Datasets:
image imagewidth (px) 600 600 | mask imagewidth (px) 600 600 | date timestamp[s]date 2023-05-25 00:00:00 2023-06-15 00:00:00 | band stringclasses 5
values | split stringclasses 3
values |
|---|---|---|---|---|
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | G | val | ||
2023-05-30T00:00:00 | B | val | ||
2023-05-30T00:00:00 | NIR | val | ||
2023-05-30T00:00:00 | R | test | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | NIR | val | ||
2023-05-30T00:00:00 | NIR | val | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | B | test | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | R | val | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | B | test | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | B | test | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | G | test | ||
2023-05-30T00:00:00 | NIR | val | ||
2023-05-30T00:00:00 | NIR | test | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | NIR | val | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | RE | val | ||
2023-05-30T00:00:00 | B | val | ||
2023-05-30T00:00:00 | RE | test | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | RE | test | ||
2023-05-30T00:00:00 | R | val | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | B | val | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | B | test | ||
2023-05-30T00:00:00 | RE | val | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | B | val | ||
2023-05-30T00:00:00 | G | val | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | RE | val | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | R | test | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | NIR | val | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | NIR | test | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | G | test | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | NIR | train | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | NIR | test | ||
2023-05-30T00:00:00 | R | test | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | R | val | ||
2023-05-30T00:00:00 | G | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | R | train | ||
2023-05-30T00:00:00 | RE | val | ||
2023-05-30T00:00:00 | G | val | ||
2023-05-30T00:00:00 | B | train | ||
2023-05-30T00:00:00 | RE | train | ||
2023-05-30T00:00:00 | G | test |
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Weedsgalore Maize Segmentation
This dataset provides real-world RGB imagery captured in maize agricultural fields, focusing on weed presence within crop canopies. It includes pixel-level segmentation masks to delineate weeds from maize plants, supporting computer vision research in precision agriculture applications. The dataset contains 780 images with pixel-level mask annotations.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the split column.
Citation
<!-- TODO: add BibTeX citation -->
This dataset was reformatted from its original format to match HuggingFace standards.
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