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Datasets:
allenai
/
molmo2-yt-vis

Tasks:
Video Classification
Object Detection
Modalities:
Tabular
Text
Formats:
parquet
Size:
1K - 10K
Tags:
video-object-tracking
video-segmentation
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Dataset Viewer
Auto-converted to Parquet Duplicate
id
stringlengths
14
17
video
stringlengths
10
10
expression
stringclasses
40 values
fps
int64
6
12
sampling_fps
int64
2
2
height
int64
180
1.08k
n_frames
int64
5
72
width
int64
320
1.92k
task
stringclasses
1 value
frame_trajectories
listlengths
1
72
mask_id
listlengths
1
13
obj_id
listlengths
1
13
yt-vis_track_0
31c53703e5
surfboard
12
2
360
54
640
track
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[ "0" ]
[ 4065 ]
yt-vis_track_1
bbaa9a036a
person
12
2
720
29
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 721, 647 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 702, ...
[ "0" ]
[ 2643 ]
yt-vis_track_2
444d4e0596
person
6
2
720
30
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 1009, 440 ], "occluded": false } ] }, { "frame": 1, "time": 0.16666666666666602, "points": [ { "id": 0, "point": [ 1009, ...
[ "0" ]
[ 930 ]
yt-vis_track_3
98a8b06e7f
monkey
12
2
720
19
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 525, 573 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 496, ...
[ "0" ]
[ 2149 ]
yt-vis_track_4
5cb0cfb98f
parrot
8
2
720
29
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 446, 370 ], "occluded": false } ] }, { "frame": 1, "time": 0.125, "points": [ { "id": 0, "point": [ 445, 375 ],...
[ "0" ]
[ 1290 ]
yt-vis_track_5
a9bb30a1f4
leopard
6
2
720
45
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 11, 490 ], "occluded": false } ] }, { "frame": 1, "time": 0.16666666666666602, "points": [ { "id": 0, "point": [ 64, 52...
[ "0" ]
[ 5459 ]
yt-vis_track_6
f2f333ad06
motorbike
8
2
720
20
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 688, 549 ], "occluded": false } ] }, { "frame": 1, "time": 0.125, "points": [ { "id": 0, "point": [ 685, 549 ],...
[ "0" ]
[ 3421 ]
yt-vis_track_7
8b30356fd0
dog
8
2
360
48
640
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 246, 103 ], "occluded": false } ] }, { "frame": 1, "time": 0.125, "points": [ { "id": 0, "point": [ 241, 105 ],...
[ "0" ]
[ 5132 ]
yt-vis_track_8
a07a15dd64
skateboard
6
2
720
36
1,280
track
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[ "0" ]
[ 2224 ]
yt-vis_track_9
a174e8d989
elephant
8
2
720
19
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 647, 217 ], "occluded": false } ] }, { "frame": 1, "time": 0.125, "points": [ { "id": 0, "point": [ 612, 239 ],...
[ "0" ]
[ 2237 ]
yt-vis_track_10
9e2d57ed8a
flying disc
8
2
720
19
1,280
track
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[ "0" ]
[ 5325 ]
yt-vis_track_11
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monkey
8
2
720
36
1,280
track
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yt-vis_track_12
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person
8
2
720
20
1,280
track
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yt-vis_track_13
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person
12
2
720
36
1,280
track
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yt-vis_track_14
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bird
12
2
720
30
1,280
track
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[ 1201 ]
yt-vis_track_15
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cat
6
2
264
19
480
track
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yt-vis_track_16
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cat
12
2
720
20
1,280
track
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yt-vis_track_17
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person
6
2
720
36
1,280
track
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yt-vis_track_18
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snake
12
2
720
30
1,280
track
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yt-vis_track_19
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person
6
2
720
36
960
track
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yt-vis_track_20
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parrot
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2
720
20
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track
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[ "0" ]
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yt-vis_track_21
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boat
12
2
360
35
640
track
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yt-vis_track_22
d14d1e9289
truck
12
2
720
20
1,280
track
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yt-vis_track_23
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car
12
2
720
36
1,280
track
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yt-vis_track_24
362c5bc56e
tennis racket
6
2
720
36
1,280
track
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yt-vis_track_25
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snake
8
2
720
36
1,280
track
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yt-vis_track_26
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boat
8
2
720
20
1,280
track
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yt-vis_track_27
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rabbit
6
2
720
19
1,280
track
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yt-vis_track_28
31c53703e5
person
12
2
360
54
640
track
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yt-vis_track_29
d708e1350c
fish
12
2
720
36
1,280
track
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yt-vis_track_30
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person
8
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720
36
1,280
track
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[ 1979 ]
yt-vis_track_31
20142b2f05
shark
12
2
720
36
1,280
track
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yt-vis_track_32
225aba51d9
person
8
2
720
18
1,280
track
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yt-vis_track_33
cb4d7fb470
snowboard
12
2
360
18
640
track
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yt-vis_track_34
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flying disc
12
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720
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track
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yt-vis_track_35
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tennis racket
6
2
720
30
1,280
track
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yt-vis_track_36
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leopard
12
2
720
36
1,280
track
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yt-vis_track_37
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skateboard
6
2
720
30
1,280
track
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yt-vis_track_38
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rabbit
6
2
720
36
1,280
track
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yt-vis_track_39
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skateboard
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720
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1,280
track
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yt-vis_track_40
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person
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720
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track
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yt-vis_track_41
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lizard
6
2
720
36
1,280
track
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yt-vis_track_42
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person
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track
