Scores on benchmarks
Model rank shown below is with respect to all public models.| .190 |
average_vision
rank 221
99 benchmarks |
|
| .020 |
neural_vision
rank 487
56 benchmarks |
|
| .009 |
V1
rank 494
28 benchmarks |
|
| .063 |
Coggan2024_fMRI.V1-rdm
v1
rank 105
|
|
|
24 images 
|
||
| .030 |
V2
rank 470
5 benchmarks |
|
| .148 |
Coggan2024_fMRI.V2-rdm
v1
rank 67
|
|
|
24 images 
|
||
| .006 |
V4
rank 489
10 benchmarks |
|
| .060 |
Coggan2024_fMRI.V4-rdm
v1
rank 79
|
|
|
24 images 
|
||
| .037 |
IT
rank 449
13 benchmarks |
|
| .483 |
Coggan2024_fMRI.IT-rdm
v1
rank 85
|
|
|
24 images 
|
||
| .359 |
behavior_vision
rank 97
43 benchmarks |
|
| .542 |
Rajalingham2018-i2n
v2
[reference]
rank 84
|
|
|
match-to-sample task
240 images 
|
||
| .319 |
Geirhos2021-error_consistency
[reference]
rank 86
17 benchmarks |
|
| .624 |
Geirhos2021colour-error_consistency
v1
[reference]
rank 57
|
|
|
640 images 
|
||
| .246 |
Geirhos2021contrast-error_consistency
v1
[reference]
rank 116
|
|
|
800 images 
|
||
| .255 |
Geirhos2021cueconflict-error_consistency
v1
[reference]
rank 92
|
|
|
1280 images 
|
||
| .060 |
Geirhos2021edge-error_consistency
v1
[reference]
rank 241
|
|
|
160 images 
|
||
| .470 |
Geirhos2021eidolonI-error_consistency
v1
[reference]
rank 86
|
|
|
800 images 
|
||
| .524 |
Geirhos2021eidolonII-error_consistency
v1
[reference]
rank 62
|
|
|
640 images 
|
||
| .433 |
Geirhos2021eidolonIII-error_consistency
v1
[reference]
rank 80
|
|
|
480 images 
|
||
| .546 |
Geirhos2021falsecolour-error_consistency
v1
[reference]
rank 60
|
|
|
560 images 
|
||
| .031 |
Geirhos2021highpass-error_consistency
v1
[reference]
rank 249
|
|
|
640 images 
|
||
| .270 |
Geirhos2021lowpass-error_consistency
v1
[reference]
rank 86
|
|
|
800 images 
|
||
| .210 |
Geirhos2021phasescrambling-error_consistency
v1
[reference]
rank 87
|
|
|
640 images 
|
||
| .260 |
Geirhos2021powerequalisation-error_consistency
v1
[reference]
rank 70
|
|
|
560 images 
|
||
| .236 |
Geirhos2021rotation-error_consistency
v1
[reference]
rank 71
|
|
|
960 images 
|
||
| .445 |
Geirhos2021silhouette-error_consistency
v1
[reference]
rank 150
|
|
|
160 images 
|
||
| .131 |
Geirhos2021sketch-error_consistency
v1
[reference]
rank 108
|
|
|
800 images 
|
||
| .456 |
Geirhos2021stylized-error_consistency
v1
[reference]
rank 62
|
|
|
800 images 
|
||
| .231 |
Geirhos2021uniformnoise-error_consistency
v1
[reference]
rank 116
|
|
|
800 images 
|
||
| .157 |
Baker2022
rank 156
3 benchmarks |
|
| .470 |
Baker2022fragmented-accuracy_delta
v1
[reference]
rank 103
|
|
|
716 images 
|
||
| .000 |
Baker2022frankenstein-accuracy_delta
v1
[reference]
rank 172
|
|
|
716 images 
|
||
| .000 |
Baker2022inverted-accuracy_delta
v1
[reference]
rank 62
|
|
|
360 images 
|
||
| .162 |
BMD2024
rank 127
4 benchmarks |
|
| .166 |
BMD2024.dotted_1Behavioral-accuracy_distance
v1
rank 94
|
|
|
100 images 
|
||
| .126 |
BMD2024.dotted_2Behavioral-accuracy_distance
v1
rank 127
|
|
|
100 images 
|
||
| .186 |
BMD2024.texture_1Behavioral-accuracy_distance
v1
rank 108
|
|
|
100 images 
|
||
| .168 |
BMD2024.texture_2Behavioral-accuracy_distance
v1
rank 128
|
|
|
100 images 
|
||
| .444 |
Ferguson2024
[reference]
rank 149
14 benchmarks |
|
| .353 |
Ferguson2024circle_line-value_delta
v1
[reference]
rank 120
|
|
|
2_way_afc task
48 images 
|
||
| .719 |
Ferguson2024color-value_delta
v1
[reference]
rank 131
|
|
|
2_way_afc task
48 images 
|
||
| .177 |
Ferguson2024convergence-value_delta
v1
[reference]
rank 208
|
|
|
2_way_afc task
48 images 
|
||
| .060 |
Ferguson2024eighth-value_delta
v1
[reference]
rank 205
|
|
|
2_way_afc task
48 images 
|
||
| .725 |
Ferguson2024gray_easy-value_delta
v1
[reference]
rank 59
|
|
|
2_way_afc task
48 images 
|
||
| .138 |
Ferguson2024gray_hard-value_delta
v1
[reference]
rank 251
|
|
|
2_way_afc task
48 images 
|
||
| .571 |
Ferguson2024half-value_delta
v1
[reference]
rank 116
|
|
|
2_way_afc task
48 images 
|
||
| .288 |
Ferguson2024juncture-value_delta
v1
[reference]
rank 92
|
|
|
2_way_afc task
48 images 
|
||
| .161 |
Ferguson2024lle-value_delta
v1
[reference]
rank 222
|
|
|
2_way_afc task
48 images 
|
||
| .583 |
Ferguson2024llh-value_delta
v1
[reference]
rank 118
|
|
|
2_way_afc task
48 images 
|
||
| .950 |
Ferguson2024quarter-value_delta
v1
[reference]
rank 16
|
|
|
2_way_afc task
48 images 
|
||
| .102 |
Ferguson2024round_f-value_delta
v1
[reference]
rank 237
|
|
|
2_way_afc task
48 images 
|
||
| .409 |
Ferguson2024round_v-value_delta
v1
[reference]
rank 156
|
|
|
2_way_afc task
48 images 
|
||
| .975 |
Ferguson2024tilted_line-value_delta
v1
[reference]
rank 28
|
|
|
2_way_afc task
48 images 
|
||
| .307 |
Hebart2023-match
v1
rank 120
|
|
|
1854 images 
|
||
| .573 |
Maniquet2024
rank 122
2 benchmarks |
|
| .500 |
Maniquet2024-confusion_similarity
v1
[reference]
rank 130
|
|
|
13600 images 
|
||
| .646 |
Maniquet2024-tasks_consistency
v1
[reference]
rank 151
|
|
|
13600 images 
|
||
| .371 |
Coggan2024_behavior-ConditionWiseAccuracySimilarity
v1
rank 98
|
|
|
22560 images 
|
||
| .396 |
engineering_vision
rank 101
25 benchmarks |
|
| .749 |
ImageNet-top1
v1
[reference]
rank 68
|
|
|
50000 images 
|
||
| .445 |
ImageNet-C-top1
[reference]
rank 62
4 benchmarks |
|
| .360 |
ImageNet-C-noise-top1
v2
[reference]
rank 110
|
|
|
||
| .412 |
ImageNet-C-blur-top1
v2
[reference]
rank 44
|
|
|
||
| .499 |
ImageNet-C-weather-top1
v2
[reference]
rank 65
|
|
|
||
| .507 |
ImageNet-C-digital-top1
v2
[reference]
rank 78
|
|
|
||
| .584 |
Geirhos2021-top1
[reference]
rank 101
17 benchmarks |
|
| .980 |
Geirhos2021colour-top1
v1
[reference]
rank 72
|
|
|
640 images 
|
||
| .829 |
Geirhos2021contrast-top1
v1
[reference]
rank 128
|
|
|
800 images 
|
||
| .207 |
Geirhos2021cueconflict-top1
v1
[reference]
rank 156
|
|
|
1280 images 
|
||
| .206 |
Geirhos2021edge-top1
v1
[reference]
rank 221
|
|
|
160 images 
|
||
| .526 |
Geirhos2021eidolonI-top1
v1
[reference]
rank 89
|
|
|
800 images 
|
||
| .570 |
Geirhos2021eidolonII-top1
v1
[reference]
rank 58
|
|
|
640 images 
|
||
| .550 |
Geirhos2021eidolonIII-top1
v1
[reference]
rank 113
|
|
|
480 images 
|
||
| .954 |
Geirhos2021falsecolour-top1
v1
[reference]
rank 107
|
|
|
560 images 
|
||
| .294 |
Geirhos2021highpass-top1
v1
[reference]
rank 214
|
|
|
640 images 
|
||
| .478 |
Geirhos2021lowpass-top1
v1
[reference]
rank 74
|
|
|
800 images 
|
||
| .656 |
Geirhos2021phasescrambling-top1
v1
[reference]
rank 74
|
|
|
640 images 
|
||
| .793 |
Geirhos2021powerequalisation-top1
v1
[reference]
rank 82
|
|
|
560 images 
|
||
| .729 |
Geirhos2021rotation-top1
v1
[reference]
rank 81
|
|
|
960 images 
|
||
| .563 |
Geirhos2021silhouette-top1
v1
[reference]
rank 60
|
|
|
160 images 
|
||
| .668 |
Geirhos2021sketch-top1
v1
[reference]
rank 71
|
|
|
800 images 
|
||
| .416 |
Geirhos2021stylized-top1
v1
[reference]
rank 100
|
|
|
800 images 
|
||
| .516 |
Geirhos2021uniformnoise-top1
v1
[reference]
rank 92
|
|
