FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

Overview

PWC

PWC

PWC

License: GPL v3

FPGA & FreeNet

Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
by Zhuo Zheng, Yanfei Zhong, Ailong Ma and Liangpei Zhang


This is an official implementation of FPGA framework and FreeNet in our TGRS 2020 paper "FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification".

We hope the FPGA framework can become a stronger and cleaner baseline for hyperspectral image classification research in the future.

News

  1. 2020/05/28, We release the code of FreeNet and FPGA framework.

Features

  1. Patch-free training and inference
  2. Fully end-to-end (w/o preprocess technologies, such as dimension reduction)

Citation

If you use FPGA framework or FreeNet in your research, please cite the following paper:

@article{zheng2020fpga,
  title={FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification},
  author={Zheng, Zhuo and Zhong, Yanfei and Ma, Ailong and Zhang, Liangpei},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2020},
  publisher={IEEE},
  note={doi: {10.1109/TGRS.2020.2967821}}
}

Getting Started

1. Install SimpleCV

pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git

2. Prepare datasets

It is recommended to symlink the dataset root to $FreeNet.

The project should be organized as:

FreeNet
├── configs     // configure files
├── data        // dataset and dataloader class
├── module      // network arch.
├── scripts 
├── pavia       // data 1
│   ├── PaviaU.mat
│   ├── PaviaU_gt.mat
├── salinas     // data 2
│   ├── Salinas_corrected.mat
│   ├── Salinas_gt.mat
├── GRSS2013    // data 3
│   ├── 2013_IEEE_GRSS_DF_Contest_CASI.tif
│   ├── train_roi.tif
│   ├── val_roi.tif

3. run experiments

1. PaviaU

bash scripts/freenet_1_0_pavia.sh

2. Salinas

bash scripts/freenet_1_0_salinas.sh

3. GRSS2013

bash scripts/freenet_1_0_grss.sh

License

This source code is released under GPLv3 license.

For commercial use, please contact Prof. Zhong ([email protected]).

Projects using FPGA/FreeNet

Welcome to pull request if you use this repo as your codebase.

You might also like...
End-to-End Object Detection with Fully Convolutional Network
End-to-End Object Detection with Fully Convolutional Network

This project provides an implementation for "End-to-End Object Detection with Fully Convolutional Network" on PyTorch.

KE-Dialogue: Injecting knowledge graph into a fully end-to-end dialogue system.
KE-Dialogue: Injecting knowledge graph into a fully end-to-end dialogue system.

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems This is the implementation of the paper: Learning Knowledge Bases with Par

Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering
Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Global Filter Networks for Image Classification
Global Filter Networks for Image Classification

Global Filter Networks for Image Classification Created by Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou This repository contains PyTorch

FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizat

⚡ Fast • 🪶 Lightweight • 0️⃣ Dependency • 🔌 Pluggable • 😈 TLS interception • 🔒 DNS-over-HTTPS • 🔥 Poor Man's VPN • ⏪ Reverse & ⏩ Forward • 👮🏿  WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

WarpDrive is a flexible, lightweight, and easy-to-use open-source reinforcement learning (RL) framework that implements end-to-end multi-agent RL on a single GPU (Graphics Processing Unit).

Simple-Image-Classification - Simple Image Classification Code (PyTorch)
Simple-Image-Classification - Simple Image Classification Code (PyTorch)

Simple-Image-Classification Simple Image Classification Code (PyTorch) Yechan Kim This repository contains: Python3 / Pytorch code for multi-class ima

Image Classification - A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

Comments
  • Some issues about the code.

    Some issues about the code.

    I try to run your code on the Pavia data set.

    Traceback (most recent call last): File "d:/FreeNet-master/train.py", line 63, in opts=args.opts) File "D:\software\anaconda\lib\site-packages\simplecv\dp_train.py", line 44, in run traindata_loader = make_dataloader(cfg['data']['train']) File "D:\software\anaconda\lib\site-packages\simplecv\data\data_loader.py", line 9, in make_dataloader raise ValueError('{} is not support now.'.format(dataloader_type)) ValueError: NewPaviaLoader is not support now.

    Looking forward to hearing from you!

    opened by zhe-meng 2
  • Test.py module

    Test.py module

    Hello!

    I really appreciate your paper and sharing the code for it. I wonder is there an option to make a test on the trainned network on another image? I saw test dict in config file, but I'm not sure it is implemented for now. Is there any plans for it or will you please suggest how can it be done better?

