The term “synthetic data” is used in computer vision to describe images that are generated by algorithms instead of being captured by a camera. These images are usually generated for training artificial intelligence (AI) models.
The use of synthetic data has several advantages over real data. First, it is easier to get a large amount of synthetic data than real data. Second, synthetic data can be generated with specific properties that are difficult to find in real data. For example, it is possible to generate images of objects that are occluded, that is, partially hidden from view. This is useful for training object detection models.
Third, synthetic data can be generated with controlled variations. This implies that the generated data can be varied systematically, for example, by changing the color of the objects in the images. This is useful for training models that are robust to changes in appearance.
Fourth, synthetic data can be generated with labels. This means that the object in the image is identified and labeled, for example, as a “car”, a “lion”, or a “human”.
Tips to Create Synthetic Data for Computer Vision
The problem with creating synthetic data for computer vision is that it’s difficult to create realistic images. The images need to be similar to real-life photos in terms of lighting, contrast, and color. If you have an image with a lot of contrast between light and dark areas, this can make it harder for your algorithm to recognize objects in the image.
If you’re using the Google Brain TensorFlow, you can use the Synthetic Images toolkit to create your own images. This toolkit includes a library of commonly used objects such as cars and faces, along with libraries for recognizing these objects and text. You can also use this toolkit to create your own custom data sets based on other datasets (like photos).
Here are a few tips to create synthetic data for computer vision:
- Use a large number of images. The more images you have, the better your results will be.
- Include both foreground and background objects in your photos (e.g., cars, trees, etc.). This will help train your model to recognize different types of objects in natural scenes as well as objects that are commonly found in images from the real world (e.g., cars).
- Try to use images from different angles and distances from your object of interest so that the model can learn how to distinguish between similar-looking objects across multiple viewpoints and distances from the camera (this helps ensure that it can generalize well beyond what was shown in the training data set).
- Look at how existing algorithms perform on images, especially in terms of labeling (how much they label correctly).
- If possible, try out various methods such as random sampling, clustering, or dimensionality reduction of the dataset before going for a more complicated method such as deep learning models or recurrent neural networks (RNNs).
The Process of Generating Synthetic Data
There are many ways to generate synthetic data for analysis. The choice of approach will depend on the AI project. The following are a few steps that are generally implemented while creating synthetic data for machine learning projects.
- Determining objectives
The objectives of the synthetic dataset should be defined first and foremost, as this will determine its use in machine learning processes. It is important to understand if there are any constraints in place that could affect the project, such as organizational policies or compliance standards. Privacy requirements should also be taken into consideration.
- Model determination
The choice of model – be it autoencoders or GANs, or even advanced deep learning models – will determine the technical expertise required and the computational resources needed for the project.
- Building a high-quality initial dataset
The quality of your synthetic data will be directly determined by the quality of the real data samples you use to generate it. Collect good samples to get good results.
- Build and train the model with the sample data collected.
To ensure that the synthetic data generated by the model is useful for the scenario, test it against real data samples. The best way to test the synthetic data created by your model is to use it in your production machine learning model, compare the results against real data samples, and make adjustments to the model accordingly.
Machine Learning-based Artificial Intelligence is widely accepted as a promising technology, with Deep Learning methods such as Convolutional Neural Networks (CNN) seeing successful implementation in many computer-vision tasks. However, DL techniques require significant amounts of training data, which can be costly, error-prone, and time-consuming to produce – especially in complex or rapidly-changing production environments. In such cases, synthetic data sets can be used to accelerate the training process.
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