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yt-vis_track_43
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giant panda
6
2
720
36
1,280
track
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yt-vis_track_44
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person
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yt-vis_track_45
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giant panda
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720
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yt-vis_track_46
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person
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track
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yt-vis_track_47
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truck
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720
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track
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yt-vis_track_48
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parrot
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720
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track
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yt-vis_track_49
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giant panda
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720
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track
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yt-vis_track_50
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train
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yt-vis_track_51
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skateboard
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yt-vis_track_52
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cat
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720
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track
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yt-vis_track_53
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duck
8
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720
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track
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yt-vis_track_54
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snowboard
12
2
720
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1,280
track
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yt-vis_track_55
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cow
8
2
720
19
1,280
track
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[ "0" ]
[ 123 ]
yt-vis_track_56
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person
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track
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[ 1263 ]
yt-vis_track_57
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shark
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2
720
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1,280
track
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yt-vis_track_58
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person
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360
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640
track
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[ "0" ]
[ 3693 ]
yt-vis_track_59
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fox
12
2
720
36
1,280
track
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yt-vis_track_60
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person
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720
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1,280
track
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[ "0" ]
[ 2022 ]
yt-vis_track_61
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monkey
12
2
720
29
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 559, 438 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 537, ...
[ "0" ]
[ 1785 ]
yt-vis_track_62
c9bf64e965
person
12
2
720
20
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 246, 576 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 242, ...
[ "0" ]
[ 2872 ]
yt-vis_track_63
3e91f10205
skateboard
6
2
720
6
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 678, 500 ], "occluded": false } ] }, { "frame": 1, "time": 0.16666666666666602, "points": [ { "id": 0, "point": [ 545, ...
[ "0" ]
[ 842 ]
yt-vis_track_64
6b25d6528a
person
8
2
720
29
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 143, 252 ], "occluded": false } ] }, { "frame": 1, "time": 0.125, "points": [ { "id": 0, "point": [ 131, 249 ],...
[ "0" ]
[ 1512 ]
yt-vis_track_65
2f6ac8644d
surfboard
12
2
720
42
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 904, 569 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 971, ...
[ "0" ]
[ 4029 ]
yt-vis_track_66
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person
6
2
720
42
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 1194, 488 ], "occluded": false } ] }, { "frame": 1, "time": 0.16666666666666602, "points": [ { "id": 0, "point": [ 1170, ...
[ "0" ]
[ 5799 ]
yt-vis_track_67
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person
6
2
720
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1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 936, 381 ], "occluded": false } ] }, { "frame": 1, "time": 0.16666666666666602, "points": [ { "id": 0, "point": [ 938, ...
[ "0" ]
[ 435 ]
yt-vis_track_68
cbc89a8163
bird
12
2
720
35
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 663, 364 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 714, ...
[ "0" ]
[ 5744 ]
yt-vis_track_69
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truck
8
2
720
20
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 476, 468 ], "occluded": false } ] }, { "frame": 1, "time": 0.125, "points": [ { "id": 0, "point": [ 421, 443 ],...
[ "0" ]
[ 3520 ]
yt-vis_track_70
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person
12
2
720
12
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 454, 253 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 553, ...
[ "0" ]
[ 4844 ]
yt-vis_track_71
f08928c6d3
car
6
2
720
20
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 144, 69 ], "occluded": false } ] }, { "frame": 1, "time": 0.16666666666666602, "points": [ { "id": 0, "point": [ 151, 8...
[ "0" ]
[ 3387 ]
yt-vis_track_72
2f2c65c2f3
monkey
8
2
720
27
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 562, 436 ], "occluded": false } ] }, { "frame": 1, "time": 0.125, "points": [ { "id": 0, "point": [ 550, 450 ],...
[ "0" ]
[ 630 ]
yt-vis_track_73
15097d5d4e
parrot
12
2
720
29
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 358, 269 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 405, ...
[ "0" ]
[ 277 ]
yt-vis_track_74
79d9e00c55
person
12
2
720
19
1,280
track
[ { "frame": 0, "time": 0, "points": [ { "id": 0, "point": [ 950, 230 ], "occluded": false } ] }, { "frame": 1, "time": 0.08333333333333301, "points": [ { "id": 0, "point": [ 949, ...
[ "0" ]
[ 1703 ]
yt-vis_track_75
a2c146ab0d
person
6
2
720
19
1,280
track
[ { "frame": 8, "time": 1.333333333333333, "points": [ { "id": 0, "point": [ 162, 376 ], "occluded": false } ] }, { "frame": 9, "time": 1.5, "points": [ { "id": 0, "point": [ 100, ...
[ "0" ]
[ 2252 ]
End of preview. Expand in Data Studio

yt-vis Tracking Dataset

Video object tracking annotations for yt-vis.

Configs

Config Description Splits
track (default) Track points across all frames train

Usage 

from datasets import load_dataset

# Default (track)
ds = load_dataset("allenai/molmo2-yt-vis", split="train")

# Specific config
ds = load_dataset("allenai/molmo2-yt-vis", "ground", split="train")
Downloads last month
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Number of rows:
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Total file size:
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