A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
-
Updated
Nov 4, 2024 - Python
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
An Open-Source Library for Training Binarized Neural Networks
QKeras: a quantization deep learning library for Tensorflow Keras
Generate a quantization parameter file for ncnn framework int8 inference
Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.
The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment
[CVPR'20] ZeroQ: A Novel Zero Shot Quantization Framework
QONNX: Arbitrary-Precision Quantized Neural Networks in ONNX
Mobilenet v1 trained on Imagenet for STM32 using extended CMSIS-NN with INT-Q quantization support
Some recent Quantizing techniques on PyTorch
Slides with modifications for a course at Tsinghua University.
BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.
This repository containts the pytorch scripts to train mixed-precision networks for microcontroller deployment, based on the memory contraints of the target device.
This repository contains source code to binarize any real-value word embeddings into binary vectors.
CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
[NeurIPS 2023 Spotlight] This project is the official implementation of our accepted NeurIPS 2023 (spotlight) paper QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution.
Low Precision(quantized) Yolov5
Binary neural networks developed by Huawei Noah's Ark Lab
Contains code for Binary, Ternary, N-bit Quantized and Hybrid CNNs for low precision experiments.
a CSK serial based train tools, rely on pytorch
Add a description, image, and links to the quantized-neural-networks topic page so that developers can more easily learn about it.
To associate your repository with the quantized-neural-networks topic, visit your repo's landing page and select "manage topics."