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Deep Learning Inference on Edge Device: Traffic Violation Detection Using OpenVino BY Chiew Jing ChengA REPORT SUBMITTED TO Universiti Tunku Abdul Rahman in partial fulfillment of the requirements
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Deep learning inference is the process of using a trained deep learning model to make predictions or decisions on new, unseen data.
Who is required to file deep learning inference on?
Researchers, data scientists, and organizations that develop or deploy deep learning models must ensure proper reporting on deep learning inference, particularly in regulated industries.
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Filling out deep learning inference requires gathering pertinent data inputs, configuring the model parameters appropriately, and documenting the outcomes of the predictions along with any necessary metrics.
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The purpose of deep learning inference is to apply learned patterns from training data to make accurate predictions or derive insights from new data that the model has not previously encountered.
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Information that must be reported includes input data characteristics, model configuration details, prediction outcomes, performance metrics, and any relevant context for the inference results.
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