CVPR15 Noisy Label Project

The repository contains the code of our CVPR15 paper Learning from Massive Noisy Labeled Data for Image Classification (paper link).

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. 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.

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}
}