Home >IEEE CIS HSO Events >Quantum CI Workshop @ SPICE Arena Penang >Experience-based & Operation-based Learning> Samples for GAIFit Application
> Sample Models and Data (GAIFit) with QCI&AI Hardware
Teaching Videos for Practice-based Learning
Generative AI Images
Stage I: QCI Data Model
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.
Stage II: QCI Knowledge Model
QCI&AI-FML Learning Platform: https://kws.oaselab.org/qciai/
Download Open CI Model ( Open GAIFit Model )
Stage III: QCI Inference Model
Download Inference Model ( GAIFit Inference Model )
Download Inference Data ( GAIFit Inference Data)
Stage IV: QCI Fine-Tuned Model
QCI&AI-FML Learning Platform: https://kws.oaselab.org/qciai/
Download Expert Data ( GAIFit Expert Data )
Download Training Data ( GAIFit Training Data )
Download Thonny and Python File
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.
Home >IEEE CIS HSO Events >Quantum CI Workshop @ SPICE Arena Penang >Experience-based & Operation-based Learning> Samples for GAIFit Application
> Sample Models and Data (GAIFit) with QCI&AI Hardware