Hugging Face's logo

Datasets:
nvidia
/
PhysicalAI-Event-Videos

Tasks:
Video Classification
Video-Text-to-Text
Modalities:
Video
Languages:
English
Size:
1K - 10K
Tags:
video-anomaly-detection
safety-events
surveillance
Synthetic
text-to-video-retrieval
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
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zhengthomastang alicli commited on
Commit
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Publish v1.0 with NVIDIA-only media archives; remove superseded media history

Browse files
Files changed (12) hide show
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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73
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74
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75
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76
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77
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78
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79
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80
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82
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87
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88
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90
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93
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94
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95
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96
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97
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98
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99
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100
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101
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102
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103
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104
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105
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106
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107
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108
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110
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112
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113
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114
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115
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117
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121
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125
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126
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127
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144
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146
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149
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150
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151
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152
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157
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221
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247
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249
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250
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251
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252
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253
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257
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258
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259
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260
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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340
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341
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342
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343
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345
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346
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347
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348
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349
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350
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351
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352
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353
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356
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357
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358
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360
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361
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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740
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744
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745
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750
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751
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752
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755
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765
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767
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768
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770
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771
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772
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780
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781
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782
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783
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785
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1039
+ video_00992.mp4,NV-VAD-v1,2
1040
+ video_00993.mp4,NV-VAD-v1,2
1041
+ video_00994.mp4,NV-VAD-v1,2
1042
+ video_00995.mp4,NV-VAD-v1,2
1043
+ video_00996.mp4,NV-VAD-v1,2
1044
+ video_00997.mp4,NV-VAD-v1,2
1045
+ video_00998.mp4,NV-VAD-v1,2
1046
+ video_00999.mp4,NV-VAD-v1,2
1047
+ video_01000.mp4,NV-VAD-v1,2
1048
+ video_01001.mp4,NV-VAD-v1,2
1049
+ video_01002.mp4,NV-VAD-v1,2
1050
+ video_01003.mp4,NV-VAD-v1,2
1051
+ video_01004.mp4,NV-VAD-v1,2
1052
+ video_01005.mp4,NV-VAD-v1,2
1053
+ video_01006.mp4,NV-VAD-v1,2
1054
+ video_01007.mp4,NV-VAD-v1,2
1055
+ video_01008.mp4,NV-VAD-v1,2
1056
+ video_01009.mp4,NV-VAD-v1,2
1057
+ video_01010.mp4,NV-VAD-v1,2
1058
+ video_01011.mp4,NV-VAD-v1,2
1059
+ video_01012.mp4,NV-VAD-v1,2
1060
+ video_01013.mp4,NV-VAD-v1,2
1061
+ video_01014.mp4,NV-VAD-v1,2
1062
+ video_01015.mp4,NV-VAD-v1,2
1063
+ video_01016.mp4,NV-VAD-v1,2
1064
+ video_01017.mp4,NV-VAD-v1,2
1065
+ video_01018.mp4,NV-VAD-v1,2
1066
+ video_01019.mp4,NV-VAD-v1,2
1067
+ video_01020.mp4,NV-VAD-v1,2
1068
+ video_01021.mp4,NV-VAD-v1,2
1069
+ video_01022.mp4,NV-VAD-v1,2
1070
+ video_01023.mp4,NV-VAD-v1,2
1071
+ video_01024.mp4,NV-VAD-v1,2
1072
+ video_01025.mp4,NV-VAD-v1,2
1073
+ video_01026.mp4,NV-VAD-v1,2
1074
+ video_01027.mp4,NV-VAD-v1,2
1075
+ video_01028.mp4,NV-VAD-v1,2
1076
+ video_01029.mp4,NV-VAD-v1,2
1077
+ video_01030.mp4,NV-VAD-v1,2
1078
+ video_01031.mp4,NV-VAD-v1,2
1079
+ video_01032.mp4,NV-VAD-v1,2
1080
+ video_01033.mp4,NV-VAD-v1,2
1081
+ video_01034.mp4,NV-VAD-v1,2
1082
+ video_01035.mp4,NV-VAD-v1,2
1083
+ video_01036.mp4,NV-VAD-v1,2
1084
+ video_01037.mp4,NV-VAD-v1,2
1085
+ video_01038.mp4,NV-VAD-v1,2
1086
+ video_01039.mp4,NV-VAD-v1,2
1087
+ video_01040.mp4,NV-VAD-v1,2
1088
+ video_01041.mp4,NV-VAD-v1,2
1089
+ video_01042.mp4,NV-VAD-v1,2
1090
+ video_01043.mp4,NV-VAD-v1,2
1091
+ video_01044.mp4,NV-VAD-v1,2
