Datasets:

leodriesch
/

agar

license: cc-by-nc-2.0
pretty_name: AGAR
task_categories:
  - object-detection
tags:
  - microbiology
  - bacteria
  - colony-counting
  - petri-dish
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    default: true
    data_files:
      - split: train
        path: data/*/train-*
      - split: validation
        path: data/*/validation-*
      - split: unassigned
        path: data/*/unassigned-*
  - config_name: bright
    data_files:
      - split: train
        path: data/bright/train-*
      - split: validation
        path: data/bright/validation-*
      - split: unassigned
        path: data/bright/unassigned-*
  - config_name: dark
    data_files:
      - split: train
        path: data/dark/train-*
      - split: validation
        path: data/dark/validation-*
      - split: unassigned
        path: data/dark/unassigned-*
  - config_name: vague
    data_files:
      - split: train
        path: data/vague/train-*
      - split: validation
        path: data/vague/validation-*
      - split: unassigned
        path: data/vague/unassigned-*
  - config_name: lower_resolution
    data_files:
      - split: train
        path: data/lower_resolution/train-*
      - split: validation
        path: data/lower_resolution/validation-*
      - split: unassigned
        path: data/lower_resolution/unassigned-*

AGAR

Mirror of the AGAR dataset by NeuroSYS Research (version 1.0, 2020): 18,000 photos of Petri dishes with 336,442 annotated colonies of five microorganisms (S. aureus, B. subtilis, P. aeruginosa, E. coli, C. albicans), plus 1,055 boxes marking defects and contaminations.

This copy repackages the original release as sharded Parquet. Images are the original JPEG bytes, not re-encoded. Annotations are taken from the per-image JSON files of the release, which match the release's COCO file exactly.

Image	Annotations

A dish from the dark subset with S. aureus and P. aeruginosa, downscaled to 1000 pixels.

Usage

from datasets import load_dataset

ds = load_dataset("leodriesch/agar", split="train")                  # all backgrounds
dark = load_dataset("leodriesch/agar", "dark", split="validation")  # one background

The images are large (up to about 4000 × 4000 pixels, 31 GB in total). Use streaming=True to avoid downloading everything.

Configurations

The release groups images by how they were photographed:

Config	Images	Description
`bright`	2,088	higher resolution, bright background
`dark`	9,649	higher resolution, dark background
`vague`	994	higher resolution, vague background
`lower_resolution`	5,269	lower resolution camera

The default config contains all four.

Splits

train / validation: the official split lists shipped with the release (training_lists/). They cover only countable images: 9,202 train and 3,067 validation.
unassigned: everything the official lists do not cover: all empty and uncountable dishes, and 4 countable images that appear in neither list.

Fields

Field	Type	Description
`image`	image	the original JPEG
`image_id`	int	file number in the release (`<image_id>.jpg`); equals the image `id` in the COCO file
`sample_id`	int	`sample_id` as written in the release's per-image JSON (see Known issues)
`background`	class	`bright`, `dark`, `vague`, `lower-resolution`
`image_type`	class	`empty`, `countable`, `uncountable`, from the ID ranges in `AGAR_id_list.md`
`width`, `height`	int	image size in pixels
`classes`	list of str	microorganisms on the dish
`colonies_number`	int	number of colonies on the dish: counts microorganism boxes only (not `Defect`/`Contamination`), `-1` for uncountable dishes
`objects.id`	list of int	box ID within the image
`objects.bbox`	list of `[x, y, width, height]`	boxes in pixels, COCO format (top-left corner)
`objects.category`	list of class	`S.aureus`, `B.subtilis`, `P.aeruginosa`, `E.coli`, `C.albicans`, `Defect`, `Contamination`

Uncountable dishes have no boxes. Use len(objects.bbox) for the number of boxes.

Original files

annotations/ holds the release's metadata unchanged:

coco_annotations.json: the COCO file (dataset/annotations.json in the release)
training_lists/: the official split lists (JSON arrays despite the .txt extension)
AGAR_id_list.md: the ID ranges per background and image type

Changes from the original release

Images and per-image JSON files are combined into sharded Parquet files, grouped by background and split. The JPEG bytes are unchanged.
Annotations are restructured: each label's x, y, width, height became objects.bbox, and class became objects.category.
Added fields: image_id (file number), image_type (from AGAR_id_list.md), and width/height (from the COCO file).
Splits: the official train/validation assignment is unchanged. The unassigned split groups the images that are in neither list.
dataset/annotations.json is renamed to annotations/coco_annotations.json. Its content and the other metadata files are unchanged.

Known issues

In 438 of the per-image JSON files, sample_id is one higher than the file number (e.g. 1000.json says 1001). The annotations themselves belong to the file with the same number; they match the COCO file under that ID. Use image_id, not sample_id.
Every official split list contains the string "annotations.json" as an entry. It was dropped here.

License and citation

AGAR is licensed under CC BY-NC 2.0: non-commercial use only, with attribution.

@misc{majchrowska2021agar,
  title         = {AGAR a microbial colony dataset for deep learning detection},
  author        = {Sylwia Majchrowska and Jaros\l{}aw Paw\l{}owski and Grzegorz Gu\l{}a and Tomasz Bonus and Agata Hanas and Adam Loch and Agnieszka Pawlak and Justyna Roszkowiak and Tomasz Golan and Zuzanna Drulis-Kawa},
  year          = {2021},
  eprint        = {2108.01234},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV}
}