{"id":10733,"date":"2020-10-27T10:13:52","date_gmt":"2020-10-27T10:13:52","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=10733"},"modified":"2023-10-16T18:04:28","modified_gmt":"2023-10-16T18:04:28","slug":"training-ai-with-cgi","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/training-ai-with-cgi\/","title":{"rendered":"Training AI with CGI"},"content":{"rendered":"\t\t
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In this article we go through how we trained a computer vision<\/a> model (AI) to detect sub-components of a Raspberry PI using only synthetic data (CGI).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Training with synthetic data is an increasingly popular way to quench the thirst of data hungry deep learning models. The datasets used for this project are available for free at app.zumolabs.ai<\/a> [1]. We want to make using synthetic data easy for everyone and plan to release more datasets in the future.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Problem<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The Raspberry Pi<\/a> is a single-board computer very popular with hobbyists. Our goal was to detect some of the sub-components that sit on the board: the pin connectors, the audio jack, and the ethernet port. Though this is a toy problem, it is not far from what you see in the real world \u2014 where automating component and defect detection using computer vision can improve the speed and reliability of manufacturing.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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To generate synthetic data, we first need a 3D model of the object. Luckily, in today\u2019s world, most objects already exist in the virtual world<\/strong\\>. Asset aggregation sites like SketchFab, TurboSquid, or Thangs, have commoditized 3D models [2]. Tip for the wise: if you can\u2019t find a model on the internet, try contacting the manufacturer directly, or scan and model the object yourself.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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We then use a game engine (such as Unity or Unreal Engine) to take thousands of images of our 3D model from a variety of camera angles and under a variety of lighting conditions. Each image has a corresponding segmentation mask, which sections out the different components in the image. In future articles we will dive deeper into the process of creating synthetic images (so stay tuned!).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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So now that we have thousands of synthetic images, we should be good right? No! It is very important to test synthetically-trained models on real data to know whether the model is successfully generalizing to real data. There is a gap between simulation-produced data and real data known as the sim2real gap<\/strong>. One way to think about it is that deep learning models will overfit on the smallest of details, and if we aren\u2019t careful many of these details may only exist in the synthetic data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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For this problem, we manually annotated a small test dataset of a dozen real images. Manual annotation is time consuming and expensive. Important to note that if we were using real images for training we would have to manually annotate thousands of images instead of just a handful for testing! That\u2019s, unfortunately, the current way of doing things, the status quo we are trying to change. Getting rid of this manual annotation process is a critical step in building better AI.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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One way we can start closing the sim2real gap is through a technique known as domain randomization <\/strong>[3][4]. This strategy involves randomizing properties of the virtual RPi, especially the visual appearance of the backgrounds and the RPi itself. This has the downstream effect of making the model we train on this data more robust to variations in color and lighting. This is also known as the network\u2019s ability to generalize.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Model and Training<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Now we get to the model. There are many different types of computer vision models to choose from. Models which leverage deep learning are the most popular right now. They work very well for detection tasks, such as this project. We used a model from PyTorch\u2019s torchvision library based on the ResNet architecture [5]. Synthetic data will work with any model architecture, so feel free to experiment and find the one that best fits your use case<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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We trained our model with four different synthetic datasets to show how domain randomization and dataset size affect performance on our real test dataset:<\/p>\n