1092
+ video_01045.mp4,NV-VAD-v1,2
1093
+ video_01046.mp4,NV-VAD-v1,2
1094
+ video_01047.mp4,NV-VAD-v1,2
1095
+ video_01048.mp4,NV-VAD-v1,2
1096
+ video_01049.mp4,NV-VAD-v1,1
1097
+ video_01050.mp4,NV-VAD-v1,2
1098
+ video_01051.mp4,NV-VAD-v1,2
1099
+ video_01052.mp4,NV-VAD-v1,2
1100
+ video_01053.mp4,NV-VAD-v1,2
1101
+ video_01054.mp4,NV-VAD-v1,2
1102
+ video_01055.mp4,NV-VAD-v1,2
1103
+ video_01056.mp4,NV-VAD-v1,2
1104
+ video_01057.mp4,NV-VAD-v1,1
1105
+ video_01058.mp4,NV-VAD-v1,2
1106
+ video_01059.mp4,NV-VAD-v1,2
1107
+ video_01060.mp4,NV-VAD-v1,2
1108
+ video_01061.mp4,NV-VAD-v1,2
1109
+ video_01062.mp4,NV-VAD-v1,2
1110
+ video_01063.mp4,NV-VAD-v1,2
1111
+ video_01064.mp4,NV-VAD-v1,2
1112
+ video_01065.mp4,NV-VAD-v1,2
1113
+ video_01066.mp4,NV-VAD-v1,2
1114
+ video_01067.mp4,NV-VAD-v1,2
1115
+ video_01068.mp4,NV-VAD-v1,2
1116
+ video_01069.mp4,NV-VAD-v1,2
1117
+ video_01070.mp4,NV-VAD-v1,2
1118
+ video_01071.mp4,NV-VAD-v1,2
1119
+ video_01072.mp4,NV-VAD-v1,2
1120
+ video_01073.mp4,NV-VAD-v1,2
1121
+ video_01074.mp4,NV-VAD-v1,2
1122
+ video_01075.mp4,NV-VAD-v1,2
1123
+ video_01076.mp4,NV-VAD-v1,2
1124
+ video_01077.mp4,NV-VAD-v1,2
1125
+ video_01078.mp4,NV-VAD-v1,2
1126
+ video_01079.mp4,NV-VAD-v1,2
1127
+ video_01080.mp4,NV-VAD-v1,2
1128
+ video_01081.mp4,NV-VAD-v1,2
1129
+ video_01082.mp4,NV-VAD-v1,2
1130
+ video_01083.mp4,NV-VAD-v1,2
1131
+ video_01084.mp4,NV-VAD-v1,2
1132
+ video_01085.mp4,NV-VAD-v1,2
1133
+ video_01086.mp4,NV-VAD-v1,2
1134
+ video_01087.mp4,NV-VAD-v1,2
1135
+ video_01088.mp4,NV-VAD-v1,2
1136
+ video_01089.mp4,NV-VAD-v1,2
1137
+ video_01090.mp4,NV-VAD-v1,2
1138
+ video_01091.mp4,NV-VAD-v1,2
1139
+ video_01092.mp4,NV-VAD-v1,2
1140
+ video_01093.mp4,NV-VAD-v1,2
1141
+ video_01094.mp4,NV-VAD-v1,2
1142
+ video_01095.mp4,NV-VAD-v1,2
1143
+ video_01096.mp4,NV-VAD-v1,2
1144
+ video_01097.mp4,NV-VAD-v1,2
1145
+ video_01098.mp4,NV-VAD-v1,2
1146
+ video_01099.mp4,NV-VAD-v1,2
1147
+ video_01100.mp4,NV-VAD-v1,2
1148
+ video_01101.mp4,NV-VAD-v1,2
1149
+ video_01102.mp4,NV-VAD-v1,2
1150
+ video_01103.mp4,NV-VAD-v1,2
1151
+ video_01104.mp4,NV-VAD-v1,2
1152
+ video_01105.mp4,NV-VAD-v1,2
1153
+ video_01106.mp4,NV-VAD-v1,2
1154
+ video_01107.mp4,NV-VAD-v1,2
1155
+ video_01108.mp4,NV-VAD-v1,2
1156
+ video_01109.mp4,NV-VAD-v1,2
1157
+ video_01110.mp4,NV-VAD-v1,2
1158
+ video_01111.mp4,NV-VAD-v1,2
1159
+ video_01112.mp4,NV-VAD-v1,2
1160
+ video_01113.mp4,NV-VAD-v1,2
1161
+ video_01114.mp4,NV-VAD-v1,2
1162
+ video_01115.mp4,NV-VAD-v1,2
1163
+ video_01116.mp4,NV-VAD-v1,1
1164
+ video_01117.mp4,NV-VAD-v1,2
1165
+ video_01118.mp4,NV-VAD-v1,2
1166
+ video_01119.mp4,NV-VAD-v1,2
1167
+ video_01120.mp4,NV-VAD-v1,2
1168
+ video_01121.mp4,NV-VAD-v1,2
1169
+ video_01122.mp4,NV-VAD-v1,2
1170
+ video_01123.mp4,NV-VAD-v1,2
1171
+ video_01124.mp4,NV-VAD-v1,2
1172
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1173
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1174
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1175
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1176
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1177
+ video_01130.mp4,NV-VAD-v1.1,2
1178
+ video_01131.mp4,NV-VAD-v1.1,2
1179
+ video_01132.mp4,NV-VAD-v1.1,2
1180
+ video_01133.mp4,NV-VAD-v1.1,2
1181
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1182
+ video_01135.mp4,NV-VAD-v1.1,2
1183
+ video_01136.mp4,NV-VAD-v1.1,2
1184
+ video_01137.mp4,NV-VAD-v1.1,2
1185
+ video_01138.mp4,NV-VAD-v1.1,2
1186
+ video_01139.mp4,NV-VAD-v1.1,2
1187
+ video_01140.mp4,NV-VAD-v1.1,2
1188
+ video_01141.mp4,NV-VAD-v1.1,2
1189
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1190
+ video_01143.mp4,NV-VAD-v1.1,2
1191
+ video_01144.mp4,NV-VAD-v1.1,2
1192
+ video_01145.mp4,NV-VAD-v1.1,2
1193
+ video_01146.mp4,NV-VAD-v1.1,2
1194
+ video_01147.mp4,NV-VAD-v1.1,2
1195
+ video_01148.mp4,NV-VAD-v1.1,2
1196
+ video_01149.mp4,NV-VAD-v1.1,2
1197
+ video_01150.mp4,NV-VAD-v1.1,2
1198
+ video_01151.mp4,NV-VAD-v1.1,2
1199
+ video_01152.mp4,NV-VAD-v1.1,2
1200
+ video_01153.mp4,NV-VAD-v1.1,2
1201
+ video_01154.mp4,NV-VAD-v1.1,2
1202
+ video_01155.mp4,NV-VAD-v1.1,2
1203
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1204
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1205
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1206
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1207
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1208
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1209
+ video_01162.mp4,NV-VAD-v1.1,2
1210
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1211
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1212
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1213
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1214
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1215
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1216
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1217
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1218
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1219
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1220
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1221
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1222
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1223
+ video_01176.mp4,NV-VAD-v1.1,2
1224
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1225
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1226
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1227
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1228
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1229
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1230
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1231
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1232
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1233
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1234
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1235
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1236
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1237
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1238
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1239
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1240
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1241
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1242
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1243
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1244
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1245
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1246
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1247
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1248
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1249
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1250
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1251
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1252
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1253
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1254
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1255
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1256
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1257
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1258
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1259
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1260
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1261
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1262
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1263
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1264
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1265
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1266
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1267
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1268
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1269
