Our Services

One of our services consists of batch analysis. This service at the beginning will use pre-trained models for specific instances but instead of doing it on image by image basis, it will process a complete batch of images in one go. We would expect a zip file with either individual images or video. On the video files we will add tracking and re-ID of the instances. We will also do text extraction from images and videos as part of the services we provide for batch datasets.

In this case our API still allows you to get the results through a REST call. So you can either integrate it into your editor or manage and display the results in our in house editor.

For batches where we have no pre-trained models we will use our Machine in the Loop pipeline where all manual aspects of a deep learning training pipeline have been substituted by algorithms. For this besides the raw data we will need examples of the objects you want to annotate. If you do not have the tools to create these examples you can either use our in-house editor or we can obtain them with our image search engine. 

In the batch processing projects, since the client is not reviewing the results in parallel, we provide two datasets, one that we are certain of the achieved accuracy and another dataset to be revised to make sure it fulfills the quality requirements.

Our manual editor based on label studio is very versatile and easy to use. It doubles as a dataset review manager and we have modified it so that it can directly call all the automated services from our API. It is ideal for companies that do not have a specific software for this task.

Our editor also allows for model composition. You will be able to narrow down the dataset by combining seamlessly different models that will allow you to create complex classes, like men only wearing black uniforms or smiling children instead of all the children in the picture.

One of our services consists of batch analysis. This service at the beginning will use pre-trained models for specific instances but instead of doing it on image by image basis, it will process a complete batch of images in one go. We would expect a zip file with either individual images or video. On the video files we will add tracking and re-ID of the instances. We will also do text extraction from images and videos as part of the services we provide for batch datasets.

In this case our API still allows you to get the results through a REST call. So you can either integrate it into your editor or manage and display the results in our in house editor.

For batches where we have no pre-trained models we will use our Machine in the Loop pipeline where all manual aspects of a deep learning training pipeline have been substituted by algorithms. For this besides the raw data we will need examples of the objects you want to annotate. If you do not have the tools to create these examples you can either use our in-house editor or we can obtain them with our image search engine. 

In the batch processing projects, since the client is not reviewing the results in parallel, we provide two datasets, one that we are certain of the achieved accuracy and another dataset to be revised to make sure it fulfills the quality requirements.