Upload folder using huggingface_hub

README.md

@@ -0,0 +1,130 @@
+---
+license:
+- cc-by-4.0
+- cc-by-nc-4.0
+pretty_name: VTOS Benchmarks (LVIS-Count, PlantSeg-OOD)
+task_categories:
+- object-detection
+- image-segmentation
+- visual-question-answering
+size_categories:
+- n<1K
+---
+
+# VTOS Benchmarks
+
+Two small benchmarks used in [VTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions
+and Observers](https://arxiv.org/abs/2606.20728) (EMNLP 2026). Code: https://github.com/jinchaogjc/VTOS.
+Each benchmark has a 60 / 20 / 100 train / val / test split.
+
+| Subset | Task | Items | Images | Source |
+|---|---|---|---|---|
+| `lvis_count/` | object counting | 180 | 180 | LVIS (images from COCO) |
+| `plantseg_ood/` | plant-disease segmentation | 180 | 180 | PlantSeg |
+
+## Layout
+
+```
+lvis_count/
+  benchmark_train.json   benchmark_val.json   benchmark_test.json
+  images/lvis_count_001.jpg ... lvis_count_180.jpg
+plantseg_ood/
+  benchmark_train.json   benchmark_val.json   benchmark_test.json
+  images/<plant>_<disease>_<id>.jpg
+```
+
+Each JSON file is a list of items. `image_path` is relative to the subset's `images/` directory.
+Every image is referenced by exactly one item.
+
+## Fields
+
+Coordinates are normalized to [0, 1]. Bounding boxes are `[x_top_left, y_top_left, width, height]`
+(FiftyOne convention).
+
+**lvis_count**
+
+| Field | Description |
+|---|---|
+| `task_id` | item identifier |
+| `image_path` | file name under `images/` |
+| `question` | counting question |
+| `target_class` | LVIS category to count |
+| `count` | ground-truth count (= number of boxes) |
+| `density_tier` | density bucket |
+| `bounding_boxes` | one box per target instance |
+| `meta_info.source_image_id` | sample id in the FiftyOne LVIS export |
+| `meta_info.source_filepath` | file path in that export; the file name is the COCO image id |
+
+**plantseg_ood**
+
+| Field | Description |
+|---|---|
+| `task_id` | item identifier |
+| `image_path` | file name under `images/` |
+| `question` | localization question |
+| `plant` | host plant |
+| `target_class` | disease name |
+| `mask_ratio` | fraction of image area covered by the disease mask |
+| `difficulty_tier` | difficulty bucket |
+| `bounding_boxes` | one box per region |
+| `segmentations` | one polygon per region, list of `[x, y]` points |
+| `meta_info` | `n_bboxes`, `n_polygons`, `source_filepath` in the FiftyOne PlantSeg export |
+
+## Sources and licenses
+
+| Subset | License | Images |
+|---|---|---|
+| `lvis_count/` | CC BY 4.0 | COCO images; copyright remains with the image owners, use subject to the Flickr Terms of Use |
+| `plantseg_ood/` | CC BY-NC 4.0 (non-commercial use only) | from PlantSeg, same license |
+
+**lvis_count.** Annotations are taken from LVIS, licensed CC BY 4.0
+(https://www.lvisdataset.org/dataset). LVIS images come from COCO. The COCO Consortium does not
+own the image copyrights; use of the images must follow the Flickr Terms of Use
+(https://cocodataset.org/#termsofuse). Per-image Flickr licenses were not verified for this subset.
+Local copy obtained from the FiftyOne LVIS export. Image files were renamed to `lvis_count_XXX.jpg`;
+image content is unmodified.
+
+**plantseg_ood.** Taken from PlantSeg (Wei et al., 2024). Zenodo lists v1 (2024-08,
+https://doi.org/10.5281/zenodo.13293891) as CC BY-NC-ND 4.0 and the latest version, v7
+(2025-11, https://zenodo.org/records/17719108), as CC BY-NC 4.0. This subset is distributed under
+CC BY-NC 4.0 and is for non-commercial use only. Local copy obtained from `Voxel51/PlantSeg-Test`
+on Hugging Face; which Zenodo version that export is based on could not be verified.
+Bounding boxes match the FiftyOne export for 177 of 180 items. GPS metadata was removed from three
+images; pixel data of all images is unmodified.
+
+## Citation
+
+If you use these benchmarks, please cite VTOS and the source datasets.
+
+```bibtex
+@misc{ge2026vtos,
+  title={{VTOS}: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observers},
+  author={Ge, Jinchao and Liu, Lingqiao and Zhao, Shuwen and Wang, Lei},
+  year={2026},
+  eprint={2606.20728},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2606.20728}
+}
+
+@inproceedings{gupta2019lvis,
+  title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
+  author={Gupta, Agrim and Doll{\'a}r, Piotr and Girshick, Ross},
+  booktitle={CVPR},
+  year={2019}
+}
+
+@inproceedings{lin2014microsoft,
+  title={Microsoft {COCO}: Common Objects in Context},
+  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence},
+  booktitle={ECCV},
+  year={2014}
+}
+
+@article{wei2024plantseg,
+  title={PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation},
+  author={Wei, Tianqi and Chen, Zhi and Yu, Xin and Chapman, Scott and Melloy, Paul and Huang, Zi},
+  journal={arXiv preprint arXiv:2409.04038},
+  year={2024}
+}
+```