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1270
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1271
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1272
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1273
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1274
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1275
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1276
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1277
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1278
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1279
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1280
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1281
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1282
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1283
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1284
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1285
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1286
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1287
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1288
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1289
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1290
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1291
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1292
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1293
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1294
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1295
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1296
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1297
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1298
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1299
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1300
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1301
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1302
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1303
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1304
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1305
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1306
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1307
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1308
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1309
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1310
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1311
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1312
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1313
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1314
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1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
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1325
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1326
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1327
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1328
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1329
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1330
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1331
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1332
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1333
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1334
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1335
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1336
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1337
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1338
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1339
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1340
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1341
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1342
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1343
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1344
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1345
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1346
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1347
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1348
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1349
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1350
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1351
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1352
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1353
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1354
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1355
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1356
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1357
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1358
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1359
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1360
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1361
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1362
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2026/train/videos.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:22a5221c0cf21e50de7cd7f265e80628973cf551a452c8243022c2af8983f9d4
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+ size 7954992209
README.md ADDED
@@ -0,0 +1,267 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ license_name: nvidia-dataset-license
4
+ license_link: >-
5
+ https://assets.ngc.nvidia.com/products/api-catalog/legal/Personas-Dataset-NVIDIA-Training-Dataset-License-Agreement-2025-11-26.pdf
6
+ pretty_name: PhysicalAI-Event-Videos
7
+ task_categories:
8
+ - video-classification
9
+ - video-text-to-text
10
+ language:
11
+ - en
12
+ size_categories:
13
+ - 1K<n<10K
14
+ tags:
15
+ - video-anomaly-detection
16
+ - safety-events
17
+ - surveillance
18
+ - synthetic
19
+ - text-to-video-retrieval
20
+ ---
21
+
22
+ # PhysicalAI-Event-Videos
23
+
24
+ PhysicalAI-Event-Videos is a video-anomaly and event dataset for developing text-to-video anomaly-search and safety-event-understanding systems. Version 1.0 provides structured annotations for 1,612 parent-video records and 3,796 labeled chunks, with 71,023 captions and queries across person-attribute, general-caption, anomaly, and action search tasks.
25
+
26
+ The redistributable media payload contains 1,486 NVIDIA-generated parent videos and 2,939 packaged chunk clips. Of those chunk clips, 2,919 have retained labels and 20 remain in the media archive without labels. CHAD and VAD-R1 media are not redistributed; the annotation index includes labels and public source pointers for those datasets.
27
+
28
+ ## Dataset highlights
29
+
30
+ ![A 3-by-3 grid of representative NVIDIA-generated normal and nonviolent event videos.](physicalai_event_videos_highlights_3x3.gif)
31
+
32
+ The montage shows nine representative NVIDIA-generated videos spanning normal activity and nonviolent retail, workplace, and public-space events. It includes selected examples from NV-Retail-Shoplifting-v0.1 and does not include CHAD or VAD-R1 source media.
33
+
34
+ ## Dataset description
35
+
36
+ The dataset covers human-activity anomalies such as falling, fighting, assault, climbing, throwing objects, shoplifting, and vaping or smoking. It also includes warehouse and industrial-safety events such as forklift incidents, forklift-person near misses, falling or damaged boxes, fire and smoke, liquid spills, and personal protective equipment violations.
37
+
38
+ Hard-negative samples depict normal or non-anomalous activity that may visually resemble an anomalous event. These samples are intended to help models distinguish true safety incidents from benign activities and reduce false-positive detections.
39
+
40
+ The videos span simulated environments including warehouses, retail spaces, offices, parking lots, streets, restaurants, parks, hospitals, schools, construction sites, stadiums, transit areas, airports, and banks.
