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

Original article was published by Nanhe Gujral on Artificial Intelligence on Medium

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.