Datasets:
Formats:
json
Languages:
English
Size:
< 1K
Tags:
visual-grounding
referring-expression-comprehension
referring-expression-segmentation
agriculture
benchmark
sample
License:
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| "description": "AgroVG-Sample is a lightweight, structure-preserving sample of AgroVG for peer review, schema inspection, and evaluation-pipeline smoke tests. It is not intended for reproducing benchmark tables.", | |
| "alternateName": "AgroVG review sample", | |
| "creator": { | |
| "@type": "Person", | |
| "name": "sauryrs", | |
| "url": "https://huggingface.co/sauryrs" | |
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| "object-detection", | |
| "image-segmentation", | |
| "English", | |
| "other", | |
| "10K - 100K", | |
| "json", | |
| "Image", | |
| "Tabular", | |
| "Text", | |
| "Datasets", | |
| "pandas", | |
| "Polars", | |
| "Croissant", | |
| "🇺🇸 Region: US", | |
| "visual-grounding", | |
| "referring-expression-comprehension", | |
| "referring-expression-segmentation", | |
| "agriculture", | |
| "benchmark", | |
| "mlcroissant" | |
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| "license": "https://choosealicense.com/licenses/other/", | |
| "url": "https://huggingface.co/datasets/sauryrs/AgroVG-Sample-Sample-Sample", | |
| "rai:dataLimitations": "AgroVG is limited to agricultural visual grounding scenarios covered by the included source datasets. Coverage is uneven across crop species, weeds, pests, diseases, fruit types, tree canopies, sensors, geographic regions, growth stages, lighting conditions, and annotation styles. The dataset combines heterogeneous sources with different native resolutions and annotation modalities. It is intended for benchmark evaluation and research, not for safety-critical agricultural decision making, pesticide application, disease treatment, yield estimation, or farm-management automation without independent domain validation.", | |
| "rai:dataBiases": "The dataset inherits selection and annotation biases from its source datasets. Some crop, weed, fruit, pest, disease, wheat-head, and tree-canopy categories are better represented than others. Imaging conditions, geographic regions, camera viewpoints, growth stages, and acquisition sensors are not uniformly sampled. Derived masks for some T2-eligible sources may reflect assumptions made by the normalization pipeline and may differ from expert instance annotations. These biases may affect model performance across underrepresented crops, environments, object sizes, occlusion levels, and sensor conditions.", | |
| "rai:personalSensitiveInformation": "AgroVG consists of agricultural imagery and associated object-level annotations. It is not intended to contain personal or sensitive human information. The released package has been checked to remove local filesystem paths, private usernames, API credentials, and internal review notes. No gender, age, health, political, religious, socio-economic, or other personal attributes are intentionally included.", | |
| "rai:dataUseCases": "AgroVG is intended to evaluate agricultural visual grounding systems. Validated use cases include benchmark evaluation of referring-expression comprehension, box-set grounding, query-level mask grounding, robustness analysis across agricultural domains, and comparison of multimodal or segmentation-oriented models. The dataset is not validated for clinical, legal, financial, farm-management, pesticide, disease-treatment, or other safety-critical operational decisions.", | |
| "rai:dataSocialImpact": "AgroVG may support research on more robust agricultural perception and improve evaluation coverage for crops, weeds, fruits, pests, plant diseases, wheat heads, and tree canopies. Potential risks include overinterpreting benchmark performance as deployment readiness, underperformance on underrepresented agricultural contexts, or misuse in automated farm decisions without expert validation. The release mitigates these risks by documenting source provenance, task definitions, evaluation protocols, and known limitations, and by positioning the dataset as a research benchmark rather than an operational decision system.", | |
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| "url": "https://conservancy.umn.edu/handle/11299/206575" | |
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