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Adam M. added photos to project Intel® AI / Colfax TASS Trainer

Medium 5541bee4 6575 4ddb b0a5 e39e3a31ef1b

Intel® AI / Colfax TASS Trainer

Introduction
There have been many versions of the TASS computer vision projects over the years, one being a version built on a Raspberry Pi 3 using a program based on the Tensorflow Inception V3 transfer learning example.

The Colfax TASS Trainer replicates the transfer learning side of the original program and is trained on the Intel AI HPC Cluster (Colfax Cluster).

Access to Intel’s Ai DevCloud is free, just register on the Intel AI DevCloud website and you will get free access to the cloud within 48 hours.

https://software.intel.com/en-us/ai-academy/tools/devcloud

Python Versions
- Python 3 (Intel, 2018 update 1)

Software requirements
- Intel Optimized TensorFlow (1.3.1)

Hardware requirements
- Access to the Intel AI HPC Cluster (Colfax Cluster)

Cloning The Repo
You will need to clone this repository to a location on your development terminal. Navigate to the directory you would like to download it to and issue the following commands.

$ git clone https://github.com/TechBubbleTechnologies/IoT-JumpWay-Intel-Examples.git
Once you have the repo, you will need to find the files in this folder located in this location

Install Requirements
Everything for this tutorial is already provided on Colfax.

Login To Colfax
Login to your Colfax Notebook area by following this link.

Upload Structure To Colfax
Once you have completed the steps above, it is time to login to Colfax and upload the structure described above. You need to upload all of the files shown in the screen shot below, with exception to the README.

Training & Testing Data

You can train and test this example without finding any additional training and testing data, but if you would like to add your own classes you will find the training data in the training/human directory.

The test data provided (Collected from Google) has two classes, 1 and 2, these folders represent Darth Vader and Yoda, in these directories (classes) are 30 images of each character, directory 1 represents Darth and directory 2 represents Yoda.

You can name these directories what you like, the name of the directory will be used in the predictions, so if the program detects Darth in an image it will return 1 as the highest prediction.

You can add as many as classes as you like, each class will slow the training down. Each class should have no less than 30 images, generally any more than 25 but less than 30 will crash the script with devision by 0 right at the end of the training process.

Start The Training
Now it is time to start the training, head on over to Colfax TASS Trainer notebook on Colfax. You do not need to execute any of the code blocks except for the first until you get to "Create training job", here you can begin to execute the blocks of code following the guide which will submit a job to train TASS on the Colfax Clusters.

Testing The Trained Model
Now time for the crunch, again, you do not need to find any additional training to test this program, but if you do want to you can add more images to the model/testing directory. The testing data provided for out of the box use of this tutorial include 2 images of Darth, 2 of Yoda, and 2 of a very handsome guy ;)

Head on over to Colfax TASS Trainer Inference notebook on Colfax. You do not need to execute any of the code blocks except for the first until you get to "Create testing job", here you can begin to execute the blocks of code following the guide which will submit a job to test TASS on the Colfax Clusters.

The output for me was as follows:

TESTING FACIAL REC

FILE: Darth1.jpg
1 (score = 0.99834)
2 (score = 0.00164)

PROVIDED IMAGE: Darth1.jpg
OBJECT DETECTED: 1
CONFIDENCE: 0.998343
...

FILE: Darth2.jpg
1 (score = 0.99692)
2 (score = 0.00311)

PROVIDED IMAGE: Darth2.jpg
OBJECT DETECTED: 1
CONFIDENCE: 0.996923
...

FILE: Yoda2.jpg
2 (score = 0.99710)
1 (score = 0.00293)

PROVIDED IMAGE: Yoda2.jpg
OBJECT DETECTED: 2
CONFIDENCE: 0.997104
...

FILE: Yoda1.jpg
2 (score = 0.99535)
1 (score = 0.00465)

FILE: VeryHansomeGuy.jpg
2 (score = 0.72019)
1 (score = 0.27821)

FILE: VeryHansomeGuy2.jpg
2 (score = 0.59470)
1 (score = 0.40845)

COMPLETED TESTING FACIAL RECOGNITION

This means that Darth was identified in each image, Yoda in 1 and the program successfully identified me as an unknown person.

Medium adam

Adam M. created project Intel® AI / Colfax TASS Trainer

Medium 5541bee4 6575 4ddb b0a5 e39e3a31ef1b

Intel® AI / Colfax TASS Trainer

Introduction There have been many versions of the TASS computer vision projects over the years, one being a version built on a Raspberry Pi 3 using a program based on the Tensorflow Inception V3 transfer learning example.

The Colfax TASS Trainer replicates the transfer learning side of the original program and is trained on the Intel AI HPC Cluster (Colfax Cluster).

