https://github.com/fuxiAIlab/ProtoSemi

https://github.com/fuxiAIlab/ProtoSemi

Last synced: 9 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/fuxiAIlab/ProtoSemi
• Owner: fuxiAIlab
• Created: 2023-07-25T01:55:50.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-07-25T01:55:59.000Z (over 1 year ago)
• Last Synced: 2024-08-02T15:30:08.262Z (3 months ago)
• Language: Python
• Size: 445 KB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# ProtoSemi

This is the official repository of paper *Rethinking Noisy Label Learning in Real-world Annotation Scenarios*.

## Setup

This implemetation is based on Python3. To run the code, you need the following dependencies:

- torch==1.7.1

- torchvision==0.8.2

- tensorboard==2.11.2

- numpy

- scikit-learn

You can simply run 

```python

pip install -r requirements.txt

```

## Repository structure

We select some important files for detailed description.

```python

|-- code 

    |-- data_preprocess # read the CIFAR-N dataset

    |-- config.py # hyperparameters

    |-- main.py 

    |-- model.py

    |-- myssl.py # semi-superivised learning

    |-- myutils.py 

    |-- sample_splits_backup.py # old sample split

    |-- sample_splits # new sample split

|-- data

    |-- CIFAR-N

```

## Run

1. You can run like the script in the following:

```python

cd code

CUDA_VISIBLE_DEVICES=0 python -u main.py --dataset cifar100 --noise_type noisy100 --lr 0.02 --epochs 500 --weight_decay 5e-4 --sample_split proto --warmups 20 --ssl mixmatch  --cos_up_bound 0.99 --cos_low_bound 0.90 --proto_epochs 1

```

1. You can reproduce the experimental results of our method by running the script:

```python

cd code

bash reproduce.sh

```

## Attribution

Parts of this code are based on the following repositories:

- [Dividemix](https://github.com/LiJunnan1992/DivideMix)

- [PES](https://github.com/tmllab/PES)

- [CIFAR-N](https://github.com/UCSC-REAL/cifar-10-100n)