--- license: other pretty_name: ConnectomeBench2 (smoke) tags: - connectomics - proofreading - 3d - electron-microscopy - mesh size_categories: - 1K ⚠️ **This is a smoke-test repo with 1,500 samples (500 per split).** The full dataset has 401,170 samples and will be uploaded after upstream loaders/viewer/Croissant are validated against this small repo. ConnectomeBench2 is a unified benchmark for **automated proofreading of connectomic neural-segmentation data**. Each row is one candidate proofreading sample (a real human merge edit, a real human split edit, or a synthetic control) 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 (full dataset, when uploaded) - 401,170 rows total · ~80/11/9 train/val/test - 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 ## 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.