One of the tasks at the end of chapter 5 is to try and improve the results gotten in the book. I’ve been trying to do just that without much success. I’ve tried different architectures namely variants of densenet and xresnet but the error_rate is about the same as in the chapter notebook.
Would be glad if you could share your approaches or ideas for improving the results. Thanks.
Has anyone gotten any further with this? I tried various transformations, mixed precision learning, deeper architectures, progressive resizing etc and I maxed out at around 95.2% accuracy, which is only marginally better than the chapter 5 results.
I note on papers with code this still seems like a good result. Ragdoll vs Birman may be beyond both humanity and DL .