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--- |
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dataset_info: |
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features: |
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- name: dataset |
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dtype: string |
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- name: condition |
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dtype: string |
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- name: trial |
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dtype: string |
|
- name: n_objects |
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dtype: int64 |
|
- name: oddity_index |
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dtype: int64 |
|
- name: images |
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sequence: image |
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- name: n_subjects |
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dtype: int64 |
|
- name: human_avg |
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dtype: float64 |
|
- name: human_sem |
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dtype: float64 |
|
- name: human_std |
|
dtype: float64 |
|
- name: RT_avg |
|
dtype: float64 |
|
- name: RT_sem |
|
dtype: float64 |
|
- name: RT_std |
|
dtype: float64 |
|
- name: DINOv2G_avg |
|
dtype: float64 |
|
- name: DINOv2G_std |
|
dtype: float64 |
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- name: DINOv2G_sem |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 384413356.563 |
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num_examples: 2019 |
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download_size: 382548893 |
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dataset_size: 384413356.563 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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## MOCHI: Multiview Object Consistency in Humans and Image models |
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We introduce a benchmark to evaluate the alignment between humans and image models on 3D shape understanding: **M**ultiview **O**bject **C**onsistency in **H**umans and **I**mage models (**MOCHI**) |
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To download dataset from huggingface, install relevant huggingface libraries |
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``` |
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pip install datasets huggingface_hub |
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``` |
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and download MOCHI |
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```python |
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from datasets import load_dataset |
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# download huggingface dataset |
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benchmark = load_dataset("tzler/MOCHI")['train'] |
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# there are 2019 trials let's pick one |
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i_trial = benchmark[1879] |
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``` |
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Here, `i_trial` is a dictionary with trial-related data including human (`human` and `RT`) and model (`DINOv2G`) performance measures: |
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``` |
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{'dataset': 'shapegen', |
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'condition': 'abstract2', |
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'trial': 'shapegen2527', |
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'n_objects': 3, |
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'oddity_index': 2, |
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'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x1000>, |
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<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x1000>, |
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<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x1000>], |
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'n_subjects': 15, |
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'human_avg': 1.0, |
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'human_sem': 0.0, |
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'human_std': 0.0, |
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'RT_avg': 4324.733333333334, |
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'RT_sem': 544.4202024405384, |
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'RT_std': 2108.530377391076, |
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'DINOv2G_avg': 1.0, |
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'DINOv2G_std': 0.0, |
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'DINOv2G_sem': 0.0}``` |
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``` |
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as well as this trial's images: |
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```python |
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plt.figure(figsize=[15,4]) |
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for i_plot in range(len(i_trial['images'])): |
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plt.subplot(1,len(i_trial['images']),i_plot+1) |
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plt.imshow(i_trial['images'][i_plot]) |
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if i_plot == i_trial['oddity_index']: plt.title('odd-one-out') |
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plt.axis('off') |
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plt.show() |
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``` |
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<img src="example_trial.png" alt="example trial"/> |
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The complete results on this benchmark, including all of the human and model (e.g., DINOv2, CLIP, and MAE at multiple sizes), can be downloaded from the github repo: |
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``` |
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git clone https://github.com/tzler/MOCHI.git |
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``` |
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And then imported with a few lines of code: |
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```python |
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import pandas |
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# load data the github repo we just cloned |
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df = pandas.read_csv('MOCHI/assets/benchmark.csv') |
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# extract trial info with the index from huggingface repo above |
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df.loc[i_trial_index]['trial'] |
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``` |
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This returns the trial, `shapegen2527`, which is the same as the huggingface dataset for this index. |
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``` |
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@misc{bonnen2024evaluatingmultiviewobjectconsistency, |
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title={Evaluating Multiview Object Consistency in Humans and Image Models}, |
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author={Tyler Bonnen and Stephanie Fu and Yutong Bai and Thomas O'Connell and Yoni Friedman and Nancy Kanwisher and Joshua B. Tenenbaum and Alexei A. Efros}, |
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year={2024}, |
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eprint={2409.05862}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2409.05862}, |
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} |
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``` |
