Why do we annotate images?

 For a computer to understand images, the training data needs to be labeled and presented in a language that the computer would eventually learn and implement by itself thus becoming artificially intelligent.The labeling methods used to generate usable training data are called Annotation techniques, or for Computer Vision, Image Annotation. The best image annotation service is provided by the Bridged Internet Inc. As reliable players for artificial intelligence and machine learning training data, they offer a range of image annotation services such as :

1. 2D/3D Bounding Boxes
2. Point Annotation
3. Line Annotation
4. Polygonal Annotation
5. Semantic Segmentation
6. Video Annotation

www.pbsdatalabelingservices.co

info@pbsdatalabelingservices.co

Computer Vision: What is the best way to collect Train and Test data(images) for object recognition job?

Basically when you are implementing a Computer Vision, some basic steps are very necessary.

1. You need to collect lot of data

2. Label these data

3. Train the Model using Algorithm and repeat the above steps till you get the desired results.

For your Model to be accurate, Active Learning is required - In Active Learning, the data is taken, trained, tuned, tested and more data is fed back into the algorithm to make it smarter, more confident, and more accurate. This approach–especially feeding data back into a classifier is called active learning.

1 Data collection - For this you can either use free datasets or paid datasets which are available online.

2 Labeling- Once you have data with you, it can be outsourced to a good data labeling company.

You can use Services of PBS data labeling services for data labeling.

ML and AI need humans to tag the data. It can be very difficult to find people to tag large datasets yourselves, not to mention the tooling and management necessary for it to be done efficiently. The overhead can be enormous for even small datasets.

We at PBS data labeling services are focused on simplifying the above problems for companies looking to create training data for their computer vision models.