The iNaturalist 2017 dataset (iNat) contains 675,170 training and validation images from 5,089 natural fine-grained categories. Those categories belong to 13 super-categories including Plantae (Plant), Insecta (Insect), Aves (Bird), Mammalia (Mammal), and so on. The iNat dataset is highly ...
added AWS S3 dataset links for 2017 and 2018 5年前 LICENSE Initial commit 8年前 README.md updating download links for all competitions 4年前 README MIT iNaturalist Competition Datasets Current Competitions 2021 Competition Previous Competitions ...
static_testset.pycan be used to load test result pickle file and get specific static info, such asclassification error images, test accuracy perclassand so on, we also can filter our dataset according to descending softmax value, it will generatenew*.csvas new dataset. ...
获取原文并翻译|示例 开具论文收录证明 >> 摘要 iNaturalist is an international social networking resource that allows users to browse and post observations, photos, videos and findings about plants, animals and the natural world around them. This interactive resource integrates database and encyclopedic ...
To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of ...
Contextual Label Smoothing with a Phylogenetic Tree on the iNaturalist 2018 Challenge DatasetContextual LabelSmoothingPhylogeneticRecognition of fine-grained visual categories (FGVC) in the natural world is a long-tailed problem, meaning recognizers must accurately recognize a large diversity of categories ...
The iNat Challenge 2017 dataset contains 5,089 species, with a combined training and validation set of 675,000 images that have been collected and verified by multiple users frominaturalist.org. The dataset features many visually similar species, captured in a wide variety of situations, from all...
This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challenge~\cite{semi-aves} at the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds sampled from the iNat-2018 dataset for a total of nearly 150...
In contrast to other image classification datasets such as ImageNet, the dataset in the iNaturalist challenge exhibits along-tailed distribution, with many species having relatively few images. It is important to enable machine learning models to handle categories in the long-tail, as the natural wo...
The iNat Challenge 2017 dataset contains 5,089 species, with a combined training and validation set of 675,000 images that have been collected and verified by multiple users from inaturalist.org. The dataset features many visually similar species, captured in a wide variety of situations, from ...