Publish PixCell dataset v2

.gitattributes

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-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
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-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
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.gitignore

@@ -1,5 +0,0 @@
-__pycache__/
-*.py[cod]
-.pytest_cache/
-.ruff_cache/
-.DS_Store

.python-version

@@ -1 +0,0 @@
-3.13.14

CITATION.cff

@@ -1,11 +1,11 @@
 cff-version: 1.2.0
 message: >-
-  If you use the PixCell Dataset,
-  please cite this dataset and the accompanying PixCell project.
+  If you use the PixCell Dataset, please cite this dataset and the
+  accompanying PixCell project.
 title: "PixCell Dataset: Representation-First Image-to-Code Curriculum"
 type: dataset
-version: depth-v1
-date-released: 2026-07-26
+version: 2.0.0
+date-released: 2026-07-27
 authors:
   - family-names: Agarwal
     given-names: Aadarsh

README.md

@@ -3,107 +3,49 @@ pretty_name: "PixCell Dataset"
 license: mit
 language:
   - en
-  - code
-annotations_creators:
-  - expert-generated
-  - machine-generated
-source_datasets:
-  - original
 task_categories:
   - image-text-to-text
 tags:
-  - image
-  - text
-  - code
-  - python
+  - image-to-code
   - code-generation
   - vision-language
   - photonics
   - gds
   - gdsfactory
-  - image-to-code
-  - multimodal
   - synthetic
   - curriculum-learning
-  - datasets
 size_categories:
   - 1K<n<10K
 configs:
-  - config_name: all
+  - config_name: depth
     default: true
     data_files:
       - split: train
-        path:
-          - data/l0/train-*.parquet
-          - data/l1/train-*.parquet
-          - data/l2/train-*.parquet
-          - data/l3/train-*.parquet
-          - data/l4/train-*.parquet
-  - config_name: core-v1
-    data_files:
-      - split: train
-        path:
-          - data/l0/train-*.parquet
-          - data/l1/train-*.parquet
-          - data/l2/train-*.parquet
-          - data/l3/train-*.parquet
-          - data/l4/train-*.parquet
-  - config_name: l0
-    data_files:
-      - split: train
-        path: data/l0/train-*.parquet
-  - config_name: l1
-    data_files:
-      - split: train
-        path: data/l1/train-*.parquet
-  - config_name: l2
-    data_files:
-      - split: train
-        path: data/l2/train-*.parquet
-  - config_name: l3
-    data_files:
-      - split: train
-        path: data/l3/train-*.parquet
-  - config_name: l4
+        path: depth/data/train-*.parquet
+      - split: validation
+        path: depth/data/validation-*.parquet
+  - config_name: core
     data_files:
       - split: train
-        path: data/l4/train-*.parquet
-  - config_name: depth-v1
+        path: core/data/train-*.parquet
+  - config_name: references
     data_files:
       - split: train
-        path: depth-v1/data/train-*.parquet
+        path: references/data/train-*.parquet
       - split: validation
-        path: depth-v1/data/validation-*.parquet
+        path: references/data/validation-*.parquet
 dataset_info:
-  - config_name: all
+  - config_name: depth
     splits:
       - name: train
-        num_examples: 738
-  - config_name: core-v1
+        num_examples: 3468
+      - name: validation
+        num_examples: 1092
+  - config_name: core
     splits:
       - name: train
         num_examples: 738
-  - config_name: l0
-    splits:
-      - name: train
-        num_examples: 177
-  - config_name: l1
-    splits:
-      - name: train
-        num_examples: 215
-  - config_name: l2
-    splits:
-      - name: train
-        num_examples: 118
-  - config_name: l3
-    splits:
-      - name: train
-        num_examples: 120
-  - config_name: l4
-    splits:
-      - name: train
-        num_examples: 108
-  - config_name: depth-v1
+  - config_name: references
     splits:
       - name: train
         num_examples: 3468
@@ -113,210 +55,136 @@ dataset_info:
 
 # PixCell Dataset
 
-The PixCell Dataset is a verified image-to-code curriculum for photonic
-geometry. Each row pairs a high-visibility component image and physical
-footprint context with a complete primitive-only GDSFactory program.
+PixCell is a verified image-to-code curriculum for reconstructing photonic
+geometry as primitive-only GDSFactory Python programs. Each model row contains
+a high-visibility component image, its physical footprint, and an audited
+program.
 
