MergeFree / segclr_inputs /README.md
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Add segclr_inputs/: per-token JSONs for SegCLR lookup (clean+merge train)
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# `segclr_inputs/` — staging JSONs for SegCLR lookup
Per-token info for **MERGER FREE** training samples, formatted so a
SegCLR maintainer can pull one embedding per token without needing
to touch the raw NPZ training data.
## Files
| File | n_samples | size | type |
|---|---|---|---|
| `segclr_inputs_clean_train.json` | 41,005 | 99 MB | B-clean (single root per sample) |
| `segclr_inputs_merge_train.json` | 188,812 | 584 MB | A-merge (two roots per sample, per-token y_part) |
## Schema
```json
{
"kind": "B-clean" | "A-merge",
"n_samples": <int>,
"samples": [
{
"source_npz": "...", // training-data NPZ
"op_id": 39183, // sample id (B) or merge op id (A)
"fov_center_um": [x, y, z],
"n_tokens": 20,
// B-clean: single root_id at sample level
"root_id": 864691136176785158,
// A-merge: two roots + post-merge proofread root
"root_a": 864...,
"root_b": 864...,
"proofread_root": 864...,
"tokens": [
{
"token_idx": 0,
"abs_um": [x, y, z], // absolute world coords (µm)
"rel_um": [x, y, z], // relative to fov_center_um
"root_id": 864..., // per-token root assignment
"sv_id": "100497...", // A-merge only: supervoxel id (uint64 → str)
"y_part": 1 // A-merge only: 0=root_a, 1=root_b, -1=ignore
},
...
]
},
...
]
}
```
## Notes for SegCLR lookup
- **A-merge tokens already have `sv_id`** — direct SegCLR embedding
lookup, no CAVE chunkedgraph resolution needed.
- **B-clean tokens have only `root_id` + `abs_um`** — SV resolution
via CAVE is required before SegCLR lookup. Happy to do this on
our side if it saves you time; let me know.
- `abs_um` is in microns (1 µm = 250 voxels in xy at 4nm, 25 voxels
in z at 40nm).
- Datastack: `minnie65_public`.
## Direct download
```
https://huggingface.co/datasets/FumingY/MergeFree/resolve/main/segclr_inputs/segclr_inputs_clean_train.json
https://huggingface.co/datasets/FumingY/MergeFree/resolve/main/segclr_inputs/segclr_inputs_merge_train.json
```
## What we'd like back
One SegCLR embedding (float32) per token, joinable back via
`(sample.op_id, token.token_idx)`. NPZ / parquet / per-sample JSON
all fine.