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--- |
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license: cc-by-sa-4.0 |
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arxiv: 2403.09193 |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: lf_label |
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dtype: string |
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- name: hf_label |
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dtype: string |
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splits: |
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- name: test |
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num_bytes: 43555505.52 |
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num_examples: 1280 |
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download_size: 43548336 |
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dataset_size: 43555505.52 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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task_categories: |
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- image-classification |
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language: |
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- en |
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tags: |
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- imagenet |
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- frequncy-bias |
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- texture-shape-bias |
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- vision-language-model |
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pretty_name: Low-/High-Frequency Cue-Conflict |
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--- |
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This dataset contains images of natural objects with low- and high-frequency cue conflicts, as described in the paper [Can We Talk Models Into Seeing the World Differently?](https://huggingface.co/papers/2403.09193). |
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The dataset consists of 1200 images where the low-frequency and high-frequency information correlate with different classes (out of 16 classes). This dataset can be used to evaluate the frequency-bias of vision models. |
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