41
+
42
+ - **Owner:** NVIDIA Corporation
43
+ - **Creation date:** June 15, 2026
44
+ - **Version:** v1.0
45
+ - **Split:** train only; validation and test splits are not included
46
+ - **Modalities:** video, audio, and text
47
+
48
+ ## License and terms of use
49
+
50
+ License: NVIDIA Dataset License Agreement.
51
+
52
+ Use of this dataset is governed by the NVIDIA Dataset License Agreement. Certain videos in this dataset were generated using Google Veo 3. The Veo-generated videos also remain subject to the applicable [Google terms](https://cloud.google.com/terms/service-terms), including restrictions on using such videos to develop a similar or competing product or service or to create or improve models similar to a Google model.
53
+
54
+ VAD-R1 and CHAD source media are not distributed. Users must obtain those datasets directly from the applicable source under its respective terms. The Public Safety Sample Videos seed dataset is not distributed.
55
+
56
+ ## Intended use
57
+
58
+ PhysicalAI-Event-Videos is intended for application developers working on video understanding and safety-related AI systems. Potential uses include:
59
+
60
+ - Video anomaly detection
61
+ - Safety-event classification
62
+ - Temporal anomaly localization
63
+ - Video-language model training
64
+ - Synthetic-to-real transfer research
65
+ - Hard-negative mining and false-positive reduction
66
+ - Industrial and warehouse safety monitoring research
67
+ - Retail and public-space anomaly detection research
68
+ - Multimodal audio, video, and text representation learning
69
+
70
+ ## Dataset characterization
71
+
72
+ ### Data collection method
73
+
74
+ The release combines annotations for NVIDIA-generated and public-source videos with a redistributable NVIDIA-generated media payload. NVIDIA-generated videos were created using Google Veo 3 and Cosmos 3 Super. Annotations also cover CHAD and VAD-R1, but those third-party source videos are not distributed; users must obtain them from their original sources. The Public Safety Sample Videos seed dataset is not distributed.
75
+
76
+ ### Labeling method
77
+
78
+ Labeling is hybrid: synthetic, automated, and manually corrected. Event labels and descriptive metadata are derived from generation specifications and prompts. Derived video chunks and associated manifests were produced through automated preprocessing. A subset of annotations received human review and correction; remaining query and caption fields may be model-generated.
79
+
80
+ ## Dataset format and contents
81
+
82
+ - **Video:** MP4 containers with H.264 video
83
+ - **Audio:** AAC audio streams embedded in MP4 files when available
84
+ - **Text:** JSON labels and provenance metadata, plus CSV manifests and summary statistics
85
+
86
+ ```text
87
+ prepare_chunks.py optional reconstruction from downloaded parent videos
88
+ README.md this dataset card
89
+ physicalai_event_videos_highlights_3x3.gif
90
+ anomaly-category-distribution.csv
91
+ anomaly-category-mapping.json
92
+ 2026/
93
+ ├── train/
94
+ │ ├── videos.zip 1,486 NVIDIA-generated parent videos
95
+ │ ├── chunks.zip 2,939 NVIDIA-generated chunk clips
96
+ │ ├── labels.json 1,612 parent records with 3,796 labeled chunks
97
+ │ └── video_manifest.csv filename-to-dataset lookup
98
+ ├── stats.json composition and per-field query counts
99
+ └── provenance.json public source and generation provenance
100
+ ```
101
+
102
+ The 2,939 packaged chunk clips comprise 2,919 labeled clips plus 20 retained media clips whose labels are not included. CHAD and VAD-R1 rows in `labels.json` identify source media through public pointers; their media is not present in the archives.
103
+
104
+ ## Label schema
105
+
106
+ `labels.json` is a list of per-video records:
107
+
108
+ ```jsonc
109
+ {
110
+ "split": "train",
111
+ "dataset": "NV-VAD-v1.1",
112
+ "video_id": "video_01126",
113
+ "anomaly_type": "person running",
114
+ "total_frames": 189,
115
+ "video_duration_sec": 7.875,
116
+ "fps": 24.0,
117
+ "video_path": "videos/video_01126.mp4",
118
+ "chunks": [
119
+ {
120
+ "chunk_index": 0,
121
+ "start_time_sec": 0.0,
122
+ "end_time_sec": 5.0,
123
+ "start_frame": 0,
124
+ "end_frame": 120,
125
+ "is_anomaly": true,
126
+ "anomaly_type": "person running",
127
+ "dense_caption": "...",
128
+ "scene_caption": "...",
129
+ "pas_queries": ["..."],
130
+ "caption_queries": ["..."],
131
+ "anomaly_queries": ["..."],
132
+ "action_queries": ["..."],
133
+ "chunk_file": "chunk_000.mp4"
134
+ }
135
+ ]
136
+ }
137
+ ```
138
+
139
+ ### Joining labels to media
140
+
141
+ | Requested media | Path inside the extracted archive |
142
+ |---|---|
143
+ | Parent video | `videos/` + `basename(video_path)` |
144
+ | Chunk clip | `chunks/<dataset>/<video_id>/<chunk_file>` |
145
+
146
+ Parent-video basenames are unique across the redistributable NVIDIA-generated videos. Chunk filenames are not unique on their own, so chunk paths are nested by `dataset` and `video_id`.
147
+
148
+ The 1,486 NVIDIA-generated parent videos use a deterministic global sequence, `video_00001.mp4` through `video_01486.mp4`. The filename does not encode its source; use `dataset` in `labels.json` or `video_manifest.csv` to distinguish releases.
