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VINAY-UMRETHE
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Dataset-SigMamba-V1-Small

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video-anomaly-detection

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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.