Datasets:

arttry
/

foxingdatasit

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
name: string
kind: string
repo_path: string
index_path: string
size: int64
sha256: string
n_samples: int64
videos: struct<ia_AboutBan1935: int64>
  child 0, ia_AboutBan1935: int64
created_at: double
upload_ok: bool
uploaded_at: double
bench_fp: string
qc_calibration: struct<lap_floor: double, ten_floor: double, n: int64, source: string>
  child 0, lap_floor: double
  child 1, ten_floor: double
  child 2, n: int64
  child 3, source: string
updated_at: double
env_fp: string
qc: struct<n_frames: int64, threads: int64, analyze_fps: double, single_thread_ms: double, parallel_spee (... 282 chars omitted)
  child 0, n_frames: int64
  child 1, threads: int64
  child 2, analyze_fps: double
  child 3, single_thread_ms: double
  child 4, parallel_speedup: double
  child 5, decide_ms: double
  child 6, accept_fraction: double
  child 7, reasons: struct<near_duplicate: int64, blur: int64>
      child 0, near_duplicate: int64
      child 1, blur: int64
  child 8, calibration: struct<lap_floor: double, ten_floor: double, n: int64, lap_p1: double, lap_p50: double, lap_p99: dou (... 4 chars omitted)
      child 0, lap_floor: double
      child 1, ten_floor: double
      child 2, n: int64
      child 3, lap_p1: double
      child 4, lap_p50: double
      child 5, lap_p99: double
  child 9, ok: bool
  child 10, seconds: double
  child 11, calibration_applied: bool
version: int64
shard: struct<samples: int64, bytes: int64, write_MBps: double, validate_MBps: double, index_ms: double, av (... 48 c
...
ruct<ms_per_image: double, psnr_vs_pillow: double, avg_bytes: double>, cv2: struct< (... 81 chars omitted)
      child 0, pillow: struct<ms_per_image: double, psnr_vs_pillow: double, avg_bytes: double>
          child 0, ms_per_image: double
          child 1, psnr_vs_pillow: double
          child 2, avg_bytes: double
      child 1, cv2: struct<ms_per_image: double, psnr_vs_pillow: double, avg_bytes: double>
          child 0, ms_per_image: double
          child 1, psnr_vs_pillow: double
          child 2, avg_bytes: double
      child 2, chosen: string
  child 1, chosen: string
  child 2, enc_ms_single: double
  child 3, enc_fps_parallel: double
  child 4, enc_threads: int64
  child 5, model_input_ms: double
  child 6, avg_jpeg_bytes: double
  child 7, n: int64
  child 8, format: string
  child 9, quality: int64
  child 10, ok: bool
  child 11, seconds: double
params: struct<n_videos: int64, decode_frames: int64, qc_frames: int64, caption_images: int64, max_batch: in (... 17 chars omitted)
  child 0, n_videos: int64
  child 1, decode_frames: int64
  child 2, qc_frames: int64
  child 3, caption_images: int64
  child 4, max_batch: int64
  child 5, apply: bool
duration_s: double
env: struct<gpus: list<item: string>, cpu_cores: int64, ram_total_gb: double, python: string, torch: stri (... 3 chars omitted)
  child 0, gpus: list<item: string>
      child 0, item: string
  child 1, cpu_cores: int64
  child 2, ram_total_gb: double
  child 3, python: string
  child 4, torch: string
to
{'caption': {'engine': Value('string'), 'precision': Value('string'), 'layout': Value('string'), 'replicas': Value('int64'), 'mock': Value('bool'), 'attn': Value('string'), 'model': Value('string'), 'runs': List({'batch': Value('int64'), 'seconds': Value('float64'), 'img_per_s': Value('float64'), 'tokens': Value('int64'), 'tokens_per_s': Value('float64'), 'peak_gb': Value('float64'), 'vram_frac': Value('float64'), 'oom': Value('bool')}), 'n_images': Value('int64'), 'avg_item_tokens': Value('float64'), 'ok': Value('bool'), 'best_batch': Value('int64'), 'best_img_per_s': Value('float64'), 'knee_batch': Value('int64'), 'stable_batch': Value('int64'), 'max_tested_batch': Value('int64'), 'token_budget': Value('int64'), 'peak_vram_gb': Value('float64'), 'tokens_per_s': Value('float64'), 'vram_cap': Value('float64'), 'pipeline_img_per_s': Value('float64'), 'caption_ok_rate': Value('float64'), 'errors': Value('int64'), 'mean_words': Value('float64'), 'flags': {'no_lighting': Value('int64'), 'no_style': Value('int64'), 'generic_opening': Value('int64')}, 'oom_events': Value('int64'), 'retries': Value('int64'), 'flash_attn': {'tested': Value('bool'), 'reason': Value('string')}, 'vllm': {'tested': Value('bool'), 'reason': Value('string')}, 'engine_final': Value('string'), 'attn_final': Value('string'), 'img_per_s_total': Value('float64'), 'seconds': Value('float64')}, 'qc_calibration': {'lap_floor': Value('float64'), 'ten_floor': Value('float64'), 'n': Value('int64'), 'source': Value('s
...
