Azure-CustomVision | Custom Vision Service is an Azure Cognitive Service | Computer Vision library
kandi X-RAY | Azure-CustomVision Summary
kandi X-RAY | Azure-CustomVision Summary
Azure-CustomVision is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, OpenCV applications. Azure-CustomVision has no bugs, it has no vulnerabilities and it has low support. However Azure-CustomVision build file is not available. You can download it from GitHub.
The Custom Vision Service is an Azure Cognitive Service that lets you build custom image classifiers. It makes it easy and fast to build, deploy, and improve an image classifier. The Custom Vision Service provides a REST API and a web interface to upload your images and train the classifier. The Custom Vision Service works best when the item you're trying to classify is prominent in your image. Few images are required to create a classifier or detector. 50 images per class are enough to start your prototype. The methods Custom Vision Service uses are robust to differences, which allows you to start prototyping with so little data. This means Custom Vision Service is not well suited to scenarios where you want to detect subtle differences. For example, minor cracks or dents in quality assurance scenarios.
The Custom Vision Service is an Azure Cognitive Service that lets you build custom image classifiers. It makes it easy and fast to build, deploy, and improve an image classifier. The Custom Vision Service provides a REST API and a web interface to upload your images and train the classifier. The Custom Vision Service works best when the item you're trying to classify is prominent in your image. Few images are required to create a classifier or detector. 50 images per class are enough to start your prototype. The methods Custom Vision Service uses are robust to differences, which allows you to start prototyping with so little data. This means Custom Vision Service is not well suited to scenarios where you want to detect subtle differences. For example, minor cracks or dents in quality assurance scenarios.
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Support
Azure-CustomVision has a low active ecosystem.
It has 2 star(s) with 2 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 0 have been closed. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Azure-CustomVision is current.
Quality
Azure-CustomVision has 0 bugs and 0 code smells.
Security
Azure-CustomVision has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Azure-CustomVision code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Azure-CustomVision does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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Azure-CustomVision releases are not available. You will need to build from source code and install.
Azure-CustomVision has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions, examples and code snippets are available.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of Azure-CustomVision
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of Azure-CustomVision
Azure-CustomVision Key Features
No Key Features are available at this moment for Azure-CustomVision.
Azure-CustomVision Examples and Code Snippets
No Code Snippets are available at this moment for Azure-CustomVision.
Community Discussions
Trending Discussions on Azure-CustomVision
QUESTION
How to upload a batch of images to Azure Custom Vision using JavaScript
Asked 2020-Aug-01 at 00:55
I want to upload a batch of 64 images to Custom Vision using the JavaScript SDK.
...ANSWER
Answered 2020-Aug-01 at 00:55I figured out that my implementation was completely messed up.
This is how I solved it for now:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Azure-CustomVision
Download and unzip customvision.zip to C drive from https://iothubstorageaccts.blob.core.windows.net/cvpic/customvision.zip. Open https://customvision.ai/ by Microsoft Edge. Click sign in with provided azure subscription account. You will see below page once login. Click NEW PROJECT provide the requested information about your service as specified in the table below image. Entry Name of your project select resource group you create in Exercise 1 in Resource Group section, other by default. Click Add images on the top of panel, locate to folder C;\Lab\customvision\gear_images\axes, select all of images by Ctrl + A, click Open, entry axes as tag at My Tag section. Click Upload 79 files. Do the same action for rest tags in C:\ gear_images\. Click Train at left of top panel to train the model by provide images of each tag. Waiting for training integration complete. You will find Precision and Recall number of your trained model. Click Mark Default at top of panel. Click Quick Test at left of top panel, entry.
Download and unzip customvision.zip to C drive from https://iothubstorageaccts.blob.core.windows.net/cvpic/customvision.zip
Open https://customvision.ai/ by Microsoft Edge
Click sign in with provided azure subscription account. You will see below page once login.
Click NEW PROJECT provide the requested information about your service as specified in the table below image. Entry Name of your project select resource group you create in Exercise 1 in Resource Group section, other by default. Click Create project
Click Add images on the top of panel, locate to folder C;\Lab\customvision\gear_images\axes, select all of images by Ctrl + A, click Open, entry axes as tag at My Tag section Click Upload 79 files Do the same action for rest tags in C:\ gear_images\
Click Train at left of top panel to train the model by provide images of each tag Waiting for training integration complete You will find Precision and Recall number of your trained model. Click Mark Default at top of panel
Click Quick Test at left of top panel, entry https://www.alpinetrek.co.uk/1500_1500_90/002-0808/berghaus-baffin-island-shell-jacket-hardshell-jacket.jpg In Image URL section, click you will see test result like below Close output panel.
