Datasets:

VINAY-UMRETHE
/

Dataset-SigMamba-V1-Small

text stringlengths 28 44
data/Normal_Videos_196_x264.txt normal
data/Normal_Videos179_x264.txt normal
data/Normal_Videos361_x264.txt normal
data/RoadAccidents017_x264.txt roadaccidents
data/Normal_Videos125_x264.txt normal
data/Normal_Videos160_x264.txt normal
data/Normal_Videos348_x264.txt normal
data/Normal_Videos470_x264.txt normal
data/Normal_Videos072_x264.txt normal
data/RoadAccidents067_x264.txt roadaccidents
data/Explosion037_x264.txt explosion
data/Normal_Videos807_x264.txt normal
data/Stealing097_x264.txt stealing
data/Normal_Videos_932_x264.txt normal
data/Normal_Videos286_x264.txt normal
data/Normal_Videos356_x264.txt normal
data/Normal_Videos700_x264.txt normal
data/Normal_Videos269_x264.txt normal
data/Normal_Videos131_x264.txt normal
data/Stealing084_x264.txt stealing
data/Normal_Videos335_x264.txt normal
data/Normal_Videos096_x264.txt normal
data/Normal_Videos458_x264.txt normal
data/Normal_Videos191_x264.txt normal
data/Normal_Videos253_x264.txt normal
data/Fighting036_x264.txt fighting
data/RoadAccidents116_x264.txt roadaccidents
data/Normal_Videos669_x264.txt normal
data/RoadAccidents018_x264.txt roadaccidents
data/RoadAccidents096_x264.txt roadaccidents
data/Burglary018_x264.txt burglary
data/Fighting013_x264.txt fighting
data/Normal_Videos742_x264.txt normal
data/Normal_Videos197_x264.txt normal
data/Vandalism008_x264.txt vandalism
data/Normal_Videos202_x264.txt normal
data/RoadAccidents093_x264.txt roadaccidents
data/Shoplifting013_x264.txt shoplifting
data/Explosion020_x264.txt explosion
data/RoadAccidents149_x264.txt roadaccidents
data/Normal_Videos818_x264.txt normal
data/Robbery002_x264.txt robbery
data/Normal_Videos_886_x264.txt normal
data/Normal_Videos_925_x264.txt normal
data/Normal_Videos_939_x264.txt normal
data/Burglary091_x264.txt burglary
data/Normal_Videos_933_x264.txt normal
data/Normal_Videos472_x264.txt normal
data/Normal_Videos553_x264.txt normal
data/Robbery092_x264.txt robbery
data/Normal_Videos178_x264.txt normal
data/Normal_Videos232_x264.txt normal
data/Normal_Videos562_x264.txt normal
data/Normal_Videos410_x264.txt normal
data/Normal_Videos559_x264.txt normal
data/Normal_Videos466_x264.txt normal
data/Robbery024_x264.txt robbery
data/RoadAccidents117_x264.txt roadaccidents
data/Arrest010_x264.txt arrest
data/Shoplifting038_x264.txt shoplifting
data/Normal_Videos081_x264.txt normal
data/Assault044_x264.txt assault
data/Robbery061_x264.txt robbery
data/Normal_Videos523_x264.txt normal
data/Fighting023_x264.txt fighting
data/Normal_Videos716_x264.txt normal
data/Normal_Videos543_x264.txt normal
data/Vandalism023_x264.txt vandalism
data/Explosion023_x264.txt explosion
data/Normal_Videos368_x264.txt normal
data/Normal_Videos720_x264.txt normal
data/Normal_Videos_938_x264.txt normal
data/Arrest011_x264.txt arrest
data/Normal_Videos339_x264.txt normal
data/Normal_Videos497_x264.txt normal
data/Normal_Videos077_x264.txt normal
data/RoadAccidents150_x264.txt roadaccidents
data/Shoplifting028_x264.txt shoplifting
data/Normal_Videos307_x264.txt normal
data/Fighting019_x264.txt fighting
data/Burglary053_x264.txt burglary
data/Robbery025_x264.txt robbery
data/Normal_Videos467_x264.txt normal
data/Burglary052_x264.txt burglary
data/Shooting028_x264.txt shooting
data/Normal_Videos128_x264.txt normal
data/Normal_Videos_150_x264.txt normal
data/Shoplifting039_x264.txt shoplifting
data/Normal_Videos_027_x264.txt normal
data/Normal_Videos055_x264.txt normal
data/Normal_Videos409_x264.txt normal
data/Normal_Videos355_x264.txt normal
data/Normal_Videos509_x264.txt normal
data/Shoplifting048_x264.txt shoplifting
data/Normal_Videos841_x264.txt normal
data/Normal_Videos_881_x264.txt normal
data/Normal_Videos806_x264.txt normal
data/RoadAccidents057_x264.txt roadaccidents
data/Shooting010_x264.txt shooting
data/Shoplifting049_x264.txt shoplifting

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SigMamba-V1-Small-Features

This dataset contains visual features encoded from the dataset using the SigLIP 2 vision encoder. These features are specifically prepared for Weakly Supervised Video Anomaly Detection (WSVAD) tasks, such as training the original VINAY-UMRETHE/SigMamba-V1-Small model.

Dataset Details

Vision Encoder: google/siglip2-base-patch16-384
Feature Dimension: 768
Sampling Rate: 1 frame every 16 frames
Normalization: L2 Normalized (Unit Hypersphere)

The dataset consists of .txt files corresponding to a video. Each file follows a matrix format of shape (T, D).

Where:

T is the number of sampled temporal segments.
D is the feature dimension. (768 for google/siglip2-base-patch16-384)

0.023145 -0.012834 ... (D values)
0.018234 -0.009123 ... (D values)

Training List (`train_list.txt`)

Format: [feature_path] [label]

Sample: data/Normal_Videos_196_x264.txt normal
Details: Simple video-level labels for weakly supervised training.

Testing List (`test_list.txt`)

Format: [feature_path] [class] [total_frames] [start1] [end1] [start2] [end2]

Sample: data/Abuse028_x264.txt Abuse 1424 165 240 -1 -1
Details: Temporal annotations for frame-level evaluation. total_frames is estimated as num_segments * 16.