the AI model using the perfectly labeled data, the model can analyze patterns in the input data and make predictions based on new data.
The performance of the AI model is decided by the quantity and the quality of the training data. The more variety of cases in the training data, the more accurate prediction the model can make by responding to a variety of situations. On the other hand, if the training data is insufficient or of low quality, the performance of the model may be degraded.
In addition, as I mentioned earlier, accurate inspection and rapid communication with the labeling workers in the inspection process may prevent mistakes and in turn, it will enhance the performance of the model. So. It is important to obtain accurate and diverse learning data to improve the performance and reliability of the AI model.
Various tools and techniques are being developed to improve the quality of AI training data. For example, the Thumbnail Viewer allows you to view thumbnails and labeling information of instances created while labeling work in one section. This allows you to visualize and check several instances at a glance, making labeling easier.
Thumbnail viewer’s multi-filtering function allows the users to select the conditions of classes and properties according to their needs, supporting them to quickly sort out unnecessary instances. Multi-filtering function boosts the searching, which greatly increases the labeling work’s efficiency.
As a result, AIMMO’s thumbnail viewer helps improve the performance and reliability of AI training data and enables quicker and more accurate labeling work. These various functions and tools bring your AI model superb performance.
Users can now select a maximum of five class and property filters. Multi-filtering functions can be applied to the tag filters and the individual folders, which can improve the speed and the scope of the verification process using the thumbnail viewer. The multi-filtering function is now enhanced enough to help the users to filter out unwanted instances and correct them, also speeds up the communication among the users by sharing the Thumbnail viewer link.
More specifically, users can copy and share the link by hovering or clicking on the specific thumbnail that needs to be forwarded. Through the forwarded link, co-workers can check to list of thumbnails under the same filtering conditions and focus on the selected thumbnails. By sharing the link, the users can accurately communicate with each other.
The introduction of a multi-filtering function to AIMMO’s Thumbnail viewer can improve the quality of training data, increase work efficiency, and further improve the performance of AI models through accurate and rapid inspection work. We hope the transition and development of ML to keep progressing with AIMMO’s technologies and features. Through these features, users can work more effectively by increasing the quality and speed of the work.
Aimmo’s services AD-DaaS, Enterprise, GTaaS
Written by Evelyn
Translation by Nora
