Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data

Related tags

Deep LearningMeta-FDMixup
Overview

1 Meta-FDMIxup

Repository for the paper :

Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021)

paper

News! the representation video loaded in 2021/10/06 in Bilibili

News! the representation video loaded in 2021/10/10 in Youtube

image

If you have any questions, feel free to contact me. My email is [email protected].

2 setup and datasets

2.1 setup

A anaconda envs is recommended:

conda create --name py36 python=3.6
conda activate py36
conda install pytorch torchvision -c pytorch
pip3 install scipy>=1.3.2
pip3 install tensorboardX>=1.4
pip3 install h5py>=2.9.0

Then, git clone our repo:

git clone https://github.com/lovelyqian/Meta-FDMixup
cd Meta-FDMixup

2.2 datasets

Totally five datasets inculding miniImagenet, CUB, Cars, Places, and Plantae are used.

  1. Following FWT-repo to download and setup all datasets. (It can be done quickly)

  2. Remember to modify your own dataset dir in the 'options.py'

  3. Under our new setting, we randomly select $num_{target}$ labeled images from the target base set to form the auxiliary set. The splits we used are provided in 'Sources/'.

3 pretrained ckps

We provide several pretrained ckps.

You can download and put them in the 'output/pretrained_ckps/'

3.1 pretrained model trained on the miniImagenet

3.2 full model meta-trained on the target datasets

Since our method is target-set specific, we have to train a model for each target dataset.

Notably, as we stated in the paper, we use the last checkpoint for target dataset, while the best model on the validation set of miniImagenet is used for miniImagenet. Here, we provide the model of 'miniImagenet|CUB' as an example.

4 usage

4.1 network pretraining

python3 network_train.py --stage pretrain  --name pretrain-model --train_aug

If you have downloaded our pretrained_model_399.tar, you can just skip this step.

4.2 pretrained model testing

# test source dataset (miniImagenet)
python network_test.py --ckp_path output/checkpoints/pretrain-model/399.tar --stage pretrain --dataset miniImagenet --n_shot 5 

# test target dataset e.g. cub
python network_test.py --ckp_path output/checkpoints/pretrain-model/399.tar --stage pretrain --dataset cub --n_shot 5

you can test our pretrained_model_399.tar in the same way:

# test source dataset (miniImagenet)
python network_test.py --ckp_path output/pretrained_ckps/pretrained_model_399.tar --stage pretrain --dataset miniImagenet --n_shot 5 


# test target dataset e.g. cub
python network_test.py --ckp_path output/pretrained_ckps/pretrained_model_399.tar --stage pretrain --dataset cub --n_shot 5

4.3 network meta-training

# traget set: CUB
python3 network_train.py --stage metatrain --name metatrain-model-5shot-cub --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set cub --n_shot 5

# target set: Cars
python3 network_train.py --stage metatrain --name metatrain-model-5shot-cars --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set cars --n_shot 5

# target set: Places
python3 network_train.py --stage metatrain --name metatrain-model-5shot-places --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set places --n_shot 5

# target set: Plantae
python3 network_train.py --stage metatrain --name metatrain-model-5shot-plantae --train_aug --warmup output/checkpoints/pretrain-model/399.tar --target_set plantae --n_shot 5

Also, you can use our pretrained_model_399.tar for warmup:

# traget set: CUB
python3 network_train.py --stage metatrain --name metatrain-model-5shot-cub --train_aug --warmup output/pretrained_ckps/pretrained_model_399.tar --target_set cub --n_shot 5

4.4 network testing

To test our provided full models:

# test target dataset (CUB)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_cub_399.tar --stage metatrain --dataset cub --n_shot 5 

# test target dataset (Cars)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_cars_399.tar --stage metatrain --dataset cars --n_shot 5 

# test target dataset (Places)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_places_399.tar --stage metatrain --dataset places --n_shot 5 

# test target dataset (Plantae)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_places_399.tar --stage metatrain --dataset plantae --n_shot 5 


# test source dataset (miniImagenet|CUB)
python network_test.py --ckp_path output/pretrained_ckps/full_model_5shot_target_cub_best_eval.tar --stage metatrain --dataset miniImagenet --n_shot 5

To test your models, just modify the 'ckp-path'.