    Thanks!

    opened by valeriylo 0
  • 运行 train.py时报错

    运行 train.py时报错

    Traceback (most recent call last): File "D:/论文代码书/代码/高光谱影像分类/全卷积FCN/FreeNet-master/train.py", line 60, in train.run(config_path=args.config_path, File "D:\Anaconda\envs\Pytorch\lib\site-packages\simplecv-0.3.4-py3.8.egg\simplecv\dp_train.py", line 29, in run cfg = config.import_config(config_path) File "D:\Anaconda\envs\Pytorch\lib\site-packages\simplecv-0.3.4-py3.8.egg\simplecv\util\config.py", line 5, in import_config m = importlib.import_module(name='{}.{}'.format(prefix, config_name)) File "D:\Anaconda\envs\Pytorch\lib\importlib_init_.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "", line 1014, in _gcd_import File "", line 991, in _find_and_load File "", line 973, in _find_and_load_unlocked ModuleNotFoundError: No module named 'configs.None'

    opened by Zhengpu-L 1
  • 在安装simpleCV时出现报错

    在安装simpleCV时出现报错

    您好,我在终端安装simpleCV时出现了以下报错: ERROR: Could not find a version that satisfies the requirement tensorboardX==1.7 (from simplecv) (from versions: none) ERROR: No matching distribution found for tensorboardX==1.7 请问您知道如何解决吗,simpleCV是基于tensorflow框架的吗,但freenet好像是基于pytorch,很抱歉打扰您

    opened by wangk98 3
Releases(v1.2)
Owner
Zhuo Zheng
CV IN RS. Ph.D. Student.
Zhuo Zheng
Geometric Deep Learning Extension Library for PyTorch

Documentation | Paper | Colab Notebooks | External Resources | OGB Examples PyTorch Geometric (PyG) is a geometric deep learning extension library for

Matthias Fey 16.5k Jan 08, 2023
Reimplement of SimSwap training code

SimSwap-train Reimplement of SimSwap training code Instructions 1.Environment Preparation (1)Refer to the README document of SIMSWAP to configure the

seeprettyface.com 111 Dec 31, 2022
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab

PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo

Atmospheric Cloud Simulation Group @ Jagiellonian University 32 Oct 18, 2022
This is code of book "Learn Deep Learning with PyTorch"

深度学习入门之PyTorch Learn Deep Learning with PyTorch 非常感谢您能够购买此书,这个github repository包含有深度学习入门之PyTorch的实例代码。由于本人水平有限,在写此书的时候参考了一些网上的资料,在这里对他们表示敬意。由于深度学习的技术在

Xingyu Liao 2.5k Jan 04, 2023
Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Chris Donahue 98 Dec 14, 2022
Code for the paper Hybrid Spectrogram and Waveform Source Separation

Demucs Music Source Separation This is the 3rd release of Demucs (v3), featuring hybrid source separation. For the waveform only Demucs (v2): Go this

Meta Research 4.8k Jan 04, 2023
A fast MoE impl for PyTorch

An easy-to-use and efficient system to support the Mixture of Experts (MoE) model for PyTorch.

Rick Ho 873 Jan 09, 2023
Official PyTorch Implementation of "Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs". NeurIPS 2020.

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs This repository is the implementation of SELAR. Dasol Hwang* , Jinyoung Pa

MLV Lab (Machine Learning and Vision Lab at Korea University) 48 Nov 09, 2022
IsoGCN code for ICLR2021

IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N

horiem 39 Nov 25, 2022
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Federico Galatolo 172 Dec 22, 2022
A 1.3B text-to-image generation model trained on 14 million image-text pairs

minDALL-E on Conceptual Captions minDALL-E, named after minGPT, is a 1.3B text-to-image generation model trained on 14 million image-text pairs for no

Kakao Brain 604 Dec 14, 2022
MegEngine implementation of YOLOX

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

旷视天元 MegEngine 77 Nov 22, 2022
Generalized hybrid model for mode-locked laser diodes with an extended passive cavity

GenHybridMLLmodel Generalized hybrid model for mode-locked laser diodes with an extended passive cavity This hybrid simulation strategy combines a tra

Stijn Cuyvers 3 Sep 21, 2022
Code release for SLIP Self-supervision meets Language-Image Pre-training

SLIP: Self-supervision meets Language-Image Pre-training What you can find in this repo: Pre-trained models (with ViT-Small, Base, Large) and code to

Meta Research 621 Dec 31, 2022
Autonomous Movement from Simultaneous Localization and Mapping

Autonomous Movement from Simultaneous Localization and Mapping About us Built by a group of Clarkson University students with the help from Professor

14 Nov 07, 2022
Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN"

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtu

68 Dec 21, 2022
Machine Unlearning with SISA

Machine Unlearning with SISA Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, N

CleverHans Lab 70 Jan 01, 2023
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.

cdf_att_classification classes = {0: 'cat', 1: 'dog', 2: 'flower'} In this project we use both Resnet and Self-attention layer for cdf-Classification.

3 Nov 23, 2022
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
Fairness Metrics: All you need to know

Fairness Metrics: All you need to know Testing machine learning software for ethical bias has become a pressing current concern. Recent research has p

Anonymous2020 1 Jan 17, 2022