Sign in to view source links and access this dataset
Description
CaliBench contains extracted logits and features from ResNet-50, ResNet-110, and DenseNet-121 models trained on CIFAR10, CIFAR100, and ImageNet. Developed by zhurong2333 and updated in March 2026, the collection facilitates research into model calibration and focal loss performance.
Use Cases
Evaluating post-hoc calibration techniques using logits from ResNet-110
Comparing feature representations between DenseNet-121 and ResNet-50 on ImageNet
Analyzing the impact of focal loss on logit distributions in CIFAR100
Strengths
Includes outputs from three distinct architectures: ResNet-50, ResNet-110, and DenseNet-121
Covers three standard benchmarks: CIFAR10, CIFAR100, and ImageNet
Provides logits specifically from focal loss calibration experiments
Limitations
Contains derived model outputs rather than raw source images
Documentation lacks specific column headers and data schema
Provenance
Source
zhurong2333 via Hugging Face
Collection Method
Extracted: Features and logits were extracted from pretrained PyTorch models during inference.
Freshness
Last updated March 2026.
ImageNet data uses torchvision.models.ResNet50_Weights.IMAGENET1K_V1; CIFAR data is specifically derived from focal loss calibration research.