10 species of monkeys with about 100 training images for each. I didn’t see any need to do the fine tuning section with this data because how do you get better than pretty much perfect right from the start? Amazing. Gonna find some other data and go again.
Hello everyone, I found this dataset on kaggle using the google dataset search which is for classifying fruit photos. So I tried my hands and got to 0.5% error rate within 4 epochs. I used resnet34 as the architecture.
Here are the images
Hi Radek, how do you approach memory problem with fastai for this competition? fastai learner loads all dataset into memory arrays, and this dataset is too huge to do it.
I was thinking that data loader accepts paths and loads tensors on demand, no? Otherwise, it would be impossible to deal with any, even relatively small modern dataset. I remember that I had out-of-memory errors even when trained a dogs breeds classifier.
As I know, PyTorch datasets API doesn’t force you to load everything into memory at once. You only need to define how to retrieve a single instance based on its index.
I’m not sure where, maybe it was with precompute=True, but in vision, fastai only loads the images a batch at a time when needed for training/validation.
Small and simple spin off from lesson1
It was one of my last hackhathone task
to do recognition of road signs.
As we can see without big hassle I achieved 98% on very unique data set black and white data three classes
AR-arrow
LD - left diagonal
RD - right diagonal
I loaded data from CSV
I created a text classifier, which is able to detect language of handwritten document based on images. Dataset contains handwritten text in four languages: Bangla , Kannada, Oriya, Persian.
Here is sample dataset:
I used the lesson 1 notebook. With resnet50, accuracy of classification is ~97%.