ImageNet-R

Overview edit

Variants: ImageNet-R

Associated Benchmarks

This dataset is used in 4 benchmarks:

• Zero-Shot Transfer Image Classification - Metrics: Accuracy
• Unsupervised Domain Adaptation - Metrics: Top 1 Error
• Prompt Engineering - Metrics: Top-1 accuracy %
• Domain Generalization - Metrics: Top-1 Error Rate

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• MMRL: Multi-Modal Representation Learning for Vision-Language Models (2025) -
• HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling (2024) -
• EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters (2024) -
• Fully Attentional Networks with Self-emerging Token Labeling (2024) -
• Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models (2023) -
• Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models (2023) -
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting (2023) -
• Consistency-guided Prompt Learning for Vision-Language Models (2023) -
• Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition (2023) -
• EVA-CLIP: Improved Training Techniques for CLIP at Scale (2023) -
• Scaling Vision Transformers to 22 Billion Parameters (2023) -
• A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others (2022) -
• Context-Aware Robust Fine-Tuning (2022) -
• AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities (2022) -
• MetaFormer Baselines for Vision (2022) -
• MaPLe: Multi-modal Prompt Learning (2022) -
• Generalized Parametric Contrastive Learning (2022) -
• Enhance the Visual Representation via Discrete Adversarial Training (2022) -
• PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) -
• Sequencer: Deep LSTM for Image Classification (2022) -