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eicircuit_resnet18_bn_20260919

Contributed by Peiyu Liu No reference provided Find on GitHub source Compare this model

Scores on benchmarks

from scoring API

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

.327	average_vision rank 91 138 benchmarks	.327 0 ceiling best median

.290	neural_vision rank 120 77 benchmarks	.290 0 ceiling best median

.343	V1 rank 117 33 benchmarks	.343 0 ceiling best median

.257	Allen2022_fmri_surface.V1 rank 134 2 benchmarks	.257 0 ceiling best median

.158	Allen2022_fmri_surface.V1-rdm v1 [reference] rank 117	.158 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.357	Allen2022_fmri_surface.V1-ridge v1 [reference] rank 144	.357 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.224	FreemanZiemba2013.V1-pls v3 [reference] rank 305	.224 0 ceiling best median
	recordings from 102 sites in V1 315 images Sample stimuli cannot be displayed publicly for this benchmark.

.515	Hebart2023_fmri.V1-ridgecv v3 rank 141	.515 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.336	Li2026.V1 rank 8 2 benchmarks	.336 0 ceiling best median

.232	Li2026.V1-rdm v1 [reference] rank 45	.232 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.439	Li2026.V1-ridgecv v1 [reference] rank 4	.439 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.494	Marques2020 [reference] rank 412 22 benchmarks	.494 0 ceiling best median

.842	V1-orientation rank 252 7 benchmarks	.842 0 ceiling best median

.889	Marques2020_DeValois1982-pref_or v1 rank 334	.889 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.806	Marques2020_Ringach2002-circular_variance v1 rank 240	.806 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.850	Marques2020_Ringach2002-cv_bandwidth_ratio v1 rank 203	.850 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.852	Marques2020_Ringach2002-opr_cv_diff v1 rank 244	.852 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.647	Marques2020_Ringach2002-or_bandwidth v1 rank 380	.647 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.997	Marques2020_Ringach2002-or_selective v1 rank 52	.997 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.853	Marques2020_Ringach2002-orth_pref_ratio v1 rank 124	.853 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.214	V1-receptive_field_size rank 418 2 benchmarks	.214 0 ceiling best median

.275	Marques2020_Cavanaugh2002-grating_summation_field v1 [reference] rank 403	.275 0 ceiling best median
	2304 images Sample stimuli cannot be displayed publicly for this benchmark.

.154	Marques2020_Cavanaugh2002-surround_diameter v1 [reference] rank 422	.154 0 ceiling best median
	2304 images Sample stimuli cannot be displayed publicly for this benchmark.

.560	V1-response_magnitude rank 418 3 benchmarks	.560 0 ceiling best median

.741	Marques2020_FreemanZiemba2013-max_noise v1 [reference] rank 275	.741 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.940	Marques2020_FreemanZiemba2013-max_texture v1 [reference] rank 94	.940 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.660	V1-response_selectivity rank 226 4 benchmarks	.660 0 ceiling best median

.724	Marques2020_FreemanZiemba2013-texture_selectivity v1 [reference] rank 249	.724 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.678	Marques2020_FreemanZiemba2013-texture_sparseness v1 [reference] rank 239	.678 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.775	Marques2020_FreemanZiemba2013-texture_variance_ratio v1 [reference] rank 147	.775 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.464	Marques2020_Ringach2002-modulation_ratio v1 rank 251	.464 0 ceiling best median
	1152 images Sample stimuli cannot be displayed publicly for this benchmark.

.710	V1-spatial_frequency rank 323 3 benchmarks	.710 0 ceiling best median

.467	Marques2020_DeValois1982-peak_sf v1 rank 402	.467 0 ceiling best median
	2112 images Sample stimuli cannot be displayed publicly for this benchmark.

.742	Marques2020_Schiller1976-sf_bandwidth v1 [reference] rank 311	.742 0 ceiling best median
	2112 images Sample stimuli cannot be displayed publicly for this benchmark.

.920	Marques2020_Schiller1976-sf_selective v1 [reference] rank 130	.920 0 ceiling best median
	2112 images Sample stimuli cannot be displayed publicly for this benchmark.

.472	V1-texture_modulation rank 388 2 benchmarks	.472 0 ceiling best median

.944	Marques2020_FreemanZiemba2013-abs_texture_modulation_index v1 [reference] rank 11	.944 0 ceiling best median
	450 images Sample stimuli cannot be displayed publicly for this benchmark.

.649	Papale2025.V1-ridgecv v3 [reference] rank 110	.649 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.035	Coggan2024_fMRI.V1-rdm v1 rank 190	.035 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.233	Zerbe2026_fmri.V1 [reference] rank 130 3 benchmarks	.233 0 ceiling best median

.242	Zerbe2026_fmri.V1-ood-ridgecv v1 [reference] rank 130	.242 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.107	Zerbe2026_fmri.V1-rdm-pearson v1 [reference] rank 133	.107 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.350	Zerbe2026_fmri.V1-tau-ridgecv v1 [reference] rank 141	.350 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.278	V2 rank 102 10 benchmarks	.278 0 ceiling best median

.361	Allen2022_fmri_surface.V2 rank 55 2 benchmarks	.361 0 ceiling best median

.254	Allen2022_fmri_surface.V2-rdm v1 [reference] rank 70	.254 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.467	Allen2022_fmri_surface.V2-ridge v1 [reference] rank 69	.467 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.257	FreemanZiemba2013.V2-pls v3 [reference] rank 170	.257 0 ceiling best median
	recordings from 103 sites in V2 315 images Sample stimuli cannot be displayed publicly for this benchmark.

.585	Hebart2023_fmri.V2-ridgecv v3 rank 74	.585 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.157	Li2026.V2 rank 106 2 benchmarks	.157 0 ceiling best median

.029	Li2026.V2-rdm v1 [reference] rank 100	.029 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.285	Li2026.V2-ridgecv v1 [reference] rank 69	.285 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.100	Coggan2024_fMRI.V2-rdm v1 rank 125	.100 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.211	Zerbe2026_fmri.V2 [reference] rank 138 3 benchmarks	.211 0 ceiling best median

.249	Zerbe2026_fmri.V2-rdm-pearson v1 [reference] rank 54	.249 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.384	Zerbe2026_fmri.V2-tau-ridgecv v1 [reference] rank 85	.384 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.239	V4 rank 144 16 benchmarks	.239 0 ceiling best median

.254	Allen2022_fmri_surface.V4 rank 122 2 benchmarks	.254 0 ceiling best median

.161	Allen2022_fmri_surface.V4-rdm v1 [reference] rank 118	.161 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.346	Allen2022_fmri_surface.V4-ridge v1 [reference] rank 120	.346 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.239	Cowley2026.V4-pls v1 [reference] rank 120	.239 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.277	Hebart2023_fmri.V4-ridgecv v3 rank 120	.277 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.060	Li2026.V4 rank 144 2 benchmarks	.060 0 ceiling best median

.000	Li2026.V4-rdm v1 [reference] rank 113	.000 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.120	Li2026.V4-ridgecv v1 [reference] rank 127	.120 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.009	MajajHong2015public.V4-reverse_pls v4 [reference] rank 164	.009 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.529	MajajHong2015.V4-pls v4 [reference] rank 91	.529 0 ceiling best median
	recordings from 88 sites in V4 2560 images Sample stimuli cannot be displayed publicly for this benchmark.

.564	Sanghavi2020.V4-pls v2 [reference] rank 147	.564 0 ceiling best median
	recordings from 47 sites in V4 5760 images Sample stimuli cannot be displayed publicly for this benchmark.

.471	SanghaviJozwik2020.V4-pls v2 [reference] rank 129	.471 0 ceiling best median
	recordings from 50 sites in V4 4916 images Sample stimuli cannot be displayed publicly for this benchmark.

.178	SanghaviMurty2020.V4-pls v2 [reference] rank 318	.178 0 ceiling best median
	recordings from 46 sites in V4 300 images Sample stimuli cannot be displayed publicly for this benchmark.

.012	Coggan2024_fMRI.V4-rdm v1 rank 224	.012 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.269	Zerbe2026_fmri.V4 [reference] rank 97 3 benchmarks	.269 0 ceiling best median

.375	Zerbe2026_fmri.V4-ood-ridgecv v1 [reference] rank 90	.375 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.148	Zerbe2026_fmri.V4-rdm-pearson v1 [reference] rank 95	.148 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.282	Zerbe2026_fmri.V4-tau-ridgecv v1 [reference] rank 126	.282 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.300	IT rank 82 18 benchmarks	.300 0 ceiling best median

.399	Allen2022_fmri_surface.IT rank 65 2 benchmarks	.399 0 ceiling best median

.339	Allen2022_fmri_surface.IT-rdm v1 [reference] rank 52	.339 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.458	Allen2022_fmri_surface.IT-ridge v1 [reference] rank 84	.458 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.352	Bracci2019.anteriorVTC-rdm v1 rank 50	.352 0 ceiling best median
	27 images Sample stimuli cannot be displayed publicly for this benchmark.

.210	Gifford2022.IT-ridgecv v3 [reference] rank 118	.210 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.279	Hebart2023_fmri.IT-ridgecv v3 rank 87	.279 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.320	Li2026.IT rank 49 2 benchmarks	.320 0 ceiling best median

.403	Li2026.IT-rdm v1 [reference] rank 35	.403 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.237	Li2026.IT-ridgecv v1 [reference] rank 80	.237 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.430	MajajHong2015.IT-pls v4 [reference] rank 185	.430 0 ceiling best median
	recordings from 168 sites in IT 2560 images Sample stimuli cannot be displayed publicly for this benchmark.

.031	MajajHong2015public.IT-reverse_pls v4 [reference] rank 133	.031 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.497	Papale2025.IT-ridgecv v3 [reference] rank 106	.497 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.451	Sanghavi2020.IT-pls v2 [reference] rank 216	.451 0 ceiling best median
	recordings from 88 sites in IT 5760 images Sample stimuli cannot be displayed publicly for this benchmark.

.389	SanghaviJozwik2020.IT-pls v2 [reference] rank 310	.389 0 ceiling best median
	recordings from 26 sites in IT 4916 images Sample stimuli cannot be displayed publicly for this benchmark.

.284	SanghaviMurty2020.IT-pls v2 [reference] rank 326	.284 0 ceiling best median
	recordings from 29 sites in IT 300 images Sample stimuli cannot be displayed publicly for this benchmark.

.265	Coggan2024_fMRI.IT-rdm v1 rank 188	.265 0 ceiling best median
	24 images Sample stimuli cannot be displayed publicly for this benchmark.

.297	Zerbe2026_fmri.IT [reference] rank 38 3 benchmarks	.297 0 ceiling best median

.212	Zerbe2026_fmri.IT-ood-ridgecv v1 [reference] rank 69	.212 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.382	Zerbe2026_fmri.IT-rdm-pearson v1 [reference] rank 32	.382 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.297	Zerbe2026_fmri.IT-tau-ridgecv v1 [reference] rank 65	.297 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.364	behavior_vision rank 113 61 benchmarks	.364 0 ceiling best median

.541	Rajalingham2018-i2n v2 [reference] rank 87	.541 0 ceiling best median
	match-to-sample task 240 images Sample stimuli cannot be displayed publicly for this benchmark.

.171	Geirhos2021-error_consistency [reference] rank 199 17 benchmarks	.171 0 ceiling best median

.372	Geirhos2021colour-error_consistency v1 [reference] rank 146	.372 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.158	Geirhos2021contrast-error_consistency v1 [reference] rank 163	.158 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.141	Geirhos2021cueconflict-error_consistency v1 [reference] rank 234	.141 0 ceiling best median
	1280 images Sample stimuli cannot be displayed publicly for this benchmark.

.055	Geirhos2021edge-error_consistency v1 [reference] rank 268	.055 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.301	Geirhos2021eidolonI-error_consistency v1 [reference] rank 178	.301 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.239	Geirhos2021eidolonII-error_consistency v1 [reference] rank 214	.239 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.258	Geirhos2021eidolonIII-error_consistency v1 [reference] rank 199	.258 0 ceiling best median
	480 images Sample stimuli cannot be displayed publicly for this benchmark.

.269	Geirhos2021falsecolour-error_consistency v1 [reference] rank 171	.269 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.015	Geirhos2021highpass-error_consistency v1 [reference] rank 304	.015 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.131	Geirhos2021lowpass-error_consistency v1 [reference] rank 180	.131 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.086	Geirhos2021phasescrambling-error_consistency v1 [reference] rank 206	.086 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.051	Geirhos2021powerequalisation-error_consistency v1 [reference] rank 239	.051 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.111	Geirhos2021rotation-error_consistency v1 [reference] rank 188	.111 0 ceiling best median
	960 images Sample stimuli cannot be displayed publicly for this benchmark.

.320	Geirhos2021silhouette-error_consistency v1 [reference] rank 199	.320 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.086	Geirhos2021sketch-error_consistency v1 [reference] rank 183	.086 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.229	Geirhos2021stylized-error_consistency v1 [reference] rank 178	.229 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.092	Geirhos2021uniformnoise-error_consistency v1 [reference] rank 194	.092 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.163	Baker2022 rank 180 3 benchmarks	.163 0 ceiling best median

.166	Baker2022fragmented-accuracy_delta v1 [reference] rank 174	.166 0 ceiling best median
	716 images Sample stimuli cannot be displayed publicly for this benchmark.

.324	Baker2022frankenstein-accuracy_delta v1 [reference] rank 141	.324 0 ceiling best median
	716 images Sample stimuli cannot be displayed publicly for this benchmark.

.000	Baker2022inverted-accuracy_delta v1 [reference] rank 77	.000 0 ceiling best median
	360 images Sample stimuli cannot be displayed publicly for this benchmark.

.179	BMD2024 rank 112 4 benchmarks	.179 0 ceiling best median

.187	BMD2024.dotted_1Behavioral-accuracy_distance v1 rank 83	.187 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.205	BMD2024.dotted_2Behavioral-accuracy_distance v1 rank 62	.205 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.155	BMD2024.texture_1Behavioral-accuracy_distance v1 rank 152	.155 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.168	BMD2024.texture_2Behavioral-accuracy_distance v1 rank 141	.168 0 ceiling best median
	100 images Sample stimuli cannot be displayed publicly for this benchmark.

.392	Ferguson2024 [reference] rank 229 14 benchmarks	.392 0 ceiling best median

.110	Ferguson2024circle_line-value_delta v1 [reference] rank 261	.110 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.794	Ferguson2024color-value_delta v1 [reference] rank 134	.794 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.385	Ferguson2024convergence-value_delta v1 [reference] rank 151	.385 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.041	Ferguson2024eighth-value_delta v1 [reference] rank 252	.041 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.215	Ferguson2024gray_easy-value_delta v1 [reference] rank 192	.215 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.022	Ferguson2024gray_hard-value_delta v1 [reference] rank 317	.022 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.161	Ferguson2024half-value_delta v1 [reference] rank 272	.161 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.629	Ferguson2024juncture-value_delta v1 [reference] rank 53	.629 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 247	.186 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

1.0	Ferguson2024llh-value_delta v1 [reference] rank 1	1.0 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.255	Ferguson2024quarter-value_delta v1 [reference] rank 183	.255 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.638	Ferguson2024round_f-value_delta v1 [reference] rank 79	.638 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

1.0	Ferguson2024round_v-value_delta v1 [reference] rank 1	1.0 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.058	Ferguson2024tilted_line-value_delta v1 [reference] rank 312	.058 0 ceiling best median
	2_way_afc task 48 images Sample stimuli cannot be displayed publicly for this benchmark.

.324	Hebart2023-match v1 rank 122	.324 0 ceiling best median
	1854 images Sample stimuli cannot be displayed publicly for this benchmark.

.411	Kato2026 rank 123 18 benchmarks	.411 0 ceiling best median

.274	Kato2026-error_consistency rank 122 9 benchmarks	.274 0 ceiling best median

.332	Kato2026Ori-error_consistency v1 rank 95	.332 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.394	Kato2026Fil-error_consistency v1 rank 126	.394 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.168	Kato2026Lin-error_consistency v1 rank 132	.168 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.386	Kato2026SilTex-error_consistency v1 rank 86	.386 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.180	Kato2026Sil-error_consistency v1 rank 151	.180 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.942	Kato2026Tex-error_consistency v1 rank 8	.942 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.024	Kato2026LinFil-error_consistency v1 rank 136	.024 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.

.038	Kato2026Dense-error_consistency v1 rank 57	.038 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.547	Kato2026-accuracy_distance rank 121 9 benchmarks	.547 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.

1.0	Kato2026Fil-accuracy_distance v1 rank 1	1.0 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.239	Kato2026Lin-accuracy_distance v1 rank 143	.239 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.679	Kato2026SilTex-accuracy_distance v1 rank 73	.679 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.145	Kato2026Sil-accuracy_distance v1 rank 157	.145 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.

.644	Kato2026LinFil-accuracy_distance v1 rank 141	.644 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.055	Kato2026Sparse-accuracy_distance v1 rank 153	.055 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.163	Kato2026Dense-accuracy_distance v1 rank 40	.163 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.805	Maniquet2024 rank 15 2 benchmarks	.805 0 ceiling best median

.950	Maniquet2024-confusion_similarity v1 [reference] rank 13	.950 0 ceiling best median
	13600 images Sample stimuli cannot be displayed publicly for this benchmark.

.661	Maniquet2024-tasks_consistency v1 [reference] rank 124	.661 0 ceiling best median
	13600 images Sample stimuli cannot be displayed publicly for this benchmark.

.288	Coggan2024_behavior-ConditionWiseAccuracySimilarity v1 rank 139	.288 0 ceiling best median
	22560 images Sample stimuli cannot be displayed publicly for this benchmark.

.275	engineering_vision rank 244 25 benchmarks	.275 0 ceiling best median

.670	ImageNet-top1 v1 [reference] rank 173	.670 0 ceiling best median
	50000 images Sample stimuli cannot be displayed publicly for this benchmark.

.514	Geirhos2021-top1 [reference] rank 197 17 benchmarks	.514 0 ceiling best median

.944	Geirhos2021colour-top1 v1 [reference] rank 189	.944 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.714	Geirhos2021contrast-top1 v1 [reference] rank 173	.714 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.202	Geirhos2021cueconflict-top1 v1 [reference] rank 182	.202 0 ceiling best median
	1280 images Sample stimuli cannot be displayed publicly for this benchmark.

.181	Geirhos2021edge-top1 v1 [reference] rank 255	.181 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.456	Geirhos2021eidolonI-top1 v1 [reference] rank 252	.456 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.470	Geirhos2021eidolonII-top1 v1 [reference] rank 236	.470 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.446	Geirhos2021eidolonIII-top1 v1 [reference] rank 256	.446 0 ceiling best median
	480 images Sample stimuli cannot be displayed publicly for this benchmark.

.914	Geirhos2021falsecolour-top1 v1 [reference] rank 182	.914 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.366	Geirhos2021highpass-top1 v1 [reference] rank 182	.366 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.385	Geirhos2021lowpass-top1 v1 [reference] rank 198	.385 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.597	Geirhos2021phasescrambling-top1 v1 [reference] rank 160	.597 0 ceiling best median
	640 images Sample stimuli cannot be displayed publicly for this benchmark.

.657	Geirhos2021powerequalisation-top1 v1 [reference] rank 181	.657 0 ceiling best median
	560 images Sample stimuli cannot be displayed publicly for this benchmark.

.630	Geirhos2021rotation-top1 v1 [reference] rank 200	.630 0 ceiling best median
	960 images Sample stimuli cannot be displayed publicly for this benchmark.

.463	Geirhos2021silhouette-top1 v1 [reference] rank 192	.463 0 ceiling best median
	160 images Sample stimuli cannot be displayed publicly for this benchmark.

.581	Geirhos2021sketch-top1 v1 [reference] rank 191	.581 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.358	Geirhos2021stylized-top1 v1 [reference] rank 216	.358 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.374	Geirhos2021uniformnoise-top1 v1 [reference] rank 192	.374 0 ceiling best median
	800 images Sample stimuli cannot be displayed publicly for this benchmark.

.192	Hermann2020 [reference] rank 240 2 benchmarks	.192 0 ceiling best median

.227	Hermann2020cueconflict-shape_bias v1 [reference] rank 244	.227 0 ceiling best median
	Sample stimuli cannot be displayed publicly for this benchmark.

.157	Hermann2020cueconflict-shape_match v1 [reference] rank 198	.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("eicircuit_resnet18_bn_20260919")
model.start_task(...)
model.start_recording(...)
model.look_at(...)

Model API

Code examples

Metadata has not been added yet.

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.

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Benchmarks BibTeX

@article{allen_massive_2022,
    title = {A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence},
    volume = {25},
    issn = {1097-6256},
    doi = {10.1038/s41593-021-00962-x},
    journal = {Nature Neuroscience},
    author = {Allen, Emily J. and St-Yves, Ghislain and Wu, Yihan and Breedlove, Jesse L.
              and Prince, Jacob S. and Dowdle, Logan T. and Nau, Matthias and Caron, Brad
              and Pestilli, Franco and Charest, Ian and Hutchinson, J. Benjamin
              and Naselaris, Thomas and Kay, Kendrick},
    year = {2022},
    pages = {116--126},
}
        @Article{Freeman2013,
                author={Freeman, Jeremy
                and Ziemba, Corey M.
                and Heeger, David J.
                and Simoncelli, Eero P.
                and Movshon, J. Anthony},
                title={A functional and perceptual signature of the second visual area in primates},
                journal={Nature Neuroscience},
                year={2013},
                month={Jul},
                day={01},
                volume={16},
                number={7},
                pages={974-981},
                abstract={The authors examined neuronal responses in V1 and V2 to synthetic texture stimuli that replicate higher-order statistical dependencies found in natural images. V2, but not V1, responded differentially to these textures, in both macaque (single neurons) and human (fMRI). Human detection of naturalistic structure in the same images was predicted by V2 responses, suggesting a role for V2 in representing natural image structure.},
                issn={1546-1726},
                doi={10.1038/nn.3402},
                url={https://doi.org/10.1038/nn.3402}
                }
        @article{li2026triplen,
    title = {Triple-N dataset: large-scale fMRI-guided dense recordings of nonhuman
             primate neural responses to natural scenes},
    author = {Li, Yipeng and Liu, Xieyi and Li, Wanru and Yang, Jia and Gong, Baoqi
              and Jin, Wei and Gong, Zhengxin and Wang, Kesheng and Luo, Jingqiu
              and Zhao, Zishuo and Bao, Pinglei},
    journal = {Nature Neuroscience},
    year = {2026},
    doi = {10.1038/s41593-026-02322-z},
    url = {https://doi.org/10.1038/s41593-026-02322-z},
}
        @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}
            }
        @article{papale_extensive_2025,
	title = {An extensive dataset of spiking activity to reveal the syntax of the ventral stream},
	volume = {113},
	issn = {08966273},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S089662732400881X},
	doi = {10.1016/j.neuron.2024.12.003},
	journal = {Neuron},
	author = {Papale, Paolo and Wang, Feng and Self, Matthew W. and Roelfsema, Pieter R.},
	year = {2025},
}
        @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}
}
        @inproceedings{zerbe_laion-fmri_2026,
      title = {{LAION}-{fMRI}: A densely sampled 7T-fMRI dataset providing broad coverage of natural image diversity},
      author = {Zerbe, Josefine and Roth, Johannes and Mell, Maggie Mae and Herholz, Peer and Knapen, Tomas and Hebart, Martin N.},
      year = {2026},
      booktitle = {Vision Sciences Society Annual Meeting},
  }
        @article{cowley2026compact,
    title={Compact deep neural network models of the visual cortex},
    author={Cowley, Benjamin R and Stan, Patricia L and Pillow, Jonathan W and Smith, Matthew A},
    journal={Nature},
    volume={652},
    number={8111},
    pages={947--954},
    year={2026},
    publisher={Nature Publishing Group},
    doi={10.1038/s41586-026-10150-1},
    url={https://doi.org/10.1038/s41586-026-10150-1}}
        @article{muzellec_reverse_2026,
      title = {Reverse predictivity for bidirectional comparison of neural networks and biological brains},
      volume = {8},
      issn = {2522-5839},
      url = {https://doi.org/10.1038/s42256-026-01204-0},
      doi = {10.1038/s42256-026-01204-0},
      number = {3},
      journal = {Nature Machine Intelligence},
      author = {Muzellec, Sabine and Kar, Kohitij},
      month = mar,
      year = {2026},
      pages = {474--488},
}
        @article {Majaj13402,
            author = {Majaj, Najib J. and Hong, Ha and Solomon, Ethan A. and DiCarlo, James J.},
            title = {Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance},
            volume = {35},
            number = {39},
            pages = {13402--13418},
            year = {2015},
            doi = {10.1523/JNEUROSCI.5181-14.2015},
            publisher = {Society for Neuroscience},
            abstract = {To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ({	extquotedblleft}face patches{	extquotedblright}) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of \~{}60,000 IT neurons and is executed as a simple weighted sum of those firing rates.SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of \&gt;100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior.},
            issn = {0270-6474},
            URL = {https://www.jneurosci.org/content/35/39/13402},
            eprint = {https://www.jneurosci.org/content/35/39/13402.full.pdf},
            journal = {Journal of Neuroscience}}
        @misc{Sanghavi_DiCarlo_2021,
  title={Sanghavi2020},
  url={osf.io/chwdk},
  DOI={10.17605/OSF.IO/CHWDK},
  publisher={OSF},
  author={Sanghavi, Sachi and DiCarlo, James J},
  year={2021},
  month={Nov}
}
        @misc{Sanghavi_Jozwik_DiCarlo_2021,
  title={SanghaviJozwik2020},
  url={osf.io/fhy36},
  DOI={10.17605/OSF.IO/FHY36},
  publisher={OSF},
  author={Sanghavi, Sachi and Jozwik, Kamila M and DiCarlo, James J},
  year={2021},
  month={Nov}
}
        @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{gifford_large_2022,
	title = {A large and rich {EEG} dataset for modeling human visual object recognition},
	volume = {264},
	issn = {10538119},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811922008758},
	doi = {10.1016/j.neuroimage.2022.119754},
	journal = {NeuroImage},
	author = {Gifford, Alessandro T. and Dwivedi, Kshitij and Roig, Gemma and Cichy, Radoslaw M.},
	year = {2022},
}
        @Article{Kar2019,
                                                    author={Kar, Kohitij
                                                    and Kubilius, Jonas
                                                    and Schmidt, Kailyn
                                                    and Issa, Elias B.
                                                    and DiCarlo, James J.},
                                                    title={Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior},
                                                    journal={Nature Neuroscience},
                                                    year={2019},
                                                    month={Jun},
                                                    day={01},
                                                    volume={22},
                                                    number={6},
                                                    pages={974-983},
                                                    abstract={Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If recurrence is critical to this behavior, then primates should outperform feedforward-only deep CNNs for images that require additional recurrent processing beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these `challenge' images. Second, using large-scale electrophysiology, we observed that behaviorally sufficient object identity solutions emerged {	extasciitilde}30{	hinspace}ms later in the IT cortex for challenge images compared with primate performance-matched `control' images. Third, these behaviorally critical late-phase IT response patterns were poorly predicted by feedforward deep CNN activations. Notably, very-deep CNNs and shallower recurrent CNNs better predicted these late IT responses, suggesting that there is a functional equivalence between additional nonlinear transformations and recurrence. Beyond arguing that recurrent circuits are critical for rapid object identification, our results provide strong constraints for future recurrent model development.},
                                                    issn={1546-1726},
                                                    doi={10.1038/s41593-019-0392-5},
                                                    url={https://doi.org/10.1038/s41593-019-0392-5}
                                                    }
        @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{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	`features.maxpool`
V2	`features.layer3.0`
V4	`features.layer2.0`
IT	`features.layer4.0`