Sold by: VITech LabÂ
Deployed on AWS
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Image recognition and classification model developed for real-time detection of the absence of masks and respirators on people.
Overview
Mask Detector - is a real-time computer vision model for detecting the absence of masks or respirators on people. The solution is a tool for ensuring epidemiological safety in laboratories, healthcare facilities, schools and universities, industrial companies, government, military facilities, etc. It is trained on the dataset manually selected and annotated by VITech Lab team. It works with live footage from CCTV cameras and detects people not wearing masks in real-time. When a violation is detected, the algorithm automatically notifies a safety engineer.
We also have a ready to use software, PPE Monitoring Platform: https://aws.amazon.com/marketplace/pp/B08BT5CV2FÂ
We provide free support during the trial period! After you've succeeded with the subscription, reach out at: support@vitechlab.comÂ
Highlights
- Mask Detector: Trained on the privately collected in VITech Lab dataset of real images from IP/CCTV cameras. The training dataset was considerably enlarged with augmented data. The model was trained on images of different resolution and accepts images of any size that are resized internally.
- Uses a custom-designed object detection architecture to detect faces and classify masks on them. The inference time is dependent on the number of faces detected in a single image. Inference latency is dependent on the hardware. Average inference latency is: ml.c4.xlarge - 3.25 s ml.c5.xlarge - 3 s ml.p2.xlarge - 250 ms ml.p3.xlarge - 120 ms ml.g4dn.xlarge - 90 ms
- Need a custom-made solution for video/image analysis? Or maybe need a custom PPE compliance detector? Reach us at support@vitechlab.com
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $15.00 |
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $3.00 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $15.00 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $15.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $15.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $15.00 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $15.00 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $15.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $15.00 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $15.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Updated face detection with a bigger model Updated mask classification models Optimized inference speed
Additional details
Inputs
- Summary
Supported content types: image/jpeg
This model accepts images in the mime-type specified above.
The image must be at least 224x224. The model resizes the image to the size specified by the user before performing the inference. Better results are achieved with 16:9 image proportions.
Content type: text/json
For every image, the model returns a single JSON file with all the detections.
The model returns JSON object, that includes an array with individual elements for each face detected. Each element has three attributes:
- box_points: includes the bounding box around the detected face. Each bounding box consists of four numbers in [X1 Y1 X2 Y2] format in the source image coordinates.
- classes: “no_mask” represents the probability score that the face in this bounding box does not have a mask. Probability is given in percents (0..100 range)
Prediction method can take four additional parameters: face_detector_arch - [resnet18, mobilenet_v2] - the architecture of mask classifier subnetwork face_crop_size - [64, 128, 192, 384] - final size, to which extracted faces will be resized before classification (higher is slower) face_detector_image_height - height of the image to be resized internally (higher is slower) face_detector_confidence - confidence value (in 0..1 range) to filter boxes with faces Use higher values for images of better quality or bigger spatial resolution.
We recommend using this model for real-time inference for better utilization of the endpoint. Optionally, batch transform is also available.
**You can find more details here: https://github.com/VITechLab/aws-sagemaker-examples/tree/master/Medical-Mask-Detector **
- Input MIME type
- image/jpeg
See Input Summary
See Input Summary
Resources
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Support
Vendor support
If you have any issues or feature requests, please write to us, and we will be happy to help you as soon as possible. We can also create custom software and models optimised for your specific use case. Reach us at: support@vitechlab.comÂ
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