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resnet_101_v1

Contributed by Anonymous User No reference provided Find on GitHub source Compare this model

CNN

44.5M parameters

Scores on benchmarks

from scoring API

Model rank shown below is with respect to all public models.

.238	average_vision rank 156 138 benchmarks	.238 0 ceiling best median

.032	neural_vision rank 490 77 benchmarks	.032 0 ceiling best median

.048	V1 rank 435 33 benchmarks	.048 0 ceiling best median

.357	Marques2020 [reference] rank 424 22 benchmarks	.357 0 ceiling best median

.498	V1-orientation rank 411 7 benchmarks	.498 0 ceiling best median

.931	Marques2020_DeValois1982-pref_or v1 rank 252	.931 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.797	Marques2020_Ringach2002-cv_bandwidth_ratio v1 rank 259	.797 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.812	Marques2020_Ringach2002-opr_cv_diff v1 rank 301	.812 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.949	Marques2020_Ringach2002-or_bandwidth v1 rank 16	.949 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.514	V1-receptive_field_size rank 285 2 benchmarks	.514 0 ceiling best median

.638	Marques2020_Cavanaugh2002-grating_summation_field v1 [reference] rank 245	.638 0 ceiling best median
	2304 images Sample stimuli cannot be displayed publicly for this benchmark.

.390	Marques2020_Cavanaugh2002-surround_diameter v1 [reference] rank 281	.390 0 ceiling best median
	2304 images Sample stimuli cannot be displayed publicly for this benchmark.

.336	V1-response_selectivity rank 419 4 benchmarks	.336 0 ceiling best median

.594	Marques2020_FreemanZiemba2013-texture_sparseness v1 [reference] rank 329	.594 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.749	Marques2020_FreemanZiemba2013-texture_variance_ratio v1 [reference] rank 172	.749 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.530	V1-spatial_frequency rank 401 3 benchmarks	.530 0 ceiling best median

.628	Marques2020_DeValois1982-peak_sf v1 rank 284	.628 0 ceiling best median
	2112 images Sample stimuli cannot be displayed publicly for this benchmark.

.964	Marques2020_Schiller1976-sf_selective v1 [reference] rank 85	.964 0 ceiling best median
	2112 images Sample stimuli cannot be displayed publicly for this benchmark.

.621	V1-surround_modulation rank 223 1 benchmark	.621 0 ceiling best median

.621	Marques2020_Cavanaugh2002-surround_suppression_index v1 [reference] rank 223	.621 0 ceiling best median
	2304 images Sample stimuli cannot be displayed publicly for this benchmark.

.025	Coggan2024_fMRI.V1-rdm v1 rank 210	.025 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.023	V2 rank 504 10 benchmarks	.023 0 ceiling best median

.135	Coggan2024_fMRI.V2-rdm v1 rank 94	.135 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.025	V4 rank 512 16 benchmarks	.025 0 ceiling best median

.245	SanghaviMurty2020.V4-pls v2 [reference] rank 16	.245 0 ceiling best median
	recordings from 46 sites in V4 300 images Sample stimuli cannot be displayed publicly for this benchmark.

.057	Coggan2024_fMRI.V4-rdm v1 rank 103	.057 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.033	IT rank 479 18 benchmarks	.033 0 ceiling best median

.463	Coggan2024_fMRI.IT-rdm v1 rank 103	.463 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.444	behavior_vision rank 48 61 benchmarks	.444 0 ceiling best median

.521	Rajalingham2018-i2n v2 [reference] rank 136	.521 0 ceiling best median
	match-to-sample task 240 images Sample stimuli cannot be displayed publicly for this benchmark.

.318	Geirhos2021-error_consistency [reference] rank 97 17 benchmarks	.318 0 ceiling best median

.551	Geirhos2021colour-error_consistency v1 [reference] rank 64	.551 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.301	Geirhos2021contrast-error_consistency v1 [reference] rank 98	.301 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.271	Geirhos2021cueconflict-error_consistency v1 [reference] rank 93	.271 0 ceiling best median
	1280 images Sample stimuli cannot be displayed publicly for this benchmark.

.107	Geirhos2021edge-error_consistency v1 [reference] rank 109	.107 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.546	Geirhos2021eidolonI-error_consistency v1 [reference] rank 57	.546 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.470	Geirhos2021eidolonII-error_consistency v1 [reference] rank 92	.470 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.465	Geirhos2021eidolonIII-error_consistency v1 [reference] rank 66	.465 0 ceiling best median
	480 images Sample stimuli cannot be displayed publicly for this benchmark.