-## Choose a configuration
+[Core curriculum gallery](assets/core.png) |
+[Depth representation overview](assets/depth.png)
 
-| Configuration | Best for | Rows | Splits |
-|---|---|---:|---|
-| `core-v1` | Curriculum training and compact task sampling | 738 | `train`: 738 |
-| `depth-v1` | Image-to-code SFT and parameter recovery | 4,560 | `train`: 3,468, `validation`: 1,092 |
-| `l0` through `l4` | Individual stages of the core curriculum | 108 to 215 | `train` |
+## Configurations
 
-`all` is the default compatibility alias for `core-v1`. Versioned work should
-pass both the configuration and matching revision.
+| Configuration | Rows | Purpose |
+|---|---:|---|
+| `depth` | 4,560 | Default corpus for supervised training and parameter recovery |
+| `core` | 738 | Compact L0 to L4 curriculum with 547 anchors and 191 supporting rows |
+| `references` | 4,560 | Target rasters, calibration, lineage, and verifier metadata |
 
-[Core gallery](galleries/all.png), [L0](galleries/l0.png),
-[L1](galleries/l1.png), [L2](galleries/l2.png),
-[L3](galleries/l3.png), [L4](galleries/l4.png),
-[Depth overview](depth-v1/galleries/overview.png)
+`core` is an exact subset of `depth/train`. `references` follows the same
+train and validation split as `depth` and joins through `opaque_id`.
 
 ## Load
 
-```bash
-pip install "datasets[vision]"
-```
-
 ```python
 from datasets import load_dataset
 
+depth = load_dataset(
+    "qpaig-mit/pixcell",
+    "depth",
+    revision="v2.0.0",
+)
+
 core = load_dataset(
     "qpaig-mit/pixcell",
-    "core-v1",
-    revision="core-v1",
+    "core",
+    revision="v2.0.0",
     split="train",
 )
 
-depth = load_dataset(
+references = load_dataset(
     "qpaig-mit/pixcell",
-    "depth-v1",
-    revision="depth-v1",
+    "references",
+    revision="v2.0.0",
 )
-
-row = core[0]
-row["image"]          # exact model input
-row["instruction"]    # footprint and display-scale context
-row["code"]           # supervised program
-row["target_image"]   # canonical physical-aspect raster
-row["footprint_um"]   # physical width and height
 ```
 
-The Parquet files load directly through `datasets`.
+## Training contract
 
-## Curriculum
+| Role | Fields |
+|---|---|
+| Model observation | `image`, `footprint_um` |
+| Supervised target | `code` |
+| Sampling and curriculum | `level`, `curriculum_order`, `representation_id`, `leakage_group_id`, `variation_role`, `realization_slot`, `is_core` |
+| Evaluator reference | the matching row from `references` |
 
-The five levels progress from the permitted primitive catalog to complete
-photonic components.
+The dataset stores no per-row prompt. Training code combines the image and
+footprint with its selected programming contract. Sampling fields organize
+batches and are model-visible only if a training pipeline explicitly includes
+them.
 
-| Level | Rows | Representations | Contents |
-|---|---:|---:|---|
-| **L0: primitives** | 177 | 93 | The 22-item primitive catalog, constructor modes, useful orientations, and scale calibration. |
-| **L1: operations** | 215 | 127 | Translation, rotation, reflection, alignment, spacing, connection, path construction, Booleans, repetition, arrays, and routing. |
-| **L2: compositions** | 118 | 99 | Small relational assemblies, connected chains, branches, repeated carriers, arrays, and radial banks. |
-| **L3: structures** | 120 | 120 | Authored multi-zone systems with routes, branches, defects, repeated media, and cyclic organization. |
-| **L4: components** | 108 | 108 | Couplers, MMIs, splitters, interferometers, resonators, crossings, gratings, cavities, converters, and free-propagation structures. |
+The maximum-visibility image preserves topology and within-axis proportions.
+The footprint supplies the physical width and height. The `references`
+configuration supplies the canonical physical-aspect raster and calibration
+used by geometry evaluators. Keep `references` out of model prompts.
+Stored calibration uses the renderer's analytic bounding box. A bbox inferred
+from thresholded antialiased pixels can differ by at most one pixel.
 
