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

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

Demonstrates knowledge distillation for image-based models in Keras.

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.

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

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💖

Automated Shorthand Recognition using Optimized DNNs

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.

DA2Lite is an automated model compression toolkit for PyTorch.

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

Vision-lanugage model example code.

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

Some DNN model optimization experiments and notebooks

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

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

quantization example for pqt & qat

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

"Vitis-AI-YOLOv3-TF2-Quantization-Evaluation" Repo for quantization of YOLOv3 on Vitis-AI using TF2, aimed to deploy model on edge devices with limited resources. Includes training & quantization scripts and evaluation metrics. Experiment with different configurations.

Successfully established a clustering model which can categorize the customers of a renowned Indian bank into several distinct groups, based on their behavior patterns and demographic details.