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jpeg image | xml unknown | __key__ string | __url__ string |
|---|---|---|---|
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... | cowpea_1850_day_17 | hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-000000.tar | |
[
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63,
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109,
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... | cowpea_3696_day_25 | hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-000000.tar | |
[
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109,
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61,
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49,
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48,
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110,
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... | cowpea_9333_day_26 | hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-000000.tar | |
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPGhlbGlvcz4KCTxwbGFudF9pbnN0YW5jZSBJRD0iMCI+Cgk(...TRUNCATED) | cowpea_7972_day_21 | "hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-00000(...TRUNCATED) | |
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPGhlbGlvcz4KCTxwbGFudF9pbnN0YW5jZSBJRD0iMCI+Cgk(...TRUNCATED) | cowpea_9172_day_11 | "hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-00000(...TRUNCATED) | |
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPGhlbGlvcz4KCTxwbGFudF9pbnN0YW5jZSBJRD0iMCI+Cgk(...TRUNCATED) | cowpea_0149_day_25 | "hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-00000(...TRUNCATED) | |
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPGhlbGlvcz4KCTxwbGFudF9pbnN0YW5jZSBJRD0iMCI+Cgk(...TRUNCATED) | cowpea_0154_day_10 | "hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-00000(...TRUNCATED) | |
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPGhlbGlvcz4KCTxwbGFudF9pbnN0YW5jZSBJRD0iMCI+Cgk(...TRUNCATED) | cowpea_2591_day_39 | "hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-00000(...TRUNCATED) | |
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPGhlbGlvcz4KCTxwbGFudF9pbnN0YW5jZSBJRD0iMCI+Cgk(...TRUNCATED) | cowpea_6237_day_11 | "hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-00000(...TRUNCATED) | |
"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPGhlbGlvcz4KCTxwbGFudF9pbnN0YW5jZSBJRD0iMCI+Cgk(...TRUNCATED) | cowpea_8876_day_16 | "hf://datasets/bbrangeo/Cowpea-Architecture-XML@b950db2734fc500c5573930a8049c38e51d30fcc/shard-00000(...TRUNCATED) |
End of preview.
Cowpea-Architecture-XML-WDS
This dataset contains simulated images of Cowpea plants paired with organ-level architecture representations in XML format, packaged in WebDataset (.tar) format for efficient high-performance training.
Dataset Structure
The dataset is sharded into .tar files, each containing up to 10,000 samples.
Each sample consists of:
.jpeg: The plant image.xml: The organ-level architecture representation.json: (Optional) Metadata
Usage with WebDataset
You can load this dataset directly in PyTorch using the webdataset library:
import webdataset as wds
from torch.utils.data import DataLoader
url = "https://huggingface.co/datasets/heesup/Cowpea-Architecture-XML-WDS/resolve/main/shard-{000000..000200}.tar"
dataset = (
wds.WebDataset(url)
.decode("rgb")
.to_tuple("jpeg", "xml")
)
dataloader = DataLoader(dataset, batch_size=16)
for images, xmls in dataloader:
# training loop
pass
Citation
If you use this dataset, please cite: "A Vision Language Model for Generating XML-based Organ-level Plant Architecture Representations of Cowpea from Simulated Images"
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