.440	Geirhos2021falsecolour-error_consistency v1 [reference] rank 95	.440 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.059	Geirhos2021highpass-error_consistency v1 [reference] rank 168	.059 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.268	Geirhos2021lowpass-error_consistency v1 [reference] rank 96	.268 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.183	Geirhos2021phasescrambling-error_consistency v1 [reference] rank 105	.183 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.234	Geirhos2021powerequalisation-error_consistency v1 [reference] rank 89	.234 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.195	Geirhos2021rotation-error_consistency v1 [reference] rank 101	.195 0 ceiling best median
	960 images Sample stimuli cannot be displayed publicly for this benchmark.

.549	Geirhos2021silhouette-error_consistency v1 [reference] rank 111	.549 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.155	Geirhos2021sketch-error_consistency v1 [reference] rank 95	.155 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.358	Geirhos2021stylized-error_consistency v1 [reference] rank 106	.358 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.253	Geirhos2021uniformnoise-error_consistency v1 [reference] rank 112	.253 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.651	Baker2022 rank 31 3 benchmarks	.651 0 ceiling best median

.670	Baker2022fragmented-accuracy_delta v1 [reference] rank 84	.670 0 ceiling best median
	716 images Sample stimuli cannot be displayed publicly for this benchmark.

.374	Baker2022frankenstein-accuracy_delta v1 [reference] rank 124	.374 0 ceiling best median
	716 images Sample stimuli cannot be displayed publicly for this benchmark.

.908	Baker2022inverted-accuracy_delta v1 [reference] rank 31	.908 0 ceiling best median
	360 images Sample stimuli cannot be displayed publicly for this benchmark.

.241	BMD2024 rank 50 4 benchmarks	.241 0 ceiling best median

.281	BMD2024.dotted_1Behavioral-accuracy_distance v1 rank 40	.281 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.182	BMD2024.dotted_2Behavioral-accuracy_distance v1 rank 81	.182 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.217	BMD2024.texture_1Behavioral-accuracy_distance v1 rank 90	.217 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.283	BMD2024.texture_2Behavioral-accuracy_distance v1 rank 47	.283 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.375	Ferguson2024 [reference] rank 247 14 benchmarks	.375 0 ceiling best median

.291	Ferguson2024circle_line-value_delta v1 [reference] rank 172	.291 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.352	Ferguson2024color-value_delta v1 [reference] rank 237	.352 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.627	Ferguson2024convergence-value_delta v1 [reference] rank 77	.627 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.027	Ferguson2024eighth-value_delta v1 [reference] rank 270	.027 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.470	Ferguson2024gray_easy-value_delta v1 [reference] rank 116	.470 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.437	Ferguson2024gray_hard-value_delta v1 [reference] rank 162	.437 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.155	Ferguson2024half-value_delta v1 [reference] rank 268	.155 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.020	Ferguson2024juncture-value_delta v1 [reference] rank 258	.020 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.186	Ferguson2024lle-value_delta v1 [reference] rank 241	.186 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.643	Ferguson2024llh-value_delta v1 [reference] rank 115	.643 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.171	Ferguson2024quarter-value_delta v1 [reference] rank 217	.171 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.961	Ferguson2024round_f-value_delta v1 [reference] rank 20	.961 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.225	Ferguson2024round_v-value_delta v1 [reference] rank 249	.225 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.682	Ferguson2024tilted_line-value_delta v1 [reference] rank 107	.682 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.297	Hebart2023-match v1 rank 138	.297 0 ceiling best median
	1854 images Sample stimuli cannot be displayed publicly for this benchmark.

.476	Kato2026 rank 65 18 benchmarks	.476 0 ceiling best median

.359	Kato2026-error_consistency rank 63 9 benchmarks	.359 0 ceiling best median

.421	Kato2026Ori-error_consistency v1 rank 49	.421 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.755	Kato2026Fil-error_consistency v1 rank 9	.755 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.388	Kato2026Lin-error_consistency v1 rank 65	.388 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.419	Kato2026SilTex-error_consistency v1 rank 76	.419 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.365	Kato2026Sil-error_consistency v1 rank 81	.365 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.838	Kato2026Tex-error_consistency v1 rank 47	.838 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.008	Kato2026LinFil-error_consistency v1 rank 143	.008 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.000	Kato2026Sparse-error_consistency v1 rank 35	.000 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.041	Kato2026Dense-error_consistency v1 rank 53	.041 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.592	Kato2026-accuracy_distance rank 75 9 benchmarks	.592 0 ceiling best median

