Scores on benchmarks
Model rank shown below is with respect to all public models.| .076 |
average_vision
rank 435
81 benchmarks |
|
| .015 |
neural_vision
rank 473
38 benchmarks |
|
| .059 |
V1
rank 456
24 benchmarks |
|
| .178 |
Coggan2024_fMRI.V1-rdm
v1
rank 34
|
|
|
24 images 
|
||
| .138 |
behavior_vision
rank 237
43 benchmarks |
|
| .098 |
Geirhos2021-error_consistency
[reference]
rank 216
17 benchmarks |
|
| .179 |
Geirhos2021colour-error_consistency
v1
[reference]
rank 193
|
|
|
640 images 
|
||
| .079 |
Geirhos2021contrast-error_consistency
v1
[reference]
rank 228
|
|
|
800 images 
|
||
| .160 |
Geirhos2021cueconflict-error_consistency
v1
[reference]
rank 163
|
|
|
1280 images 
|
||
| .084 |
Geirhos2021edge-error_consistency
v1
[reference]
rank 135
|
|
|
160 images 
|
||
| .077 |
Geirhos2021eidolonI-error_consistency
v1
[reference]
rank 274
|
|
|
800 images 
|
||
| .173 |
Geirhos2021eidolonII-error_consistency
v1
[reference]
rank 218
|
|
|
640 images 
|
||
| .123 |
Geirhos2021eidolonIII-error_consistency
v1
[reference]
rank 239
|
|
|
480 images 
|
||
| .218 |
Geirhos2021falsecolour-error_consistency
v1
[reference]
rank 174
|
|
|
560 images 
|
||
| .048 |
Geirhos2021highpass-error_consistency
v1
[reference]
rank 179
|
|
|
640 images 
|
||
| .090 |
Geirhos2021lowpass-error_consistency
v1
[reference]
rank 199
|
|
|
800 images 
|
||
| .051 |
Geirhos2021phasescrambling-error_consistency
v1
[reference]
rank 218
|
|
|
640 images 
|
||
| .013 |
Geirhos2021powerequalisation-error_consistency
v1
[reference]
rank 281
|
|
|
560 images 
|
||
| .063 |
Geirhos2021rotation-error_consistency
v1
[reference]
rank 210
|
|
|
960 images 
|
||
| .052 |
Geirhos2021silhouette-error_consistency
v1
[reference]
rank 290
|
|
|
160 images 
|
||
| .044 |
Geirhos2021sketch-error_consistency
v1
[reference]
rank 225
|
|
|
800 images 
|
||
| .159 |
Geirhos2021stylized-error_consistency
v1
[reference]
rank 188
|
|
|
800 images 
|
||
| .061 |
Geirhos2021uniformnoise-error_consistency
v1
[reference]
rank 192
|
|
|
800 images 
|
||
| .091 |
Baker2022
rank 159
3 benchmarks |
|
| .274 |
Baker2022fragmented-accuracy_delta
v1
[reference]
rank 119
|
|
|
716 images 
|
||
| .000 |
Baker2022frankenstein-accuracy_delta
v1
[reference]
rank 151
|
|
|
716 images 
|
||
| .000 |
Baker2022inverted-accuracy_delta
v1
[reference]
rank 58
|
|
|
360 images 
|
||
| .146 |
BMD2024
rank 131
4 benchmarks |
|
| .166 |
BMD2024.dotted_1Behavioral-accuracy_distance
v1
rank 86
|
|
|
100 images 
|
||
| .126 |
BMD2024.dotted_2Behavioral-accuracy_distance
v1
rank 113
|
|
|
100 images 
|
||
| .134 |
BMD2024.texture_1Behavioral-accuracy_distance
v1
rank 132
|
|
|
100 images 
|
||
| .157 |
BMD2024.texture_2Behavioral-accuracy_distance
v1
rank 122
|
|
|
100 images 
|
||
| .019 |
Ferguson2024
[reference]
rank 256
14 benchmarks |
|
| .270 |
Ferguson2024gray_hard-value_delta
v1
[reference]
rank 183
|
|
|
2_way_afc task
48 images 
|
||
| .233 |
Hebart2023-match
v1
rank 152
|
|
|
1854 images 
|
||
| .417 |
Maniquet2024
rank 199
2 benchmarks |
|
| .164 |
Maniquet2024-confusion_similarity
v1
[reference]
rank 231
|
|
|
13600 images 
|
||
| .671 |
Maniquet2024-tasks_consistency
v1
[reference]
rank 84
|
|
|
13600 images 
|
||
| .095 |
Coggan2024_behavior-ConditionWiseAccuracySimilarity
v1
rank 182
|
|
|
22560 images 
|
||
| .104 |
engineering_vision
rank 279
25 benchmarks |
|
| .520 |
Geirhos2021-top1
[reference]
rank 160
17 benchmarks |
|
| .948 |
Geirhos2021colour-top1
v1
[reference]
rank 155
|
|
|
640 images 
|
||
| .855 |
Geirhos2021contrast-top1
v1
[reference]
rank 97
|
|
|
800 images 
|
||
| .158 |
Geirhos2021cueconflict-top1
v1
[reference]
rank 238
|
|
|
1280 images 
|
||
| .194 |
Geirhos2021edge-top1
v1
[reference]
rank 205
|
|
|
160 images 
|
||
| .498 |
Geirhos2021eidolonI-top1
v1
[reference]
rank 144
|
|
|
800 images 
|
||
| .511 |
Geirhos2021eidolonII-top1
v1
[reference]
rank 147
|
|
|
640 images 
|
||
| .498 |
Geirhos2021eidolonIII-top1
v1
[reference]
rank 165
|
