Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
PPE Detector for Laboratory Safety Free trial
By:
Latest Version:
1.0
Image recognition and classification model to detect PPE non-compliance in laboratory environments in real-time
Product 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/B08BT5CV2F We provide free support during the trial period! After you've succeeded with the subscription, reach out at: support@vitechlab.com
Key Data
Version
Type
Model Package
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
Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us
Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$5.00/hr
running on ml.p3.2xlarge
Model Batch Transform$20.00/hr
running on ml.p3.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$3.825/host/hr
running on ml.p3.2xlarge
SageMaker Batch Transform$3.825/host/hr
running on ml.p3.2xlarge
About Free trial
Try this product for 5 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $5.00 | |
ml.m5.4xlarge | $5.00 | |
ml.m5d.24xlarge | $5.00 | |
ml.m4.16xlarge | $5.00 | |
ml.m5.2xlarge | $5.00 | |
ml.p3.16xlarge | $5.00 | |
ml.c5d.4xlarge | $5.00 | |
ml.m4.2xlarge | $5.00 | |
ml.c5.2xlarge | $5.00 | |
ml.c5d.9xlarge | $5.00 | |
ml.p3.2xlarge Vendor Recommended | $5.00 | |
ml.c4.2xlarge | $5.00 | |
ml.m4.10xlarge | $5.00 | |
ml.m5.24xlarge | $5.00 | |
ml.m5d.xlarge | $5.00 | |
ml.p2.xlarge | $5.00 | |
ml.m5.12xlarge | $5.00 | |
ml.m5d.4xlarge | $5.00 | |
ml.p2.16xlarge | $5.00 | |
ml.c4.4xlarge | $5.00 | |
ml.m5.xlarge | $5.00 | |
ml.c5.9xlarge | $5.00 | |
ml.c5.4xlarge | $5.00 | |
ml.m4.xlarge | $5.00 | |
ml.m5d.2xlarge | $5.00 | |
ml.p3.8xlarge | $5.00 | |
ml.m5d.12xlarge | $5.00 | |
ml.c4.8xlarge | $5.00 | |
ml.p2.8xlarge | $5.00 | |
ml.t2.xlarge | $5.00 | |
ml.c5.18xlarge | $5.00 | |
ml.c5d.18xlarge | $5.00 | |
ml.t2.2xlarge | $5.00 | |
ml.c5d.2xlarge | $5.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
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-Detector
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
PPE Detector for Laboratory Safety
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.com
AWS Infrastructure
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.
Learn MoreRefund Policy
We do not offer refunds at this time.
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