Facial Expression Recognition

phamquiluan

AI model preview image
The facial-expression-recognition model is a system that uses a Residual Masking Network to recognize and classify facial expressions in images. It is trained on a dataset of facial expressions labeled with corresponding emotions. The model takes an input image of a face and processes it to identify the facial features and expressions present. It then classifies the expression into one of several predefined categories, such as happiness, sadness, anger, etc. The model uses residual connections and masking techniques to improve accuracy and extract important information from the input images. This enables the model to accurately recognize and classify facial expressions in real-time applications.

Use cases

The facial-expression-recognition model can be used in a variety of practical applications, particularly in the field of computer vision and human-computer interaction. For example, it can be utilized in emotion detection systems for human-computer interfaces, allowing devices to understand and respond to the emotions of users. This can enhance user experiences in applications such as virtual reality, gaming, and social media. The model can also find applications in the field of psychology and neuroscience, enabling researchers to analyze and understand emotional responses in individuals and groups. Additionally, this model can be integrated into security systems for detecting suspicious or abnormal behavior based on facial expressions, enhancing the capabilities of surveillance systems in public spaces. Overall, the facial-expression-recognition model has the potential to revolutionize various industries and create innovative products that can better understand and respond to human emotions.

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Pricing

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Avg run time
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Hardware
Nvidia T4 GPU
Prediction

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Overview

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PropertyValue
Creatorphamquiluan
Model NameFacial Expression Recognition
Description
Facial Expression Recognition using Residual Masking Network
TagsImage-to-Text
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Runs11,592
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PropertyValue
Cost per Run$-
Prediction HardwareNvidia T4 GPU
Average Completion Time-