Imagenette is a subset of 10 easily classified classes from the Imagenet
dataset. It was originally prepared by Jeremy Howard of FastAI. The objective
behind putting together a small version of the Imagenet dataset was mainly
because running new ideas/algorithms/experiments on the whole Imagenet take a
lot of time.
This version of the dataset allows researchers/practitioners to quickly try out
ideas and share with others. The dataset comes in three variants:
- Full size
- 320 px
- 160 px
Note: The v2 config correspond to the new 70/30 train/valid split (released
in Dec 6 2019).
