Caltech-101

Overview edit

Variants: Caltech-101, Caltech-101, 202 Labels

Associated Benchmarks

This dataset is used in 8 benchmarks:

• Zero-Shot Learning - Metrics: Accuracy
• Image Clustering - Metrics: Accuracy
• Density Estimation - Metrics: Negative ELBO, NLL, MMD-L2, COV-L2
• Fine-Grained Image Classification - Metrics: Top-1 Error Rate, Accuracy
• Unsupervised Anomaly Detection - Metrics: AUC (outlier ratio = 0.5)
• Prompt Engineering - Metrics: Harmonic mean
• Semi-Supervised Image Classification - Metrics: Accuracy
• Semantic correspondence - Metrics: IoU, LT-ACC, IoU (weak), LT-ACC (weak)

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models (2025) -
• MMRL: Multi-Modal Representation Learning for Vision-Language Models (2025) -
• Stochastic Subsampling With Average Pooling (2024) -
• HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling (2024) -
• Let Go of Your Labels with Unsupervised Transfer (2024) -
• Label Propagation for Zero-shot Classification with Vision-Language Models (2024) -
• Prompt Learning via Meta-Regularization (2024) -
• PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise (2024) -
• PromptKD: Unsupervised Prompt Distillation for Vision-Language Models (2024) -
• Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models (2023) -
• DePT: Decoupled Prompt Tuning (2023) -
• Read-only Prompt Optimization for Vision-Language Few-shot Learning (2023) -
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting (2023) -
• Consistency-guided Prompt Learning for Vision-Language Models (2023) -
• Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive Fields (2023) -
• Reduction of Class Activation Uncertainty with Background Information (2023) -
• How to Use Dropout Correctly on Residual Networks with Batch Normalization (2023) -
• On the Ideal Number of Groups for Isometric Gradient Propagation (2023) -
• Learning Domain Invariant Prompt for Vision-Language Models (2022) -
• MaPLe: Multi-modal Prompt Learning (2022) -