finalproject123/Anomaly_Detection_I3D_Model

Model Card for Anomaly Detection in Videos

This model card provides information about a TensorFlow model designed to detect anomalies in videos. The model processes video frames to predict whether a given video segment is normal or contains anomalies.

Model Details

Model Description

-This model is based on a 3D Convolutional Neural Network (3D CNN) and is designed to analyze video data for anomaly detection. It has been trained on the UCF-Crime dataset and can classify videos into two categories: normal or anomalous.

Developed by: YOR Group Funded by [optional]: [More Information Needed] Model type: 3D Convolutional Neural Network (3D CNN) Language(s) (NLP): N/A License: MIT License Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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Uses

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Citation [optional]

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