5 citing

If you find our paper or this code useful for your research, please cite us:

@article{fu2021meta,
  title={Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data},
  author={Fu, Yuqian and Fu, Yanwei and Jiang, Yu-Gang},
  journal={arXiv preprint arXiv:2107.11978},
  year={2021}
}

6 Note

Notably, our code is built upon the implementation of FWT-repo.

Owner
Fu Yuqian
Fu Yuqian
GitHub Repository
PyTorch implementation of PSPNet segmentation network

pspnet-pytorch PyTorch implementation of PSPNet segmentation network Original paper Pyramid Scene Parsing Network Details This is a slightly different

Roman Trusov 532 Dec 29, 2022
SuperSDR: multiplatform KiwiSDR + CAT transceiver integrator

SuperSDR SuperSDR integrates a realtime spectrum waterfall and audio receive from any KiwiSDR around the world, together with a local (or remote) cont

Marco Cogoni 30 Nov 29, 2022
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis (CVPR2022)

Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis Multi-View Consistent Generative Adversarial Networks for 3D-aware

Xuanmeng Zhang 78 Dec 10, 2022
Black box hyperparameter optimization made easy.

BBopt BBopt aims to provide the easiest hyperparameter optimization you'll ever do. Think of BBopt like Keras (back when Theano was still a thing) for

Evan Hubinger 70 Nov 03, 2022
Racing line optimization algorithm in python that uses Particle Swarm Optimization.

Racing Line Optimization with PSO This repository contains a racing line optimization algorithm in python that uses Particle Swarm Optimization. Requi

Parsa Dahesh 6 Dec 14, 2022
Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

An official implementation of paper Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

11 Nov 23, 2022
Unofficial PyTorch implementation of TokenLearner by Google AI

tokenlearner-pytorch Unofficial PyTorch implementation of TokenLearner by Ryoo et al. from Google AI (abs, pdf) Installation You can install TokenLear

Rishabh Anand 46 Dec 20, 2022
Scientific Computation Methods in C and Python (Open for Hacktoberfest 2021)

Sci - cpy README is a stub. Do expand it. Objective This repository is meant to be a ready reference for scientific computation methods. Do ⭐ it if yo

Sandip Dutta 7 Oct 12, 2022
Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation

Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation Overview This example will show how to validate the status of our firewall before and a

Calvin Remsburg 1 Jan 07, 2022
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis [Paper] [Online Demo] The following results are obtained by our SCUNet with purely syn

Kai Zhang 312 Jan 07, 2023
Code repository accompanying the paper "On Adversarial Robustness: A Neural Architecture Search perspective"

On Adversarial Robustness: A Neural Architecture Search perspective Preparation: Clone the repository: https://github.com/tdchaitanya/nas-robustness.g

Chaitanya Devaguptapu 4 Nov 10, 2022
It's a powerful version of linebot

CTPS-FINAL Linbot-sever.py 主程式 Algorithm.py 推薦演算法，媒合餐廳端資料與顧客端資料 config.ini 儲存 channel-access-token、channel-secret 資料 Preface 生活在成大將近4年，我們每天的午餐時間看著形形色色

1 Oct 17, 2022
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors In this paper, we propose a novel local descriptor-based fra

Haiping Wang 80 Dec 15, 2022
Generative Art Using Neural Visual Grammars and Dual Encoders

Generative Art Using Neural Visual Grammars and Dual Encoders Arnheim 1 The original algorithm from the paper Generative Art Using Neural Visual Gramm

DeepMind 231 Jan 05, 2023
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision This is the repository for our Paper/Contribution to the WI2022 in Nürnber

Maximilian Harl 6 Jan 17, 2022
Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data.

Deep Learning Dataset Maker Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data. How to use Down

deepbands 25 Dec 15, 2022
Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models

Python scripts to detect faces using Python with the BlazeFace Tensorflow Lite models. Tested on Windows 10, Tensorflow 2.4.0 (Python 3.8).

Ibai Gorordo 46 Nov 17, 2022
Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

Yeongjoon 31 Dec 19, 2022
Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

Real-ESRGAN Colab Demo for Real-ESRGAN . Portable Windows executable file. You can find more information here. Real-ESRGAN aims at developing Practica

Xintao 17.2k Jan 02, 2023
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

MIRA Lab 33 Dec 07, 2022