Video: generate synthetic data with Stable Diffusion to augment computer vision datasets
Oct 26, 2022
Building image datasets is hard work. Instead of scraping, cleaning and labeling images, why not generate them directly with a Stable Diffusion model?
In this video, I show you how to generate new images with a Stable Diffusion model and the diffusers library, in order to augment an image classification dataset. Then, I add the new images to the original dataset, and push the augmented dataset to the Hugging Face hub. Finally, I fine-tune an existing model on the augmented dataset.
- Code: https://gitlab.com/juliensimon/huggingface-demos/-/tree/main/food102
- Food101 dataset: https://huggingface.co/datasets/food101
- Original model: https://huggingface.co/juliensimon/autotrain-food101-1471154053
- How the original model was created with AutoTrain: https://youtu.be/uFxtl7QuUvo
- Stable Diffusion model: https://huggingface.co/runwayml/stable-diffusion-v1-5
- Stable Diffusion Space: https://huggingface.co/spaces/runwayml/stable-diffusion-v1-5
- Diffusers library: https://github.com/huggingface/diffusers
- Food102 dataset: https://huggingface.co/datasets/juliensimon/food102
- New model: https://huggingface.co/juliensimon/swin-food102