-`depth-v1` retains the complete core and adds seven controlled geometry
-settings to 546 representation anchors.
+`opaque_id` is a stable join key derived from the v1 source ID. It is not an
+anonymization mechanism.
 
 ## Split design
 
-The split holds out parameter settings within known representations.
-
-| Split | Contents | Rows |
+| Split | Construction | Rows |
 |---|---|---:|
-| `train` | All 738 core rows plus five new settings for each of 546 expanded representations | 3,468 |
-| `validation` | Two further settings for each expanded representation | 1,092 |
+| `train` | 738 core rows plus five additional settings for 546 representations | 3,468 |
+| `validation` | Two held-out settings for each expanded representation | 1,092 |
 
-```text
-train      738 + (5 * 546) = 3,468
-validation         2 * 546 = 1,092
-```
+Validation measures parameter recovery within known representations. Every
+representation is present in training.
 
-Training receives the anchors and most controlled settings. Validation receives
-a fixed parameter holdout for every expanded representation. This gives a
-76/24 training-to-validation ratio and keeps all 547 representations available
-during training.
+## Curriculum
 
-Exact image-plus-instruction pairs and code hashes stay unique across the two
-splits. `leakage_group_id` records related aliases and dependencies. Use that
-field to construct a group-disjoint split for unseen-representation studies.
+| Level | Rows | Representations | Contents |
+|---|---:|---:|---|
+| L0 | 177 | 93 | Primitive catalog, constructor modes, orientation, and scale |
+| L1 | 215 | 127 | Placement, transforms, alignment, connection, repetition, and routing |
+| L2 | 118 | 99 | Relational assemblies, chains, branches, arrays, and radial banks |
+| L3 | 120 | 120 | Multi-zone structures, repeated media, defects, and cyclic organization |
+| L4 | 108 | 108 | Couplers, MMIs, interferometers, resonators, crossings, gratings, cavities, and converters |
 
-## Row contract
+## Verification
 
-| Purpose | Fields |
-|---|---|
-| Model input | `image`, `instruction` |
-| Supervised target | `code` |
-| Physical reference | `target_image`, `footprint_um` |
-| Curriculum identity | `level`, `family`, `concept_id`, `representation_id`, `grammar_id`, `variation_role` |
-| Alias and dependency grouping | `leakage_group_id` |
-| Construction metadata | `primitive_calls`, `prerequisite_ids`, `port_signature_json`, `parameters_json`, `topology_json` |
-| Integrity and provenance | SHA-256 fields, `view_policy`, source digest, and generator version |
-
-`image` is a maximum-visibility view: thin gaps, rails, teeth, bends, and
-defects remain legible through declared per-axis display magnification. The
-class-blind `instruction` supplies that display scale and the physical
-footprint. `target_image` preserves the physical aspect ratio.
-
-The full schema and release inventory are in
-[`metadata/catalog.json`](metadata/catalog.json).
-The additional `depth-v1` lineage and controlled-variation fields are
-documented in [`depth-v1/README.md`](depth-v1/README.md).
-
-## Construction and quality
-
-Core examples enumerate primitives, operations, compositions, structures, and
-photonic components. The depth builder identifies live geometry parameters and
-emits a fixed schedule of controlled, visible settings.
-
-Every row passes compilation and execution, primitive-only source checks,
-declared port and connectivity checks, structural witnesses, calibrated
-geometry comparison, footprint agreement, and artifact hashing. Release
-manifests and verification evidence are stored under [`factory/`](factory/)
-and [`depth-v1/factory/`](depth-v1/factory/).
-
-The dataset targets executable geometric reconstruction at a declared raster
-and physical footprint. Electromagnetic performance and foundry qualification
-are evaluated in downstream device workflows.
-
-All images, instructions, and programs are synthetic.
-
-## Reproduce
-
-The source capsule under `factory/core-v1/` and the depth release scripts
-rebuild the published rows from a clean PixCell checkout.
-
-<details>
-<summary>Rebuild and verify both releases</summary>
-
-```bash
-cd dataset
-python3.13 -m venv .venv
-source .venv/bin/activate
-python -m pip install -r requirements-lock.txt
-
-python scripts/build_all.py
-python scripts/validate_release.py --root . --execute-programs
-python depth-v1/scripts/build.py --dataset-root . --output depth-v1
-python depth-v1/scripts/validate.py \
-  --release-root depth-v1 \
-  --dataset-root . \
-  --execute-programs
-python -m unittest discover -s tests -v
-python -m pytest depth-v1/tests -q
-```
+Published rows pass deterministic compilation and execution, the
+primitive-only source policy, structural checks, calibrated geometry
+comparison, footprint agreement, and artifact hashing.
 