|
|
480 images 
|
||
| .929 |
Geirhos2021falsecolour-top1
v1
[reference]
rank 136
|
|
|
560 images 
|
||
| .500 |
Geirhos2021highpass-top1
v1
[reference]
rank 69
|
|
|
640 images 
|
||
| .419 |
Geirhos2021lowpass-top1
v1
[reference]
rank 135
|
|
|
800 images 
|
||
| .539 |
Geirhos2021phasescrambling-top1
v1
[reference]
rank 191
|
|
|
640 images 
|
||
| .598 |
Geirhos2021powerequalisation-top1
v1
[reference]
rank 188
|
|
|
560 images 
|
||
| .674 |
Geirhos2021rotation-top1
v1
[reference]
rank 130
|
|
|
960 images 
|
||
| .319 |
Geirhos2021silhouette-top1
v1
[reference]
rank 235
|
|
|
160 images 
|
||
| .488 |
Geirhos2021sketch-top1
v1
[reference]
rank 218
|
|
|
800 images 
|
||
| .329 |
Geirhos2021stylized-top1
v1
[reference]
rank 210
|
|
|
800 images 
|
||
| .381 |
Geirhos2021uniformnoise-top1
v1
[reference]
rank 155
|
|
|
800 images 
|
||
How to use
from brainscore_vision import load_model
model = load_model("resnet50_eMMCR_Vanilla")
model.start_task(...)
model.start_recording(...)
model.look_at(...)
Benchmarks bibtex
@inproceedings{santurkar2019computer,
title={Computer Vision with a Single (Robust) Classifier},
author={Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Andrew Ilyas and Logan Engstrom and Aleksander Madry},
booktitle={ArXiv preprint arXiv:1906.09453},
year={2019}
}
@article{geirhos2021partial,
title={Partial success in closing the gap between human and machine vision},
author={Geirhos, Robert and Narayanappa, Kantharaju and Mitzkus, Benjamin and Thieringer, Tizian and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021},
url={https://openreview.net/forum?id=QkljT4mrfs}
}
@article{BAKER2022104913,
title = {Deep learning models fail to capture the configural nature of human shape perception},
journal = {iScience},
volume = {25},
number = {9},
pages = {104913},
year = {2022},
issn = {2589-0042},
doi = {https://doi.org/10.1016/j.isci.2022.104913},
url = {https://www.sciencedirect.com/science/article/pii/S2589004222011853},
author = {Nicholas Baker and James H. Elder},
keywords = {Biological sciences, Neuroscience, Sensory neuroscience},
abstract = {Summary
A hallmark of human object perception is sensitivity to the holistic configuration of the local shape features of an object. Deep convolutional neural networks (DCNNs) are currently the dominant models for object recognition processing in the visual cortex, but do they capture this configural sensitivity? To answer this question, we employed a dataset of animal silhouettes and created a variant of this dataset that disrupts the configuration of each object while preserving local features. While human performance was impacted by this manipulation, DCNN performance was not, indicating insensitivity to object configuration. Modifications to training and architecture to make networks more brain-like did not lead to configural processing, and none of the networks were able to accurately predict trial-by-trial human object judgements. We speculate that to match human configural sensitivity, networks must be trained to solve a broader range of object tasks beyond category recognition.}
}
@misc{ferguson_ngo_lee_dicarlo_schrimpf_2024,
title={How Well is Visual Search Asymmetry predicted by a Binary-Choice, Rapid, Accuracy-based Visual-search, Oddball-detection (BRAVO) task?},
url={osf.io/5ba3n},
DOI={10.17605/OSF.IO/5BA3N},
publisher={OSF},
author={Ferguson, Michael E, Jr and Ngo, Jerry and Lee, Michael and DiCarlo, James and Schrimpf, Martin},
year={2024},
month={Jun}
}
@article {Maniquet2024.04.02.587669,
author = {Maniquet, Tim and de Beeck, Hans Op and Costantino, Andrea Ivan},
title = {Recurrent issues with deep neural network models of visual recognition},
elocation-id = {2024.04.02.587669},
year = {2024},
doi = {10.1101/2024.04.02.587669},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2024/04/10/2024.04.02.587669},
eprint = {https://www.biorxiv.org/content/early/2024/04/10/2024.04.02.587669.full.pdf},
journal = {bioRxiv}
}