units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'encode': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'caption': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'validate': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'upload': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'download': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}}, 'videos': Value('int64'), 'candidate_frames': Value('float64'), 'selected_frames': Value('float64'), 'accept_fraction': Value('float64'), 'accepted_images': Value('float64'), 'dataset_gb': Value('float64'), 'bottleneck': Value('string'), 'total_hours': Value('float64'), 'session_hours_usable': Value('float64'), 'sessions_needed': Value('int64'), 'weekly_gpu_hours_assumed': Value('float64'), 'weeks_of_gpu_quota': Value('float64'), 'effective_images_per_s': Value('float64'), 'shard_count': Value('int64'), 'budgets': List({'gpu_hours': Value('float64'), 'images': Value('float64'), 'images_per_video': Value('float64'), 'max_images_per_video': Value('int64'), 'equivalent_sampling_ratio': Value('float64'), 'dataset_gb': Value('float64')}), 'summary': Value('string')}, 'updated_at': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              name: string
              kind: string
              repo_path: string
              index_path: string
              size: int64
              sha256: string
              n_samples: int64
              videos: struct<ia_AboutBan1935: int64>
                child 0, ia_AboutBan1935: int64
              created_at: double
              upload_ok: bool
              uploaded_at: double
              bench_fp: string
              qc_calibration: struct<lap_floor: double, ten_floor: double, n: int64, source: string>
                child 0, lap_floor: double
                child 1, ten_floor: double
                child 2, n: int64
                child 3, source: string
              updated_at: double
              env_fp: string
              qc: struct<n_frames: int64, threads: int64, analyze_fps: double, single_thread_ms: double, parallel_spee (... 282 chars omitted)
                child 0, n_frames: int64
                child 1, threads: int64
                child 2, analyze_fps: double
                child 3, single_thread_ms: double
                child 4, parallel_speedup: double
                child 5, decide_ms: double
                child 6, accept_fraction: double
                child 7, reasons: struct<near_duplicate: int64, blur: int64>
                    child 0, near_duplicate: int64
                    child 1, blur: int64
                child 8, calibration: struct<lap_floor: double, ten_floor: double, n: int64, lap_p1: double, lap_p50: double, lap_p99: dou (... 4 chars omitted)
                    child 0, lap_floor: double
                    child 1, ten_floor: double
                    child 2, n: int64
                    child 3, lap_p1: double
                    child 4, lap_p50: double
                    child 5, lap_p99: double
                child 9, ok: bool
                child 10, seconds: double
                child 11, calibration_applied: bool
              version: int64
              shard: struct<samples: int64, bytes: int64, write_MBps: double, validate_MBps: double, index_ms: double, av (... 48 c
              ...
