Datasets:

iejMac
/

CLIP-Kinetics700

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 8 new columns ({'0.151', '0.283', '-5KQ66BBWC4', '0.811', '0.077', '1', '80', '0902'}) and 5 missing columns ({'time_start', 'split', 'label', 'youtube_id', 'time_end'}).

This happened while the csv dataset builder was generating data using

hf://datasets/iejMac/CLIP-Kinetics700/data/annotations/AVA-Kinetics/ava_kinetics_v1_0/ava_train_v2.2.csv (at revision 9e6b82e3134265d63fad9308eb996dbe21b2653c)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              -5KQ66BBWC4: string
              0902: int64
              0.077: double
              0.151: double
              0.283: double
              0.811: double
              80: double
              1: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1142
              to
              {'label': Value(dtype='string', id=None), 'youtube_id': Value(dtype='string', id=None), 'time_start': Value(dtype='int64', id=None), 'time_end': Value(dtype='int64', id=None), 'split': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1316, in compute_config_parquet_and_info_response
                  parquet_operations, partial = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 909, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 8 new columns ({'0.151', '0.283', '-5KQ66BBWC4', '0.811', '0.077', '1', '80', '0902'}) and 5 missing columns ({'time_start', 'split', 'label', 'youtube_id', 'time_end'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/iejMac/CLIP-Kinetics700/data/annotations/AVA-Kinetics/ava_kinetics_v1_0/ava_train_v2.2.csv (at revision 9e6b82e3134265d63fad9308eb996dbe21b2653c)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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label string	youtube_id string	time_start int64	time_end int64	split string
clay pottery making	---0dWlqevI	19	29	train
news anchoring	---aQ-tA5_A	9	19	train
using bagging machine	---j12rm3WI	14	24	train
javelin throw	--07WQ2iBlw	1	11	train
climbing a rope	--0NTAs-fA0	29	39	train
sipping cup	--0l35AkU34	68	78	train
flipping pancake	--33Lscn6sk	4	14	train
tickling	--3OAstUWtU	45	55	train
watering plants	--3lTx87ebQ	23	33	train
eating spaghetti	--3ouPhoy2A	20	30	train
dribbling basketball	--4-0ihtnBU	58	68	train
calligraphy	--4NLFGNfAs	11	21	train
playing tennis	--56QUhyDQM	185	195	train
brushing floor	--5Tg3gW4s4	8	18	train
chiseling stone	--5kVeU1uco	58	68	train
crossing eyes	--5urtlw6gs	49	59	train
mountain climber (exercise)	--65_UAU7ao	0	10	train
tap dancing	--6q_33gNew	132	142	train
planing wood	--6zxbYq5M4	207	217	train
using inhaler	--71SekUwOA	13	23	train
playing saxophone	--7VUM9MKg4	136	146	train
slapping	--7goKgS4kc	15	25	train
shaking head	--8FQVwWH0M	3	13	train
driving tractor	--9s8lCov-I	2	12	train
delivering mail	--AC9Wionlg	0	10	train
riding a bike	--DiygSPius	52	62	train
calligraphy	--Dt5ovTecs	3	13	train
putting in contact lenses	--EZD1uEVhM	63	73	train
climbing a rope	--EaS9P7ZdQ	13	23	train
shaving head	--FTpoPhxcA	570	580	train
doing jigsaw puzzle	--GEr5-PyTI	0	10	train
playing bass guitar	--GF746y6UM	496	506	train
playing with trains	--GaSxELz-8	39	49	train
digging	--ILYNHl3e4	541	551	train
brushing teeth	--IPbe5ZMCI	2	12	train
washing hair	--JGqNj9Bv0	33	43	train
opening bottle (not wine)	--KkHPoWdW8	25	35	train
finger snapping	--Kr1PZaPDI	1	11	train
crossing eyes	--LrHkjbax8	3	13	train
doing nails	--MW2zcDiNg	197	207	train
making balloon shapes	--Nixnc9DQg	17	27	train
pouring beer	--O2qCO6zBI	25	35	train
preparing salad	--O4p15yejk	351	361	train
playing violin	--PwWfN1Ae4	97	107	train
feeding birds	--PyMoD3_eg	20	30	train
wading through water	--QG-bjOFSA	0	10	train
cutting watermelon	--QfWXBoeA0	125	135	train
petting cat	--Qq8Y0ywHA	2	12	train
playing drums	--RVKBMp_2M	79	89	train
doing aerobics	--RtHwqbEWA	139	149	train
sausage making	--RuMpNXawk	277	287	train
playing ukulele	--SOz3xjWfA	37	47	train
throwing knife	--SSk3r84yw	0	10	train
skiing slalom	--SrV9bGzRA	197	207	train
knitting	--T4dtRsSSg	25	35	train
head stand	--U3pqp_C5g	139	149	train
dining	--UW3CcUqaU	20	30	train
hurdling	--VnA3ztuZg	57	67	train
throwing water balloon	--VtCsmlD18	47	57	train
tiptoeing	--WckmtKBpc	17	27	train
news anchoring	--XQ47BGhdc	46	56	train
opening present	--XR9XONO2U	460	470	train
changing gear in car	--XWWLL8Spk	0	10	train
playing cymbals	--Y25nDn2Wk	60	70	train
chopping meat	--YKMD5LYtw	89	99	train
making paper aeroplanes	--YXAAC0WWA	35	45	train
folding clothes	--ZrYtRjYfs	87	97	train
playing pinball	--_3Wbv-1YY	319	329	train
changing gear in car	--_fOePZDoY	41	51	train
vacuuming floor	--bQb-k_Tjs	9	19	train
shuffling feet	--cIG2WqOf4	20	30	train
recording music	--cLZr4EsEk	133	143	train
historical reenactment	--cOOZbreL4	129	139	train
surfing water	--coBvtS-eQ	57	67	train
playing drums	--d6UCSGoHg	51	61	train
bowling	--dVV4_CSvw	33	43	train
snowkiting	--feYHvtteA	25	35	train
lighting fire	--gMWIQlLvs	122	132	train
eating cake	--gtKHP3Q8E	0	10	train
inflating balloons	--gx7yb1-x0	298	308	train
letting go of balloon	--h55t9J0Xo	7	17	train
calligraphy	--hZSQ7Q7qs	145	155	train
chopping meat	--iGd5Zf7-k	8	18	train
bee keeping	--iIcadFu9c	90	100	train
cutting apple	--ijHZ19K_M	25	35	train
drooling	--ixUOhZodk	56	66	train
gymnastics tumbling	--jD1Yu5ZnQ	37	47	train
archaeological excavation	--jfcGztatc	6	16	train
clapping	--jktjgj81k	5	15	train
giving or receiving award	--kbTDDIiP0	62	72	train
cutting cake	--kyWqclQ24	256	266	train
visiting the zoo	--lmBtwsdBc	0	10	train
marriage proposal	--loSjz82iU	83	93	train
petting animal (not cat)	--lrRHlpK68	218	228	train
egg hunting	--lvipPLwp0	0	10	train
planting trees	--mBL7P45yE	394	404	train
biking through snow	--mI_-gaZLk	18	28	train
playing ping pong	--mLOzFpQoo	7	17	train
brushing teeth	--mQfG6Wu48	28	38	train
dribbling basketball	--mTlk9ommA	48	58	train

