Scores on benchmarks
Model rank shown below is with respect to all public models.| .076 |
average_vision
rank 444
81 benchmarks |
|
| .015 |
neural_vision
rank 479
38 benchmarks |
|
| .059 |
V1
rank 465
24 benchmarks |
|
| .178 |
Coggan2024_fMRI.V1-rdm
v1
rank 34
|
|
|
||
| .138 |
behavior_vision
rank 233
43 benchmarks |
|
| .098 |
Geirhos2021-error_consistency
[reference]
rank 226
17 benchmarks |
|
| .179 |
Geirhos2021colour-error_consistency
v1
[reference]
rank 192
|
|
|
||
| .079 |
Geirhos2021contrast-error_consistency
v1
[reference]
rank 238
|
|
|
||
| .160 |
Geirhos2021cueconflict-error_consistency
v1
[reference]
rank 161
|
|
|
||
| .084 |
Geirhos2021edge-error_consistency
v1
[reference]
rank 134
|
|
|
||
| .077 |
Geirhos2021eidolonI-error_consistency
v1
[reference]
rank 284
|
|
|
||
| .173 |
Geirhos2021eidolonII-error_consistency
v1
[reference]
rank 228
|
|
|
||
| .123 |
Geirhos2021eidolonIII-error_consistency
v1
[reference]
rank 248
|
|
|
||
| .218 |
Geirhos2021falsecolour-error_consistency
v1
[reference]
rank 172
|
|
|
||
| .048 |
Geirhos2021highpass-error_consistency
v1
[reference]
rank 178
|
|
|
||
| .090 |
Geirhos2021lowpass-error_consistency
v1
[reference]
rank 197
|
|
|
||
| .051 |
Geirhos2021phasescrambling-error_consistency
v1
[reference]
rank 227
|
|
|
||
| .013 |
Geirhos2021powerequalisation-error_consistency
v1
[reference]
rank 290
|
|
|
||
| .063 |
Geirhos2021rotation-error_consistency
v1
[reference]
rank 209
|
|
|
||
| .052 |
Geirhos2021silhouette-error_consistency
v1
[reference]
rank 299
|
|
|
||
| .044 |
Geirhos2021sketch-error_consistency
v1
[reference]
rank 225
|
|
|
||
| .159 |
Geirhos2021stylized-error_consistency
v1
[reference]
rank 187
|
|
|
||
| .061 |
Geirhos2021uniformnoise-error_consistency
v1
[reference]
rank 190
|
|
|
||
| .091 |
Baker2022
rank 158
3 benchmarks |
|
| .274 |
Baker2022fragmented-accuracy_delta
v1
[reference]
rank 118
|
|
|
||
| .000 |
Baker2022frankenstein-accuracy_delta
v1
[reference]
rank 150
|
|
|
||
| .000 |
Baker2022inverted-accuracy_delta
v1
[reference]
rank 58
|
|
|
||
| .146 |
BMD2024
rank 131
4 benchmarks |
|
| .166 |
BMD2024.dotted_1Behavioral-accuracy_distance
v1
rank 86
|
|
|
||
| .126 |
BMD2024.dotted_2Behavioral-accuracy_distance
v1
rank 112
|
|
|
||
| .134 |
BMD2024.texture_1Behavioral-accuracy_distance
v1
rank 132
|
|
|
||
| .157 |
BMD2024.texture_2Behavioral-accuracy_distance
v1
rank 122
|
|
|
||
| .019 |
Ferguson2024
[reference]
rank 251
14 benchmarks |
|
| .270 |
Ferguson2024gray_hard-value_delta
v1
[reference]
rank 179
|
|
|
||
| .233 |
Hebart2023-match
v1
rank 149
|
|
|
||
| .417 |
Maniquet2024
rank 198
2 benchmarks |
|
| .164 |
Maniquet2024-confusion_similarity
v1
[reference]
rank 229
|
|
|
||
| .671 |
Maniquet2024-tasks_consistency
v1
[reference]
rank 83
|
|
|
||
| .095 |
Coggan2024_behavior-ConditionWiseAccuracySimilarity
v1
rank 181
|
|
|
||
| .104 |
engineering_vision
rank 285
25 benchmarks |
|
| .520 |
Geirhos2021-top1
[reference]
rank 159
17 benchmarks |
|
| .948 |
Geirhos2021colour-top1
v1
[reference]
rank 153
|
|
|
||
| .855 |
Geirhos2021contrast-top1
v1
[reference]
rank 95
|
|
|
||
| .158 |
Geirhos2021cueconflict-top1
v1
[reference]
rank 246
|
|
|
||
| .194 |
Geirhos2021edge-top1
v1
[reference]
rank 213
|
|
|
||
| .497 |
Geirhos2021eidolonI-top1
v1
[reference]
rank 143
|
|
|
||
| .511 |
Geirhos2021eidolonII-top1
v1
[reference]
rank 146
|
|
|
||
| .498 |
Geirhos2021eidolonIII-top1
v1
[reference]
rank 164
|
|
|
||
| .929 |
Geirhos2021falsecolour-top1
v1
[reference]
rank 134
|
|
|
||
| .500 |
Geirhos2021highpass-top1
v1
[reference]
rank 69
|
|
|
||
| .419 |
Geirhos2021lowpass-top1
v1
[reference]
rank 134
|
|
|
||
| .539 |
Geirhos2021phasescrambling-top1
v1
[reference]
rank 190
|
|
|
||
| .598 |
Geirhos2021powerequalisation-top1
v1
[reference]
rank 186
|
|
|
||
| .674 |
Geirhos2021rotation-top1
v1
[reference]
rank 128
|
|
|
||
| .319 |
Geirhos2021silhouette-top1
v1
[reference]
rank 243
|
|
|
||
| .487 |
Geirhos2021sketch-top1
v1
[reference]
rank 216
|
|
|
||
| .329 |
Geirhos2021stylized-top1
v1
[reference]
rank 208
|
|
|
||
| .381 |
Geirhos2021uniformnoise-top1
v1
[reference]
rank 153
|
|
|
||
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}
}