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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021highpass-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.875
2	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.858
3	convnext_xlarge:fb_in22k_ft_in1k	.851
4	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.827
5	vit_large_patch14_clip_224:laion2b_ft_in1k	.812
6	vit_large_patch14_clip_224:openai_ft_in1k	.807
7	convnext_large:fb_in22k_ft_in1k	.806
8	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.793
9	dinov2_vitl14_reg_lc	.769
10	convnext_large_in22k_supervised	.723
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.722
12	dinov2_vitl14_lc	.721
13	fixres_resnext101_32x48d_wsl	.716
14	convnext_base:clip_laiona_augreg_ft_in1k_384	.709
15	dinov2_vitg14_lc	.677
16	vit_huge_clip336_laion	.652
17	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.652
18	dinov2_vitg14_reg_lc	.638
19	vit_large_clip336_openai	.636
20	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.592
21	convnext_base_fb_in22k_ft_in1k	.584
22	CLIP_resnet50	.550
23	CLIP_resnet50_float32	.550
24	vit_base_patch16_clip_224:openai_ft_in1k	.506
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.493
26	convnext_tiny:in12k_ft_in1k	.457
27	convnext_tiny_imagenet_full_seed-0	.442
28	convnext_xxlarge_clip	.437
29	CLIP-RN50	.422
30	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.419
31	effnetv2m_custom384	.410
32	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.404
33	convnext_large_clip_laion2b_finelayers	.386
34	SWSL_resnext101_32x8d	.380
35	resnext101_32x48d_wsl	.367
36	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.364
37	convnext_large_imagenet_full_seed-0	.355
38	dinov2_vitl14_lc_res336	.353
39	convnext_small_imagenet_full_seed-0	.330
40	convnext_base_fb_in1k	.327
41	cvt_cvt-13-384-in1k_4_LucyV4	.305
42	resnet_152_v2	.303
43	resnext101_32x16d_wsl	.294
44	cv_18_dagger_408_pretrained	.293
45	resnext101_32x32d_wsl	.291
46	convnext_base_imagenet_full_seed-0	.287
47	resnet50-VITO-8deg-cc	.285
48	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.262
49	vision_transformer_vit_large_patch16_224	.248
50	eva02_large_in22k	.234
51	convnext_tiny_sup	.226
52	focalnet_tiny_lrf_in1k	.220
53	resnet-152_v2_pytorch	.202
54	cvt_cvt-21-384-in1k_4_LucyV4	.200
55	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.196
56	cvt_cvt-21-224-in1k_4_LucyV4	.195
57	vit_relpos_base_patch16_clsgap_224:sw_in1k	.189
58	pnasnet_large_halfPrecision	.177
59	pnasnet_large	.177
60	resnext101_32x8d_wsl	.176
61	convnext_base_fb_in1k_gram_aligned_dinov3	.175
62	convnext_femto_ols:d1_in1k	.175
63	AlexNet_SIN_fov4	.169
64	regnety_032	.160
65	SWSL_resnet50	.154
66	cvt_cvt-13-224-in1k_4_LucyV4	.151
67	pnasnet_large_pytorch	.150
68	nasnet_large	.149
69	inception_v3	.143
70	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.142
71	CLIP_ViT-B_32	.139
72	inception_v4	.139
73	pnasnet_large-tf	.139
74	inception_v3_pytorch	.139
75	inception_v3-tf	.135
76	densenet-169	.132
77	convnext_small_imagenet_100_seed-0	.131
78	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.127
79	resnet_101_v2	.123
80	resnet50_st_wzc_seed6_2_323	.121
81	resnet-152_v2-tf	.120
82	xception	.118
83	tv_efficientnet-b1	.116
84	densenet-169-keras	.116
