Pytorch code to run SNCF

Paper (ECCV 2022): SNCF

Install requirements using conda (cuda version 10.2)

conda env create -f environment.yml conda activate sncf

Edit the mypath.py file

mypath.py lists the location of the datasets For the Places365 dataset, we use the small images (256x256) test set available at http://places2.csail.mit.edu/download.html (4.4G) For ImageNet32 see https://patrykchrabaszcz.github.io/Imagenet32/

Pretrained unsupervised weights and noise clustering

This is a link to pretrained unsupervised weights and noise detection for seed 1 of this repository: CIFAR-100, miniImageNet

Visualizing the linear separation of contrastive features on the 2D hypersphere for CIFAR-10

Learn unsupervised features using iMix + N-pairs - projection size for the contrastive feature is 2 for a 2D hypersphere

CUDA_VISIBLE_DEVICES=0 python main_unsup.py --dataset cifar10 --ood-noise 0.2 --id-noise 0.0 --epochs 2000 --batch-size 256 --net preresnet18 --lr 0.01 --steps 1000 1500 --seed 1 --exp-name cifar10_test --proj-size 2 --mixup

Visualize the separation

CUDA_VISIBLE_DEVICES=0 python plot_sphere.py --weights checkpoints/preresnet18_cifar10/cifar10_test/1.0/preresnet18_cifar10.pth.tar --id-noise 0.2 --ood-noise 0.2

Note that a higher linear separation will be observed if the projection size is higher. We use 128 in the paper. For higher separations you could visualize using Umap to map to 2D before reapplying L2 normalization.

Retreiving noisy samples using OPTICS on unsupervised contrastive features

Phase 1 - learn the unsupervised features

CUDA_VISIBLE_DEVICES=0 python main_unsup.py --dataset cifar100 --ood-noise 0.2 --id-noise 0.0 --epochs 2000 --batch-size 256 --net preresnet18 --lr 0.01 --steps 1000 1500 --seed 1 --exp-name cifar100_test_unsup --proj-size 128 --mixup

Note that there is no point in adding in-distribution noise here since the algorithm is unsupervised

Phase 2 - extract the clusters

CUDA_VISIBLE_DEVICES=0 python evaluate_clust.py --weights checkpoints/preresnet18_cifar100/cifar100_test_unsup/1.0/preresnet18_cifar100.pth.tar --ood-noise 0.2 --id-noise 0.2

Both the noisy label assignment file and the ood clusters will be stored in noise_files/{dataset}/ (created automatically if it does not exist)

Phase 3 - train the noise robust algorithm

CUDA_VISIBLE_DEVICES=0 python main_sup.py --dataset cifar100 --ood-noise 0.2 --id-noise 0.2 --epochs 100 --batch-size 256 --net preresnet18 --lr 0.1 --steps 50 80 --seed 1 --exp-name cifar100_sup_test --warmup 30 --proj-size 128 --mixup --cont --ts --noise-file noise_files/cifar100/clean_noisy_cifar100_0.2_0.2.pth.tar

The file train.sh lists commands to run the algorithm

Cite us if we helped your research

@inproceedings{2022_ECCV_SNCF,
  title="{Addressing out-of-distribution label noise in webly-labelled data}",
  author="Paul Albert and Eric Arazo and Noel O'Connor and Kevin McGuinness",
  booktitle="{European Conference on Computer Vision (ECCV)}",
  year="2022"
}

PaulAlbert31 / SNCF