Ubuntu 18.04, MXNet-cu92 1.3.1, Gluon-Cv 0.4 (Master)
I’m trying to finetuneing a gluoncv pretrained
ResNet50_v1d classification model. But there is something strange going on.
When I finetuned it with 256x256 image resized from some dataset of size 512x512, everything was fine. However, When I tried to finetune it with 384x384 image, the accuracy just wouldn’t go up, It kept going up and down.
At first, I thought it has something to do with the mxnet
imread and mxnet
ResizeAug. so I rewrote my own dataset class with cv2
imread and cv2
resize. Now, the accuracy is going up, but at a much lower rate which is no way compared to the rate when using 256x256 image. It usually took about 3 more epochs to
get the accuracy up by 0.1% .(the total training epoch is 50)
BTW, below are different resize function that I have tried:
image = cv2.resize(image, (self.size, self.size), cv2.INTER_AREA)
resize_aug = mx.image.ResizeAug(size=size) image = resize_aug(image)
I checked they both use Area-based (resampling using pixel area relation) interpretation strategy for resize.
I wonder if it may have something to do with batch_size since I halved the batch_size when using larger image.
Isn’t larger image supposed to give better result?