-The rebuilds reproduce these frozen logical release digests:
+The dataset targets geometric reconstruction of single-layer photonic layouts.
+Optical performance and foundry qualification are handled by downstream device
+evaluation.
+
+The release manifest binds every shard to the frozen source digests:
 
 ```text
-core-v1   e1c509dca337ce485efcb2b35101a052e06b12331b63a2f21a36c93b3682ea89
-depth-v1  676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b
+core   e1c509dca337ce485efcb2b35101a052e06b12331b63a2f21a36c93b3682ea89
+depth  676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b
 ```
 
-</details>
-
-See [`factory/README.md`](factory/README.md) for the replay contract. Dataset
-rows contain executable Python. Inspect code from untrusted revisions before
-execution.
-
-## Training export
-
-Convert a level to OpenAI-style multimodal `messages`:
-
-```bash
-python scripts/to_vlm_messages.py \
-  --root . \
-  --level l4 \
-  --output /tmp/pixcell-l4.jsonl \
-  --image-mode data-uri
-```
+Source, release tools, and training recipes are available in the
+[PixCell v2.0.0 source release](https://github.com/QPG-MIT/PixCell/tree/dataset-v2.0.0).
 
-## Recommended use
+## Versions
 
-Use `depth-v1` for image-to-code SFT and within-representation parameter
-recovery. Use `core-v1` or the individual levels for staged training, task
-sampling, and component-focused training. Use a representation-disjoint
-benchmark for unseen-component evaluation.
+Use configuration `depth` with revision `v2.0.0` to pin this release. The
+original `core-v1` and `depth-v1` releases remain available at their matching
+Hugging Face revisions.
 
-## License and citation
+Version 2.0.0 is a breaking schema release. It makes `depth` the default,
+removes the duplicated per-row instruction, and moves target rasters and
+calibration into `references`. Pin revision `core-v1` or `depth-v1` for the
+original row schema.
 
-The dataset and release tools use the [MIT License](LICENSE). Dependency
-notices are in [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md). Citation
-metadata is available in [CITATION.cff](CITATION.cff).
+## Citation
 
 ```bibtex
-@misc{agarwal2026pixcelldataset,
-  title        = {PixCell Dataset: Representation-First Image-to-Code Curriculum},
-  author       = {Aadarsh Agarwal and Kenaish Al Qubaisi and Dirk Englund},
-  year         = {2026},
-  howpublished = {Hugging Face dataset},
-  note         = {Version depth-v1},
-  url          = {https://huggingface.co/datasets/qpaig-mit/pixcell}
+@dataset{agarwal2026pixcell,
+  title   = {PixCell Dataset: Representation-First Image-to-Code Curriculum},
+  author  = {Aadarsh Agarwal and Kenaish Al Qubaisi and Dirk Englund},
+  year    = {2026},
+  version = {2.0.0},
+  url     = {https://huggingface.co/datasets/qpaig-mit/pixcell}
 }
 ```
 
-The citation record will include the paper's arXiv identifier when available.
-Project source: [QPG-MIT/PixCell](https://github.com/QPG-MIT/PixCell).
+The citation record will be updated with the paper's arXiv identifier when it
+is available.

data/l0/train-00000-of-00001.parquet → core/data/train-00000-of-00002.parquet

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+size 18799084

data/l1/train-00000-of-00001.parquet → core/data/train-00001-of-00002.parquet

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+oid sha256:924091f420a1235fdc0ff953a86e4cbc638430e9aef7e30a63c6b8479d4d2923
+size 10528449

