a collection of computer vision projects&tools. 计算机视觉方向项目和工具集合。

A curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.

This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.

Demonstrates knowledge distillation for image-based models in Keras.

Efficient in-memory representation for ONNX, in Python

TinyML & Edge AI: On-device inference, model quantization, embedded ML, ultra-low-power AI for microcontrollers and IoT devices.

Code of the ICASSP 2022 paper "Gradient Variance Loss for Structure Enhanced Super-Resolution"

This repository shows how to train a custom detection model with the TFOD API, optimize it with TFLite, and perform inference with the optimized model.

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

本仓库包含了完整的深度学习应用开发流程，以经典的手写字符识别为例，基于LeNet网络构建。推理部分使用torch、onnxruntime以及openvino框架💖

Enhanced BR2804-1700KV Motor Field Oriented Control with a Tiny Neural Network

This repository includes code for the paper "Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks" published in IEEE TCOM, focusing on autonomous cybersecurity (physical-layer authentication and cross-layer intrusion detection) using AutoML techniques.

NCNN Framework: High-performance neural network inference for mobile, embedded, and edge AI deployment.

Automated Shorthand Recognition using Optimized DNNs

Vision-lanugage model example code.

ptdeco is a library for model optimization by matrix decomposition built on top of PyTorch

Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications, and run pre-trained deep learning models for computer vision on-premise.

Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks

DA2Lite is an automated model compression toolkit for PyTorch.

A deep learning framework that implements Early Exit strategies in Convolutional Neural Networks (CNNs) using Deep Q-Learning (DQN). This project enhances computational efficiency by dynamically determining the optimal exit point in a neural network for image classification tasks on CIFAR-10.

Learn linear quantization techniques using the Quanto library and downcasting methods with the Transformers library to compress and optimize generative AI models effectively.

Mobile AI: iOS CoreML, Android TFLite, on-device inference, ONNX, TensorRT, and ML deployment for smartphones.

Hands-on course materials for ML engineers to implement and optimize Mixture of Experts models: PyTorch, transformers (educational)

A curated list of awesome open source tools and commercial products for autoML hyperparameter tuning 🚀

This project uses YOLOv5 architecture for creating guns and knifes real time detection

compares different pretrained object classification with per-layer and per-channel quantization using pytorch

Can You Eat That? 🍄 High-precision predictive classification achieving 100% accuracy using Random Forest & XGBoost. Optimized via GridSearchCV to ensure zero-false-negative outcomes in safety-critical biological data

Some DNN model optimization experiments and notebooks

Convert and optimize BirdNET models for ONNX Runtime inference on GPUs, CPUs, and embedded devices

40x faster AI inference: ONNX to TensorRT optimization with FP16/INT8 quantization, multi-GPU support, and deployment