Access to Intel’s Ai DevCloud is free, just register on the Intel AI DevCloud website and you will get free access to the cloud within 48 hours.

https://software.intel.com/en-us/ai-academy/tools/devcloud

Python Versions - Python 3 (Intel, 2018 update 1)

Software requirements - Intel Optimized TensorFlow (1.3.1)

Hardware requirements - Access to the Intel AI HPC Cluster (Colfax Cluster)

Cloning The Repo You will need to clone this repository to a location on your development terminal. Navigate to the directory you would like to download it to and issue the following commands.

$ git clone https://github.com/TechBubbleTechnologies/IoT-JumpWay-Intel-Examples.git Once you have the repo, you will need to find the files in this folder located in this location

Install Requirements Everything for this tutorial is already provided on Colfax.

Login To Colfax Login to your Colfax Notebook area by following this link.

Upload Structure To Colfax Once you have completed the steps above, it is time to login to Colfax and upload the structure described above. You need to upload all of the files shown in the screen shot below, with exception to the README.

Training & Testing Data

You can train and test this example without finding any additional training and testing data, but if you would like to add your own classes you will find the training data in the training/human directory.

The test data provided (Collected from Google) has two classes, 1 and 2, these folders represent Darth Vader and Yoda, in these directories (classes) are 30 images of each character, directory 1 represents Darth and directory 2 represents Yoda.

You can name these directories what you like, the name of the directory will be used in the predictions, so if the program detects Darth in an image it will return 1 as the highest prediction.

You can add as many as classes as you like, each class will slow the training down. Each class should have no less than 30 images, generally any more than 25 but less than 30 will crash the script with devision by 0 right at the end of the training process.

Start The Training Now it is time to start the training, head on over to Colfax TASS Trainer notebook on Colfax. You do not need to execute any of the code blocks except for the first until you get to "Create training job", here you can begin to execute the blocks of code following the guide which will submit a job to train TASS on the Colfax Clusters.

Testing The Trained Model Now time for the crunch, again, you do not need to find any additional training to test this program, but if you do want to you can add more images to the model/testing directory. The testing data provided for out of the box use of this tutorial include 2 images of Darth, 2 of Yoda, and 2 of a very handsome guy ;)

Head on over to Colfax TASS Trainer Inference notebook on Colfax. You do not need to execute any of the code blocks except for the first until you get to "Create testing job", here you can begin to execute the blocks of code following the guide which will submit a job to test TASS on the Colfax Clusters.

The output for me was as follows:

TESTING FACIAL REC

FILE: Darth1.jpg 1 (score = 0.99834) 2 (score = 0.00164)

PROVIDED IMAGE: Darth1.jpg OBJECT DETECTED: 1 CONFIDENCE: 0.998343 ...

FILE: Darth2.jpg 1 (score = 0.99692) 2 (score = 0.00311)

PROVIDED IMAGE: Darth2.jpg OBJECT DETECTED: 1 CONFIDENCE: 0.996923 ...

FILE: Yoda2.jpg 2 (score = 0.99710) 1 (score = 0.00293)

PROVIDED IMAGE: Yoda2.jpg OBJECT DETECTED: 2 CONFIDENCE: 0.997104 ...

FILE: Yoda1.jpg 2 (score = 0.99535) 1 (score = 0.00465)

FILE: VeryHansomeGuy.jpg 2 (score = 0.72019) 1 (score = 0.27821)

FILE: VeryHansomeGuy2.jpg 2 (score = 0.59470) 1 (score = 0.40845)

COMPLETED TESTING FACIAL RECOGNITION

This means that Darth was identified in each image, Yoda in 1 and the program successfully identified me as an unknown person.

Adam

Adam M. added photos to project BubbleNLU Deep Learning NLU Engine

Medium eb938210 949d 4405 9a2b 3f9c9262053b

BubbleNLU Deep Learning NLU Engine

BubbleNLU is a state of the art Deep Learning NLU Engine (Natural Language Understanding). The project has been created to provide the TechBubble Technologies EcoSystem with a more advanced and efficient A.I. assistant, TIA, who is soon to replace TOA in all TechBubble Technologies systems including the IoT JumpWay / IntelliLan products and services and A.I. Hybrid Application products.

CURRENT FEATURES:

Updatable default responses
Trained / interfaced via API
Intent classification
Intent classification threshold sent as API parameter
Entity training /
Entity synonym training / classification
Dialogue context management
Automatic dialogue context resetting if user changes conversation
Intent actions
Multiple models / users
Repetition handling
Updatable repetition responses
Updatable repetition ignore count
Updatable repetition ignore count cutoff
Served on secure Nginx server

FUTURE FEATURES INCLUDE:

Internal actions for updating user details such as name, age, location etc
Slotfilling with automatic cancellation if user changes conversation
Sentiment analysis
Webhooks
Managed via TechBubble ARC
Hosted on Colfax

AREAS THAT HAVE IMPROVED THE ENGINE:

Automatic Dialogue Resetting: One issue with other services I have used was getting caught in a loop when contexts were set. In my implementation, once the context has been incorrectly matched, if the following intent does not match the context it will reset the context and provide the response to the current intent request.

