Brevitas: neural network quantization in PyTorch

More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt

yolo model qat and deploy with deepstream&tensorrt

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.

针对pytorch模型的自动化模型结构分析和修改工具集，包含自动分析模型结构的模型压缩算法库

Zero-label image classification via OpenCLIP knowledge distillation

Implementation of YOLOv9 QAT optimized for deployment on TensorRT platforms.

Kubernetes Reference Deployment

QAT(quantize aware training) for classification with MQBench

FakeQuantize with Learned Step Size(LSQ ) as Observer in PyTorch

The project focuses on Intel’s enterprise AI and cloud native foundation for Red Hat OpenShift Container Platform (RHOCP) solution enablement and innovation including Intel data center hardware features, Intel technology enhanced AI platform and the referenced AI workloads provisioning for OpenShift.

mi-optimize is a versatile tool designed for the quantization and evaluation of large language models (LLMs). The library's seamless integration of various quantization methods and evaluation techniques empowers users to customize their approaches according to specific requirements and constraints, providing a high level of flexibility.

quantization example for pqt & qat

Training U-Net based Convolutional Neural Network model to automatically identify and delineate areas of qat agriculture in Sentinel-2 multispectral imagery.

Official website of qat programming language...

Build AI model to classify beverages for blind individuals

Vagrant installation script

Combidata is a flexible and powerful Python library designed for generating various combinations of test data based on defined cases and rules. It is especially useful for testing, debugging, and analyzing software applications and systems.

EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.

Quantization of Models : Post-Training Quantization(PTQ) and Quantize Aware Training(QAT)