Answers.Body: <p>I am not absolutely sure, but I guess it is due to the domain gap. As far as I have seen in my project where YOLOv3 was trained on synthetic images, it performed better when the model was trained and tested on the same domain (synthetic), while the performance drops when we introduce real images for testing. So when you include real images along with synthetic images, you might have to use some domain adaptation methods to improve the performance.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-05-21T08:23:06.617Z
Answers.LastActivityDate: 2021-05-21T08:23:06.617Z
Answers.UserDisplayName: Saurabh Band
Answers.Body: <p>A model trained on synthetic data can sometimes outperform a model trained on both synthetic and real data for a few reasons it is due to following reasons</p>
  <h3>1. Consistency of Synthetic Data:</h3>
  <h4>Controlled Environments:</h4>
  <p>Synthetic data offers a highly controlled environment, where the objects are always clear, and correctly labeled, and the backgrounds are controlled. There is little to no noise, occlusion, or ambiguities that could confuse the model during training.
  Precision of Bounding Boxes: The bounding boxes in synthetic data are usually perfectly aligned, while manually drawn bounding boxes in real data may have human error or imprecision, which could slightly degrade model performance.</p>
  <h3>2. Bias and Noise in Real Data:</h3>
  <h4>Imperfect Labeling:</h4>
  <p>Real data often includes mistakes or inaccuracies, especially when manually labeled. This can introduce noise into the training process, making it harder for the model to learn the correct patterns.
  Variety and Complexity of Real-world Scenarios: Real images might have occlusions, lighting variations, reflections, and other complexities that can make it more difficult for the model to generalize well from the data. If the synthetic data does not contain such challenging scenarios, the model may find learning easier.</p>
  <h3>3. Overfitting to Real-world Anomalies:</h3>
  <p>A model trained on both synthetic and real data could overfit to specific real-world characteristics that don’t generalize well. For instance, it could learn specific patterns in the noise or background of real images that are not relevant for object detection in the real world.</p>
  <h3>4. Synthetic Data Can be Richer:</h3>
  <p>####More Data:</p>
  <p>Synthetic data can be generated in large quantities, providing the model with far more diverse training examples, which can improve generalization to unseen examples, especially if the real data is limited or not sufficiently diverse.</p>
  <h4>Augmentation:</h4>
  <p>Synthetic data generation allows for creating variations (different lighting conditions, angles, rotations, etc.) that might be harder to collect in real-world data, thus enriching the training set.</p>
  <h3>5. Domain Gap Between Synthetic and Real Data:</h3>
  <p>While mixing real and synthetic data can help generalization, the differences between synthetic and real data (often referred to as the domain gap) can cause issues. If the synthetic and real data are too different, the model could struggle to combine both. This gap could lead to underperformance if the model cannot reconcile the differences effectively.</p>
  <p>That said, in many cases, the combination of synthetic and real data can outperform either one alone, especially if efforts are made to close the domain gap (e.g., using domain adaptation techniques). Synthetic data's advantages lie in scalability and precision, but real data remains essential for generalizing to real-world conditions.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-10-04T06:51:09.490Z
Answers.LastActivityDate: 2024-10-04T06:51:09.490Z
Answers.UserDisplayName: Keval Pandya
Body: <p>Suppose we want to train a model to detect various objects.   Let's say we have training data of those objects in various backgrounds along with their bounding boxes. Basically these objects have been three dimensionally created and the bounding boxes have been drawn on them. Then these have been "synthetically inserted" into various blank backgrounds. </p>
  
  <p>Why would a model trained only on this data do better than a model that has this data along with "real" data of these objects with their bounding boxes manually drawn? </p>
  
Tags: <convolutional-neural-networks><object-recognition><object-detection>
Title: YOLOv3 Synthetic Data Training
UserDisplayName: Guessguy112233
ThreadText: # YOLOv3 Synthetic Data Training
  
  (Asked by: username_0 on 2020-01-11)
  
  Suppose we want to train a model to detect various objects. Let's say we have training data of those objects in various backgrounds along with their bounding boxes. Basically these objects have been three dimensionally created and the bounding boxes have been drawn on them. Then these have been "synthetically inserted" into various blank backgrounds.
  
  Why would a model trained only on this data do better than a model that has this data along with "real" data of these objects with their bounding boxes manually drawn?
  
  **Tags:** convolutional-neural-networks, object-recognition, object-detection
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-05-21. Score: 0
  
  I am not absolutely sure, but I guess it is due to the domain gap. As far as I have seen in my project where YOLOv3 was trained on synthetic images, it performed better when the model was trained and tested on the same domain (synthetic), while the performance drops when we introduce real images for testing. So when you include real images along with synthetic images, you might have to use some domain adaptation methods to improve the performance.
  
  
  ---
  
  ### Answer by username_2 on 2024-10-04. Score: 0
  
  A model trained on synthetic data can sometimes outperform a model trained on both synthetic and real data for a few reasons it is due to following reasons
  
  ### 1. Consistency of Synthetic Data:
  
  #### Controlled Environments:
  
  Synthetic data offers a highly controlled environment, where the objects are always clear, and correctly labeled, and the backgrounds are controlled. There is little to no noise, occlusion, or ambiguities that could confuse the model during training.
  Precision of Bounding Boxes: The bounding boxes in synthetic data are usually perfectly aligned, while manually drawn bounding boxes in real data may have human error or imprecision, which could slightly degrade model performance.
  
  ### 2. Bias and Noise in Real Data:
  
  #### Imperfect Labeling:
  
  Real data often includes mistakes or inaccuracies, especially when manually labeled. This can introduce noise into the training process, making it harder for the model to learn the correct patterns.
  Variety and Complexity of Real-world Scenarios: Real images might have occlusions, lighting variations, reflections, and other complexities that can make it more difficult for the model to generalize well from the data. If the synthetic data does not contain such challenging scenarios, the model may find learning easier.
  
  ### 3. Overfitting to Real-world Anomalies:
  
  A model trained on both synthetic and real data could overfit to specific real-world characteristics that don’t generalize well. For instance, it could learn specific patterns in the noise or background of real images that are not relevant for object detection in the real world.
  
  ### 4. Synthetic Data Can be Richer:
  
  ####More Data:
  
  Synthetic data can be generated in large quantities, providing the model with far more diverse training examples, which can improve generalization to unseen examples, especially if the real data is limited or not sufficiently diverse.
  
  #### Augmentation:
  
  Synthetic data generation allows for creating variations (different lighting conditions, angles, rotations, etc.) that might be harder to collect in real-world data, thus enriching the training set.
  
  ### 5. Domain Gap Between Synthetic and Real Data:
  
  While mixing real and synthetic data can help generalization, the differences between synthetic and real data (often referred to as the domain gap) can cause issues. If the synthetic and real data are too different, the model could struggle to combine both. This gap could lead to underperformance if the model cannot reconcile the differences effectively.
  
  That said, in many cases, the combination of synthetic and real data can outperform either one alone, especially if efforts are made to close the domain gap (e.g., using domain adaptation techniques). Synthetic data's advantages lie in scalability and precision, but real data remains essential for generalizing to real-world conditions.
  
  
  ---
  
  