Chat
Online
Inquiry
Home > Double protective clothing

Double protective clothing

Shanghai Sunland Industrial Co., Ltd is the top manufacturer of Personal Protect Equipment in China, with 20 years’experience. We are the Chinese government appointed manufacturer for government power,personal protection equipment , medical instruments,construction industry, etc. All the products get the CE, ANSI and related Industry Certificates. All our safety helmets use the top-quality raw material without any recycling material.

Why Choose Us
01
Solutions to meet different needs

We provide exclusive customization of the products logo, using advanced printing technology and technology, not suitable for fading, solid and firm, scratch-proof and anti-smashing, and suitable for various scenes such as construction, mining, warehouse, inspection, etc. Our goal is to satisfy your needs. Demand, do your best.

02
Highly specialized team and products

Professional team work and production line which can make nice quality in short time.

03
We trade with an open mind

We abide by the privacy policy and human rights, follow the business order, do our utmost to provide you with a fair and secure trading environment, and look forward to your customers coming to cooperate with us, openly mind and trade with customers, promote common development, and work together for a win-win situation.

04
24 / 7 guaranteed service

The professional team provides 24 * 7 after-sales service for you, which can help you solve any problems

Certificate of Honor
Get in touch with usCustomer satisfaction is our first goal!
Email us
— We will confidentially process your data and will not pass it on to a third party.
Double protective clothing
Mask Detection from the edge to the cloud — with ...
Mask Detection from the edge to the cloud — with ...

14/10/2020, · Apply a ,mask, / no ,mask, binary classifier on the cropped face. (Model trained for this project) Create an event if no ,mask, is detected and store it on local storage. The logic is detailed in the main() function of my detect_,mask,.py. 2. Collecting the events. Once the Pi records an event, we send it to a server for archiving and further processing.

Object detection using Mask R-CNN on a custom dataset | by ...
Object detection using Mask R-CNN on a custom dataset | by ...

Returns: ,masks,: A bool array of shape [height, width, instance count] with one ,mask, per instance. class_ids: a 1D array of class IDs of the instance ,masks,. """ def load_,mask,(self, image_id): # get details of image info = self.image_info[image_id] # define anntation file location path = info['annotation'] # load XML boxes, w, h = self.extract_boxes(path) # create one array for all ,masks,, each ...

Training Instance Segmentation Models Using Mask R-CNN on ...
Training Instance Segmentation Models Using Mask R-CNN on ...

Conclusion. In this post, you learned about training instance segmentation models using the ,Mask R-CNN, architecture with the TLT. The post showed taking an open-source COCO dataset with one of the pretrained models from NGC and training and optimizing with TLT to deploying the model on the ,edge, using the DeepStream SDK.

[PDF] Real-time Mask Detection on Google Edge TPU ...
[PDF] Real-time Mask Detection on Google Edge TPU ...

After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing ,masks, in order to reduce risk of front-line workers. In addition, processing user data locally is a great way to address both privacy and network bandwidth issues. In this paper, we present a light-weighted model for detecting whether people in a particular area wear ,masks,, which can also be ...

Real-time Mask Detection on Google Edge TPU | Papers With Code
Real-time Mask Detection on Google Edge TPU | Papers With Code

Get the latest machine learning methods with code. Browse our catalogue of tasks and access state-of-the-art solutions. Tip: you can also follow us on Twitter

[2010.04427] Real-time Mask Detection on Google Edge TPU
[2010.04427] Real-time Mask Detection on Google Edge TPU

9/10/2020, · After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing ,masks, in order to reduce risk of front-line workers. In addition, processing user data locally is a great way to address both privacy and network bandwidth issues. In this paper, we present a light-weighted model for detecting whether people in a particular area wear ,masks,, which can also be ...

Training Instance Segmentation Models Using Mask R-CNN on ...
Training Instance Segmentation Models Using Mask R-CNN on ...

Conclusion. In this post, you learned about training instance segmentation models using the ,Mask R-CNN, architecture with the TLT. The post showed taking an open-source COCO dataset with one of the pretrained models from NGC and training and optimizing with TLT to deploying the model on the ,edge, using the DeepStream SDK.

An Edge TPU demo project. Starting from videos and ending ...
An Edge TPU demo project. Starting from videos and ending ...

Last blogpost, the dark secrets of how the ,Edge TPU, works were unveiled.In this blogpost, we’ll use the ,Edge TPU, to create our very own demo project! The goal of this blogpost is to give you a step-by-step guide of how to perform object detection on the ,Edge TPU,.At the end of this blogpost we will be able to detect a set of tools: screwdrivers, cutters and pliers.

TensorFlow models on the Edge TPU | Coral
TensorFlow models on the Edge TPU | Coral

In order for the ,Edge TPU, to provide high-speed neural network performance with a low-power cost, the ,Edge TPU, supports a specific set of neural network operations and architectures. This page describes what types of models are compatible with the ,Edge TPU, and how you can create them, either by compiling your own TensorFlow model or retraining an existing model with transfer-learning.

Real-time Mask Detection on Google Edge TPU
Real-time Mask Detection on Google Edge TPU

After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing ,masks, in order to reduce risk of front-line workers. In addition, processing user data locally is a great way to address both privacy and network bandwidth issues. In this paper, we present a light-weighted model for detecting whether people in a particular area wear ,masks,, which can also be ...