a python package for calibrating the deep learning models
from calibrator import LocalCalibrator
import torch
val_logits = torch .randn (1000 , 10 )
val_labels = torch .randint (0 , 10 , (1000 ,))
test_logits = torch .randn (1000 , 10 )
calibrator = LocalCalibrator ()
eps_opt = calibrator .fit (val_logits , val_labels )
calibrated_probability = calibrator .calibrate (test_logits )
Datasets
Method
Method Description
ECE
ACE
MCE
CECE
PIECE
CIFAR-10
CIFAR-100
ImageNet-1K
Post-Hoc Calibration methods
Calibration methods
Description
Paper
Source Code
Status
Temperature Scaling (TS)
ICML 2017
paper
code
✅ Implemented
Parameterized Temperature Scaling (PTS)
ECCV 2022
paper
code
🔜 Pending
Ensemble Temperature Scaling (ETS)
ICML 2020
paper
code
🔜 Pending
Class-based Temperature Scaling (CTS)
EUSIPCO 2021
paper
unavailable
🔜 Pending
Group Calibration with Temperature Scaling (GCTS)
NeurIPS 2023
paper
code
🔜 Pending
Proximity-informed Calibration (PROCAL)
NeurIPS 2023
paper
code
🔜 Pending
Isotonic Regression
Histogram Binning
Platt Scaling
Bayesian Binning into Quantiles (BBQ)
AAAI 2015
paper
🔜 Pending
BetaCal
Scaling-Binning Calibrator
NeuIPS 2019
paper
code
🔜 Pending
Dirichlet calibration
NeurIPS 2019
paper
code
🔜 Pending
Train-time Calibration Methods
Calibration Losses
Description
Paper
Source Code
Status
Focal Loss
Dual Focal Loss
Adaptive Focal Loss
Metrics
Description
Paper
Source Code
Status
Expected Calibration Error (ECE)
AAAI 2015
paper
code
✅ Implemented
Maximum Calibration Error (MCE)
AAAI 2015
paper
code
🔜 Pending
Adaptive Calibration Error (ACE)
CVPRW 2019
paper
code
✅ Implemented
Classwise Expected Calibration Error (CECE)
NeurIPS 2019
paper
code
✅ Implemented
Negative Log Likelihood (NLL)
Accuracy
Proximity-informed Expected Calibration Error (PIECE)
NeurIPS 2023
paper
code
🔜 Pending
Pre-trained Model Weights
Datasets
Description
Paper
Source Code
Status
CIFAR-10
CIFAR-100
ImageNet
ImageNet-100
ImageNet-1000
Datasets
Description
Paper
Source Code
Status
CIFAR-10
CIFAR-100
ImageNet
ImageNet-100
ImageNet-1000