| """ |
| 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 |
|
|
|
|
| |
| _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) |
| |
| 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: |
| |
| continue |
| except FileNotFoundError: |
| |
| pass |
|
|
