Facial Expression Recognition
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.
- Cost per run
- Avg run time
- Nvidia T4 GPU
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Summary of this model and related resources.
|Facial Expression Recognition
Facial Expression Recognition using Residual Masking Network
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How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run
|Nvidia T4 GPU
|Average Completion Time