{"url":"/dataset/post-hoc-calibration","name":"Post-hoc Calibration","full_name":null,"description_markdown":"# Post-hoc Calibration Dataset\r\n\r\nThis 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.\r\n\r\n## 🧾 Dataset Characteristics\r\n\r\n- 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**.\r\n- Datasets are split into **training and test sets**, tailored specifically for post-hoc calibration tasks.\r\n- Pretrained networks include **ResNet**, **WideResNet**, **DenseNet**, **Swin Transformer**, and task-specific fine-tuned architectures.\r\n\r\n## 🎯 Motivation and Content Summary\r\n\r\nModern 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:\r\n- Ground-truth labels and classification results (logits) from diverse architectures\r\n- Calibration tasks across datasets with varying number of classes and granularity\r\n- Consistent experimental setup for fair comparison across different calibration methods\r\n\r\n## 💡 Potential Use Cases\r\n\r\n- Developing and benchmarking **post-hoc calibration algorithms**\r\n- Evaluating the generalization ability of calibration methods across domains and data distributions\r\n- Supporting studies in neural network confidence estimation and uncertainty quantification\r\n\r\n\r\n---\r\n\r\n> 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).","description_withheld":null,"homepage":"https://huggingface.co/datasets/WJHuang/calibration","introduced_date":"2025-06-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/h-calibration-rethinking-classifier","title":"h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective","first_author":"Wenjian Huang","url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Post-hoc Calibration"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}