---
dataset_info:
  features:
  - name: dataset
    dtype: string
  - name: condition
    dtype: string
  - name: trial
    dtype: string
  - name: n_objects
    dtype: int64
  - name: oddity_index
    dtype: int64
  - name: images
    sequence: image
  - name: n_subjects
    dtype: int64
  - name: human_avg
    dtype: float64
  - name: human_sem
    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
  - name: DINOv2G_sem
    dtype: float64
  splits:
  - name: train
    num_bytes: 384413356.563
    num_examples: 2019
  download_size: 382548893
  dataset_size: 384413356.563
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

## MOCHI: Multiview Object Consistency in Humans and Image models

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**)

To download dataset from huggingface, install relevant huggingface libraries

```
pip install datasets huggingface_hub
```
and download MOCHI 

```python
from datasets import load_dataset

# download huggingface dataset 
benchmark = load_dataset("tzler/MOCHI")['train']

# there are 2019 trials let's pick one 
i_trial = benchmark[1879]
```

Here, `i_trial` is a dictionary with trial-related data including human (`human` and `RT`) and model (`DINOv2G`) performance measures: 

```
{'dataset': 'shapegen',
 'condition': 'abstract2',
 'trial': 'shapegen2527',
 'n_objects': 3,
 'oddity_index': 2,
 'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x1000>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x1000>,
  <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x1000>],
 'n_subjects': 15,
 'human_avg': 1.0,
 'human_sem': 0.0,
 'human_std': 0.0,
 'RT_avg': 4324.733333333334,
 'RT_sem': 544.4202024405384,
 'RT_std': 2108.530377391076,
 'DINOv2G_avg': 1.0,
 'DINOv2G_std': 0.0,
 'DINOv2G_sem': 0.0}```

```

as well as this trial's images: 

```python
plt.figure(figsize=[15,4])
for i_plot in range(len(i_trial['images'])):
  plt.subplot(1,len(i_trial['images']),i_plot+1)
  plt.imshow(i_trial['images'][i_plot])
  if i_plot == i_trial['oddity_index']: plt.title('odd-one-out')
  plt.axis('off')
plt.show()
```
<img src="example_trial.png" alt="example trial"/>

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: 

```
git clone https://github.com/tzler/MOCHI.git
```
And then imported with a few lines of code:

```python
import pandas 
# load data the github repo we just cloned  
df = pandas.read_csv('MOCHI/assets/benchmark.csv')
# extract trial info with the index from huggingface repo above
df.loc[i_trial_index]['trial']
```
This returns the trial, `shapegen2527`, which is the same as the huggingface dataset for this index. 


```
@misc{bonnen2024evaluatingmultiviewobjectconsistency,
      title={Evaluating Multiview Object Consistency in Humans and Image Models}, 
      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},
      year={2024},
      eprint={2409.05862},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2409.05862}, 
}
```