https://github.com/cysu/noisy_label

Code for the CVPR15 paper "Learning from Massive Noisy Labeled Data for Image Classification"
https://github.com/cysu/noisy_label

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Code for the CVPR15 paper "Learning from Massive Noisy Labeled Data for Image Classification"

• Host: GitHub
• URL: https://github.com/cysu/noisy_label
• Owner: Cysu
• Created: 2015-10-06T02:57:15.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2019-02-06T19:35:01.000Z (almost 6 years ago)
• Last Synced: 2023-11-07T18:02:24.138Z (about 1 year ago)
• Language: Python
• Homepage: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiao_Learning_From_Massive_2015_CVPR_paper.pdf
• Size: 586 KB
• Stars: 109
• Watchers: 7
• Forks: 31
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
# CVPR15 Noisy Label Project

The repository contains the code of our CVPR15 paper *Learning from Massive Noisy Labeled Data for Image Classification* ([paper link](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiao_Learning_From_Massive_2015_CVPR_paper.pdf)).

## Installation

1.  Clone this repository

        # Make sure to clone with --recursive to get the modified Caffe

        git clone --recursive https://github.com/Cysu/noisy_label.git

2.  Build the Caffe

        cd external/caffe

        # Now follow the Caffe installation instructions here:

        #   http://caffe.berkeleyvision.org/installation.html

        # If you're experienced with Caffe and have all of the requirements installed

        # and your Makefile.config in place, then simply do:

        make -j8 && make py

        cd -

3.  Setup an experiment directory. You can either create a new one under external/, or make a link to another existing directory.

        mkdir -p external/exp

    or

        ln -s /path/to/your/exp/directory external/exp

## CIFAR-10 Experiments

1.  Download the CIFAR-10 data (python version).

        scripts/cifar10/download_cifar10.sh

2.  Synthesize label noise and prepare LMDBs. Will corrupt the labels of 40k randomly selected training images, while leaving other 10k image labels unchanged.

        scripts/cifar10/make_db.sh 0.3

    The parameter 0.3 controls the level of label noise. Can be any number between [0, 1].

3.  Run a series of experiments

        # Train a CIFAR10-quick model using only the 10k clean labeled images

        scripts/cifar10/train_clean.sh

        # Baseline:

        # Treat 40k noisy labels as ground truth and finetune from the previous model

        scripts/cifar10/train_noisy_gt_ft_clean.sh

        # Our method

        scripts/cifar10/train_ntype.sh

        scripts/cifar10/init_noisy_label_loss.sh

        scripts/cifar10/train_noisy_label_loss.sh

We provide the training logs in `logs/cifar10/` for reference.

## Clothing1M Experiments

Clothing1M is the dataset we proposed in our paper.

1.  Download the dataset. Please contact *tong.xiao.work[at]gmail[dot]com* to get the download link. Untar the images and unzip the annotations under `external/exp/datasets/clothing1M`. The directory structure should be

        external/exp/datasets/clothing1M/

        ├── category_names_chn.txt

        ├── category_names_eng.txt

        ├── clean_label_kv.txt

        ├── clean_test_key_list.txt

        ├── clean_train_key_list.txt

        ├── clean_val_key_list.txt

        ├── images

        │   ├── 0

        │   ├── ⋮

        │   └── 9

        ├── noisy_label_kv.txt

        ├── noisy_train_key_list.txt

        ├── README.md

        └── venn.png

2.  Make the LMDBs and compute the matrix C to be used.

        scripts/clothing1M/make_db.sh

3.  Run experiments for our method

        # Download the ImageNet pretrained CaffeNet

        wget -P external/exp/snapshots/ http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel

        # Train the clothing prediction CNN using only the clean labeled images

        scripts/clothing1M/train_clean.sh

        # Train the noise type prediction CNN

        scripts/clothing1M/train_ntype.sh

        # Train the whole net using noisy labeled data

        scripts/clothing1M/init_noisy_label_loss.sh

        scripts/clothing1M/train_noisy_label_loss.sh

We provide the training logs in `logs/clothing1M/` for reference. A final trained model is also provided [here](https://drive.google.com/open?id=0B67_d0rLRTQYMkthcV91NmtSX0E). To test the performance, please download the model, place it under `external/exp/snapshots/clothing1M/`, and then

    # Run the test

    external/caffe/build/tools/caffe test \

        -model models/clothing1M/noisy_label_loss_test.prototxt \

        -weights external/exp/snapshots/clothing1M/noisy_label_loss_inference.caffemodel \

        -iterations 106 \

        -gpu 0

## Tips

The self-brewed `external/caffe` supports data parallel with multiple GPUs using MPI. One can accelerate the training / test process by

1.  Compile the caffe with MPI enabled

2.  Tweak the training shell scripts to use multiple GPUs, for example, `mpirun -n 2 ... -gpu 0,1`

Detailed instructions are listed [here](https://github.com/Cysu/caffe).

## Reference

    @inproceedings{xiao2015learning,

      title={Learning from Massive Noisy Labeled Data for Image Classification},

      author={Xiao, Tong and Xia, Tian and Yang, Yi and Huang, Chang and Wang, Xiaogang},

      booktitle={CVPR},

      year={2015}

    }