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Datasets:
Odeinjul
/
bigann-1b-static-search-eval-pq

Tags:
approximate-nearest-neighbor
product-quantization
pq
bigann
static-search-eval
billion-scale-ann
License:
Dataset card Files
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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
completed_at: string
dataset: string
folder: string
sources: list<item: struct<bytes: int64, final: string, header: struct<count: int64, dim: int64, k: int64, qu (... 81 chars omitted)
  child 0, item: struct<bytes: int64, final: string, header: struct<count: int64, dim: int64, k: int64, query_count:  (... 69 chars omitted)
      child 0, bytes: int64
      child 1, final: string
      child 2, header: struct<count: int64, dim: int64, k: int64, query_count: int64>
          child 0, count: int64
          child 1, dim: int64
          child 2, k: int64
          child 3, query_count: int64
      child 3, source: string
      child 4, url: string
      child 5, archive: string
      child 6, member: string
elapsed_seconds: int64
threads: int64
ks: int64
m_values: list<item: int64>
  child 0, item: int64
train_fraction: null
input_vector_path: string
faiss_fallback_used: bool
end_time: string
mode: string
output_dir: string
seed: int64
start_time: string
training_iterations: int64
completed: list<item: struct<m: int64, mode: string>>
  child 0, item: struct<m: int64, mode: string>
      child 0, m: int64
      child 1, mode: string
tool: string
status: string
to
{'completed': List({'m': Value('int64'), 'mode': Value('string')}), 'dataset': Value('string'), 'elapsed_seconds': Value('int64'), 'end_time': Value('string'), 'faiss_fallback_used': Value('bool'), 'input_vector_path': Value('string'), 'ks': Value('int64'), 'm_values': List(Value('int64')), 'mode': Value('string'), 'output_dir': Value('string'), 'seed': Value('int64'), 'start_time': Value('string'), 'status': Value('string'), 'threads': Value('int64'), 'tool': Value('string'), 'train_fraction': Value('null'), 'training_iterations': Value('int64')}
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
              completed_at: string
              dataset: string
              folder: string
              sources: list<item: struct<bytes: int64, final: string, header: struct<count: int64, dim: int64, k: int64, qu (... 81 chars omitted)
                child 0, item: struct<bytes: int64, final: string, header: struct<count: int64, dim: int64, k: int64, query_count:  (... 69 chars omitted)
                    child 0, bytes: int64
                    child 1, final: string
                    child 2, header: struct<count: int64, dim: int64, k: int64, query_count: int64>
                        child 0, count: int64
                        child 1, dim: int64
                        child 2, k: int64
                        child 3, query_count: int64
                    child 3, source: string
                    child 4, url: string
                    child 5, archive: string
                    child 6, member: string
              elapsed_seconds: int64
              threads: int64
              ks: int64
              m_values: list<item: int64>
                child 0, item: int64
              train_fraction: null
              input_vector_path: string
              faiss_fallback_used: bool
              end_time: string
              mode: string
              output_dir: string
              seed: int64
              start_time: string
              training_iterations: int64
              completed: list<item: struct<m: int64, mode: string>>
                child 0, item: struct<m: int64, mode: string>
                    child 0, m: int64
                    child 1, mode: string
              tool: string
              status: string
              to
              {'completed': List({'m': Value('int64'), 'mode': Value('string')}), 'dataset': Value('string'), 'elapsed_seconds': Value('int64'), 'end_time': Value('string'), 'faiss_fallback_used': Value('bool'), 'input_vector_path': Value('string'), 'ks': Value('int64'), 'm_values': List(Value('int64')), 'mode': Value('string'), 'output_dir': Value('string'), 'seed': Value('int64'), 'start_time': Value('string'), 'status': Value('string'), 'threads': Value('int64'), 'tool': Value('string'), 'train_fraction': Value('null'), 'training_iterations': Value('int64')}
              because column names don't match

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bigann-1b-static-search-eval-pq

Product-quantization artifacts for the BIGANN 1B static search-evaluation dataset. File names follow the Kaggle static-search-eval upload convention: pq_m<M>.pqcodes, pq_m<M>.pqmeta, and pq_m<M>.pqcodebook.

Files

  • pq_m8.pqcodes
  • pq_m8.pqmeta
  • pq_m8.pqcodebook
  • pq_m16.pqcodes
  • pq_m16.pqmeta
  • pq_m16.pqcodebook
  • pq_m32.pqcodes
  • pq_m32.pqmeta
  • pq_m32.pqcodebook
  • pq_manifest.json
  • source_manifest.json

Source Data

  • Base vectors: base.1B.u8bin from https://dl.fbaipublicfiles.com/billion-scale-ann-benchmarks/bigann/base.1B.u8bin
  • Public queries: query.public.10K.u8bin from https://dl.fbaipublicfiles.com/billion-scale-ann-benchmarks/bigann/query.public.10K.u8bin
  • 1B ground truth: GT_1B_v2.tgz/GT_1B/bigann-1B from https://dl.fbaipublicfiles.com/billion-scale-ann-benchmarks/GT_1B_v2.tgz

The Big ANN Benchmarks page lists BIGANN as CC0.

Dataset Properties

  • Vector count: 1000000000
  • Dimension: 128
  • Dtype: uint8
  • Metric: l2
  • Query count: 10000

PQ

  • Implementation path: cpp
  • Fallback used: false
  • Thread count: 224
  • ks: 256
  • Training iterations: 20
  • Seed: 42
  • Train fraction: full dataset
  • Elapsed seconds: 20003
  • Build start: 2026-07-17 07:23:29 UTC
  • Build end: 2026-07-17 12:56:52 UTC

PQ Artifact Sizes

  • pq_m8.pqcodes: 8000000029 bytes
  • pq_m16.pqcodes: 16000000029 bytes
  • pq_m32.pqcodes: 32000000029 bytes
  • Each .pqmeta: 29 bytes
  • Each .pqcodebook: 131105 bytes

Download 

hf download Odeinjul/bigann-1b-static-search-eval-pq \
  --repo-type dataset \
  --include "pq_m8.*" \
  --include "pq_m16.*" \
  --include "pq_m32.*" \
  --include "pq_manifest.json" \
  --include "source_manifest.json" \
  --local-dir bigann-1b-static-search-eval-pq

Expected total PQ payload size is approximately 56 GB.
Downloads last month
5
Total file size:
56 GB