Verify API by post URL endpoint. Click Predictions at top of panel, select result picture you just test, and click delete. Click View Endpoint Open Postman on the desktop, entry POST URL with first URL In above output panel. Entry header by key/value above Entry Body with raw and JSON format with {"Url": "https://www.alpinetrek.co.uk/1500_1500_90/002-0808/berghaus-baffin-island-shell-jacket-hardshell-jacket.jpg"} Click Send, you will see test result like below
Open Visual Studio Code on desktop, select Python 3.5 at left of bottom panel. clicK File at left of top panel, click Open File…, locate to C;\Lab\customvision\classify.py. Open portal https://customvision.ai/, click sign in if your session is expired. Click setting icon at left of top panel,. Copy Training Key and Prediction Key under resource group you created in Exercise 1 (e.g. techsubmit not Limited trial). Replace training_key and prediction_key in classify.py. Replace project name classifylabfreeze with a new project name, different from project created in Exercise 2. Replace PredictionEndpoint with Prediction Key above. Click F5, select Python at output command. Waiting for process complete, you will see the whole project was created, image uploaded, trained and verified automatically. (you could open https://customvision.ai to check project status, e.g. how many images uploaded).
Open Visual Studio Code on desktop, select Python 3.5 at left of bottom panel. clicK File at left of top panel, click Open File…, locate to C;\Lab\customvision\classify.py click Open
Open portal https://customvision.ai/, click sign in if your session is expired. Click setting icon at left of top panel, Copy Training Key and Prediction Key under resource group you created in Exercise 1 (e.g. techsubmit not Limited trial). Replace training_key and prediction_key in classify.py Replace project name classifylabfreeze with a new project name, different from project created in Exercise 2 Replace PredictionEndpoint with Prediction Key above Click F5, select Python at output command Waiting for process complete, you will see the whole project was created, image uploaded, trained and verified automatically. (you could open https://customvision.ai to check project status, e.g. how many images uploaded)
Open https://customvision.ai/ by Microsoft Edge. Click sign in with provided azure subscription account. You will see below page once login. Click NEW PROJECT provide the requested information about your service as specified in the table below image. Entry Name of your project select resource group you create in Exercise 1 in Resource Group section, select Object Detection (preview) in Project Type section. Click Add images on the top of panel, locate to folder C;\Lab\customvision\gear_images_detection, select all of images by Ctril + A, click Open. Click Upload 251 files.
Open https://customvision.ai/ by Microsoft Edge
Click sign in with provided azure subscription account. You will see below page once login.
Click NEW PROJECT provide the requested information about your service as specified in the table below image. Entry Name of your project select resource group you create in Exercise 1 in Resource Group section, select Object Detection (preview) in Project Type section. Click Create project
Click Add images on the top of panel, locate to folder C;\Lab\customvision\gear_images_detection, select all of images by Ctril + A, click Open Click Upload 251 files
After uploaded all files, click first picture, select boundary of helmets in the picture and add tag helmets like below. Entry helmets in Add Region Tag section first time. Click next icon and do same action of rest of all pictures, it might take couple minutes. (you don’t need tag all of pictures, but at least 30 tagged pictures need)
Click Train at left of top panel to train model by provide images of each tag.
You will find Precision, Recall and M.A.P number of your trained model. Click Mark Default at top of panel
Click Quick Test at left of top panel, entry. In Image URL section, click -> you will see test result like below
Verify API by post URL endpoint. Click Predictions at top of panel, select result picture you just test, and click delete. Click View Endpoint Open Postman on the desktop, entry POST URL with first URL In above output panel. Entry header by key/value above Entry Body with raw and JSON format with {"Url": "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Cologne_Germany_Industrial-work-with-Personal-Protective-Equipment-04.jpg/1200px-Cologne_Germany_Industrial-work-with-Personal-Protective-Equipment-04.jpg"} Click Send, you will see test result like below
Download and unzip customvision.zip to C drive from https://iothubstorageaccts.blob.core.windows.net/cvpic/customvision.zip
Open https://customvision.ai/ by Microsoft Edge
Click sign in with provided azure subscription account. You will see below page once login.
