| --- |
| 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](https://agar.neurosys.com) 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 |
|
|
| ```python |
| 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](https://creativecommons.org/licenses/by-nc/2.0/): non-commercial use only, with attribution. |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
