The repository contains the code of our CVPR15 paper Learning from Massive Noisy Labeled Data for Image Classification (paper link).
-
Clone this repository
# Make sure to clone with --recursive to get the modified Caffe git clone --recursive https://github.com/Cysu/noisy_label.git
-
Build the Caffe
cd external/caffe # Now follow the Caffe installation instructions here: # https://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 -
-
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
-
Download the CIFAR-10 data (python version).
scripts/cifar10/download_cifar10.sh
-
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].
-
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 is the dataset we proposed in our paper.
-
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 beexternal/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
-
Make the LMDBs and compute the matrix C to be used.
scripts/clothing1M/make_db.sh
-
Run experiments for our method
# Download the ImageNet pretrained CaffeNet wget -P external/exp/snapshots/ https://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
The self-brewed external/caffe
supports data parallel with multiple GPUs using MPI. One can accelerate the training / test process by
- Compile the caffe with MPI enabled
- Tweak the training shell scripts to use multiple GPUs, for example,
mpirun -n 2 ... -gpu 0,1
Detailed instructions are listed here.
@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}
}