{"url":"/dataset/barknet-1-0","name":"BarkNet 1.0","full_name":null,"description_markdown":"23,000 cropped images of tree bark, for 23 species of trees around Quebec City, Canada. The images were captured at a distance between 20-60 cm away from the trunk. Labels include: individual tree ID, its species, and its DBH (diameter at breast height). Pictures were taken with four different devices: Nexus 5, Samsung Galaxy S5, Samsung Galaxy S7, and a Panasonic Lumix DMC-TS5 camera. The dataset is sufficiently large to train a Deep network such as ResNet for species recognition.","description_withheld":null,"homepage":"https://github.com/ulaval-damas/tree-bark-classification","introduced_date":"2018-03-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/tree-species-identification-from-bark-images","title":"Tree Species Identification from Bark Images Using Convolutional Neural Networks","first_author":"Mathieu Carpentier","url":null},"license":{"name":"MIT","url":"https://github.com/ulaval-damas/tree-bark-classification/blob/master/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semi-Supervised Image Classification","url":"/task/semi-supervised-image-classification","datasets_with_task":"/datasets/task/semi-supervised-image-classification"}],"languages":[],"variants":["BarkNet 1.0"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}