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https://github.com/26hzhang/coteaching
A TensorFlow implementation of "Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels"
https://github.com/26hzhang/coteaching
deep-learning noisy-label-learning noisy-labels pytorch tensorflow
Last synced: about 1 month ago
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A TensorFlow implementation of "Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels"
- Host: GitHub
- URL: https://github.com/26hzhang/coteaching
- Owner: 26hzhang
- License: mit
- Created: 2019-06-02T14:25:56.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-06-14T05:10:12.000Z (over 5 years ago)
- Last Synced: 2023-10-12T20:36:31.976Z (about 1 year ago)
- Topics: deep-learning, noisy-label-learning, noisy-labels, pytorch, tensorflow
- Language: Python
- Size: 37.1 KB
- Stars: 6
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CoTeaching
This repository reproduces the NeurIPS'18 paper
[Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](
https://papers.nips.cc/paper/8072-co-teaching-robust-training-of-deep-neural-networks-with-extremely-noisy-labels.pdf)
by TensorFlow.
- TensorFlow implementation, see all `*_tf.py` files.
- Adapt original co-teaching PyTorch implementation to PyTorch `1.1.0`, see all `*_th.py` files. The original PyTorch
implementation is provided by the author "[Bo Han](https://bhanml.github.io)" as: [[bhanML/Co-teaching]](
https://github.com/bhanML/Co-teaching).
## Requirements
The codes are developed and tested on MacOS (`python==3.7.x`, CPU) and Ubuntu 18.04 (`python==3.6.x`, NVIDIA GeForce GTX
1080 Ti GPU with `CUDA==10.0`) with following environment:
- tensorflow==1.13.1 (>=1.8.0)
- pytorch==1.1.0 (>=0.4.1)
- numpy==1.14.6 (>=1.14.2)
## Setups
**On MacOS**
Install TensorFlow via:
```bash
$ pip3 install tensorflow==1.13.1
```
Install PyTorch via:
```bash
$ pip3 install torch torchvision
```
**On Ubuntu**
Install TensorFlow via:
```bash
$ pip3 install tensorflow==1.13.1 # CPU version
$ pip3 install tensorflow-gpu==1.13.1 # GPU version
```
Install PyTorch via:
```bash
# CPU version
$ pip3 install https://download.pytorch.org/whl/cpu/torch-1.1.0-cp36-cp36m-linux_x86_64.whl
$ pip3 install https://download.pytorch.org/whl/cpu/torchvision-0.3.0-cp36-cp36m-linux_x86_64.whl
# GPU version
$ pip3 install https://download.pytorch.org/whl/cu100/torch-1.1.0-cp36-cp36m-linux_x86_64.whl
$ pip3 install https://download.pytorch.org/whl/cu100/torchvision-0.3.0-cp36-cp36m-linux_x86_64.whl
```
## Usage
Here is an example for TensorFlow:
```bash
$ python3 main_tf.py --dataset cifar10 --noise_type symmetric --noise_rate 0.5
```
Here is an example for PyTorch:
```bash
$ python3 main_th.py --dataset cifar10 --noise_type symmetric --noise_rate 0.5
```
## Performance
Performance on benchmark datasets reported by the Author:
| (Flipping, Rate) | MNIST | CIFAR-10 | CIFAR-100 |
| ---------------: | -----: | -------: | --------: |
| (Pair, 45%) | 87.58% | 72.85% | 34.40% |
| (Symmetry, 50%) | 91.68% | 74.49% | 41.23% |
| (Symmetry, 20%) | 97.71% | 82.18% | 54.36% |
Performance on benchmark datasets derived by the codes in this repository:
> `th` means PyTorch while `tf` means TensorFlow.
| (Flipping, Rate) | MNIST (th -- tf) | CIFAR-10 (th -- tf) | CIFAR-100 (th -- tf) |
| ---------------: | ---------------: | ------------------: | -------------------: |
| (Pair, 45%) | 88.63% -- 94.16% | 72.88% -- 76.04% | 34.05% -- 35.24% |
| (Symmetry, 50%) | 92.34% -- 98.05% | 74.56% -- 79.64% | 41.17% -- 49.09% |
| (Symmetry, 20%) | 97.84% -- 99.16% | 82.87% -- 87.02% | 54.11% -- 59.55% |
> The model structure and parameters setting of TensorFlow version are almost same as those of PyTorch version,
but the performance of TensorFlow version is generally better than the PyTorch version, I think it maybe caused by the
internal implementation of some functions are different between these two frameworks.