End of preview.

Dataset Card for CLIP-Kinetics70

Dataset Description

Dataset Summary

CLIP-Kinetics700 is a compressed version of the Kinetics700 dataset using OpenAI's CLIP model.

The original dataset is ~700 GB making it difficult to use and hold in memory on one machine. By downsampling each video to 1 FPS and encoding the frames using CLIP we we're able to compress the dataset to ~8 GB making it very memory-friendly and easy to use.

Dataset Preprocessing

clip-video-encode is a tool you can use to easily and efficiently compute CLIP embeddings from video frames. We used it to generate the embeddings for this dataset.

Dataset Structure

Data Format

We formatted this as a WebDataset for better data-loading performance when training the models. Each split contains a list of tar files each with 10000 data samples. This format can be read and used easily using the EmbeddingWebDatasetReader from clip-video-encode.

CLIP-Kinetics700
 ├── splits.csv
 ├── ds_00000.tar
 |     ├── vid_00000.npy
 |     ├── vid_00000.txt
 |     ├── vid_00000.json
 |     ├── vid_00001.npy
 |     ├── vid_00001.txt
 |     ├── vid_00001.json
 |     └── ...
 |     ├── vid_10000.npy
 |     ├── vid_10000.txt
 |     ├── vid_10000.json
 ├── ds_00001.tar
 |     ├── vid_10001.npy
 |     ├── vid_10001.txt
 |     ├── vid_10001.json
 │     ...
 ...

Data Fields

vid.npy: the numpy array with the per-frame embeddings. Shape -> (n_frames, 512)
vid.cap: the "caption" of the video. In this case it is the Kinetics700 label.
vid.json: additional metadata - YouTube video ID, start time, end time.

Data Splits

Train - 536489 samples | 54 tar's
Validation - 33966 samples | 4 tar's
Test - 64532 samples | 7 tar's

Dataset Creation

Source Data

Data was sourced from DeepMind's Kinetics700 dataset and downloaded using this convenient repository.

Simple Experiments

Using this repository we evaluate CLIP-Kinetics700 with the following simple methods:

Zero-shot Evaluation

	Accuracy
Top-1	0.31
Top-5	0.56
mean(Top1, Top5)	0.44

Linear-probe Evaluation

	Accuracy
Top-1	0.41
Top-5	0.65
mean(Top1, Top5)	0.53

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