Pl@ntNet-300K-v2 Image Dataset

Pl@ntNet-300K-v2 is an image dataset designed for evaluating set-valued classification methods. It is derived from the Pl@ntNet citizen observatory database and comprises 306,087 images spanning 1,000 plant species.The new version provides improved images resolution and better naming for the species. Key Features The dataset reflects two notable characteristics, intrinsic to both the image acquisition process and the morphological diversity of plants: Strong class imbalance: A few species account for the majority of images. Visual similarity: Many species are visually indistinguishable, posing challenges even for expert identification. These attributes make Pl@ntNet-300K-v2 particularly suited for benchmarking set-valued classification approaches. Dataset Structure Image Organization Images are partitioned into train, test, and val sets, stored in directories labeled 0000 to 0999. Metadata Files 1. plantnet300K_metadata.csv Contains 306,087 image entries, each with the following fields: Field Description species_id Numerical species index (0–999) PN_observation_id Unique Pl@ntNet observation identifier organ Plant organ in the image (leaf, flower, other, habit, fruit, etc.) author Photographer’s identity license Image license type (cc-by-sa, cc-by-nc, cc-by-nc-sa) split Dataset partition (train, test, val) PN_hash Image hash name 2. species_metadata.csv Provides taxonomic and conservation details for each species (aligned with the World Checklist of Vascular Plants, v13): Field Description species_id Numerical species index (matches image directories) full_species Full species name (with author) species Species name (without author) genus Genus name family Family name epithet Species epithet author Species name author(s) unmatched_terms Unresolved terms (e.g., spp., f.) iucn_status IUCN conservation status (e.g., EX, CR, EN, VU, LC, DD, NE) Resources Scientific Publication The dataset and baseline results are described in: Garcin et al. (2021), NeurIPS Datasets and Benchmarks Utilities PyTorch tools for data loading and model training: GitHub Repository Citation If you use this dataset, please cite the following publication: @inproceedings{Garcin_Joly_Bonnet_Affouard_Lombardo_Chouet_Servajean_Lorieul_Salmon2021, author = {Garcin, C. and Joly, A. and Bonnet, P. and Affouard, A. and Lombardo, J.-C. and Chouet, M. and Servajean, M. and Lorieul, T. and Salmon, J.}, booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, pdf = {https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7e7757b1e12abcb736ab9a754ffb617a-Paper-round2.pdf}, title = {Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution}, year = {2021}, comment = {[Code]} }
Publisher name: Zenodo
Last updated: 2026-02-20T14:25:54Z