Training Models with Synthetic Data: OpenAI Dall-E Dataset Generation with Edge Impulse
In this video
Harnessing the Power of Synthetic Datasets with Edge Impulse and OpenAI's DALL-E
Edge Impulse, a leading platform for developing machine learning applications, now enables the creation of synthetic data sets leveraging OpenAI's image generation tool, DALL-E. DALL-E is an advanced AI model that generates unique images from text prompts, facilitating the creation of diverse and rich datasets without having to take a single photo yourself.
DALL-E: A Revolution in Synthetic Data Generation
Getting Started with DALL-E
To get started with DALL-E, you need to install local requirements including Python, pip, Jupyter notebooks, and OpenAI itself. It's possible to run the example in Google Colab if you don't want to install these locally.
After installing the necessary software, you need to set up your OpenAI API key. This can be done by logging into OpenAI, navigating to the API keys section, and setting your API key as an environment variable on your machine.
With the OpenAI API key set up, you can proceed to generate images. The API's
image.create function accepts a prompt and generates the corresponding image. It's advisable to be as descriptive as possible in your prompts to get the most accurate results.
Potential Biases and Variations
When using generative AI like DALL-E, it's crucial to be aware of potential biases that may be introduced either inherently in the model or through the prompts you provide. Additionally, the AI might 'hallucinate' — generating unrealistic images, such as someone with an abnormal number of fingers. As such, careful validation of generated data is necessary to ensure you don't introduce these biases into your model.
To create variations of a single image, you can use the variations tool. It's a powerful feature, especially when dealing with a small dataset, as it generates slightly different versions of a given image, adding more diversity to your dataset.
Integrating DALL-E with Edge Impulse
Dataset Generation and Upload
Once you have generated a dataset with various images, you can upload these images to an Edge Impulse project to develop an image classification model. In the demonstration, the model was trained to detect whether a person in front of a webcam was wearing gloves or not.
Harnessing Transformation Blocks
Edge Impulse also offers transformation blocks, a powerful Enterprise feature, that enables you to create a data pipeline with Docker containers. In this scenario, the transformation block was used to generate data from scratch.
The block contained a script that accepted various parameters such as prompt, label, image, variations, and size. After adding your OpenAI API key as a secret, you can set up the DALL-E image generator as your transformation block and enter your desired parameters.
Building and Deploying the Model
Once your data is ready, you can design your impulse in Edge Impulse, choosing an image width that suits your needs. Add the processing block, and opt for transfer learning as it works well even on small datasets.
Once the features are generated and the model is trained, you can deploy it to your device of choice and test it out. In the demonstration, the model successfully differentiated between hands with and without gloves in real-time.
Potential Applications and Benefits
The ability to create synthetic data has several potential applications and benefits. It's useful if you lack access to the end environment for data collection, if data collection is expensive, or if you wish to create a proof of concept before committing resources.
Furthermore, synthetic data can augment existing datasets. If you have a small but valuable dataset, dataset synthesis can enlarge it to provide a more representative sample. The OpenAI API proves useful for this purpose, as you can feed in existing data and specify areas where you want it to
tinymlmachine learningmledge machine learningedge mlembedded machine learningembedded mledge aigenerative aisynthetic dataopenaiopenai dall-eopenai api