1.0	Kato2026Ori-accuracy_distance v1 rank 1	1.0 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.993	Kato2026Fil-accuracy_distance v1 rank 48	.993 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.470	Kato2026Lin-accuracy_distance v1 rank 72	.470 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.668	Kato2026SilTex-accuracy_distance v1 rank 81	.668 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.301	Kato2026Sil-accuracy_distance v1 rank 89	.301 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

1.0	Kato2026Tex-accuracy_distance v1 rank 1	1.0 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.697	Kato2026LinFil-accuracy_distance v1 rank 84	.697 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.156	Kato2026Sparse-accuracy_distance v1 rank 77	.156 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.042	Kato2026Dense-accuracy_distance v1 rank 78	.042 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.768	Maniquet2024 rank 27 2 benchmarks	.768 0 ceiling best median

.831	Maniquet2024-confusion_similarity v1 [reference] rank 38	.831 0 ceiling best median
	13600 images Sample stimuli cannot be displayed publicly for this benchmark.

.705	Maniquet2024-tasks_consistency v1 [reference] rank 45	.705 0 ceiling best median
	13600 images Sample stimuli cannot be displayed publicly for this benchmark.

.351	Coggan2024_behavior-ConditionWiseAccuracySimilarity v1 rank 105	.351 0 ceiling best median
	22560 images Sample stimuli cannot be displayed publicly for this benchmark.

.392	engineering_vision rank 105 25 benchmarks	.392 0 ceiling best median

.736	ImageNet-top1 v1 [reference] rank 94	.736 0 ceiling best median
	50000 images Sample stimuli cannot be displayed publicly for this benchmark.

.434	ImageNet-C-top1 [reference] rank 78 4 benchmarks	.434 0 ceiling best median

.349	ImageNet-C-noise-top1 v2 [reference] rank 113	.349 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.399	ImageNet-C-blur-top1 v2 [reference] rank 58	.399 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.481	ImageNet-C-weather-top1 v2 [reference] rank 84	.481 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.507	ImageNet-C-digital-top1 v2 [reference] rank 87	.507 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.583	Geirhos2021-top1 [reference] rank 112 17 benchmarks	.583 0 ceiling best median

.977	Geirhos2021colour-top1 v1 [reference] rank 101	.977 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.839	Geirhos2021contrast-top1 v1 [reference] rank 130	.839 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.199	Geirhos2021cueconflict-top1 v1 [reference] rank 191	.199 0 ceiling best median
	1280 images Sample stimuli cannot be displayed publicly for this benchmark.

.238	Geirhos2021edge-top1 v1 [reference] rank 207	.238 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.541	Geirhos2021eidolonI-top1 v1 [reference] rank 72	.541 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.578	Geirhos2021eidolonII-top1 v1 [reference] rank 60	.578 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.583	Geirhos2021eidolonIII-top1 v1 [reference] rank 92	.583 0 ceiling best median
	480 images Sample stimuli cannot be displayed publicly for this benchmark.

.975	Geirhos2021falsecolour-top1 v1 [reference] rank 57	.975 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.375	Geirhos2021highpass-top1 v1 [reference] rank 165	.375 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.474	Geirhos2021lowpass-top1 v1 [reference] rank 91	.474 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.656	Geirhos2021phasescrambling-top1 v1 [reference] rank 87	.656 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.766	Geirhos2021powerequalisation-top1 v1 [reference] rank 114	.766 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.680	Geirhos2021rotation-top1 v1 [reference] rank 146	.680 0 ceiling best median
	960 images Sample stimuli cannot be displayed publicly for this benchmark.

.494	Geirhos2021silhouette-top1 v1 [reference] rank 161	.494 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.631	Geirhos2021sketch-top1 v1 [reference] rank 123	.631 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.418	Geirhos2021stylized-top1 v1 [reference] rank 111	.418 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.488	Geirhos2021uniformnoise-top1 v1 [reference] rank 127	.488 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.206	Hermann2020 [reference] rank 205 2 benchmarks	.206 0 ceiling best median

.254	Hermann2020cueconflict-shape_bias v1 [reference] rank 207	.254 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.157	Hermann2020cueconflict-shape_match v1 [reference] rank 196	.157 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

How to use

from brainscore_vision import load_model
model = load_model("resnet_101_v1")
model.start_task(...)
model.start_recording(...)
model.look_at(...)

