This is a PyTorch implementation of the ECCV2018 paper "Learning to Navigate for Fine-grained Classification" (Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang).
I tried NTS-Net with 320x 320 microscopic images , it worked fine. However , now I have image sizes of > 65 to 192 which I have to classify. I tried padding > 65 size images to 192 and trained NTS-Net model. However, the accuracy reduced to 40%.
Would it be possible to use your model for low image sizes. Tweaking for the patch sizes and shallow Resnet model.
I tried NTS-Net with 320x 320 microscopic images , it worked fine. However , now I have image sizes of > 65 to 192 which I have to classify. I tried padding > 65 size images to 192 and trained NTS-Net model. However, the accuracy reduced to 40%. Would it be possible to use your model for low image sizes. Tweaking for the patch sizes and shallow Resnet model.
Would appreciate your suggestion.