149
+
150
+ ## Annotation examples
151
+
152
+ Each labeled chunk contains temporal bounds, an anomaly decision and free-text anomaly type, two complementary captions, and four query sets. The example below is an excerpt from an NVIDIA-generated forklift-person near-miss chunk.
153
+
154
+ | Annotation type | Example |
155
+ |---|---|
156
+ | Temporal localization | `0.0-5.0 seconds` |
157
+ | Anomaly flag | `is_anomaly: true` |
158
+ | Anomaly type | `near miss forklift person` |
159
+ | Dense caption | A forklift drives down a warehouse aisle and nearly collides with two employees standing in its path, forcing them to step aside quickly. 00:00-00:02: Two warehouse employees in safety vests stand in the aisle discussing a clipboard while a forklift approaches from the distance. 00:02-00:04: The forklift continues to drive forward toward the two employees, who remain stationary and focused on their documents. 00:04-00:07: As the forklift closes the distance rapidly, the employees notice it and quickly step out of the way to avoid a collision. |
160
+ | Scene caption | The scene takes place indoors within a large warehouse aisle, flanked by towering orange industrial shelving units filled with boxed inventory. The camera is positioned at a fixed, eye-level angle looking down the center of the aisle. The area is brightly lit with cool, overhead industrial lighting, and the general activity involves personnel in safety vests coordinating logistics and operating a forklift. |
161
+ | Person-attribute search (PAS) | `person wearing yellow vest and grey shirt`; `person wearing yellow vest and black pants` |
162
+ | General caption query | `warehouse aisle with orange industrial shelving units`; `two employees discussing a clipboard in a warehouse` |
163
+ | Anomaly query | `person in yellow safety vest nearly hit by a forklift`; `forklift almost colliding with warehouse employees` |
164
+ | Action query | `a forklift driving`; `people standing in a path` |
165
+
166
+ `anomaly_type` remains free text. The derived anomaly families below are provided for summary reporting and do not replace the original labels.
167
+
168
+ ## Dataset quantification
169
+
170
+ ### Annotation coverage
171
+
172
+ - **Parent records:** 1,612
173
+ - **Labeled chunks:** 3,796
174
+ - **Anomalous labeled chunks:** 2,483
175
+ - **Normal labeled chunks:** 1,313
176
+ - **Captions and queries:** 71,023 total
177
+
178
+ ### Query counts
179
+
180
+ | Query field | Count |
181
+ |---|---:|
182
+ | Person-attribute search (`pas_queries`) | 18,103 |
183
+ | General caption (`caption_queries`) | 18,823 |
184
+ | Anomaly (`anomaly_queries`) | 18,913 |
185
+ | Action (`action_queries`) | 15,184 |
186
+ | **Total** | **71,023** |
187
+
188
+ ### Source coverage
189
+
190
+ | Source dataset | Parent records | Labeled chunks | Media availability in this release |
191
+ |---|---:|---:|---|
192
+ | `CHAD` | 46 | 392 | Source pointers only |
193
+ | `NV-Retail-Shoplifting-v0.1` | 200 | 399 | NVIDIA-generated media included |
194
+ | `NV-VAD-v1` | 925 | 1,799 | NVIDIA-generated media included |
195
+ | `NV-VAD-v1.1` | 361 | 721 | NVIDIA-generated media included |
196
+ | `VAD-R1` | 80 | 485 | Source pointers only |
197
+ | **Total** | **1,612** | **3,796** | |
198
+
199
+ ### Redistributable NVIDIA-generated media
200
+
201
+ - **Parent videos:** 1,486
202
+ - **Labeled chunk clips:** 2,919
203
+ - **Retained but unlabeled chunk clips:** 20
204
+ - **Packaged chunk clips:** 2,939
205
+
206
+ ## Anomaly-category distribution
207
+
208
+ `anomaly_type` is a free-text field rather than a closed categorical label. For reporting, each of the 2,483 anomalous chunks is assigned to one mutually exclusive family using a documented, deterministic first-match mapping. These derived families do not replace the original labels.
209
+
210
+ | Derived anomaly family | Labeled chunks | Share of anomalous chunks |
211
+ |---|---:|---:|
212
+ | Fall / collapse | 379 | 15.3% |
213
+ | Fight / assault | 340 | 13.7% |
214
+ | Smoking / vaping | 282 | 11.4% |
215
+ | Climbing / jumping | 201 | 8.1% |
216
+ | Throwing / vandalism | 164 | 6.6% |
217
+ | Running | 160 | 6.4% |
218
+ | Fire / smoke / explosion | 145 | 5.8% |
219
+ | Falling / damaged objects | 137 | 5.5% |
220
+ | Traffic / parking safety | 125 | 5.0% |
221
+ | Retail theft / concealment | 111 | 4.5% |
222
+ | Forklift incident | 91 | 3.7% |
223
+ | Liquid spill / leak | 91 | 3.7% |
224
+ | Animal-related hazard | 91 | 3.7% |
225
+ | Generic warehouse-safety violation | 82 | 3.3% |
226
+ | PPE violation | 32 | 1.3% |
227
+ | Weather / environmental hazard | 27 | 1.1% |
228
+ | Other / ambiguous | 25 | 1.0% |
229
+
230
+ Percentages use anomalous labeled chunks as the denominator and may not sum to exactly 100% because values are rounded to one decimal place. See [`anomaly-category-distribution.csv`](anomaly-category-distribution.csv) for the summary table and [`anomaly-category-mapping.json`](anomaly-category-mapping.json) for the ordered mapping rules.
