good annotations v bad annotations students

Good annotations v bad annotations students full#

Needless to say, this is not something that's going to happen for most researchers. In other words, if you spend 3 hours creating an image annotation that has 7 labels and depth perception runs in about 1 day, then it's likely to take 14 days for your algorithm to recognize every label in the image. The other drawback to this is that creating overly detailed annotations takes a lot of time, and it's likely that you won't spend the time at the same rate that you'll get accurate results. However, these types of annotations can be very detailed and accurate if the person designing them knows quite a bit about what they're annotating. This would help the computer recognize human info, but because many algorithms are set up so that they can't handle a large number of labels or words, it's usually just not used.

An example of this would be labeling an image with a large number of words designed to describe the contents of an image. To make up for this wasted information, many computer vision researchers use overly detailed annotations. This means that not only does it produce very inaccurate results every time an algorithm runs on this data, but it also wastes a lot of valuable data. If a computer is trained on this data, it may label 50% of the adults as adults, 10% of the children as being students, and 90% of the children as being students because these are the 'only' labels that get labeled. For example, a classroom may have one group of children labeled as children, another labeled as being a student, and another labeled as being an adult. Humans are often trained on inaccurate teaching datasets that are not relevant or complete. Re-training the dataset can be a very bad decision for your data and will decrease accuracy of your training data.īad annotations are created by humans when they label things or when they make errors in labeling something. For example, have you ever had a teacher say to you 'do this exercise now and do it again next time'? This is called re-training the dataset and is something many AI engineers don't like to do. While this study looks at human annotations, it's important to note that humans are often trained using datasets that are not comprised of relevant data. This shows that using a computer to make image annotations can be very inaccurate. One study compared the ability of humans to label an image as containing a horse against a computer's ability to label the same image as containing a horse, with the computer achieving an accuracy rate of 56% (humans had an accuracy rate of 92%).

Good annotations v bad annotations students full#

Many researchers have published research about this, including datasets that are full of bad or nonsense annotations. We want to do anything we can to make sure that our training data is actually useful and accurate.īad annotations are more common than you may think. Why Do We Need To Correct Bad Annotations?īeing able to identify and correct bad annotations is important in several important areas: making data more accurate, improving performance of the model, and updating the overall quality of the annotation dataset. This could cause problems for machine learning if an algorithm thinks that it's recognizing something that's supposed to be in the picture. An example of this is when humans annotate a picture of a dog, but the annotated image contains a dog with eyes in its head (a common caricature).

However, the problem with image annotations is that many are not accurate or complete. Image annotations are useful because they provide useful information to a machine learning task. In fact, it's been shown by many researchers that having accurate image annotations can significantly improve machine learning results by providing large sets of training data or improving the accuracy of the data when 'teaching' algorithms to recognize patterns better. By not having good image annotations, you're missing out on a lot of benefits that image annotations can provide. Image annotations are commonly used in computer vision and other AI tasks, but they don't get much attention.