Datasets:
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Dataset script for VLM Plant Sim - enables proper JSONL loading with explicit schema definition.
This prevents HF's auto-inference from failing on complex multi-valued fields.
"""
import json
from typing import Generator
import datasets
from datasets import Features, SplitGenerator, Value, DatasetInfo
# Dataset configuration
_CITATION = """\
@misc{yun2026usingvisionlanguagefoundation,
title={Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning},
author={Heesup Yun and Isaac Kazuo Uyehara and Earl Ranario and Lars Lundqvist and Christine H. Diepenbrock and Brian N. Bailey and J. Mason Earles},
year={2026},
eprint={2603.08930},
archivePrefix={arXiv},
primaryClass={cs.CV},
}
"""
_DESCRIPTION = """\
VLM Plant Sim is a multi-source plant and crop image dataset combining HELIOS synthetic renders,
real drone orthophoto patches, and annotated field data for evaluating Vision Language Models
in generating plant simulation configurations.
"""
_HOMEPAGE = "https://huggingface.co/datasets/heesup/vlm-plant-sim"
_LICENSE = "apache-2.0"
class VlmPlantSimDataset(datasets.GeneratorBasedBuilder):
"""HF Dataset builder for VLM Plant Sim."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="default", version=VERSION, description="Default config"),
]
DEFAULT_CONFIG_NAME = "default"
def _info(self) -> DatasetInfo:
"""Define the dataset schema explicitly."""
features = Features({
"id": Value("string"),
"split": Value("string"),
"source": Value("string"),
"dap": Value("int32"),
"bed": Value("int32"),
"tier": Value("int32"),
"image_path": Value("string"),
"annotation_path": Value("string"),
"scene_json_path": Value("string"),
"camera_json_path": Value("string"),
"params_json_path": Value("string"),
"classes_txt_path": Value("string"),
"boxes_txt_path": Value("string"),
"masks_json_path": Value("string"),
"notes": Value("string"),
})
return DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Define the splits."""
return [
SplitGenerator(
name="synthetic",
gen_kwargs={"manifest_file": "manifests/synthetic.jsonl"},
),
SplitGenerator(
name="real",
gen_kwargs={"manifest_file": "manifests/real.jsonl"},
),
SplitGenerator(
name="real_dap10_boxes",
gen_kwargs={"manifest_file": "manifests/initial_plant.jsonl"},
),
]
def _generate_examples(self, manifest_file: str) -> Generator:
"""Generate examples from JSONL manifest file."""
idx = 0
try:
with open(manifest_file, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
try:
record = json.loads(line)
# Ensure all expected fields are present
example = {
"id": record.get("id", ""),
"split": record.get("split", ""),
"source": record.get("source", ""),
"dap": int(record.get("dap", 0)),
"bed": int(record.get("bed", record.get("tier", record.get("location", 0)))),
"tier": int(record.get("tier", record.get("bed", record.get("location", 0)))),
"image_path": record.get("image_path", ""),
"annotation_path": record.get("annotation_path", ""),
"scene_json_path": record.get("scene_json_path", ""),
"camera_json_path": record.get("camera_json_path", ""),
"params_json_path": record.get("params_json_path", ""),
"classes_txt_path": record.get("classes_txt_path", ""),
"boxes_txt_path": record.get("boxes_txt_path", ""),
"masks_json_path": record.get("masks_json_path", ""),
"notes": record.get("notes", ""),
}
yield idx, example
idx += 1
except (json.JSONDecodeError, ValueError) as e:
# Skip invalid lines
continue
except FileNotFoundError:
# File not found - return empty result
pass
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