TensorFlow implementation of AlexNet and its training and testing on ImageNet ILSVRC 2012 dataset

Overview

AlexNet training on ImageNet LSVRC 2012

alt text

This repository contains an implementation of AlexNet convolutional neural network and its training and testing procedures on the ILSVRC 2012 dataset, all using TensorFlow.

Folder tf contains code in the "classic TensorFlow" framework whereas code in the tf_eager directory has been developed with TensorFlow's new impearative style, TensorFlow eager.

The two implementations are independent and refer to the READMEs inside the folders for specific instruction on how to train and to test.

References

  • Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Inforamtion Processing Systems 25, 2012.
  • Olga Russakovsky°, Jia Deng°, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (° = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015
Owner
Matteo Dunnhofer
A bear experimenting AI.
Matteo Dunnhofer
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

ChongjianGE 89 Dec 02, 2022
Backend code to use MCPI's python API to make infinite worlds with custom generation

inf-mcpi Backend code to use MCPI's python API to make infinite worlds with custom generation Does not save player-placed blocks! Generation is still

5 Oct 04, 2022
Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

Point-Unet This is an unofficial implementation of the MICCAI 2021 paper Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segment

Namt0d 9 Dec 07, 2022
A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.

WebDataset WebDataset is a PyTorch Dataset (IterableDataset) implementation providing efficient access to datasets stored in POSIX tar archives and us

1.1k Jan 08, 2023
PyTorch Lightning + Hydra. A feature-rich template for rapid, scalable and reproducible ML experimentation with best practices. ⚡🔥⚡

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

Łukasz Zalewski 2.1k Jan 09, 2023
Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving Abstract In this paper, we introduce SalsaNext f

308 Jan 04, 2023
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification [NeurIPS 2021] Abstract Multiple instance learn

132 Dec 30, 2022
TensorFlow port of PyTorch Image Models (timm) - image models with pretrained weights.

TensorFlow-Image-Models Introduction Usage Models Profiling License Introduction TensorfFlow-Image-Models (tfimm) is a collection of image models with

Martins Bruveris 227 Dec 20, 2022
Apache Flink

Apache Flink Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities. Learn more about Flin

The Apache Software Foundation 20.4k Dec 30, 2022
Global Rhythm Style Transfer Without Text Transcriptions

Global Prosody Style Transfer Without Text Transcriptions This repository provides a PyTorch implementation of AutoPST, which enables unsupervised glo

Kaizhi Qian 193 Dec 30, 2022
The Deep Learning with Julia book, using Flux.jl.

Deep Learning with Julia DL with Julia is a book about how to do various deep learning tasks using the Julia programming language and specifically the

Logan Kilpatrick 67 Dec 25, 2022
Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes

Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes [Paper] Method overview 4DMatch Benchmark 4DMatch is a benchmark for matc

103 Jan 06, 2023
A playable implementation of Fully Convolutional Networks with Keras.

keras-fcn A re-implementation of Fully Convolutional Networks with Keras Installation Dependencies keras tensorflow Install with pip $ pip install git

JihongJu 202 Sep 07, 2022
Code for the paper "Unsupervised Contrastive Learning of Sound Event Representations", ICASSP 2021.

Unsupervised Contrastive Learning of Sound Event Representations This repository contains the code for the following paper. If you use this code or pa

Eduardo Fonseca 81 Dec 22, 2022
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020 Oral) Figure: Face image editing controlled via style images and segmenta

Peihao Zhu 579 Dec 30, 2022
Unofficial implementation of Fast-SCNN: Fast Semantic Segmentation Network

Fast-SCNN: Fast Semantic Segmentation Network Unofficial implementation of the model architecture of Fast-SCNN. Real-time Semantic Segmentation and mo

Philip Popien 69 Aug 11, 2022
Machine Learning automation and tracking

The Open-Source MLOps Orchestration Framework MLRun is an open-source MLOps framework that offers an integrative approach to managing your machine-lea

873 Jan 04, 2023
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

CharacterGAN Implementation of the paper "CharacterGAN: Few-Shot Keypoint Character Animation and Reposing" by Tobias Hinz, Matthew Fisher, Oliver Wan

Tobias Hinz 181 Dec 27, 2022
code and data for paper "GIANT: Scalable Creation of a Web-scale Ontology"

GIANT Code and data for paper "GIANT: Scalable Creation of a Web-scale Ontology" https://arxiv.org/pdf/2004.02118.pdf Please cite our paper if this pr

Excalibur 39 Dec 29, 2022
Existing Literature about Machine Unlearning

Machine Unlearning Papers 2021 Brophy and Lowd. Machine Unlearning for Random Forests. In ICML 2021. Bourtoule et al. Machine Unlearning. In IEEE Symp

Jonathan Brophy 213 Jan 08, 2023