metadata
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 MOCHI (Multiview Obect Consistency in Humans and Image models), a benchmark to evaluate the alignment between humans and image models on 3D shape understanding.
To download dataset from huggingface, install relevant huggingface libraries
pip install datasets huggingface_hub
and download MOCHI
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:
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()