              ruct<ms_per_image: double, psnr_vs_pillow: double, avg_bytes: double>, cv2: struct< (... 81 chars omitted)
                    child 0, pillow: struct<ms_per_image: double, psnr_vs_pillow: double, avg_bytes: double>
                        child 0, ms_per_image: double
                        child 1, psnr_vs_pillow: double
                        child 2, avg_bytes: double
                    child 1, cv2: struct<ms_per_image: double, psnr_vs_pillow: double, avg_bytes: double>
                        child 0, ms_per_image: double
                        child 1, psnr_vs_pillow: double
                        child 2, avg_bytes: double
                    child 2, chosen: string
                child 1, chosen: string
                child 2, enc_ms_single: double
                child 3, enc_fps_parallel: double
                child 4, enc_threads: int64
                child 5, model_input_ms: double
                child 6, avg_jpeg_bytes: double
                child 7, n: int64
                child 8, format: string
                child 9, quality: int64
                child 10, ok: bool
                child 11, seconds: double
              params: struct<n_videos: int64, decode_frames: int64, qc_frames: int64, caption_images: int64, max_batch: in (... 17 chars omitted)
                child 0, n_videos: int64
                child 1, decode_frames: int64
                child 2, qc_frames: int64
                child 3, caption_images: int64
                child 4, max_batch: int64
                child 5, apply: bool
              duration_s: double
              env: struct<gpus: list<item: string>, cpu_cores: int64, ram_total_gb: double, python: string, torch: stri (... 3 chars omitted)
                child 0, gpus: list<item: string>
                    child 0, item: string
                child 1, cpu_cores: int64
                child 2, ram_total_gb: double
                child 3, python: string
                child 4, torch: string
              to
              {'caption': {'engine': Value('string'), 'precision': Value('string'), 'layout': Value('string'), 'replicas': Value('int64'), 'mock': Value('bool'), 'attn': Value('string'), 'model': Value('string'), 'runs': List({'batch': Value('int64'), 'seconds': Value('float64'), 'img_per_s': Value('float64'), 'tokens': Value('int64'), 'tokens_per_s': Value('float64'), 'peak_gb': Value('float64'), 'vram_frac': Value('float64'), 'oom': Value('bool')}), 'n_images': Value('int64'), 'avg_item_tokens': Value('float64'), 'ok': Value('bool'), 'best_batch': Value('int64'), 'best_img_per_s': Value('float64'), 'knee_batch': Value('int64'), 'stable_batch': Value('int64'), 'max_tested_batch': Value('int64'), 'token_budget': Value('int64'), 'peak_vram_gb': Value('float64'), 'tokens_per_s': Value('float64'), 'vram_cap': Value('float64'), 'pipeline_img_per_s': Value('float64'), 'caption_ok_rate': Value('float64'), 'errors': Value('int64'), 'mean_words': Value('float64'), 'flags': {'no_lighting': Value('int64'), 'no_style': Value('int64'), 'generic_opening': Value('int64')}, 'oom_events': Value('int64'), 'retries': Value('int64'), 'flash_attn': {'tested': Value('bool'), 'reason': Value('string')}, 'vllm': {'tested': Value('bool'), 'reason': Value('string')}, 'engine_final': Value('string'), 'attn_final': Value('string'), 'img_per_s_total': Value('float64'), 'seconds': Value('float64')}, 'qc_calibration': {'lap_floor': Value('float64'), 'ten_floor': Value('float64'), 'n': Value('int64'), 'source': Value('s
              ...
              units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'encode': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'caption': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'validate': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'upload': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}, 'download': {'units': Value('float64'), 'rate': Value('float64'), 'unit': Value('string'), 'hours': Value('float64')}}, 'videos': Value('int64'), 'candidate_frames': Value('float64'), 'selected_frames': Value('float64'), 'accept_fraction': Value('float64'), 'accepted_images': Value('float64'), 'dataset_gb': Value('float64'), 'bottleneck': Value('string'), 'total_hours': Value('float64'), 'session_hours_usable': Value('float64'), 'sessions_needed': Value('int64'), 'weekly_gpu_hours_assumed': Value('float64'), 'weeks_of_gpu_quota': Value('float64'), 'effective_images_per_s': Value('float64'), 'shard_count': Value('int64'), 'budgets': List({'gpu_hours': Value('float64'), 'images': Value('float64'), 'images_per_video': Value('float64'), 'max_images_per_video': Value('int64'), 'equivalent_sampling_ratio': Value('float64'), 'dataset_gb': Value('float64')}), 'summary': Value('string')}, 'updated_at': Value('float64')}
              because column names don't match
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Video-frame image/caption dataset

Frames sampled from user-provided videos (deterministic hash sampling), quality filtered and de-duplicated, captioned with Qwen/Qwen3-VL-8B-Instruct. Stored as WebDataset shards (data/train-*.tar: <id>.jpg + <id>.json), with per-shard Parquet indexes in index/ and resume state in control/. Load with webdataset (wds.WebDataset("hf://datasets/<repo>/data/train-{000001..00000N}.tar")) or stream the tar files. Respect the licenses of the source videos (see source_name / license fields in each JSON).
Downloads last month: 49
Total file size:
2.73 MB