--- license: apache-2.0 --- # EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients. This repository contains the data of the second versio
iNat-Anim Dataset iNat-Anim (iNaturalist + Animalia) is a multi-modal few-shot image classification benchmark. It consists of 195,605 images across 673 species from an array of animal classes. The
This data repository includes the features and the trained backbone parameters used in the ICLR 2022 Paper "On the Importance of Firth Bias Reduction in Few-Shot Classification". The code accompanying
In recent years, the success of large-scale visionlanguage models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-shot inference through
Delaunay data set learn2learn l2l for meta-learning and few-shot learning. We split it into 3 meta-train, meta-val and meta-test sets. For details of original data see: https://github.com/camillegonti