231
+
232
+ ## Preparing chunks from downloaded videos
233
+
234
+ `chunks.zip` is ready to use. If parent videos are downloaded separately, the optional `prepare_chunks.py` script reconstructs labeled time windows without making network requests:
235
+
236
+ ```bash
237
+ python3 prepare_chunks.py \
238
+ --labels 2026/train/labels.json \
239
+ --videos-dir /path/to/downloaded/videos \
240
+ --output-dir 2026/train/chunks \
241
+ --workers 4
242
+ ```
243
+
244
+ The script searches the video directory recursively, reads exact start and end times from `labels.json`, and writes `chunks/<dataset>/<video_id>/<chunk_file>`. It requires Python 3, `ffmpeg`, and `ffprobe`. Use `--dry-run` to validate inputs or `--dataset NAME` to prepare a subset. Reconstructed clips match the labeled time windows but may not be byte-identical across ffmpeg versions.
245
+
246
+ ## Public source pages
247
+
248
+ These are public third-party landing pages, not NVIDIA-controlled storage:
249
+
250
+ - **CHAD:** [official repository](https://github.com/TeCSAR-UNCC/CHAD) and [primary video archive](https://drive.google.com/file/d/13am4hfhicErcozAYgkmQm02_K-cCmtkQ/view?usp=sharing)
251
+ - **VAD-R1:** [public Hugging Face dataset](https://huggingface.co/datasets/wbfwonderful/Vad-R1)
252
+
253
+ CHAD and VAD-R1 media must be obtained from their original sources under the applicable terms. This release does not redistribute that media.
254
+
255
+ ## Known limitations
256
+
257
+ 1. **Third-party source dependency.** CHAD and VAD-R1 annotations require users to obtain the corresponding media separately and comply with each source dataset's terms.
258
+ 2. **Model-generated text.** A subset of annotations received human review and correction, while remaining query and caption fields may contain model errors or inconsistencies.
259
+ 3. **Free-text anomaly types.** `anomaly_type` is not a closed ontology. The supplied category mapping is a reporting aid based on deterministic text matching.
260
+ 4. **Near-duplicate scenarios.** Many NVIDIA-generated videos share related generation scenarios. Split by generation scenario, not only by `video_id`, to reduce train-evaluation leakage.
261
+ 5. **Synthetic-to-real gap.** Models trained on simulated media may not transfer directly to real environments, camera systems, or populations without additional validation.
262
+
263
+ ## Ethical considerations
264
+
265
+ NVIDIA believes Trustworthy AI is a shared responsibility and has established policies and practices to enable development for a wide array of AI applications. Developers should work with their organizations to ensure this dataset meets the requirements of the relevant industry and use case and addresses unforeseen product misuse.
266
+
267
+ Users should evaluate performance, safety, fairness, privacy, and failure modes in the intended deployment context. Synthetic data may still encode artifacts or biases from prompts and generation systems, and third-party annotations should be assessed with their source context and terms.
anomaly-category-distribution.csv ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ anomaly_family,chunk_count,percent_of_anomalous_chunks
2
+ Fall / collapse,379,15.3
3
+ Fight / assault,340,13.7
4
+ Smoking / vaping,282,11.4
5
+ Climbing / jumping,201,8.1
6
+ Throwing / vandalism,164,6.6
7
+ Running,160,6.4
8
+ Fire / smoke / explosion,145,5.8
9
+ Falling / damaged objects,137,5.5
10
+ Traffic / parking safety,125,5.0
11
+ Retail theft / concealment,111,4.5
12
+ Forklift incident,91,3.7
13
+ Liquid spill / leak,91,3.7
14
+ Animal-related hazard,91,3.7
15
+ Generic warehouse-safety violation,82,3.3
16
+ PPE violation,32,1.3
17
+ Weather / environmental hazard,27,1.1
18
+ Other / ambiguous,25,1.0
anomaly-category-mapping.json ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "source_field": "anomaly_type",
3
+ "unit": "labeled chunks with is_anomaly == true",
4
+ "assignment": "first matching rule wins; otherwise Other / ambiguous",
5
+ "rules": [
6
+ {
7
+ "family": "Retail theft / concealment",
8
+ "regex": "shoplift|stealing|conceal|security tag|suspicious handoff|theft|steal"
9
+ },
10
+ {
11
+ "family": "Smoking / vaping",
12
+ "regex": "smoking|vaping|\\bvape\\b"
13
+ },
14
+ {
15
+ "family": "Animal-related hazard",
16
+ "regex": "sheep|bear|elephant|dog|leopard|lion|animal"
17
+ },
18
+ {
19
+ "family": "Fire / smoke / explosion",
20
+ "regex": "fire|smoke|explosion"
21
+ },
22
+ {
23
+ "family": "Forklift incident",
24
+ "regex": "forklift"
25
+ },
26
+ {
27
+ "family": "Liquid spill / leak",
28
+ "regex": "spill|leak|flood"
29
+ },
30
+ {
31
+ "family": "PPE violation",
32
+ "regex": "ppe violation"
33
+ },
34
+ {
35
+ "family": "Falling / damaged objects",
36
+ "regex": "boxes falling|damaged boxes|falling_objects|falling object"
37
+ },
38
+ {
39
+ "family": "Fall / collapse",
40
+ "regex": "person falling|person_falling|falling|collaps|lying motionless|lying on the ground|person lying|lying upside down"
41
+ },
42
+ {
43
+ "family": "Fight / assault",
44
+ "regex": "fight|assault|physical_abuse|attack|grappling|pushing each other"
45
+ },
46
+ {
47
+ "family": "Climbing / jumping",
