Answers.Body: <p>You can use conditional GANs for synthesizing data with labels/bounding boxes.</p>
  <p>Conditional GANs are GANs where, besides the normal random noise (prior), you also insert additional information which has to be taken into account during data generation. You then also pass this same information to the discriminator. The discriminator has to discriminate the real/fake data, taking into account the labels.</p>
  <p>Resources:
  <a href="https://keras.io/examples/generative/conditional_gan/" rel="nofollow noreferrer">Keras blog about conditional GANs</a>.
  <a href="https://arxiv.org/abs/1411.1784" rel="nofollow noreferrer">First ever paper on conditional GANs</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-30T10:34:49.173Z
Answers.Text: If the response sufficiently answered your question, please check it as the correct answer using the checkmark so that future visitors know that the question was solved.
Answers.UserDisplayName: Robin van Hoorn
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-28T11:41:43.370Z
Answers.LastActivityDate: 2022-12-28T11:41:43.370Z
Answers.UserDisplayName: Robin van Hoorn
Body: <p>I was building a YOLOv5 object detection model, and was looking into researching synthetic methods like GANs to increase the size of my training set in an unsupervised manner.</p>
  <p>I know that few-shot GANs can be used to &quot;hallucinate&quot; images and labels for a classification task, but how can they be extended to hallucinate images and labels in <a href="https://www.edge-ai-vision.com/2022/04/exploring-data-labeling-and-the-6-different-types-of-image-annotation/#:%7E:text=YOLO%3A%20In%20the%20YOLO%20labeling,coordinates%2C%20height%2C%20and%20width." rel="nofollow noreferrer">YOLO format</a> (basically lists out each bounding box and class)?</p>
  <p>Is there some way that I can train a GAN on images / YOLO labels, and get it to hallucinate more images / labels?</p>
  
Tags: <neural-networks><convolutional-neural-networks><generative-adversarial-networks><yolo><few-shot-learning>
Title: Using GANs to generate data augmentations for YOLOv5
UserDisplayName: Nebula
ThreadText: # Using GANs to generate data augmentations for YOLOv5
  
  (Asked by: username_0 on 2022-12-27)
  
  I was building a YOLOv5 object detection model, and was looking into researching synthetic methods like GANs to increase the size of my training set in an unsupervised manner.
  
  I know that few-shot GANs can be used to "hallucinate" images and labels for a classification task, but how can they be extended to hallucinate images and labels in [YOLO format](https://www.edge-ai-vision.com/2022/04/exploring-data-labeling-and-the-6-different-types-of-image-annotation/#:%7E:text=YOLO%3A%20In%20the%20YOLO%20labeling,coordinates%2C%20height%2C%20and%20width.) (basically lists out each bounding box and class)?
  
  Is there some way that I can train a GAN on images / YOLO labels, and get it to hallucinate more images / labels?
  
  **Tags:** neural-networks, convolutional-neural-networks, generative-adversarial-networks, yolo, few-shot-learning
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-12-28. Score: 1 | Accepted answer
  
  You can use conditional GANs for synthesizing data with labels/bounding boxes.
  
  Conditional GANs are GANs where, besides the normal random noise (prior), you also insert additional information which has to be taken into account during data generation. You then also pass this same information to the discriminator. The discriminator has to discriminate the real/fake data, taking into account the labels.
  
  Resources:
  [Keras blog about conditional GANs](https://keras.io/examples/generative/conditional_gan/).
  [First ever paper on conditional GANs](https://arxiv.org/abs/1411.1784)
  
  > **Comments:**
  > **username_1**: If the response sufficiently answered your question, please check it as the correct answer using the checkmark so that future visitors know that the question was solved.  
  
  ---
  
  