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--- |
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license: mit |
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task_categories: |
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- image-classification |
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- token-classification |
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language: |
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- en |
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pretty_name: Post-hoc Calibration Dataset |
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tags: |
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- deep learning |
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- neural network classifier |
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- classification calibration |
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- network calibration |
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- canonical calibration |
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- deep learning calibration |
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--- |
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# Post-hoc Calibration Dataset |
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This repository contains datasets designed for evaluating and developing **post-hoc calibration** methods for deep neural network classifiers. Each dataset includes precomputed logits and labels, divided into clear training and test splits. |
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## Dataset Overview |
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Datasets provided here cover popular benchmark tasks, including CIFAR-10, CIFAR-100, SVHN, Stanford Cars (CARS), CUB-200 Birds (BIRDS), and ImageNet. Dataset composition here for post-hoc calibration are listed below: |
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| Dataset | # Classes | Training Set Size | Test Set Size | |
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|--------------|-----------|-------------------|---------------| |
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| CIFAR-10 | 10 | 5000 | 10000 | |
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| SVHN | 10 | 6000 | 26032 | |
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| CIFAR-100 | 100 | 5000 | 10000 | |
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| CARS | 196 | 4020 | 4020 | |
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| BIRDS | 200 | 2897 | 2897 | |
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| ImageNet | 1000 | 25000 | 25000 | |
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## Included Pre-trained Networks on Classification Datasets |
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Each `.p` file, which represents one calibration task, contains ground-truth labels and predicted logits from specific pre-trained neural network architectures, as listed below: |
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- **`probs_resnet110_c10_logits.p`**: ResNet110 on CIFAR-10 |
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- **`probs_resnet_wide32_c10_logits.p`**: WideResNet32 on CIFAR-10 |
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- **`probs_densenet40_c10_logits.p`**: DenseNet40 on CIFAR-10 |
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- **`probs_resnet110_c100_logits.p`**: ResNet110 on CIFAR-100 |
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- **`probs_resnet_wide32_c100_logits.p`**: WideResNet32 on CIFAR-100 |
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- **`probs_densenet40_c100_logits.p`**: DenseNet40 on CIFAR-100 |
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- **`probs_resnet152_SD_SVHN_logits.p`**: ResNet152 SD on SVHN |
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- **`probs_resnet50NTSNet_birds_logits.p`**: ResNet50NTSNet on BIRDS |
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- **`probs_resnet50_cars_logits.p`**: ResNet50 on CARS |
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- **`probs_resnet101scratch_cars_logits.p`**: ResNet101 from scratch on CARS |
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- **`probs_resnet101_cars_logits.p`**: ResNet101 on CARS (initialized with ImageNet weights) |
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- **`probs_densenet161_imgnet_logits.p`**: DenseNet161 on ImageNet |
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- **`probs_pnasnet5large_imgnet_logits.p`**: PNASNet5large on ImageNet |
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- **`probs_resnet152_imgnet_logits.p`**: ResNet152 on ImageNet |
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- **`probs_swintiny_imgnet_logits.p`**: Swin Transformer (tiny) on ImageNet |
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## Data Loading |
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Each dataset is stored as a Python pickle file (`.p`). Load the datasets with the following Python snippet: |
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```python |
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import pickle |
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with open('path_to_dataset.p', 'rb') as f: |
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(x_logits_train, y_train), (x_logits_test, y_test) = pickle.load(f) |
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``` |
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- `x_logits_train`, `x_logits_test`: The logits (raw neural network outputs). |
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- `y_train`, `y_test`: Ground truth labels. |
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## Reference |
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More detailed description for the dataset can be found in the following paper: |
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``` |
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Huang W, Cao G, Xia J, Chen J, Wang H, Zhang J. h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective[J]. |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025. |
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``` |
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The official GitHub implementation of the above `H-Calibration` study using the dataset is accessible here: |
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- [h-Calibration GitHub Repository](https://github.com/WenjianHuang93/h-Calibration) |
