Update README.md

README.md

@@ -45,3 +45,61 @@ configs:
   - 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 
+
+```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="assets/example_trial.png" alt="example trial"/>