deep-learningobject-detectionyolotransfer-learningdarknet

YOLOv4 Transfer Learning/ Fine tuning

I have trained a model of YOLOv4 by using my original dataset and the custom yolov4 configuration file, which I will refer to as my 'base' YOLOv4 model.

Now I want to use this base model that I have created to train the model again using images that I have manually augmented. I am trying to retrain my models to try and increase the mAP and AP. So I want to use the weights from my base model to train a new yolov4 model with the manually augmented images.

I have seen on the YOLOv4 wiki page that using stopbackward = 1 freezes the layers so weights in these layers would not be updated, however this reduces accuracy. Also there was another piece of information that I read where ./darknet partial cfg/yolov4.cfg yolov4.weights yolov4.conv.137 137 takes out the first 137 layers. Does this mean that the first 137 layers are frozen in the network or does this mean you are only training on the 137 layers?

My questions are:

  1. Which code actually does freeze layers so I can do transfer learning on the base YOLOv4 model I have created?
  2. Which layers would you recommend freezing,the first 137 before the first YOLO layer in the network?

Thank you in advance!

Solution

  • To answer your questions:

    1. If you want to use transfer learning, you don't have to freeze any layers. You should simply start training with the weights you have stored from your first run. So instead of darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 you can run darknet.exe detector train data/obj.data yolo-obj.cfg backup/your_weights_file. The weights are stored in the backup folder build\darknet\x64\backup\. So for example, the command could look like this: darknet.exe detector train data/obj.data yolo-obj.cfg backup/yolov4_2000.weights

    2. Freezing layers can save time during training. What is a good solution is to first train the model with the first layers frozen, and later unfreeze the layers to finetune your learning. I am not sure what is a good amount of layers to freeze in the first run, maybe can you test it with trial and error.