---
pretty_name: AgroVG
license: other
task_categories:
- object-detection
- image-segmentation
tags:
- visual-grounding
- referring-expression-comprehension
- referring-expression-segmentation
- agriculture
- benchmark
- mlcroissant
size_categories:
- 10K<n<100K
language:
- en
configs:
- config_name: t1_annotations
  data_files:
  - split: dev
    path: t1/annotations/dev.jsonl
  - split: test
    path: t1/annotations/test.jsonl
- config_name: t1_queries
  data_files:
  - split: dev
    path: t1/queries/queries_dev.jsonl
  - split: test
    path: t1/queries/queries_test.jsonl
- config_name: t2_annotations
  data_files:
  - split: dev
    path: t2/annotations/dev.jsonl
  - split: test
    path: t2/annotations/test.jsonl
- config_name: t2_queries
  data_files:
  - split: dev
    path: t2/queries/queries_dev.jsonl
  - split: test
    path: t2/queries/queries_test.jsonl
---

# AgroVG

AgroVG is a large-scale benchmark for agricultural visual grounding. It contains two evaluation tasks:

- **T1: box-set visual grounding**, where a model receives an agricultural referring expression and returns a set of bounding boxes.
- **T2: query-level mask grounding**, where a model receives an agricultural referring expression and returns a binary segmentation mask.

This repository is the anonymized public-release package prepared for peer review and archival hosting. It contains only release data and metadata; construction scripts, model predictions, and evaluation code should be hosted in a separate code repository.

Dataset URL for review: https://huggingface.co/datasets/sauryrs/AgroVG

## Dataset Composition

| Task | Images | Queries | Output target | Main files |
|---|---:|---:|---|---|
| T1 | 6,526 | 6,526 | Bounding-box set | `t1/annotations`, `t1/images`, `t1/queries` |
| T2 | 3,545 | 3,545 | Query-level binary mask | `t2/annotations`, `t2/images`, `t2/instance_maps`, `t2/queries` |
| Total | 10,071 | 10,071 | Boxes or masks | `t1`, `t2` |

## Repository Structure

```text
AgroVG/
  README.md
  LICENSE
  CITATION.cff
  croissant.json
  NOTICE
  source_licenses.json
  t1/
    annotations/
      annotations.jsonl
      dev.jsonl
      test.jsonl
      final_audit_table.csv
      policy.json
      final_stats.json
      split_stats.json
      kept_ids.txt
      dropped_ids.txt
    images/
      <source>/
    queries/
      queries_dev.jsonl
      queries_test.jsonl
      query_policy.json
      query_stats.json
  t2/
    annotations/
      annotations.jsonl
      dev.jsonl
      test.jsonl
      final_audit_table.csv
      policy.json
      final_stats.json
      split_stats.json
      kept_ids.txt
      dropped_ids.txt
    images/
      <source>/
    instance_maps/
      <source>/
    queries/
      queries_dev.jsonl
      queries_test.jsonl
      query_policy.json
      query_stats.json
```

All `rgb_relpath` and `mask_relpath` fields are relative to the corresponding task root (`t1/` or `t2/`).

## Annotation Records

Each image annotation record contains:

- `image_id`: stable image identifier.
- `source`: normalized source-dataset key.
- `scene_family`: benchmark-level scene family.
- `sensor_type`: imaging context when available.
- `task_candidates`: task list, e.g. `["T1"]` or `["T2"]`.
- `annotation_type`: `bbox` for T1 and `instance_mask` for T2.
- `rgb_relpath`: relative RGB image path.
- `mask_relpath`: relative uint16 instance-map path for T2.
- `width`, `height`: image dimensions.
- `group_id`: group identifier used to avoid split leakage.
- `split_source`: source-side split label when available.
- `benchmark_split`: `dev` or `test`.
- `instances`: normalized instance annotations.
- `meta`: source-specific metadata retained for auditability.

T1 instances include `object_id`, `class_family_global`, source-derived class labels, and `bbox_xyxy`. T2 instances additionally include `instance_local_id`, mask-derived geometry, mask area statistics, and mask-generation provenance when applicable.

## Query Records

Each query record contains the common fields:

- `query_id`
- `image_id`
- `benchmark_split`
- `source`
- `scene_family`
- `sensor_type`
- `rgb_relpath`
- `width`
- `height`
- `group_id`
- `query`
- `query_type`
- `program_type`
- `template_id`
- `target_object_ids`
- `target_count`
- `is_empty`
- `meta`

T1 query records additionally include `target_boxes`. T2 query records additionally include `mask_relpath` and `target_instance_local_ids`.

## Splits

AgroVG uses `dev` and `test` splits. Split construction is group-aware: records sharing a `group_id` are assigned to the same benchmark split to reduce leakage across visually related images.

## Evaluation

T1 evaluates set-level box grounding with IoU-thresholded matching. T2 evaluates query-level mask grounding with overlap-based mask metrics and separate target-absent accuracy. Evaluation scripts are not included in this data-only package and should be released in the accompanying code repository.

## Licensing

AgroVG-specific annotations, query templates, audit metadata, and derived benchmark metadata are released under Creative Commons Attribution 4.0 International (CC BY 4.0), included in `LICENSE`.

Images and source-derived annotations retain the licenses, terms, and redistribution permissions of their original source datasets. Source-level license and attribution information is summarized in `source_licenses.json` and `NOTICE`. Non-public permission correspondence is intentionally not included in this anonymized review package.

## Responsible Use and Limitations

AgroVG is intended for research on agricultural visual grounding, referring-expression comprehension, referring segmentation, and robustness analysis across agricultural domains. It is not intended for direct deployment in farm-management, pesticide, disease-treatment, yield-estimation, or safety-critical agricultural decision systems without additional domain validation.

Known limitations include source-dataset heterogeneity, uneven geographic and crop coverage, possible source-specific annotation biases, and the fact that AgroVG normalizes existing source labels rather than re-certifying all taxonomic labels from scratch.

## Citation

This anonymized release is associated with a NeurIPS 2026 submission. Please cite the accompanying anonymous submission during review. The citation metadata in `CITATION.cff` should be updated with final author names, venue information, and DOI after the review process.