PHOEBI

Phase-contrast Optical bEnchmark for Bacterial Identification — a benchmark and framework for open-world identification of mixed bacterial cultures from optical phase-contrast microscopy.

At a glance

The release contains two subsets that share the same imaging pipeline:

Subset	Species	Combinations	Images
PHOEBI-6 (primary)	6 (bs, bt, fj, ka, mx, pf)	40	~120,000
PHOEBI-4 (legacy)	4 (b, f, k, p)	14	~14,000

Both subsets:

• 1024×1024×3 JPEG images at 1000× total magnification (100× oil-immersion, NA 1.25).
• Random 80/10/10 split (PHOEBI-6 also ships a leave-combinations-out (LCO) split) at seed 1337.
• Croissant 1.0 metadata in croissant.json.
• Released under CC BY 4.0.

The 4-class subset was collected under a separate microscopy session and is included to support cross-session replication of the compositional-collapse finding (paper §4.5, Appendix E.4).

Species (PHOEBI-6)

Code	Latin name	Gram	Motility	Cell length
bs	Bacillus subtilis	+	peritrichous flagella	4–10 µm
bt	Bacillus thermoamylovorans	+	peritrichous flagella	~4 µm
fj	Flavobacterium johnsoniae	−	gliding	5–10 µm
ka	Klebsiella aerogenes	−	peritrichous flagella	1–3 µm (encapsulated)
mx	Myxococcus xanthus	−	gliding	5–10 µm
pf	Pseudomonas fluorescens	−	polar flagella	1.5–3 µm

Directory layout

.
├── croissant.json                 # Croissant 1.0 metadata (core + RAI)
├── LICENSE                        # CC BY 4.0
├── README.md                      # this file
├── splits.json                    # PHOEBI-6 random + LCO split definitions (seed 1337)
├── images.tar.gz                  # PHOEBI-6: ~120,000 1024×1024 images
├── splits_4class.json             # PHOEBI-4 random split definition (seed 1337)
└── images_4class.tar.gz           # PHOEBI-4: ~14,000 1024×1024 images

After extraction the layout is:

images/                            # PHOEBI-6
├── bs/                            # singletons
├── bs_ka/                         # pairs (combo encoded by underscore-joined codes)
├── bs_ka_fj/                      # triples
└── bs_bt_mx_ka_fj_pf/             # six-species mixture

images_4class/                     # PHOEBI-4
├── b/                             # 4-class singletons
├── b_f/                           # 4-class pairs
├── b_f_k/                         # 4-class triples
└── b_f_k_p/                       # 4-class quadruple

Each image is a 1024×1024×3 RGB JPEG. The PHOEBI-6 label vector is the multi-hot indicator over [bs, bt, fj, ka, mx, pf]; PHOEBI-4 over [b, f, k, p]. Example: bs_ka_fj → [1, 0, 1, 1, 0, 0].

Splits

splits.json (PHOEBI-6) and splits_4class.json (PHOEBI-4) define the protocols.

Random 80/10/10. Image-level split with a fixed seed, intended for in-distribution closed-set characterisation.

Leave-combinations-out (LCO), seed 1337 (PHOEBI-6 only). Holds out entire species combinations under three constraints: combination disjointness (held-out combinations never appear in train or val), species coverage (every species appears in at least one trained-on combination, so the protocol tests compositional generalization rather than novel-class detection), and order coverage (held-out set spans a range of combination orders).

The PHOEBI-6 LCO held-out combinations at seed 1337 are: bt, bs_pf, ka_fj, bs_mx_fj, bs_ka_pf, mx_ka_fj, bs_bt_ka_fj, bs_mx_fj_pf, bs_bt_mx_ka_fj_pf.

Wet-lab protocol

Cultures were inoculated from glycerol stocks into nutrient broth (8 g L⁻¹), sterilised by autoclave (121 °C, 15 min), and grown at 30 °C with orbital shaking at 250 rpm for 72–120 hours. Once each culture reached its characteristic growth stage, Petri dishes were prepared from the broth and imaged on an inverted phase-contrast microscope (100× oil-immersion, NA 1.25). For PHOEBI-6 three pairwise combinations (bs+bt, bt+fj, ka+pf) and the five-species combinations were not collected and are absent from the release.

Tasks supported

1. Multi-label species presence detection in mixed cultures.
2. Compositional generalization (LCO protocol).
3. Open-set rejection (leave-one-class-out evaluation; see paper §4.4).
4. Novel-class discovery (multi-label compositional NCD; see paper §4.4).

Tasks NOT supported

• Proportion / composition estimation. Ground-truth species ratios are unavailable; growth rates differ across species and the wet-lab procedure records only species presence, not relative abundance.
• Clinical pathogen identification. No stained-smear protocol; the dataset must not be used for clinical decisions without independent validation.

Loading the data

A minimal Python loader using only the standard library:

import json
from PIL import Image
import numpy as np

# Assume images.tar.gz has been extracted next to splits.json
with open("splits.json") as f:
    splits = json.load(f)

class_names = splits["class_names"]   # ['bs', 'bt', 'fj', 'ka', 'mx', 'pf']

for entry in splits["splits"]["train"]:
    img = np.array(Image.open(entry["path"]).convert("RGB"))   # 1024 x 1024 x 3
    label = np.asarray(entry["label"], dtype=np.int64)         # 6-vector multi-hot
    combo = entry["combo"]                                     # combination token, e.g. "bs_ka"
    # ... your pipeline ...

For the LCO protocol, use the helper at baselines.supervised_multilabel_heldout.select_heldout(seed=1337) in the companion code release for an apples-to-apples held-out set.

Citation

@misc{phoebi2026,
  title        = {{PHOEBI}: An Open-World Benchmark for Bacterial Identification in
                  Phase-Contrast Microscopy},
  author       = {Anonymous Authors},
  year         = {2026},
  note         = {Under review at the NeurIPS 2026 Datasets and Benchmarks Track},
  howpublished = {OpenReview: \url{https://openreview.net/PLACEHOLDER}},
}

(Author and institution information will be added on acceptance.)

Contact

During the double-blind review period, please use the OpenReview discussion thread for the corresponding submission. Public contact information will be added on acceptance.