Model API

Code examples

Curated metadata

At-a-glance

Architecture Probable: show confidence source Probable — Existing plugin metadata; primary sources need review.: CNN
Parameters Probable: show confidence source Probable — Existing plugin metadata; primary sources need review.: 44.5M
Input resolution: Not documented
Recurrent: Not documented
Supervision: Not documented
Weights provider: Not documented
Trainable layers Probable: show confidence source Probable — Existing plugin metadata; primary sources need review.: 105 layers
Checkpoint: Not documented

Training and data

Not documented

Objective:: Not documented
Loss:: Not documented
Learning rate:: Not documented
Batch size:: Not documented
Preprocessing:: Not documented

Creators and licensing

Organization:: Not documented
Creators:: Not documented
License:: Not documented

Dashed Not documented fields are awaiting curation.

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.

Historical trend

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Benchmarks BibTeX

@article {Marques2021.03.01.433495,
	author = {Marques, Tiago and Schrimpf, Martin and DiCarlo, James J.},
	title = {Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior},
	elocation-id = {2021.03.01.433495},
	year = {2021},
	doi = {10.1101/2021.03.01.433495},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Primate visual object recognition relies on the representations in cortical areas at the top of the ventral stream that are computed by a complex, hierarchical network of neural populations. While recent work has created reasonably accurate image-computable hierarchical neural network models of those neural stages, those models do not yet bridge between the properties of individual neurons and the overall emergent behavior of the ventral stream. One reason we cannot yet do this is that individual artificial neurons in multi-stage models have not been shown to be functionally similar to individual biological neurons. Here, we took an important first step by building and evaluating hundreds of hierarchical neural network models in how well their artificial single neurons approximate macaque primary visual cortical (V1) neurons. We found that single neurons in certain models are surprisingly similar to their biological counterparts and that the distributions of single neuron properties, such as those related to orientation and spatial frequency tuning, approximately match those in macaque V1. Critically, we observed that hierarchical models with V1 stages that better match macaque V1 at the single neuron level are also more aligned with human object recognition behavior. Finally, we show that an optimized classical neuroscientific model of V1 is more functionally similar to primate V1 than all of the tested multi-stage models, suggesting room for further model improvements with tangible payoffs in closer alignment to human behavior. These results provide the first multi-stage, multi-scale models that allow our field to ask precisely how the specific properties of individual V1 neurons relate to recognition behavior.HighlightsImage-computable hierarchical neural network models can be naturally extended to create hierarchical {\textquotedblleft}brain models{\textquotedblright} that allow direct comparison with biological neural networks at multiple scales {\textendash} from single neurons, to population of neurons, to behavior.Single neurons in some of these hierarchical brain models are functionally similar to single neurons in macaque primate visual cortex (V1)Some hierarchical brain models have processing stages in which the entire distribution of artificial neuron properties closely matches the biological distributions of those same properties in macaque V1Hierarchical brain models whose V1 processing stages better match the macaque V1 stage also tend to be more aligned with human object recognition behavior at their output stageCompeting Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2021/08/13/2021.03.01.433495},
	eprint = {https://www.biorxiv.org/content/early/2021/08/13/2021.03.01.433495.full.pdf},
	journal = {bioRxiv}
}
        @article{Cavanaugh2002,
            author = {Cavanaugh, James R. and Bair, Wyeth and Movshon, J. A.},
            doi = {10.1152/jn.00692.2001},
            isbn = {0022-3077 (Print) 0022-3077 (Linking)},
            issn = {0022-3077},
            journal = {Journal of Neurophysiology},
            mendeley-groups = {Benchmark effects/Done,Benchmark effects/*Surround Suppression},
            number = {5},
            pages = {2530--2546},
            pmid = {12424292},
            title = {{Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons}},
            url = {http://www.physiology.org/doi/10.1152/jn.00692.2001},
            volume = {88},
            year = {2002}
            }
        @article{Freeman2013,
            author = {Freeman, Jeremy and Ziemba, Corey M. and Heeger, David J. and Simoncelli, E. P. and Movshon, J. A.},
            doi = {10.1038/nn.3402},
            issn = {10976256},
            journal = {Nature Neuroscience},
            number = {7},
            pages = {974--981},
            pmid = {23685719},
            publisher = {Nature Publishing Group},
            title = {{A functional and perceptual signature of the second visual area in primates}},
            url = {http://dx.doi.org/10.1038/nn.3402},
            volume = {16},
            year = {2013}
            }
        @article{Schiller1976,
            author = {Schiller, P. H. and Finlay, B. L. and Volman, S. F.},
            doi = {10.1152/jn.1976.39.6.1352},
            issn = {0022-3077},
            journal = {Journal of neurophysiology},
            number = {6},
            pages = {1334--1351},
            pmid = {825624},
            title = {{Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial Frequency}},
            url = {http://www.ncbi.nlm.nih.gov/pubmed/825624},
            volume = {39},
            year = {1976}
            }
        @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}
}
        @misc{Sanghavi_Murty_DiCarlo_2021,
  title={SanghaviMurty2020},
  url={osf.io/fchme},
  DOI={10.17605/OSF.IO/FCHME},
  publisher={OSF},
  author={Sanghavi, Sachi and Murty, N A R and DiCarlo, James J},
  year={2021},
  month={Nov}
}
        @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}
        }

metadata.yaml · Schema v2.0

This annotated template shows the structure of a model’s metadata.yaml. Each section groups related information; comments explain the expected types and allowed values.