depth-v1/README.md

@@ -1,47 +0,0 @@
-# PixCell depth-v1
-
-`depth-v1` is a separate, core-compatible release of controlled geometry
-realizations around the 547 approved PixCell representations.
-
-The immutable 738-row PixCell core is retained in full. Its 547 semantic rows
-are the `r00` anchors; the other 191 core rows remain as `core-extra` records.
-Exactly 546 anchors have seven additional, model-visible geometry states. One
-L2 anchor remains anchor-only because its sole mutable literal is a global
-translation erased by maximum-visibility normalization; synthesizing its
-descendants would create repeated inputs with different code labels.
-
-| Release split | Contents | Rows |
-|---|---|---:|
-| `train` | 738 core rows plus `r01`–`r05` descendants | 3,468 |
-| `validation` | `r06`–`r07` descendants | 1,092 |
-| **Total** | 546 complete eight-state groups, one anchor-only representation, and core extras | **4,560** |
-
-This release does not alter `core-v1`. Every row binds its parent core example,
-parent code hash, and frozen core digest.
-
-## Row format
-
-Rows preserve all 30 core columns, including the model image, physical-aspect
-target, parametric Python program, footprint, topology, hashes, and curriculum
-metadata. The appended depth fields record:
-
-- parent and release provenance;
-- geometry and realization identity;
-- controlled transform axes and parameter deltas;
-- dependency, pose, render, and split grouping;
-- verification evidence (hashes and metrics) and generator version; and
-- a deterministic logical-row digest.
-
-Images remain embedded in Parquet, so the release loads without a custom
-dataset script. The Hugging Face feature declaration is provided by
-[`scripts/schema.py`](scripts/schema.py).
-
-## Integrity
-
-`scripts/schema.py` defines the stable column order, Hugging Face features,
-logical row and release digests, deterministic Parquet shard names, the exact
-frozen population contract, and manifest, freeze, and checksum helpers.
-
-The logical digest hashes every non-payload field, including the stored hashes
-of code and images, in ascending row-ID order. Freeze and checksum records bind
-the generated release artifacts without changing any file in the parent core.

depth-v1/factory/core_reference_snapshot.json

@@ -1,23 +0,0 @@
-{
-  "core_digest_sha256": "e1c509dca337ce485efcb2b35101a052e06b12331b63a2f21a36c93b3682ea89",
-  "reference_artifacts": {
-    "data/l0/train-00000-of-00001.parquet": "e0c5bda1b192a901349aeda2cf9287e22d62b5b791c421955e2ffba9ca2785da",
-    "data/l1/train-00000-of-00001.parquet": "fc37aea17af1a127e46f89604c95e490b8c361d5105a4cd77581f1d93dfcfc44",
-    "data/l2/train-00000-of-00001.parquet": "420d2b75f1cd38e2e3629495b9da36a07bc24ec9b81139816528e9332c7d4007",
-    "data/l3/train-00000-of-00001.parquet": "0dea653b7f8a7d5c5e773606ead614f460fae3f40e8588e67d48d314b053c4b2",
-    "data/l4/train-00000-of-00001.parquet": "05c8162b50feb869224e998a7d43fe81eee40d12371019fb27ae6aef3557a16d",
-    "galleries/all.png": "5fe87e14eb72e0a76cb39bcac1d7cc739861948dc299c400f1a746763c2793d9",
-    "galleries/l0.png": "838dc208f2bddb3c9cfa140ef09cb8d350f866c9b380d285b3c4d6afc3bd9c0a",
-    "galleries/l1.png": "e6cb27d4d4230ceb2c0b8eb5cde759af53759b2c3c05abbd9fd72b05302400d8",
-    "galleries/l2.png": "e730702cd6c321f50d48305ff7952434990ed48dc87c1a41699bf893e364489b",
-    "galleries/l3.png": "5fb0387dfc4be86ac455528e75f9dc33f8979ea5a860812159445f851f2eefd9",
-    "galleries/l4.png": "eca24f18ece945ee76f316e9c97fd48d5c9a52a52b8fdc04eba0857b49376463",
-    "metadata/audit_report.json": "2cb6122042f1b231adc4c6de642f64663f42759a2bf3dcee305c9d255064b0ae",
-    "metadata/catalog.json": "c22125e4201e3ccac233b0d6898a86cda232a5371ad8a68e19b44112aa680511",
-    "metadata/source_reports/l0.json": "654d69c5a9954f4a251450f85ce6143635bbfb26e6f1f01f8e22777223c98d23",
-    "metadata/source_reports/l1.json": "6e95ccff6ea3dfeec8df3c31cbbafbb487986d94736d8ec74d17189edc3da290",
-    "metadata/source_reports/l2.json": "95d17fe874a904df8ef2a2aca5a563d3cc5e72237e611278083181bf1ec9465a",
-    "metadata/source_reports/l3.json": "b5dbd40ef517ec23353d98a6326f8ec649b0ec88ec2f0d2b2fdc622b4d8a6f1a",
-    "metadata/source_reports/l4.json": "a7cfc8be204209ef7997447f20db339a52e327ad6ecdabbcb921c54111de9eeb"
-  }
-}

