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README.md

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----
-license: mit
----
+---
+license: other
+pretty_name: ConnectomeBench2 (smoke)
+tags:
+  - connectomics
+  - proofreading
+  - 3d
+  - electron-microscopy
+  - mesh
+size_categories:
+  - 1K<n<10K
+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
+
+> ⚠️ **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.

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