|
800 images 
|
||
| .204 |
Hermann2020
[reference]
rank 200
2 benchmarks |
|
| .245 |
Hermann2020cueconflict-shape_bias
v1
[reference]
rank 203
|
|
|
||
| .163 |
Hermann2020cueconflict-shape_match
v1
[reference]
rank 165
|
|
|
||
How to use
from brainscore_vision import load_model
model = load_model("resnet_152_v1")
model.start_task(...)
model.start_recording(...)
model.look_at(...)
Brain Encoding Response Generator (BERG)
Through the BERG you can easily generate neural responses to images of your choice using any Brain-Score vision model.
For more information on how to use BERG, see the documentation and tutorial.
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 {Rajalingham240614,
author = {Rajalingham, Rishi and Issa, Elias B. and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J.},
title = {Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks},
elocation-id = {240614},
year = {2018},
doi = {10.1101/240614},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Primates{ extemdash}including humans{ extemdash}can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks{ extemdash}such as those obtained here{ extemdash}could serve as direct guides for discovering such models.SIGNIFICANCE STATEMENT Recently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.},
URL = {https://www.biorxiv.org/content/early/2018/02/12/240614},
eprint = {https://www.biorxiv.org/content/early/2018/02/12/240614.full.pdf},
journal = {bioRxiv}
}
@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}
}
@INPROCEEDINGS{5206848,
author={J. {Deng} and W. {Dong} and R. {Socher} and L. {Li} and {Kai Li} and {Li Fei-Fei}},
booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition},
title={ImageNet: A large-scale hierarchical image database},
year={2009},
volume={},
number={},
pages={248-255},
}
@ARTICLE{Hendrycks2019-di,
title = "Benchmarking Neural Network Robustness to Common Corruptions
and Perturbations",
author = "Hendrycks, Dan and Dietterich, Thomas",
abstract = "In this paper we establish rigorous benchmarks for image
classifier robustness. Our first benchmark, ImageNet-C,
standardizes and expands the corruption robustness topic,
while showing which classifiers are preferable in
safety-critical applications. Then we propose a new dataset
called ImageNet-P which enables researchers to benchmark a
classifier's robustness to common perturbations. Unlike
recent robustness research, this benchmark evaluates
performance on common corruptions and perturbations not
worst-case adversarial perturbations. We find that there are
negligible changes in relative corruption robustness from
AlexNet classifiers to ResNet classifiers. Afterward we
discover ways to enhance corruption and perturbation
robustness. We even find that a bypassed adversarial defense
provides substantial common perturbation robustness.
Together our benchmarks may aid future work toward networks
that robustly generalize.",
month = mar,
year = 2019,
archivePrefix = "arXiv",
primaryClass = "cs.LG",
eprint = "1903.12261",
url = "https://arxiv.org/abs/1903.12261"
}
@article{hermann2020origins,
title={The origins and prevalence of texture bias in convolutional neural networks},
author={Hermann, Katherine and Chen, Ting and Kornblith, Simon},
journal={Advances in Neural Information Processing Systems},
volume={33},
pages={19000--19015},
year={2020},
url={https://proceedings.neurips.cc/paper/2020/hash/db5f9f42a7157abe65bb145000b5871a-Abstract.html}
}