Required: schema_version, domain, and models keyed by model identifier. Other fields may be omitted or set to null when undocumented. Use [] for empty lists.

The values below are illustrative, not this model’s metadata. Metadata changes are proposed in repository pull requests and published after merge.

# metadata.yaml — proposed schema v2.0
# Example values below are illustrative, not metadata for a real model.
# Required: schema_version, domain, and models keyed by exact model identifier.
# Other fields may be omitted or set to null when undocumented.
# Use [] for an empty list. Do not use "unknown" as a numeric/boolean value.
# Keep identifiers exact, including checkpoint suffixes.

schema_version: "2.0"  # String; quote the version.

domain: vision  # Must match the repository's registered domain.
models:
  example-model:
    sources:
      example-source:
        kind: other  # paper | huggingface | other | unreviewed
        url: https://example.org/model-card
        citation: Illustrative source, not evidence for a real model.
    model:
      display_name: Example model
      version: null  # String, e.g. "1.0".
      architecture:
        family: convolutional_neural_network  # String describing the architecture family.
        description: A convolutional model trained for image classification.
      parameter_count: 25000000  # Nonnegative integer; count, not "25M".
      parameter_count_exact: false  # Boolean; false means the count is approximate.
      trainable_layers: "All layers"  # Text; can describe partial fine-tuning.
      recurrent: false  # Boolean.
      input_resolution:
        channels: 3  # Nonnegative integer.
        height: 224  # Nonnegative integer, pixels.
        width: 224  # Nonnegative integer, pixels.
      visual_degrees: null  # Nonnegative number; vision models only.
      visual_degrees_description: null  # Text explaining the field of view.
      supervision:
        type: supervised  # String, e.g. supervised, self_supervised, weakly_supervised.
        description: Trained using class labels.

    training:
      process: null  # Text describing the training procedure.
      objective: null  # Text describing what the model learns.
      loss: null  # Text, e.g. "Cross-entropy".
      learning_rate: null  # Text; supports schedules, e.g. "0.1 with cosine decay".
      batch_size: null  # Text; supports different values across training stages.
      preprocessing: null  # Text describing normalization and augmentation.

    data:
      summary: null  # Text summarizing the data used.
      training_datasets:
        - identifier: example-training-set  # Optional dataset identifier.
          name: Example training set  # Required for each dataset entry.
          role: training  # training | pretraining | fine_tuning
          description: null  # Text describing this dataset's use.

    eval:
      test_datasets: []  # Entries: {identifier: optional, name: required, description: optional}.
      validation_datasets: []  # Same entry structure; role is implied by the list.

    io:
      modality: image  # String, e.g. image or text.
      interface: null  # Text describing the model's interface.
      input_format: null  # Text describing accepted inputs and their shape.
      output_format: null  # Text describing returned values and their shape.
      tokenizer: null  # Text; language models, where applicable.

    provenance:
      weights_provider: null  # Organization or repository providing the weights.
      checkpoint: null  # Exact checkpoint identifier.
      source_url: null  # Link to the original code or model card.
      curation_confidence: null  # Text summarizing confidence in the curated record.

    legal:
      license: null  # License identifier or full terms; distinguish code and weights.

    people:
      creators: []  # List of names, e.g. ["Example Researcher"].
      organizations: []  # List of organization names.

    use:
      applications: []  # List of intended applications (strings).
      users: []  # List of intended user groups (strings).
      limitations: []  # List of known limitations (strings).
      biases: []  # List of known biases (strings).

    lineage:
      base_models:
        - identifier: example-base  # Optional; exact identifier in the same domain.
          name: Example base model  # Required for each base-model entry.
          relationship: fine_tuned_from  # variant_of | fine_tuned_from | derived_from

    # Optional verification records. Omit this section if no verification is recorded.
    # Paths refer to fields in this YAML document, not database columns.
    assertions:
      - path: /model/architecture
        status: probable  # verified | probable | uncertain | undocumented
        sources: [example-source]  # References the structured sources above.

Region	Layer
V1	`layer1.2`
V2	`layer3.9`
V4	`layer3.2`
IT	`layer4.0`