depth-v1/factory/freeze.json

@@ -1,88 +0,0 @@
-{
-  "anchor_only_parent_ids": [
-    "l2_02_rotated_edge_aligned_cluster__s03__proposal_b"
-  ],
-  "anchor_only_representation_count": 1,
-  "approved_representation_count": 547,
-  "complete_depth_representation_count": 546,
-  "depth_schema_version": "pixcell-depth-row-v1",
-  "geometry_state_count": 8,
-  "logical_release_sha256": "676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b",
-  "manifest": {
-    "path": "metadata/manifest.json",
-    "sha256": "e40017affd7bc3fc1acf39ff05167681c8436fc38a1cbe8ba953149079373133"
-  },
-  "parent_core_digest_sha256": "e1c509dca337ce485efcb2b35101a052e06b12331b63a2f21a36c93b3682ea89",
-  "reference_artifacts": {
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-    "data/train-00001-of-00007.parquet": "c51c77d5f076bd6960b1452eeccf351fecff58e98189c7ab593ed4d648c617d0",
-    "data/train-00002-of-00007.parquet": "24c4ea04eb13ab8ccab55f4301cf8b8292eb2a23d9dbe5bf5a7b1743c7aba1ae",
-    "data/train-00003-of-00007.parquet": "73929f04bdaed1e9ccb72b9c4a53910da71429e32cfb1a6c90c8154c9849ee8b",
-    "data/train-00004-of-00007.parquet": "7c59c6af12beebd505c2b4dabdd398498e9a8c218805e89f5af5a9da451327de",
-    "data/train-00005-of-00007.parquet": "e0786acb9dd5ea1c05fccab30b7292dd89d59e402bfb52321af8f57f6ba1631f",
-    "data/train-00006-of-00007.parquet": "425467eeeda20953ec62514b447de3034b215c6b7b1d15fa7ff46c425b3b7432",
-    "data/validation-00000-of-00003.parquet": "8e823f8ce2dbacfd402e1e4db646fef316f165d362b8928ccdb0d1079a792d22",
-    "data/validation-00001-of-00003.parquet": "8b974b438ecda3d3f53d5dc4b84969d0ef68fa40aecde2195a081726c39a202d",
-    "data/validation-00002-of-00003.parquet": "77109cf40718db1e33c25d4998c22aca1d0f13c4ffe01b271e0b36fd22f14a30",
-    "factory/accepted_manifest.json": "c89f6131d8ee41b26e4cbacef6fe935fe7f98c01726f846689ff67ed131b0a72",
-    "factory/anchor_manifest.json": "89f50e714b0f07bf8c053575f964f3294076298c312e4f31233540dbd6fb5887",
-    "factory/core_reference_snapshot.json": "363818781a7e4708de2b8fc99e0f4119dd5c91fc6b57cb2752542d503b5df088",
-    "factory/generation_plan.json": "f81cab2e287e2d7cb7c3ff809c59a4f57719fa24474aefde9202ee7b8c11eec5",
-    "factory/variation_contracts.json": "02b5ae3838fcd4f66d0e7bec00ba7733145868287256d179a7655f5bb9a14c8d",
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-    "metadata/audit_report.json": "b8b26d46f48d3b5e882c3f7b0e906f31eaa5449d9bf7db4f6b168fd7d484fa11",
-    "metadata/catalog.json": "595b700aaea0e8f9883d0f5aad588d5c1dc98f1aa12db8d19ac37e3095b90032",
-    "metadata/gallery_manifest.json": "5f62dae4cd32f4cfa268c785368d6833e56b6f213e118301afad9407bb5fa640",
-    "metadata/manifest.json": "e40017affd7bc3fc1acf39ff05167681c8436fc38a1cbe8ba953149079373133"
-  },
-  "release_name": "depth-v1",
-  "row_count": 4560,
-  "schema_version": "pixcell-depth-freeze-v1"
-}