---
license: other
size_categories:
- 100K<n<1M
pretty_name: ConnectomeBench2
task_categories:
- image-classification
tags:
- connectomics
- proofreading
- 3d
- electron-microscopy
- mesh
configs:
- config_name: default
  data_files:
  - split: train
    path: train/train-*.parquet
  - split: validation
    path: val/val-*.parquet
  - split: test
    path: test/test-*.parquet
---

# ConnectomeBench2

ConnectomeBench2 is a unified benchmark for **automated proofreading of connectomic neural-segmentation data**. This is the dataset for the paper [ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading](https://huggingface.co/papers/2606.21116).

The official codebase is available on GitHub at [timfarkas/ConnectomeBench2](https://github.com/timfarkas/ConnectomeBench2).

**401,170 samples** across 4 species (mouse, fly, human, zebrafish) and 5 sample types (real merge edits, real split edits, synthetic adjacent / junction / synapse controls), with the associated mesh geometry and electron-microscopy (EM) renderings.

Downstream trainers should treat this dataset as the single source of truth for sample identity, labels, train/validation/test split, and which task(s) a row is valid for.

## Context: Connectomic Proofreading

**Connectomics** scans and automatically segments neurons to create large-scale brain maps at cellular resolution. Two types of **segmentation errors** can occur in this process, which need to be corrected (= **proofreading**):

- **False Splits** — corrected via merge corrections
- **False Merges** — corrected via split corrections

*Merge corrections* (of false splits) are applied to *multiple segments* that need to be correctly merged together. *Split corrections* (of false merges) are applied to *single segments* that need to be correctly split apart.

For this reason, this dataset contains renderings of both *single-segment* (pre-split or post-merge) and *dual-segment* (post-split or pre-merge) mesh geometry, where possible. EM data is provided in dual format only — segmentation on imaging level is contiguous, so the single-version can be derived from the union of the dual.

## Renderings (geometry and EM imaging data)

![channel decomposition: synapse 2-mask vs junction single-mask](figures/channel_decomposition.png)

(top: synapse merge-pair — both masks populated; bottom: junction control — single-mask only, mask B / seg B empty)

**Geometry files** (the `geometry` and `geometry_single` columns) are compressed `.npz` payloads that decode to `(3, 7, 224, 224) float16` arrays — three 2D views (front, side, top) × seven channels:

| ch | content |
|---|---|
| 0 | silhouette |
| 1 | depth |
| 2 | normal_x |
| 3 | normal_y |
| 4 | normal_z |
| 5 | mask A |
| 6 | mask B (empty in single-segment renders) |

Note that single and dual segment renders differ not only in mask channels, but also subtly differ in all other channels, due to slight differences in mesh geometry from merging/splitting.

**Free split-mask labels.** For `split_edit` rows, the dual-segment render (post-split) provides ground-truth split-mask labels (Mask A / Mask B channels) for the corresponding single-segment render (pre-split) — split-mask-generation tasks get pixel-level supervision without extra labeling.

**EM coverage.** EM views are not present on every sample. Coverage by `sample_type` (full dataset):

| sample_type | rows | has_em |
|---|---|---|
| adjacent_control | 121,333 | 100% |
| junction_control | 38,272 | 100% |
| synapse_control | 18,182 | 100% |
| merge_edit | 146,461 | 38% |
| split_edit | 77,213 | 23% |
| **total** | **401,170** | **63% (37% null)** |

real human edits (merge_edit, split_edit) only got EM rendered on a stratified subset; synthetic controls all have EM. Filter by `has_em` if your task requires it.

**EM imaging files** (`em_xy` / `em_xz` / `em_yz` / `em_best` columns) are PNG-encoded 3-channel slices:

| ch | content |
|---|---|
| 0 | raw EM intensity |
| 1 | segment A mask |
| 2 | segment B mask |

Four imaging views per sample: three cardinal slices (xy, xz, yz) + a `best` slice at an oblique angle that maximizes the visible area of both segments (sum of their logs).

For single-segment tasks, segment A and B should be merged (and B zeroed). The `best` view may leak some dual-label information (it takes both labels into account); we advise against testing single-segment tasks on `em_best`.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("jeffbbrown2/connectomebench2-smoke", split="train")
sample = ds[0]
# sample["em_xy"] is a PIL Image (HF auto-decodes)
# sample["geometry"] is bytes — decode with:
import io, numpy as np
geom = np.load(io.BytesIO(sample["geometry"]))["arr_0"]   # shape (3, 7, 224, 224) float16
```

Or with raw `pyarrow`:

```python
import pyarrow.parquet as pq
import numpy as np, io
df = pq.read_table("train/train-00000.parquet").to_pandas()
geom = np.load(io.BytesIO(df.iloc[0]["geometry"]))["arr_0"]
```

The `metadata/{train,val,test}.parquet` sidecars contain identifier/label/modality columns only (no image bytes) — useful for fast filtering or inspection.

## Columns

### Identifiers
- **`combined_sample_hash`** — primary key (md5 hex 32-char of `f"{source_archive}|{source_archive_sample_hash}"`); guaranteed unique across the dataset.
- **`source_archive_sample_hash`** — legacy 12-char hex hash from upstream; kept for traceability, not unique alone.
- **`source_archive`** — name of the originating render archive (e.g. `edits_and_adj_controls_fly`, `junction_controls_mouse`, `synapse_controls_fly`). 10 distinct values (5 archives × species).

### Sample identity
- **`sample_type: str`** — single source of truth for what kind of sample this row is. Five values:
  - `merge_edit` — positive merge-correction edit
  - `split_edit` — positive split-correction edit
  - `adjacent_control` — synthetic negative for merge-correction (segments adjacent to genuine correction)
  - `junction_control` — putative junction in proofread neuron (negative merge-error-id sample)
  - `synapse_control` — synapse pair across neurons (negative merge-correction)
- **`same_neuron: bool`** — derived from sample_type:
  - `True` for `merge_edit`, `junction_control`
  - `False` for `split_edit`, `adjacent_control`, `synapse_control`
- **`species: str`** — `fly` / `mouse` / `human` / `zebrafish`.

### Image content
- **`geometry`** — bytes; compressed npz (key `"arr_0"`) decoding to `(3, 7, 224, 224) float16`. Null when the sample has no dual-segment render.
- **`geometry_single`** — same shape/dtype, single-segment version. Null when not present.
- **`em_xy` / `em_xz` / `em_yz` / `em_best`** — PIL Images (3-channel PNG, `(224, 224, 3) uint8`). Null when the row has no EM views.
- **`has_single_mask: bool`** — convenience flag.
- **`has_dual_mask: bool`** — convenience flag.
- **`has_em: bool`** — true if any `em_*` column is non-null.
- **`present_slots: list[str]`** — modality tags actually present (e.g. `["geometry", "geometry_single", "em_xy", "em_xz", "em_yz", "em_best"]`).

### Task routing & labels
- **`task_routing: list[str]`** — which downstream task(s) this row can serve as training data for:
  - `false_split_correction` — merge-correction task; fires when `sample_type ∈ {merge_edit, synapse_control, adjacent_control}` AND `has_dual_mask`.
  - `false_merge_identification` — merge-error binary classification; fires when `sample_type ∈ {split_edit, junction_control}` AND `has_single_mask`.
  - `split_mask_generation` — pixel-level split prediction; fires when `sample_type == split_edit` AND `has_single_mask`.
- **`false_split_correction_label: bool`** = `same_neuron`. Populated for all rows; trainers filter by `task_routing`.
- **`false_merge_identification_label: bool`** = `not same_neuron`. Populated for all rows; trainers filter by `task_routing`.

**Usage note.** Downstream training scripts must load the appropriate geometry render per task:
- **Merge Correction** of false splits should use **dual-segment** renders
- **Split Correction** of false merges should use **single-segment** renders
  - Furthermore, fuse A/B channels of EM images and **discard `em_best`** (it sees both labels at oblique angle and can leak ground truth)

Otherwise, ground-truth task or label information may leak to the model and bias performance.

### Train/val/test split
- **`split: str`** — `train` / `validation` / `test`. ~80/10/10 split assigned by spatial location of the proofreading sample (`interface_point_nm`), matched via cube splits (50µm cubes tiling the volume and randomly split).

### Other
- **`metadata: str`** — JSON-stringified original metadata struct. Parse with `json.loads`. Useful keys: `operation_id`, `source_operation_id`, `strategy`, `image_types`, `interface_point_nm`, `before_root_ids`, `after_root_ids`, …

## Counts

- **401,170 rows** total · ~80/11/9 train (319,727) / validation (43,517) / test (37,926)
- 251,499 rows with EM views; all 401,170 have geometry
- **~2.2M model-level samples** (EM × 4 views + geom × 3 views), or **~2.8M** counting dual + single geom separately
- 506 parquet shards (~240 MB each)

## Layout

```
README.md
shards.csv                    metadata across shards (path, sha256, n_samples, size)
train/train-*.parquet         WebDataset-style parquet shards with image bytes
val/val-*.parquet
test/test-*.parquet
metadata/                     sidecar parquets with identifiers + labels (no bytes)
  train.parquet
  val.parquet
  test.parquet
demo.parquet                  stratified mini-shard (one-line preview)
figures/
  channel_decomposition.png
```

## Sources & License

Derived from the following upstream connectomic proofreading datasets:
- **MICrONS** (mouse cortex)
- **FlyWire** (Drosophila brain)
- **H01** (human cortex)
- Zebrafish larval connectome

License = `other`; users must comply with upstream licenses (which may differ across species/sources). Final outbound license will be set after upstream license review.

## Citation

If you use ConnectomeBench2, please cite:

```
Brown, J., Farkas, T., Razgar, G., Boyden, E. S.
ConnectomeBench2: A unified benchmark for automated connectomic proofreading.
(2026, in submission). Brown J. and Farkas T. contributed equally as first authors.
```

Please also cite the upstream connectome sources used by this dataset:
- MICrONS (mouse cortex): https://www.microns-explorer.org/cortical-mm3
- FlyWire (Drosophila): https://flywire.ai/
- H01 (human cortex): https://h01-release.storage.googleapis.com/landing.html
- Zebrafish (fish1): https://fish1-release.storage.googleapis.com/index.html