Click NEW PROJECT provide the requested information about your service as specified in the table below image. Entry Name of your project select resource group you create in Exercise 1 in Resource Group section, other by default. Click Create project
Click Add images on the top of panel, locate to folder C;\Lab\customvision\gear_images\axes, select all of images by Ctrl + A, click Open, entry axes as tag at My Tag section Click Upload 79 files Do the same action for rest tags in C:\ gear_images\
Click Train at left of top panel to train the model by provide images of each tag Waiting for training integration complete You will find Precision and Recall number of your trained model. Click Mark Default at top of panel
Click Quick Test at left of top panel, entry https://www.alpinetrek.co.uk/1500_1500_90/002-0808/berghaus-baffin-island-shell-jacket-hardshell-jacket.jpg In Image URL section, click you will see test result like below Close output panel.
Verify API by post URL endpoint. Click Predictions at top of panel, select result picture you just test, and click delete. Click View Endpoint Open Postman on the desktop, entry POST URL with first URL In above output panel. Entry header by key/value above Entry Body with raw and JSON format with {"Url": "https://www.alpinetrek.co.uk/1500_1500_90/002-0808/berghaus-baffin-island-shell-jacket-hardshell-jacket.jpg"} Click Send, you will see test result like below
Open Visual Studio Code on desktop, select Python 3.5 at left of bottom panel. clicK File at left of top panel, click Open File…, locate to C;\Lab\customvision\classify.py. Open portal https://customvision.ai/, click sign in if your session is expired. Click setting icon at left of top panel,. Copy Training Key and Prediction Key under resource group you created in Exercise 1 (e.g. techsubmit not Limited trial). Replace training_key and prediction_key in classify.py. Replace project name classifylabfreeze with a new project name, different from project created in Exercise 2. Replace PredictionEndpoint with Prediction Key above. Click F5, select Python at output command. Waiting for process complete, you will see the whole project was created, image uploaded, trained and verified automatically. (you could open https://customvision.ai to check project status, e.g. how many images uploaded).
Open Visual Studio Code on desktop, select Python 3.5 at left of bottom panel. clicK File at left of top panel, click Open File…, locate to C;\Lab\customvision\classify.py click Open
Open portal https://customvision.ai/, click sign in if your session is expired. Click setting icon at left of top panel, Copy Training Key and Prediction Key under resource group you created in Exercise 1 (e.g. techsubmit not Limited trial). Replace training_key and prediction_key in classify.py Replace project name classifylabfreeze with a new project name, different from project created in Exercise 2 Replace PredictionEndpoint with Prediction Key above Click F5, select Python at output command Waiting for process complete, you will see the whole project was created, image uploaded, trained and verified automatically. (you could open https://customvision.ai to check project status, e.g. how many images uploaded)
Open https://customvision.ai/ by Microsoft Edge. Click sign in with provided azure subscription account. You will see below page once login. Click NEW PROJECT provide the requested information about your service as specified in the table below image. Entry Name of your project select resource group you create in Exercise 1 in Resource Group section, select Object Detection (preview) in Project Type section. Click Add images on the top of panel, locate to folder C;\Lab\customvision\gear_images_detection, select all of images by Ctril + A, click Open. Click Upload 251 files.
Open https://customvision.ai/ by Microsoft Edge
Click sign in with provided azure subscription account. You will see below page once login.
Click NEW PROJECT provide the requested information about your service as specified in the table below image. Entry Name of your project select resource group you create in Exercise 1 in Resource Group section, select Object Detection (preview) in Project Type section. Click Create project
Click Add images on the top of panel, locate to folder C;\Lab\customvision\gear_images_detection, select all of images by Ctril + A, click Open Click Upload 251 files
After uploaded all files, click first picture, select boundary of helmets in the picture and add tag helmets like below. Entry helmets in Add Region Tag section first time. Click next icon and do same action of rest of all pictures, it might take couple minutes. (you don’t need tag all of pictures, but at least 30 tagged pictures need)
Click Train at left of top panel to train model by provide images of each tag.
You will find Precision, Recall and M.A.P number of your trained model. Click Mark Default at top of panel
Click Quick Test at left of top panel, entry. In Image URL section, click -> you will see test result like below
Verify API by post URL endpoint. Click Predictions at top of panel, select result picture you just test, and click delete. Click View Endpoint Open Postman on the desktop, entry POST URL with first URL In above output panel. Entry header by key/value above Entry Body with raw and JSON format with {"Url": "https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Cologne_Germany_Industrial-work-with-Personal-Protective-Equipment-04.jpg/1200px-Cologne_Germany_Industrial-work-with-Personal-Protective-Equipment-04.jpg"} Click Send, you will see test result like below
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