Datasets:

Dhi-Technologies
/

thermal-perception-benchmark

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
passed: bool
agc.minmax: bool
agc.percentile: bool
agc.plateau: bool
agc.tile_clahe: bool
ssl.zero_loss_on_perfect_reconstruction: bool
probe.separable_accuracy: double
probe.ok: bool
activity_score: double
boxes_xyxy: list<item: list<item: int64>>
  child 0, item: list<item: int64>
      child 0, item: int64
n_vehicles: int64
activity_bucket: string
netd_k: double
dtype: string
n_people: int64
seed: int64
ambient_k: double
frame_index: int64
perceptual_hash: int64
shape_hw: list<item: int64>
  child 0, item: int64
to
{'frame_index': Value('int64'), 'seed': Value('int64'), 'n_people': Value('int64'), 'n_vehicles': Value('int64'), 'boxes_xyxy': List(List(Value('int64'))), 'perceptual_hash': Value('int64'), 'activity_score': Value('float64'), 'activity_bucket': Value('string'), 'ambient_k': Value('float64'), 'netd_k': Value('float64'), 'shape_hw': List(Value('int64')), 'dtype': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              passed: bool
              agc.minmax: bool
              agc.percentile: bool
              agc.plateau: bool
              agc.tile_clahe: bool
              ssl.zero_loss_on_perfect_reconstruction: bool
              probe.separable_accuracy: double
              probe.ok: bool
              activity_score: double
              boxes_xyxy: list<item: list<item: int64>>
                child 0, item: list<item: int64>
                    child 0, item: int64
              n_vehicles: int64
              activity_bucket: string
              netd_k: double
              dtype: string
              n_people: int64
              seed: int64
              ambient_k: double
              frame_index: int64
              perceptual_hash: int64
              shape_hw: list<item: int64>
                child 0, item: int64
              to
              {'frame_index': Value('int64'), 'seed': Value('int64'), 'n_people': Value('int64'), 'n_vehicles': Value('int64'), 'boxes_xyxy': List(List(Value('int64'))), 'perceptual_hash': Value('int64'), 'activity_score': Value('float64'), 'activity_bucket': Value('string'), 'ambient_k': Value('float64'), 'netd_k': Value('float64'), 'shape_hw': List(Value('int64')), 'dtype': Value('string')}
              because column names don't match

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Thermal Perception Sample Corpus (A5)

Product: thermal-perception ("thermalcore"): perception with no visible light. A radiometric data engine, self-supervised pretraining harness, and eval protocols for thermal-native models, validated with CPU-only math (no trained checkpoints, no GPU required for what's in this repo today).

Synthetic data, real-data validation in progress

These are physics-flavored synthetic 16-bit radiometric frames (a microbolometer-style model: cool background with spatial gradients, warm bodies at realistic body/vehicle temperatures, per-column fixed-pattern noise, NETD temporal noise, hot-pixel defects), built so the engine's normalization, dedup, and self-supervised math can be verified against exact physics, independent of any real sensor. No real thermal camera footage is included, and real-sensor validation has not been performed yet; that's explicitly what the repo's emit-recipe GPU-phase step is for.

What's in this dataset

sample_corpus_raw_uint16.npz: 30 synthetic radiometric frames, (30, 240, 320) uint16 array (key: frames), each pixel a raw sensor count over a 253.15 K to 393.15 K span (linear; see the repo's synth.temperature_to_raw for the exact mapping).
manifest.jsonl: one row per frame: seed, n_people/n_vehicles (ground truth), boxes_xyxy (warm-body ground-truth boxes), perceptual_hash plus activity_score / activity_bucket (empty/sparse/busy, computed by the repo's real corpus-builder functions, thermalcore.corpus.perceptual_hash / activity_score), ambient_k, netd_k.
selfcheck_results.json: output of thermalcore.cli selfcheck --seed 0.

Of these 30 sample frames: 2 empty, 6 sparse, 22 busy (by the corpus builder's own activity bucketing); this sample was not curated to look good, it's an unfiltered draw.

Measured result (from this repo, `thermalcore.cli selfcheck --seed 0`)

{
  "passed": true,
  "agc.minmax": true, "agc.percentile": true, "agc.plateau": true, "agc.tile_clahe": true,
  "ssl.zero_loss_on_perfect_reconstruction": true,
  "probe.separable_accuracy": 0.94,
  "probe.ok": true
}

This validates the math (AGC normalization variants, SSL loss correctness, a linear-probe separability sanity check on synthetic classes); it is not a real-world thermal perception accuracy number. The repo's own test suite (36 tests) is CPU-only and green; no GPU pretraining has been run.

Reproduce with: PYTHONPATH=src .venv/bin/python -m thermalcore.cli selfcheck --seed 0

Method card, no trained weights

There is deliberately no trained checkpoint here. This product is the data engine, pretraining harness, and eval protocols, with the math validated CPU-only. The GPU pretraining run is emitted as a recipe (thermalcore emit-recipe), not a model, so nothing on this org implies a thermal foundation model exists when it does not. When compute is connected and a checkpoint is actually trained, it will be published separately and labeled as such.