Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
69 lines
2.1 KiB
YAML
69 lines
2.1 KiB
YAML
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Global Wheat 2020 dataset https://www.global-wheat.com/ by University of Saskatchewan
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# Documentation: https://docs.ultralytics.com/datasets/detect/globalwheat2020/
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# Example usage: yolo train data=GlobalWheat2020.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── GlobalWheat2020 ← downloads here (7.0 GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: GlobalWheat2020 # dataset root dir
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train: # train images (relative to 'path') 3422 images
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- images/arvalis_1
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- images/arvalis_2
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- images/arvalis_3
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- images/ethz_1
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- images/rres_1
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- images/inrae_1
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- images/usask_1
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val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
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- images/ethz_1
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test: # test images (optional) 1276 images
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- images/utokyo_1
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- images/utokyo_2
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- images/nau_1
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- images/uq_1
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# Classes
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names:
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0: wheat_head
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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from pathlib import Path
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from ultralytics.utils.downloads import download
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# Download
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dir = Path(yaml["path"]) # dataset root dir
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urls = [
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"https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip",
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"https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip",
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]
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download(urls, dir=dir)
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# Make Directories
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for p in "annotations", "images", "labels":
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(dir / p).mkdir(parents=True, exist_ok=True)
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# Move
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for p in (
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"arvalis_1",
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"arvalis_2",
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"arvalis_3",
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"ethz_1",
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"rres_1",
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"inrae_1",
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"usask_1",
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"utokyo_1",
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"utokyo_2",
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"nau_1",
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"uq_1",
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):
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(dir / "global-wheat-codalab-official" / p).rename(dir / "images" / p) # move to /images
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f = (dir / "global-wheat-codalab-official" / p).with_suffix(".json") # json file
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if f.exists():
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f.rename((dir / "annotations" / p).with_suffix(".json")) # move to /annotations
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