Machines are replacing humans in routine and manual jobs because of the faster processing speed and storing knowledge advantage they have over humans. One can even leverage their speed and turn them into intelligent machines. Here is where the Training data comes into the picture. By feeding them with relevant data, machines can be trained to mimic the human brain and learn to process information.
Training data even though is a simple concept, forms the basis to the way cutting-edge technologies like machine learning and deep learning programs work. It is an initial dataset that helps a program or an algorithm find relationships, understand, learn and produce sophisticated results.
The performance of the ML and DL models depends on the quality and quantity of the training data.
Why Training Data Matters?
One can describe training data as well-structured or labeled data that helps to sharpen your ML models. You will require vast amounts of data to train your models with high accuracy.
A great model requires training data at a large scale and has to be labeled in a way that will work for training your algorithm or model. By feeding the self-driving car models with a picture of the road won’t be enough. They should be fed with labeled images where every object such as a street sign, vehicle, pedestrian and more have to be annotated.
In case of projects that require sentiment analysis, the algorithm has to be fed with labeled data that will help it to understand sarcasm or slang.
How to collect Training Data?
Data Labeler can be a good partner in your quest for training data. We have the expertise and experience in labeling millions of images and videos daily for some of the top innovative companies in the world.
Whether you are looking for text, image, video or any kind of data annotation services, we are here to help you in collecting world-class training data for any industry.
From autonomous vehicles and drones to agriculture, retail and sports analytics, we are adept at supporting all image and video annotation types. We specialize in the following:
- Bounding Box Annotation
- Polygonal Annotation
- Semantic Segmentation
- Cuboid Annotations
- Line annotation
- Text annotation
- Select & Multi-select annotation