{"url":"/dataset/cornhub","name":"CornHub","full_name":"Instance-Segmentation Dataset of Corn Cobs","description_markdown":"# 🌽 CornHub: Instance-Segmentation Dataset of Corn Cobs\r\n\r\n**Version:** 1.0.0  \r\n**Date:** 2025-05-18  \r\n**License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)  \r\n**Author:** Sebastian Borukało  \r\n\r\n\r\n## 📦 Description\r\n\r\n**CornHub** is an instance-segmentation dataset of corn cobs in real field conditions. It contains **304 high-resolution RGB images** in three variants:\r\n\r\n- **FULL** (2432×2432 px)  \r\n- **LITE** (1024×1024 px)  \r\n- **ULTRALITE** (512×512 px)  \r\n\r\nCaptured at varying distances (close-up and wide-angle), lighting conditions (sunny, overcast), and viewpoints, each corn cob is annotated as its own grayscale mask (single channel):\r\n\r\n- **0** = background  \r\n- **1, 2, 3…** = individual corn cob instances  \r\n\r\nImages and masks are provided in **PNG** format.\r\n\r\nUse CornHub to train and evaluate instance-segmentation models in agriculture, field robotics, and computer vision research.\r\n\r\n### DataSet view:\r\n\r\n|     Version      |         Filename          |    Resolution    |\r\n|------------------|:-------------------------:|:----------------:|\r\n| **Full**         | `CornHub_FULL_2432.zip`   | 2432×2432 px     |\r\n| **Lite**         | `CornHub_LITE_1024.zip`   | 1024×1024 px     |\r\n| **Ultra-Lite**   | `CornHub_ULTRALITE_512.zip` | 512×512 px     |\r\n\r\n\r\n## 📂 Download Options\r\n\r\nAvailable on:\r\n\r\n- [Kaggle](https://www.kaggle.com/datasets/ciapserr/cornhub-instance-segmentation-dataset)  \r\n- [Zenodo](https://doi.org/10.5281/zenodo.15453713)  \r\n- [Papers With Code](https://paperswithcode.com/dataset/cornhub )\r\n\r\n## 🤝 Credits\r\n\r\nThis dataset is free to use and you can use it in any project or paper, \r\nhowever I would be glad if you would cite me and give credit - as it is stated in the license.\r\n\r\n**CornHub: Instance-Segmentation Dataset of Corn Cobs**  \r\n**Author:** Sebastian Borukało \r\n\r\n**DOI:** [https://doi.org/10.5281/zenodo.15453713](https://doi.org/10.5281/zenodo.15453713)  \r\n**DOI:** 10.5281/zenodo.15453713","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.15453713","introduced_date":"2025-05-18","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Image Segmentation","url":"/task/image-segmentation","datasets_with_task":"/datasets/task/image-segmentation"},{"name":"Deep Learning","url":"/task/deep-learning","datasets_with_task":"/datasets/task/deep-learning"}],"languages":[],"variants":["CornHub"],"data_loaders":[],"num_papers_in_archive":0,"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."}