Answers.Body: <p>Assuming that you do have a dataset (images + labels/bounding boxes) in a format that's required by the training model, you can fine-tune your existing model. You can choose to unlock the final few layers or leave all the layers unlocked during the training process. When I had performed such an experiment with RetinaNet I chose to unlock final few conv layers and was able to achieve slightly higher accuracy. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-10-05T06:03:24.267Z
Answers.LastActivityDate: 2018-10-05T06:03:24.267Z
Answers.UserDisplayName: sachinpuranik99
Body: <p>(Cross-posting here from the data science stack exchange, as my question didn't get any replies. I hope it's okay!)</p>
  
  <p>I've been playing around with YOLOv3 and obtaining some good results on the ~20 custom classes I trained. However, one or two classes look like they can use some additional training data (not a lot, say about 10% more data), which I can provide.</p>
  
  <p>What is the most efficient way to train my model now? Do I need to start training from scratch? Can I just throw in my additional data (with the appropriate changes to the config files etc.) and run the training based on the weight matrix I already acquired, but for a small number of iterations? (1000?) Or is this more like a transfer learning problem now?</p>
  
  <p>Thanks for all tips!</p>
  
Tags: <deep-learning><training><object-recognition>
Title: Add training data to YOLO post-training
UserDisplayName: sharonh
ThreadText: # Add training data to YOLO post-training
  
  (Asked by: username_0 on 2018-10-02)
  
  (Cross-posting here from the data science stack exchange, as my question didn't get any replies. I hope it's okay!)
  
  I've been playing around with YOLOv3 and obtaining some good results on the ~20 custom classes I trained. However, one or two classes look like they can use some additional training data (not a lot, say about 10% more data), which I can provide.
  
  What is the most efficient way to train my model now? Do I need to start training from scratch? Can I just throw in my additional data (with the appropriate changes to the config files etc.) and run the training based on the weight matrix I already acquired, but for a small number of iterations? (1000?) Or is this more like a transfer learning problem now?
  
  Thanks for all tips!
  
  **Tags:** deep-learning, training, object-recognition
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-10-05. Score: 1
  
  Assuming that you do have a dataset (images + labels/bounding boxes) in a format that's required by the training model, you can fine-tune your existing model. You can choose to unlock the final few layers or leave all the layers unlocked during the training process. When I had performed such an experiment with RetinaNet I chose to unlock final few conv layers and was able to achieve slightly higher accuracy.
  
  
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