48
+ "regex": "climb|jump"
49
+ },
50
+ {
51
+ "family": "Throwing / vandalism",
52
+ "regex": "throw|vandal|dropping (items|an object)"
53
+ },
54
+ {
55
+ "family": "Running",
56
+ "regex": "running|people running|suspicious_running"
57
+ },
58
+ {
59
+ "family": "Weather / environmental hazard",
60
+ "regex": "sand|tornado|heavy wind|debris"
61
+ },
62
+ {
63
+ "family": "Traffic / parking safety",
64
+ "regex": "parking|\\bcar\\b|vehicle|road|bicycle|skateboard|scooter|sidewalk"
65
+ },
66
+ {
67
+ "family": "Generic warehouse-safety violation",
68
+ "regex": "warehouse_safety_violation"
69
+ }
70
+ ],
71
+ "total_anomalous_chunks": 2483
72
+ }
physicalai_event_videos_highlights_3x3.gif ADDED

Git LFS Details

  • SHA256: 6939f5e70cea8112679955d33ca2e85f0ccd9765e5dbc21fea529891bd53b4dd
  • Pointer size: 132 Bytes
  • Size of remote file: 5.43 MB
prepare_chunks.py ADDED
@@ -0,0 +1,192 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Reconstruct labeled PhysicalAI-Event-Videos chunks from downloaded parents."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
7
+ import concurrent.futures
8
+ import json
9
+ import os
10
+ import shutil
11
+ import subprocess
12
+ import sys
13
+ from pathlib import Path
14
+
15
+
16
+ VIDEO_SUFFIXES = {".avi", ".mkv", ".mov", ".mp4", ".webm"}
17
+
18
+
19
+ def safe_part(value: object, field: str) -> str:
20
+ text = str(value or "")
21
+ if not text or text in {".", ".."} or Path(text).name != text:
22
+ raise ValueError(f"unsafe {field}: {text!r}")
23
+ return text
24
+
25
+
26
+ def parent_basename(record: dict) -> str:
27
+ source = record.get("source_video") or {}
28
+ for value in (
29
+ record.get("video_path"),
30
+ source.get("path"),
31
+ source.get("repository_path"),
32
+ source.get("archive_member"),
33
+ ):
34
+ if value:
35
+ return Path(str(value)).name
36
+ raise ValueError(f"no parent-video path for {record.get('dataset')}/{record.get('video_id')}")
37
+
38
+
39
+ def index_parents(root: Path, wanted: set[str]) -> tuple[dict[str, Path], dict[str, list[Path]]]:
40
+ found: dict[str, Path] = {}
41
+ duplicates: dict[str, list[Path]] = {}
42
+ for path in root.rglob("*"):
43
+ if not path.is_file() or path.suffix.lower() not in VIDEO_SUFFIXES or path.name not in wanted:
44
+ continue
45
+ if path.name in found:
46
+ duplicates.setdefault(path.name, [found[path.name]]).append(path)
47
+ else:
48
+ found[path.name] = path
49
+ return found, duplicates
50
+
51
+
52
+ def probe(path: Path, ffprobe: str, expected_duration: float) -> tuple[bool, str]:
53
+ proc = subprocess.run(
54
+ [
55
+ ffprobe, "-v", "error", "-show_entries",
56
+ "format=duration:stream=codec_name,pix_fmt", "-of", "json", str(path),
57
+ ],
58
+ capture_output=True,
59
+ text=True,
60
+ )
61
+ if proc.returncode:
62
+ return False, proc.stderr.strip()[-300:]
63
+ try:
64
+ data = json.loads(proc.stdout)
65
+ duration = float(data["format"]["duration"])
66
+ video = next(stream for stream in data["streams"] if stream.get("codec_name"))
67
+ except (KeyError, StopIteration, TypeError, ValueError, json.JSONDecodeError) as exc:
68
+ return False, f"invalid ffprobe output: {exc}"
69
+ if abs(duration - expected_duration) > 0.10:
70
+ return False, f"duration {duration:.3f}s != {expected_duration:.3f}s"
71
+ if video.get("codec_name") != "h264" or video.get("pix_fmt") != "yuv420p":
72
+ return False, f"unexpected video format {video.get('codec_name')}/{video.get('pix_fmt')}"
73
+ return True, ""
74
+
75
+
76
+ def encode(job: tuple[Path, Path, float, float], ffmpeg: str, ffprobe: str, force: bool) -> tuple[Path, str]:
77
+ source, output, start, end = job
78
+ duration = end - start
79
+ if output.exists() and not force:
80
+ valid, _ = probe(output, ffprobe, duration)
81
+ if valid:
82
+ return output, "skipped"
83
+
84
+ output.parent.mkdir(parents=True, exist_ok=True)
85
+ temporary = output.with_name(output.name + ".part.mp4")
86
+ temporary.unlink(missing_ok=True)
87
+ proc = subprocess.run(
88
+ [
89
+ ffmpeg, "-nostdin", "-hide_banner", "-loglevel", "error", "-y",
90
+ "-ss", f"{start:.6f}", "-i", str(source), "-t", f"{duration:.6f}",
91
+ "-c:v", "libx264", "-preset", "fast", "-crf", "23",
92
+ "-pix_fmt", "yuv420p", "-movflags", "+faststart", "-an", str(temporary),
93
+ ],
94
+ capture_output=True,
95
+ text=True,
96
+ )
97
+ if proc.returncode:
98
+ temporary.unlink(missing_ok=True)
99
+ raise RuntimeError(f"ffmpeg failed for {source}: {proc.stderr.strip()[-400:]}")
100
+ valid, error = probe(temporary, ffprobe, duration)
101
+ if not valid:
102
+ temporary.unlink(missing_ok=True)
103
+ raise RuntimeError(f"validation failed for {source}: {error}")
104
+ os.replace(temporary, output)
105
+ return output, "written"
106
+
107
+
108
+ def main() -> int:
109
+ parser = argparse.ArgumentParser(description=__doc__)
110
+ parser.add_argument("--labels", required=True, type=Path)
111
+ parser.add_argument("--videos-dir", required=True, type=Path)
112
+ parser.add_argument("--output-dir", required=True, type=Path)
113
+ parser.add_argument("--workers", type=int, default=4)
114
+ parser.add_argument("--dataset", action="append", help="Process only this dataset; repeatable.")
