Leveraging Convolutional Neural Networks for Face Mask Detection
Bibliographic record
Abstract
COVID-19 has made face masks an imperative whenever an individual is going out in public. However, many people are remiss in fulfilling their duty to society. They are deviating from the lockdown norms and violating the regulatory measures set by the government. Such a situation only proliferates the spread of COVID-19 and makes it difficult to control it. In this paper, we use Convolutional Neural Networks (CNNs) to detect whether a person is wearing a face mask. This research uses TensorFlow and Keras to build a CNN which detects face masks with an accuracy of over 98% within 10 epochs. This algorithm will be a boon in places like malls or public areas where automated doors can be shut tight if the prospect trying to enter the store is not wearing a mask. Overall, this paper will help create products that can be used to safely break the COVID-19 chain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".