by Team Diffgram | Apr 7, 2022 | Training Data 101
The quality of your Training Data is defined by the quality of annotations and labels. Your annotators can label your training data with the tools you (or your data labeling software vendor) provide them. The right instance type will help you label easily and...
by Team Diffgram | Apr 5, 2022 | Training Data 101
What is Image Annotation? Image annotation is the process of annotating or labeling images in a given dataset. These data sets are used to train an ML model. By annotating images, you add labels to the features of the images that you expect your ML algorithm to...
by Anthony Sarkis | Mar 20, 2022 | Training Data 101
Data Labeling is transferring human knowledge to the computer by annotating data. In Data Labeling a raw media element (image, text, video, 3d, audio, etc.) is loaded along with a set of labels (Schema). The user reviews the media and labels it. For example, declaring...
by Team Diffgram | Feb 22, 2022 | Training Data 101
The quality of your annotations and training data depends on how well your annotation workforce executes the tasks. Guides are readable instructions that help improve the quality of annotations created by your labelers. Diffgram allows you to create detailed guides...
by Team Diffgram | Feb 22, 2022 | Training Data 101
You can better leverage Diffgram when your team perfectly knows how to label data for machine learning efforts. Diffgram’s data labeling tool allows you to effectively manage your data labeling tasks without having to leave the platform. No more isolated tools....
by Team Diffgram | Feb 15, 2022 | Training Data 101
To annotate your text training data on Diffgram, you first need to create a project. Here is a step-by-step guide to help you get started with your training data labeling on Diffgram. The process is standard and the same across all different data types viz Image,...
