Machines are much faster at processing and storing knowledge compared to humans. But how can one leverage their speed to create intelligent machines? The answer to this question — make them feed on relevant data. This is also referred to as Training data.
Machine learning models are not too different from a human child. When a child observes a new object, say for example a dog and receives constant feedback from its environment, the child is able to learn this new piece of knowledge.
Machines too can learn when they see enough relevant data. Using this you can model algorithms to find relationships, detect patterns, understand complex problems and make decisions.
Eventually, the quality, variety, and quantity of your training data determine the success of your machine learning models.
The form and content of the training data often referred to as labeled or human labeled data or ground truth dataset is designed for to train specific ML models with an end application in perspective. Here, from a few examples of labeled images to train various types of computer vision models.
Training Data is nothing but enriched or labeled data you need to train your models. You might just need to collect more of it to sharpen your model accuracy. But, the chances of using your data is pretty low because, as you build a great model you need great training data at scale.
Publicly available datasets are unorganized and semantically difficult to classify into very specialized classes automatically (rain at night?, players in soccer uniform?, top view shots of images?) The only way is to go over them all and select manually.
And, we could be a good partner in your journey just after all we annotated millions of images a day for some of the world’s most innovative companies. Whether it’s bounding boxes, dots, semantic segmentation or any sorts of shape, we can help you collect high-quality training data with high precision and recall value.
What kind of data you want us to label? Here are some use cases we do,
Image Annotation for autonomous vehicles, drones, agriculture, satellite imagery, video surveillance and sports analytics.
We also support all image annotation types,
Training Data is labeled data used to train your machine learning algorithms and increase accuracy.
Every machine learning model needs to be tested in the real world to measure how robust its predictions are. This is data that it has never seen before. Just as a student comes across fresh problems while in an exam, models too, need to be similarly challenged so as to evaluate their performance.
While training a model on a particular dataset, we need to ensure that it does not overfit on that data distribution. Thus, the annotated data which we feed into the model is split into training and validation data. This ensures that the learning of the machine learning model is generalized across the dataset.
Every dataset is unique in terms of its content. With a fair bit of domain knowledge, one should decide how to split their annotated dataset into train and test pairs. The ratio of the split is usually around an 80:30 (or) 75:25 depending on how stringently you wish to test the performance of your models.
Every domain has different algorithms and different kinds of data that it requires. But, the general consensus within the machine learning community is that more the data, the better off you are creating a robust model.
There are numerous domains online where you can find training datasets. A lot of research groups also share the labeled image datasets they have collected with the rest of the community to further machine learning research in a particular direction.
With the widespread adoption of technology, it is now become comparatively easier to collect vast amounts of data. Such a drastic change in the volume, variety, and veracity of data has been termed by the community as Big Data. Once you annotate and enrich this data, it can be used as training data for your algorithms.
Training data, as we mentioned above, is labeled data used to teach AI models (or) machine learning algorithms. Whereas, the Test dataset is the sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.