Dataloader for CIFAR-N

CIFAR-10N

noise_label = torch.load('./data/CIFAR-10_human.pt')
clean_label = noise_label['clean_label']
worst_label = noise_label['worse_label']
aggre_label = noise_label['aggre_label']
random_label1 = noise_label['random_label1']
random_label2 = noise_label['random_label2']
random_label3 = noise_label['random_label3']

CIFAR-100N

noise_label = torch.load('./data/CIFAR-100_human.pt')
clean_label = noise_label['clean_label']
noisy_label = noise_label['noisy_label']

Training on CIFAR-N with the Cross-Entropy loss

CIFAR-10N

# NOISE_TYPE: [clean, aggre, worst, rand1, rand2, rand3]
# Use human annotations
CUDA_VISIBLE_DEVICES=0 python3 main.py --dataset cifar10 --noise_type NOISE_TYPE --is_human
# Use the synthetic noise that has the same noise transition matrix as human annotations
CUDA_VISIBLE_DEVICES=0 python3 main.py --dataset cifar10 --noise_type NOISE_TYPE

CIFAR-100N

# NOISE_TYPE: [clean100, noisy100]
# Use human annotations
CUDA_VISIBLE_DEVICES=0 python3 main.py --dataset cifar100 --noise_type NOISE_TYPE --is_human
# Use the synthetic noise that has the same noise transition matrix as human annotations
CUDA_VISIBLE_DEVICES=0 python3 main.py --dataset cifar100 --noise_type NOISE_TYPE

Additional dataset information

We include additional side information during the noisy-label collection in side_info_cifar10N.csv and side_info_cifar100N.csv. A brief introduction of these two files:

Image-batch: a subset of indexes of the CIFAR training images.
Worker-id: the encrypted worker id on Amazon Mechanical Turk.
Work-time-in-seconds: the time (in seconds) a worker spent on annotating the corresponding image batch.

Human annotated noisy labels for CIFAR-10 and CIFAR-100.

Related tags

Overview

Dataloader for CIFAR-N

CIFAR-10N

CIFAR-100N

Training on CIFAR-N with the Cross-Entropy loss

CIFAR-10N

CIFAR-100N

Additional dataset information

Owner

[email protected]

Simple Tensorflow implementation of "Adaptive Convolutions for Structure-Aware Style Transfer" (CVPR 2021)

[CVPR 2022 Oral] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Label Hallucination for Few-Shot Classification

A new video text spotting framework with Transformer

Computer vision - fun segmentation experience using classic and deep tools :)

Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

A large-scale database for graph representation learning

Reinforcement Learning for Automated Trading

OpenLT: An open-source project for long-tail classification

A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

A trusty face recognition research platform developed by Tencent Youtu Lab

Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

The code for "Deep Level Set for Box-supervised Instance Segmentation in Aerial Images".

Implementation of "Learning to Match Features with Seeded Graph Matching Network" ICCV2021

Numba-accelerated Pythonic implementation of MPDATA with examples in Python, Julia and Matlab

Tensorflow 2 implementation of our high quality frame interpolation neural network

Deep motion generator collections

[CVPR'21] Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration