README.md

metadata

pretty_name: PixCell Dataset
license: mit
language:
  - en
task_categories:
  - image-text-to-text
tags:
  - image-to-code
  - code-generation
  - vision-language
  - photonics
  - gds
  - gdsfactory
  - synthetic
  - curriculum-learning
size_categories:
  - 1K<n<10K
configs:
  - config_name: depth
    default: true
    data_files:
      - split: train
        path: depth/data/train-*.parquet
      - split: validation
        path: depth/data/validation-*.parquet
  - config_name: core
    data_files:
      - split: train
        path: core/data/train-*.parquet
  - config_name: references
    data_files:
      - split: train
        path: references/data/train-*.parquet
      - split: validation
        path: references/data/validation-*.parquet
dataset_info:
  - config_name: depth
    splits:
      - name: train
        num_examples: 3468
      - name: validation
        num_examples: 1092
  - config_name: core
    splits:
      - name: train
        num_examples: 738
  - config_name: references
    splits:
      - name: train
        num_examples: 3468
      - name: validation
        num_examples: 1092

PixCell Dataset

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.

Core curriculum gallery | Depth representation overview

Configurations

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 is an exact subset of depth/train. references follows the same train and validation split as depth and joins through opaque_id.

Load

from datasets import load_dataset

depth = load_dataset(
    "qpaig-mit/pixcell",
    "depth",
    revision="v2.0.0",
)

core = load_dataset(
    "qpaig-mit/pixcell",
    "core",
    revision="v2.0.0",
    split="train",
)

references = load_dataset(
    "qpaig-mit/pixcell",
    "references",
    revision="v2.0.0",
)

Training contract

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 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.

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.

opaque_id is a stable join key derived from the v1 source ID. It is not an anonymization mechanism.

Split design

Split	Construction	Rows
train	738 core rows plus five additional settings for 546 representations	3,468
validation	Two held-out settings for each expanded representation	1,092

Validation measures parameter recovery within known representations. Every representation is present in training.

Curriculum

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

Verification

Published rows pass deterministic compilation and execution, the primitive-only source policy, structural checks, calibrated geometry comparison, footprint agreement, and artifact hashing.

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:

core   e1c509dca337ce485efcb2b35101a052e06b12331b63a2f21a36c93b3682ea89
depth  676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b

Source, release tools, and training recipes are available in the PixCell v2.0.0 source release.

Versions

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.

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.

Citation

@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 be updated with the paper's arXiv identifier when it is available.