Sample Models and Data (GAIFit) with QCI&AI Hardware

▪ QCI&AI-FML Learning Platform (English)

▪ Stages I to III (English) 

▪ Stages I to III (Chinese)

Intelligent Travel: Dataset 1 

Intelligent Environment: Dataset 2  

Intelligent Local Food: Dataset 3 

QCI&AI-FML Learning Platform: https://kws.oaselab.org/qciai/ 

QCI Agent & GAI Image: https://kws.oaselab.org/kws-ai/ 

Data Collection Template: ( Download )

Data Collection Sample: ( Data Collection Steps 1-4 by NUTN Team )

Descriptions

Step 1: Use QCI&AI Learning Tool to collect AIoT data Distance and Light and shooting images.

Step 2: Use QCI Agent & GAI Image to generate texts and conduct a human evaluation to collect HEGAIText.

Step 3: Use QCI Agent & GAI Image to generate images and conduct a human evaluation to collect HEGAIImage.

QCI&AI-FML Learning Platform: https://kws.oaselab.org/qciai/ 

Download Open CI Model ( Open GAIFit Model )

Download Inference Model ( GAIFit Inference Model )

Download Inference Data ( GAIFit Inference Data)

QCI&AI-FML Learning Platform: https://kws.oaselab.org/qciai/ 

Download Expert Data ( GAIFit Expert Data )

Download Training Data ( GAIFit Training Data )

Download Thonny 4.1.7: ( Windows ) ｜ ( Mac ) 

Download Open Learning Tool Python File ( Python File )

const.py: Set the (1) Wi-Fi name and password, and (2) the MQTT Server's username, password, and topic name.

QCIGAIModel_DataCollection.py:  Execute this code to start collecting the GAI data, including distance and light, from the learning tool.

MQTTGAIManual(two-way).py:  Execute this code to (1) receive data from the QCI&AI-FML learning platform and make a response to the received data, also (2) press left-hand bottom to send data manually back to QCI&AI-FML learning platform.