Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
images: int64
sigmas: list<item: double>
child 0, item: double
peaks: list<item: double>
child 0, item: double
results: struct<flux2_klein: struct<texture_psnr: double, texture_ssim: double, targets: struct<sigma0.5_peak (... 2206 chars omitted)
child 0, flux2_klein: struct<texture_psnr: double, texture_ssim: double, targets: struct<sigma0.5_peak16: struct<mean_rete (... 651 chars omitted)
child 0, texture_psnr: double
child 1, texture_ssim: double
child 2, targets: struct<sigma0.5_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5 (... 590 chars omitted)
child 0, sigma0.5_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 1, sigma0.5_peak48: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 2, sigma1.0_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_bel
...
sigma1.0_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 3, sigma1.0_peak48: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 4, sigma2.0_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 5, sigma2.0_peak48: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
kid_std: double
l1_std: double
lpips: double
psnr_std: double
ms_ssim: double
ssim_std: double
dists_std: double
l1: double
psnr: double
ssim: double
protocol: string
dists: double
pred_dir: string
lpips_std: double
fid: double
n: int64
pairs: string
kid: double
ms_ssim_std: double
to
{'protocol': Value('string'), 'pairs': Value('string'), 'pred_dir': Value('string'), 'n': Value('int64'), 'psnr': Value('float64'), 'ssim': Value('float64'), 'ms_ssim': Value('float64'), 'l1': Value('float64'), 'lpips': Value('float64'), 'dists': Value('float64'), 'psnr_std': Value('float64'), 'ssim_std': Value('float64'), 'ms_ssim_std': Value('float64'), 'l1_std': Value('float64'), 'lpips_std': Value('float64'), 'dists_std': Value('float64'), 'fid': Value('float64'), 'kid': Value('float64'), 'kid_std': Value('float64')}
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 483, 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
images: int64
sigmas: list<item: double>
child 0, item: double
peaks: list<item: double>
child 0, item: double
results: struct<flux2_klein: struct<texture_psnr: double, texture_ssim: double, targets: struct<sigma0.5_peak (... 2206 chars omitted)
child 0, flux2_klein: struct<texture_psnr: double, texture_ssim: double, targets: struct<sigma0.5_peak16: struct<mean_rete (... 651 chars omitted)
child 0, texture_psnr: double
child 1, texture_ssim: double
child 2, targets: struct<sigma0.5_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5 (... 590 chars omitted)
child 0, sigma0.5_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 1, sigma0.5_peak48: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 2, sigma1.0_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_bel
...
sigma1.0_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 3, sigma1.0_peak48: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 4, sigma2.0_peak16: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
child 5, sigma2.0_peak48: struct<mean_retention: double, median_retention: double, lost_frac_below_0.5: double, n: int64>
child 0, mean_retention: double
child 1, median_retention: double
child 2, lost_frac_below_0.5: double
child 3, n: int64
kid_std: double
l1_std: double
lpips: double
psnr_std: double
ms_ssim: double
ssim_std: double
dists_std: double
l1: double
psnr: double
ssim: double
protocol: string
dists: double
pred_dir: string
lpips_std: double
fid: double
n: int64
pairs: string
kid: double
ms_ssim_std: double
to
{'protocol': Value('string'), 'pairs': Value('string'), 'pred_dir': Value('string'), 'n': Value('int64'), 'psnr': Value('float64'), 'ssim': Value('float64'), 'ms_ssim': Value('float64'), 'l1': Value('float64'), 'lpips': Value('float64'), 'dists': Value('float64'), 'psnr_std': Value('float64'), 'ssim_std': Value('float64'), 'ms_ssim_std': Value('float64'), 'l1_std': Value('float64'), 'lpips_std': Value('float64'), 'dists_std': Value('float64'), 'fid': Value('float64'), 'kid': Value('float64'), 'kid_std': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
IRForge artifacts
Data, checkpoints, predictions, and detection artifacts of IRForge, which adapts image-conditioned foundation flow
models (FLUX.2-klein-base-4B, Qwen-Image-2.1) to RGB-to-infrared translation and uses them as data engines for infrared
small target detection. The code and the reproduction guide (docs/reproduce.md) are in the IRForge repository.
The layout mirrors the code's IRFORGE_WORK directory. MANIFEST.json lists every file with its size; each *.tar
unpacks into the directory given by its unpack_to entry. experiments/tools/fetch_release.py PART ... downloads
parts and unpacks them (raw, paper, checkpoints, predictions, detection, backgrounds).
| Path | Content |
|---|---|
data/raw/20260922pbw/*.tar |
paired RGB-infrared images with train.txt / test.txt: FLIR-aligned (LWIR) and VEDAI (NIR), full sets and the 1,000-pair Selected subsets |
runs/RUN/saves/RUN/checkpoints/checkpoint-N/ |
PEFT LoRA adapters (Flow-Factory checkpoints): Stage 1 translators, reward-tuned continuations (checkpoint-0 is the starting point), DMD2 / TDM students |
eval/RUN/SETTING/SPLIT/ |
metrics.json, sampling.json (and merge.json for interpolated translators), predictions.tar with the translated test images |
irstd/datasets.tar |
synthesized SIRST detection datasets in the BasicIRSTD layout, including the SIRST320 split |
irstd/runs/DATASET/MODEL_seedS/ |
detector scores (test_scores.json, val_scores.json) of every detection run |
irstd/backgrounds/*.tar |
translated Places365 background pools, 4,200 scenes each |
irstd/*.json, irstd/*.npz, runs/*/train.log |
background analysis, DINOv2 domain probes, and RL training logs read by the paper tables |
release/inputs_make_*.txt |
every file the paper's table and figure scripts read |
Licenses and attribution
- Adapters of FLUX.2-klein-base-4B: the base model is Apache-2.0.
- Adapters and students of Qwen-Image-2.1 derive from a model under the Qwen Research License (non-commercial research and evaluation only); the same terms apply to them.
- FLIR-aligned derives from the Teledyne FLIR ADAS dataset (aligned by Zhang et al., ICIP 2020), VEDAI from Razakarivony and Jurie (2016), SIRST from NUAA-SIRST (Dai et al., 2021), and the background scenes from the Places365 validation set (Zhou et al., 2018). These images, and the translations and detection data derived from them, remain subject to the original datasets' terms; use them for non-commercial research.
- Downloads last month
- 92