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Videomae Base Finetuned Kinetics Finetuned Dcsass Shoplifting Subset
A video classification model based on the VideoMAE architecture, fine-tuned specifically for shoplifting behavior detection
Downloads 23
Release Time : 5/1/2024
Model Overview
This model is a video classification model based on the VideoMAE architecture, pre-trained on the Kinetics dataset and fine-tuned for shoplifting detection scenarios.
Model Features
Efficient Video Understanding
Utilizes the VideoMAE architecture with masked autoencoder pre-training for efficient video feature learning
Scenario-specific Optimization
Specifically fine-tuned for shoplifting detection scenarios to improve recognition accuracy for targeted behaviors
Lightweight Inference
The base version model is suitable for practical deployment scenarios, balancing performance and computational resource requirements
Model Capabilities
Video Action Classification
Abnormal Behavior Detection
Real-time Video Analysis
Use Cases
Security Surveillance
Shoplifting Behavior Detection
Real-time analysis of surveillance footage to identify potential theft behaviors
Validation set accuracy reached 88.74%
Retail Analytics
Customer Behavior Analysis
Identifying abnormal customer behavior patterns in stores
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