115
+ parser.add_argument("--dry-run", action="store_true", help="Resolve inputs and outputs without encoding.")
116
+ parser.add_argument("--force", action="store_true", help="Replace valid existing chunks.")
117
+ args = parser.parse_args()
118
+
119
+ if args.workers < 1:
120
+ parser.error("--workers must be at least 1")
121
+ labels = json.loads(args.labels.read_text(encoding="utf-8"))
122
+ selected = set(args.dataset or [])
123
+ records = [record for record in labels if not selected or record.get("dataset") in selected]
124
+ if selected - {record.get("dataset") for record in labels}:
125
+ parser.error(f"unknown dataset(s): {sorted(selected - {r.get('dataset') for r in labels})}")
126
+
127
+ wanted = {parent_basename(record) for record in records}
128
+ parents, duplicates = index_parents(args.videos_dir, wanted)
129
+ if duplicates:
130
+ for name, paths in sorted(duplicates.items())[:10]:
131
+ print(f"duplicate parent basename {name}: {', '.join(map(str, paths))}", file=sys.stderr)
132
+ return 2
133
+
134
+ jobs: list[tuple[Path, Path, float, float]] = []
135
+ missing: list[str] = []
136
+ for record in records:
137
+ dataset = safe_part(record.get("dataset"), "dataset")
138
+ video_id = safe_part(record.get("video_id"), "video_id")
139
+ basename = parent_basename(record)
140
+ source = parents.get(basename)
141
+ if source is None:
142
+ missing.append(basename)
143
+ continue
144
+ for chunk in record.get("chunks", []):
145
+ chunk_file = safe_part(
146
+ chunk.get("chunk_file") or f"chunk_{int(chunk['chunk_index']):03d}.mp4",
147
+ "chunk_file",
148
+ )
149
+ start = float(chunk["start_time_sec"])
150
+ end = float(chunk["end_time_sec"])
151
+ if start < 0 or end <= start:
152
+ raise ValueError(f"invalid interval {start}..{end} for {dataset}/{video_id}/{chunk_file}")
153
+ jobs.append((source, args.output_dir / dataset / video_id / chunk_file, start, end))
154
+
155
+ if missing:
156
+ print(f"missing {len(missing)} parent videos; examples: {', '.join(sorted(missing)[:10])}", file=sys.stderr)
157
+ return 2
158
+ if len({job[1] for job in jobs}) != len(jobs):
159
+ raise RuntimeError("duplicate output chunk paths")
160
+
161
+ print(f"resolved {len(records):,} parent videos and {len(jobs):,} labeled chunks")
162
+ if args.dry_run:
163
+ print("dry run passed; no files written")
164
+ return 0
165
+
166
+ ffmpeg = shutil.which("ffmpeg")
167
+ ffprobe = shutil.which("ffprobe")
168
+ if not ffmpeg or not ffprobe:
169
+ print("ffmpeg and ffprobe must both be available on PATH", file=sys.stderr)
170
+ return 2
171
+
172
+ counts = {"written": 0, "skipped": 0}
173
+ failures: list[str] = []
174
+ with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as pool:
175
+ futures = [pool.submit(encode, job, ffmpeg, ffprobe, args.force) for job in jobs]
176
+ for index, future in enumerate(concurrent.futures.as_completed(futures), 1):
177
+ try:
178
+ _, status = future.result()
179
+ counts[status] += 1
180
+ except Exception as exc: # keep processing independent clips
181
+ failures.append(str(exc))
182
+ if index % 100 == 0 or index == len(futures):
183
+ print(f"processed {index:,}/{len(futures):,}")
184
+
185
+ print(f"written {counts['written']:,}; skipped {counts['skipped']:,}; failed {len(failures):,}")
186
+ for failure in failures[:10]:
187
+ print(f"ERROR: {failure}", file=sys.stderr)
188
+ return 1 if failures else 0
189
+
190
+
191
+ if __name__ == "__main__":
192
+ raise SystemExit(main())