Post Entity Processing: A lot of the time on other services I have used, when the intent has been matched and entity has been matched, it would return the correct response and entity type, but the wrong entity definition, in my case, it would break the application as entity definitions are what matches the actions to the relevant entry in the database. This would lead to situations such the client being redirected to a product page that did not exist. In my implementation, fallback responses can be added to the data, the engine will check to see if there is a valid entity reference or synonym that matches, and if not will return one of the provided fallbacks, also, it will clear the entity totally if no actual entity was provided in the training data for that intent.

Repetition Management: None of the services I used handled repetition, in my implementation repeat limits can be set and a repeat string which will be added to the beginning of the response for each repeat count. I response before the repeat limit, the bot will respond with a predefined warning that it will no longer respond to the question / statement if asked again, and then if asked again will ignore the user for the duration of a predefined time period.

Adam

Adam M. added photos to project TASS PVL Computer Vision Hub

Medium 639cd5fa 019c 4c7c 9985 21c578b68350

TASS PVL Computer Vision Hub

DESCRIPTION:
TASS PVL is an open source version of the technology used to power TASS that allows developers to create their own IoT connected, Artficially Intelligent Assistant. TASS PVL utilizes the Intel® Computer Vision SDK Beta to bring industry standard computer vision to the project. TASS PVL can connect to either an Intel® Realsense camera or a webcam via USB.

First, TASS PVL detects if there is a face, or faces, present in the frames passed to it from the camera, and if so passes the frames through the computer vision algorythm to determine whether the face is a known person or an intruder. In the event of a known person or intruder, TASS PVL communicates with the IoT JumpWay which can then communicate with IoT devices and applications based on predefined rules, for instance, controlling other devices on the network or raising alarms in applications etc.

INTEL® TECHNOLOGY
TASS PVL uses the following Intel technologies:

- Intel® Core i7 NUC
- Intel® Computer Vision SDK Beta
- Intel® Realsense (R200,F200)

IOT CONNECTIVITY:
The IoT connectivity is managed by the TechBubble IoT JumpWay, the TechBubble Technologies IoT PaaS which primarily, at this point, uses secure MQTT protocol. Rules can be set up that can be triggered by sensor values/warning messages/device status messages and identified known people or intruder alerts. These rules allow connected devices to interact with each other autonomously, providing an automated smart home/business environment.

ARTIFICIAL INTELLIGENCE:
TASS PVL uses the Intel Computer Vision SDK Beta to provide the system with Artificial Intelligence. For other uses of A.I. used in TASS Hub, follow this link.

INTELLILAN MANAGEMENT:
The IntelliLan Management Console/Applications are essentially IoT JumpWay applications, capable of controlling all IntelliLan devices on their network and communicating with the IoT JumpWay. Users can use the console and manage their devices using their voice which is powered by TIA, an A.I. agent developed to assist home and business owners to use TechBubble web and IoT systems.

Adam

Adam M. added photos to project TASS PVL Computer Vision Hub

Medium 6aac905f 2830 45a5 b481 0983343eecd0

TASS PVL Computer Vision Hub

DESCRIPTION:
TASS PVL is an open source version of the technology used to power TASS that allows developers to create their own IoT connected, Artficially Intelligent Assistant. TASS PVL utilizes the Intel® Computer Vision SDK Beta to bring industry standard computer vision to the project. TASS PVL can connect to either an Intel® Realsense camera or a webcam via USB.

First, TASS PVL detects if there is a face, or faces, present in the frames passed to it from the camera, and if so passes the frames through the computer vision algorythm to determine whether the face is a known person or an intruder. In the event of a known person or intruder, TASS PVL communicates with the IoT JumpWay which can then communicate with IoT devices and applications based on predefined rules, for instance, controlling other devices on the network or raising alarms in applications etc.

INTEL® TECHNOLOGY
TASS PVL uses the following Intel technologies:

- Intel® Core i7 NUC
- Intel® Computer Vision SDK Beta
- Intel® Realsense (R200,F200)

IOT CONNECTIVITY:
The IoT connectivity is managed by the TechBubble IoT JumpWay, the TechBubble Technologies IoT PaaS which primarily, at this point, uses secure MQTT protocol. Rules can be set up that can be triggered by sensor values/warning messages/device status messages and identified known people or intruder alerts. These rules allow connected devices to interact with each other autonomously, providing an automated smart home/business environment.

