May. 26, 2021
Powered by artificial intelligence and machine learning, computer vision can help digitally transform your business. Today, sophisticated computer vision AI models can learn to recognize a wide variety of faces, objects, concepts, and actions, just as well as—if not even better than—humans can. But…
There’s just one problem: where are you going to get the data? For best results during AI training, you need to collect potentially hundreds or thousands of images or videos. The more training data you can obtain, the better your computer vision models can learn how to classify different visual phenomena.
For example, suppose that you want to build a computer vision model that can differentiate between images of dogs and cats. If you only use high-quality photographs as your training data, with each animal close up and facing the camera, then your model might struggle to generalize to real-world situations, with types of data that it hasn’t seen before. Some difficulties with image recognition are:
In order to be responsive to these issues, your computer vision model needs to be trained on large amounts of labeled, high-quality, and highly diverse data. Unfortunately, generating this type of data manually can be difficult and time-consuming.
One solution is to perform data augmentation: increasing the amount of training data by making slight modifications to each image. For example, an image of a dog may be slightly rotated, flipped, shifted, or cropped (or all of the above). This will help the model learn the underlying truth about what a dog looks like, rather than over-fitting by learning to recognize the image itself and knowing that “dog” is the right answer. A single image can produce a dozen or more augmented images that can help your computer vision model extrapolate better in real-world scenarios.
In addition to simple data augmentations, there’s another solution to the problem of data sparsity: you can generate synthetic data.
If you have a realistic 3D model of the object you want to recognize, you can instantly generate hundreds or thousands of images of that 3D object with synthetic data generation.
By using synthetic data generation, computer vision users can rapidly build and iterate AI models and deploy them to the edge.
Chooch is a computer vision vendor that helps businesses of all sizes and industries—from healthcare and construction to retail and geospatial—build cutting-edge computer vision models. Our synthetic data generation feature makes it easy for users to increase the diversity and versatility of their training data. All you need are two files: an .OBJ file that describes the object’s 3D geometry, and an .MTL file that describes the object’s appearance and textures.