Hugging Face's logo

Datasets:
shiniagarwal
/
rav4-semantic-video-index-v2

Tasks:
Image-to-Text
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Dataset Viewer
Auto-converted to Parquet Duplicate
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
query_index: int64
start_timestamp: int64
end_timestamp: int64
class_label: large_string
number_of_supporting_detections: int64
youtube_url: large_string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 857
to
{'query_index': Value('int64'), 'class_labels': List(Value('string')), 'start_timestamp': Value('int64'), 'end_timestamp': Value('int64'), 'number_of_supporting_detections': Value('int64'), 'youtube_url': Value('large_string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2083, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 180, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 143, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              query_index: int64
              start_timestamp: int64
              end_timestamp: int64
              class_label: large_string
              number_of_supporting_detections: int64
              youtube_url: large_string
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 857
              to
              {'query_index': Value('int64'), 'class_labels': List(Value('string')), 'start_timestamp': Value('int64'), 'end_timestamp': Value('int64'), 'number_of_supporting_detections': Value('int64'), 'youtube_url': Value('large_string')}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning:The task_categories "video-retrieval" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

RAV4 Image to Video Semantic Retrieval Dataset

This dataset contains outputs from a system that searches a video using an image of a car part.

We give the system a picture (for example a hood or door), and it returns the moments in the video where that same part appears.

The system matches meaning (car parts), not exact pixels.

How the system works

  1. The video is converted into frames (1 frame per second)
  2. A trained detector finds car parts in every frame
  3. All detections are stored in a searchable table
  4. A query image is analyzed to detect its parts
  5. The system finds timestamps where the same parts appear
  6. Nearby timestamps are grouped into watchable clips

Both intermediate and final results are provided.

Files

1. video_detections/data.parquet

This is the video index.

Each row represents one detected object in one frame.

Columns:

  • video_id — id of the video
  • frame_index — frame number (sampled at 1 FPS)
  • timestamp — second in the original video
  • class_label — detected car part (hood, bumper, mirror, glass, etc.)
  • x_min, y_min, x_max, y_max — bounding box location
  • confidence_score — model confidence for that detection

Low confidence detections may later be ignored during retrieval.

2. retrieval_raw/data.parquet

This file stores the first retrieval matches.

Process:

  • detect parts in query image
  • search the video index
  • merge consecutive seconds into short segments

Each row is one continuous appearance of a component.

Columns:

  • query_index — query image id
  • start_timestamp — beginning of appearance
  • end_timestamp — end of appearance
  • class_label — matched component
  • number_of_supporting_detections — number of frames inside the segment containing the object
  • youtube_url — verification link

These segments can still be small and fragmented.

3. retrieval_final/data.parquet

This file stores the final search results returned to the user.

We improve the raw segments using two ideas:

  1. Confidence filtering
    Detections with very low model confidence are ignored.

  2. Temporal consistency
    If the object appears in many nearby frames, we trust it more and merge segments into longer clips.

Columns:

  • query_index — query image id
  • class_labels — list of detected components used for retrieval (multiple parts may match)
  • start_timestamp — beginning of clip
  • end_timestamp — end of clip
  • number_of_supporting_detections — total detections inside the clip
  • youtube_url — direct video link

Difference between raw and final

retrieval_raw.parquet
Short appearance segments directly from detection timeline

retrieval_final.parquet
Longer reliable clips after:

  • removing weak detections
  • merging nearby segments

Goal

Enable semantic video search:

Given an image of a car component, return the moments in the video where that component appears.
Downloads last month
3
Number of rows:
3,392
Total file size:
106 kB