I have searched around the internet but found very little information around this, I don't understand what each variable/value represents in yolo's .cfg
files. So I was hoping some of you could help, I don't think I'm the only one having this problem, so if anyone knows 2 or 3 variables please post them so that people who needs such info in the future might find them.
The main one that I'd like to know are :
subdivisions
decay
momentum
channels
filters
activation
The neural network model architecture is stored in the yolov3. cfg file, and the pre-trained weights of the neural network are stored in yolov3. weights . There is a file called coco. names that has the list of 80 object class that the model will be able to detect.
This could probably be named something better but the mask tells the layer which of the bounding boxes it is responsible for predicting. The first yolo layer predicts 6,7,8 because those are the largest boxes and it's at the coarsest scale. The 2nd yolo layer predicts some smallers ones, etc.
Download the weights and cfg file from https://pjreddie.com/darknet/yolo/ by clicking on the yellow links on the page, marked by red boxes here: Save the yolov2. cfg and the yolov2.
Here is my current understanding of some of the variables. Not necessarily correct though:
On the left we have a single channel with 4x4 pixels, The reorganization layer reduces the size to half then creates 4 channels with adjacent pixels in different channels.
Many things are more or less self-explanatory (size, stride, batch_normalize, max_batches, width, height). If you have more questions, feel free to comment.
Again, please keep in mind that I am not 100% certain about many of those.
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