ARTIFICIAL INTELLIGENCE:
TASS PVL uses the Intel Computer Vision SDK Beta to provide the system with Artificial Intelligence. For other uses of A.I. used in TASS Hub, follow this link.

INTELLILAN MANAGEMENT:
The IntelliLan Management Console/Applications are essentially IoT JumpWay applications, capable of controlling all IntelliLan devices on their network and communicating with the IoT JumpWay. Users can use the console and manage their devices using their voice which is powered by TIA, an A.I. agent developed to assist home and business owners to use TechBubble web and IoT systems.

Adam

Adam M. added photos to project TASS PVL Computer Vision Hub

Medium dc2adc7a 1579 429e b803 829246f1aa62

TASS PVL Computer Vision Hub

DESCRIPTION:
TASS PVL is an open source version of the technology used to power TASS that allows developers to create their own IoT connected, Artficially Intelligent Assistant. TASS PVL utilizes the Intel® Computer Vision SDK Beta to bring industry standard computer vision to the project. TASS PVL can connect to either an Intel® Realsense camera or a webcam via USB.

First, TASS PVL detects if there is a face, or faces, present in the frames passed to it from the camera, and if so passes the frames through the computer vision algorythm to determine whether the face is a known person or an intruder. In the event of a known person or intruder, TASS PVL communicates with the IoT JumpWay which can then communicate with IoT devices and applications based on predefined rules, for instance, controlling other devices on the network or raising alarms in applications etc.

INTEL® TECHNOLOGY
TASS PVL uses the following Intel technologies:

- Intel® Core i7 NUC
- Intel® Computer Vision SDK Beta
- Intel® Realsense (R200,F200)

IOT CONNECTIVITY:
The IoT connectivity is managed by the TechBubble IoT JumpWay, the TechBubble Technologies IoT PaaS which primarily, at this point, uses secure MQTT protocol. Rules can be set up that can be triggered by sensor values/warning messages/device status messages and identified known people or intruder alerts. These rules allow connected devices to interact with each other autonomously, providing an automated smart home/business environment.

ARTIFICIAL INTELLIGENCE:
TASS PVL uses the Intel Computer Vision SDK Beta to provide the system with Artificial Intelligence. For other uses of A.I. used in TASS Hub, follow this link.

INTELLILAN MANAGEMENT:
The IntelliLan Management Console/Applications are essentially IoT JumpWay applications, capable of controlling all IntelliLan devices on their network and communicating with the IoT JumpWay. Users can use the console and manage their devices using their voice which is powered by TIA, an A.I. agent developed to assist home and business owners to use TechBubble web and IoT systems.

Adam

Adam M. added photos to project TASS PVL Computer Vision Hub

Medium 639cd5fa 019c 4c7c 9985 21c578b68350

TASS PVL Computer Vision Hub

DESCRIPTION:
TASS PVL is an open source version of the technology used to power TASS that allows developers to create their own IoT connected, Artficially Intelligent Assistant. TASS PVL utilizes the Intel® Computer Vision SDK Beta to bring industry standard computer vision to the project. TASS PVL can connect to either an Intel® Realsense camera or a webcam via USB.

First, TASS PVL detects if there is a face, or faces, present in the frames passed to it from the camera, and if so passes the frames through the computer vision algorythm to determine whether the face is a known person or an intruder. In the event of a known person or intruder, TASS PVL communicates with the IoT JumpWay which can then communicate with IoT devices and applications based on predefined rules, for instance, controlling other devices on the network or raising alarms in applications etc.

INTEL® TECHNOLOGY
TASS PVL uses the following Intel technologies:

- Intel® Core i7 NUC
- Intel® Computer Vision SDK Beta
- Intel® Realsense (R200,F200)

IOT CONNECTIVITY:
The IoT connectivity is managed by the TechBubble IoT JumpWay, the TechBubble Technologies IoT PaaS which primarily, at this point, uses secure MQTT protocol. Rules can be set up that can be triggered by sensor values/warning messages/device status messages and identified known people or intruder alerts. These rules allow connected devices to interact with each other autonomously, providing an automated smart home/business environment.

ARTIFICIAL INTELLIGENCE:
TASS PVL uses the Intel Computer Vision SDK Beta to provide the system with Artificial Intelligence. For other uses of A.I. used in TASS Hub, follow this link.

INTELLILAN MANAGEMENT:
The IntelliLan Management Console/Applications are essentially IoT JumpWay applications, capable of controlling all IntelliLan devices on their network and communicating with the IoT JumpWay. Users can use the console and manage their devices using their voice which is powered by TIA, an A.I. agent developed to assist home and business owners to use TechBubble web and IoT systems.

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