927 Open source projects that are alternatives of or similar to sparsezoo

Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint

High level network definitions with pre-trained weights in TensorFlow

Pytorch Imagenet Models Example + Transfer Learning (and fine-tuning)

Training & Quantization of embedded friendly Deep Learning / Machine Learning / Computer Vision models

Classification models trained on ImageNet. Keras.

PaddleSlim is an open-source library for deep model compression and architecture search.

PyTorch Model Compression

A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.

PyTorch to Keras model convertor

RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

OpenMMLab Image Classification Toolbox and Benchmark

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

A curated list of awesome edge machine learning resources, including research papers, inference engines, challenges, books, meetups and others.

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

Unofficial Pytorch implementation of Deep Compression in CIFAR10

A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework

Tensorflow Faster RCNN for Object Detection

PyTorch*-based Neural Network Compression Framework for enhanced OpenVINO™ inference

Address book for computer vision models.

PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models

HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

Towards deepfake detection that actually works

YOLO ModelCompression MultidatasetTraining

Train a state-of-the-art yolov3 object detector from scratch!

One Person, One Model, One World: Learning Continual User Representation without Forgetting

Embedded and mobile deep learning research resources

Pruning and quantization for SSD. Model compression.

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

reference pytorch code for named entity tagging

Awesome machine learning model compression research papers, tools, and learning material.

Your local Flux surgeon

Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64🌟 GhostNet1.3x 75.78🌟

Multiclass image classification using Convolutional Neural Network

No description or website provided.

Infrastructures™ for Machine Learning Training/Inference in Production.

ImageNet pre-trained models with batch normalization for the Caffe framework

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Tutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101

🛠️ Tools for Transformers compression using PyTorch Lightning ⚡

Reference implementation for Blueprint Separable Convolutions (CVPR 2020)

A caffe implementation of MobileNet-YOLO detection network

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

MobileNetV2_YOLOV3 for ncnn framework

PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

yolov3 network slimming剪枝的一种实现

Implementing YOLO using ResNet as the feature extraction network

PyTorch implementations of recent Computer Vision tricks

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

Repository for Single Shot MultiBox Detector and its variants, implemented with pytorch, python3.

I transfer the backend of yolov3 into Mobilenetv1，VGG16，ResNet101 and ResNeXt101

Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”

YOLO-v2, ResNet-32, GoogLeNet-lite

🍅 Deploy NCNN on mobile phones. Support Android and iOS. 移动端NCNN部署，支持Android与iOS。

It contains the coursework and the practice I have done while learning Deep Learning.🚀 👨‍💻💥 🚩🌈

QAT(quantize aware training) for classification with MQBench