Omkar2118/datasets huggingface dataset:
Herbarium-19

The FGVC 2019 Herbarium Challenge is to identify melastome species from herbarium specimens provided by the New York Botanical Garden (NYBG). We have provided a curated dataset of over 46,000 herbarium specimens for over 680 species of the flowering plant family Melastomataceae.

Link: https://www.kaggle.com/c/herbarium-2019-fgvc6
Imagenet100

ImageNet-100 is a subset of ImageNet-1k Dataset from ImageNet Large Scale Visual Recognition Challenge 2012. It contains random 100 classes as specified in Labels.json file.
Link: https://www.kaggle.com/datasets/ambityga/imagenet100
Terra Incognita

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems.

Link: https://arxiv.org/abs/1807.04975