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Timesformer Base Finetuned K400 Continual Lora Ucf101
A video classification model based on the TimeSformer architecture, pre-trained on the Kinetics-400 dataset and fine-tuned on the UCF101 dataset, utilizing LoRA technology for continual learning.
Downloads 17
Release Time : 10/11/2023
Model Overview
This model is a deep learning model for video action recognition, specifically designed based on the Transformer architecture to process temporal information in videos, excelling in action recognition tasks.
Model Features
Temporal Attention Mechanism
Utilizes the TimeSformer architecture to effectively capture temporal information and spatial features in videos.
Efficient Fine-tuning
Employs LoRA (Low-Rank Adaptation) technology for continual learning, reducing training parameters while maintaining model performance.
High Performance
Achieves an accuracy of 97.67% on the UCF101 dataset, demonstrating excellent performance.
Model Capabilities
Video Action Recognition
Temporal Feature Extraction
Video Content Classification
Use Cases
Video Analysis
Action Recognition System
Identifies human actions in videos, such as running, jumping, etc.
97.67% accuracy on the UCF101 dataset
Video Content Moderation
Automatically detects specific actions or behaviors in videos.
Intelligent Surveillance
Abnormal Behavior Detection
Detects abnormal or suspicious behaviors in surveillance videos.
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