An open-source project for Windows developers to learn how to add AI with local models and APIs to Windows apps.
A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.
#计算机科学#The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
#计算机科学#The Qualcomm® AI Hub apps are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
基于react和antd开发的cron表达式生成组件 React and Antd based cron expression generation components
Mobilenet v1 trained on Imagenet for STM32 using extended CMSIS-NN with INT-Q quantization support
#安卓#Simplified AI runtime integration for mobile app development
A Toolbox for Binarized Spectral Compressive Imaging (NeurIPS 2023)
This repository containts the pytorch scripts to train mixed-precision networks for microcontroller deployment, based on the memory contraints of the target device.
#人脸识别#Low-Precision YOLO on PYNQ with FINN
#安卓#Kotlin bindings for Edgerunner
Official PyTorch repository for Quaternion Generative Adversarial Networks.
The repository supports TensorRT, QNN platform inference, 2D obstacle detection yolo series (yolov5, yolov8, yolo11, yolox), semantic segmentation and so on.
javascript literal object manipulation plug-in in code file | 代码文件中的js字面量对象操作插件
Scalable Quantum Neural Network builds and trains a large-scale QNN in a modular fashion. SQNN is evaluated with a binary classification task on the MNIST dataset.
In this repository, I classify the Iris dataset using Qutrits and IBM Quantum pulse technology.
Hybrid Quantum Neural Network for classification of the MNIST Dataset using Classiq
Feed forward QNN
QNN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling.
Testing and playing around with QNNs using the mnist dataset