Sold by: VITech LabÂ
Deployed on AWS
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Image recognition and classification model to detect PPE non-compliance in laboratory environments in real-time
Overview
The PPE Detector for Laboratory Safety - is a real-time computer vision model for identifying PPE non-compliance in a laboratory or healthcare facilities or life science manufacturing sites. 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 any of four objects: Coat, Glasses, Glove, Mask. Notifications are sent when the absence of PPE is detected. The ML model can be used in pharmaceutical or medical devices manufacturing, laboratories, universities, research centres and healthcare facilities.
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
- 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 resolutions and accepts images of any size that are resized internally.
- Uses a custom-designed object detection architecture to detect people and classify different lab clothes on them. The inference time is dependent on the number of faces detected in a single image. Inference latency is dependent on the hardware.
- 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
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Pricing
Free trial
Try this product free for 5 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.p3.2xlarge instance type, real-time mode | $5.00 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
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We do not offer refunds at this time.
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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
1.0 version release
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 320x320. The model resizes the image to 640x640 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 person detected. Each element has two attributes:
- box_points: includes the bounding box around the detected person. Each bounding box consists of four numbers in [X1 Y1 X2 Y2] format in the source image coordinates.
- classes: tuple (class name, confidence) that represent the probability score that the person in this bounding box does not wear a “class” object. Probability is given in percentages (0..100 range) Supported classes are: “no_gloves”, “no_glasses”, “no_mask”, “no_coat”
Prediction method takes no additional parameters.
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/Laboratory-PPE-DetectorÂ
- Input MIME type
- image/jpeg
See Input Summary
See Input Summary
Resources
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
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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