I'm doing a AlexNet fine tuning for face detection following this: link
The only difference with the link is that I am using another dataset (facescrub and some images from imagenet as negative examples).
I noticed the accuracy increasing too fast, in 50 iterations it goes from 0.308 to 0.967 and when it is about 0.999 I stop the training and use the model using the same python script as the above link.
I use for testing an image from the dataset and the result is nowhere near good, test image result. As you can see the box in the faces is too big (and the dataset images are tightly cropped), not to mention the box not containing a face.
My solver and train_val files are exactly the same, only difference is batch sizes and max iter size.
The reason was that my dataset has way more face examples than non-face examples. I tried the same setup with the same number of positive and negative examples and now the accuracy increases slower.
