Omkar2118/datasets huggingface dataset:
Post-hoc Calibration

# Post-hoc Calibration Dataset

This dataset collection is designed for the evaluation and development of **post-hoc calibration methods** for deep neural network classifiers. It provides **precomputed logits and labels** across a range of standard classification benchmarks, enabling rigorous, reproducible calibration research.

## 🧾 Dataset Characteristics

- Contains logits and ground-truth labels from **pretrained neural networks** on popular datasets: **CIFAR-10, CIFAR-100, SVHN, Stanford Cars (CARS), CUB-200 (BIRDS), and ImageNet**.
- Datasets are split into **training and test sets**, tailored specifically for post-hoc calibration tasks.
- Pretrained networks include **ResNet**, **WideResNet**, **DenseNet**, **Swin Transformer**, and task-specific fine-tuned architectures.

## 🎯 Motivation and Content Summary

Modern neural networks often produce poorly calibrated probability estimates, which can hinder decision-making in risk-sensitive applications. This dataset addresses the need for **standardized benchmarks** for post-hoc calibration by offering a unified collection of:
- Ground-truth labels and classification results (logits) from diverse architectures
- Calibration tasks across datasets with varying number of classes and granularity
- Consistent experimental setup for fair comparison across different calibration methods

## 💡 Potential Use Cases

- Developing and benchmarking **post-hoc calibration algorithms**
- Evaluating the generalization ability of calibration methods across domains and data distributions
- Supporting studies in neural network confidence estimation and uncertainty quantification


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> For more information, refer to the paper *"h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective"* (TPAMI 2025) and the [official GitHub repository](https://github.com/WenjianHuang93/h-Calibration).

Introducing paper: h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective