Add segclr_inputs/: per-token JSONs for SegCLR lookup (clean+merge train)

.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+segclr_inputs/segclr_inputs_clean_train.json filter=lfs diff=lfs merge=lfs -text
+segclr_inputs/segclr_inputs_merge_train.json filter=lfs diff=lfs merge=lfs -text

segclr_inputs/README.md

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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.

segclr_inputs/segclr_inputs_clean_train.json

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segclr_inputs/segclr_inputs_merge_train.json

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