85	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.116
86	resnet50_ewconplus_wzc_seed6_2_323	.115
87	ViT_B_16_imagenet1k	.111
88	xception-keras	.110
89	seresnext50_32x4d	.103
90	resnet34_imagenet_full	.101
91	resnet_50_v2	.100
92	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.100
93	BiT-S-R50x3	.100
94	alexnet_training_seed_05	.096
95	alexnet_training_seed_01	.096
96	alexnet_training_seed_01_fov4	.096
97	alexnet_training_seed_01_fov12	.096
98	alexnet_training_seed_01_fov16	.096
99	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.095
100	deit_small_imagenet_1_seed-0	.095
101	deit_small_imagenet_10_seed-0	.095
102	resnet50_imagenet_10_seed-0	.095
103	imagenet_l2_10_0	.094
104	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.093
105	BiT-S-R152x2	.093
106	resnet50_FractalDB	.092
107	resnet50_ewconplus_wzc_seed5_2__323	.092
108	resnet50-vicregl0p9	.091
109	barlow-twins-resnet50	.091
110	alexnet_linf_8_robust	.090
111	alexnet_l2_3_robust	.090
112	resnet50_linf_8_robust	.089
113	inception_v4-tf	.089
114	resnet50_ewconplus_wzc_seed2_2__323	.088
115	alexnet_training_seed_07	.088
116	effnetb1_272x240	.088
117	alexnet_robust_correct	.086
118	custom_model_cv_18_dagger_408	.085
119	AdvProp_efficientnet-b4	.083
120	AT_efficientnet-b4	.083
121	mobilenet_v2_0_5_224	.082
122	blt_vs	.081
123	vonealexnet_gaussian_noise_std4_fixed	.081
124	mobilenet_v1_0.5_192	.080
125	resnet50_linf_4_robust	.079
126	densenet-201-keras	.078
127	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.078
128	resnet50_random_l2_perturb	.077
129	mobilenet_v1_0.25_128	.075
130	alexnet_training_seed_02	.075
131	alexnet_training_seed_04	.075
132	alexnet_training_seed_08	.075
133	alexnet_training_seed_10	.075
134	artResNet18_1	.075
135	resnet101	.075
136	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.075
137	resnet_50_v1_spiking_l4a	.074
138	alexnet_fov12	.074
139	BiT-S-R101x3	.074
140	convnext_small_imagenet_10_seed-0	.073
141	resnet50_byol	.073
142	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
143	texture_shape_alexnet_trained_on_SIN	.073
144	alexnet_training_seed_03	.072
145	mobilenet_v1_0.75_160	.072
146	vgg-19-keras	.072
147	nasnet_large-tf	.071
148	resnet50-vicreg	.071
149	alexnet_fov16	.067
150	alexnet_fov4	.067
151	BiT-S-R152x4	.066
152	voneresnet-50-robust	.066
153	vgg-16-keras	.066
154	mobilenet_v1_0.25_192	.065
155	antialias-resnet152	.064
156	mobilenet_v1_1.0_224	.064
157	AdvProp_efficientnet-b8	.064
158	resnet50-moclr8deg	.063
159	mobilenet_v2_0_5_192	.062
160	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.061
161	resnet50-SIN_IN_IN	.060
162	resnet_SIN_IN_FT_IN	.060
163	resnet50_robust_l2_eps1	.060
164	resnet_101_v1	.059
165	mobilenet_v1_0.75_128	.059
166	alexnet_reduced_aliasing_early_checkpoint	.059
167	konkle_alexnetgn_ipcl_ref01_primary_model	.059
168	AdvProp_efficientnet-b0	.056
169	resnet50_l2_3_robust	.056
170	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.056
171	AlexNet_SIN	.055
172	resnet-50-robust	.055
173	imagenet_l2_3_0	.055
174	resnet50_robust_l2_eps3	.055
175	mobilenet_v2_1.0_160-tf	.055
176	mobilenet_v1_0.25_160	.055
177	densenet-201	.054
178	densenet_201_pytorch	.054
179	efficientnet_b0	.054
180	mobilenet_v2_1_0_128	.054
181	vonegrcnn_62e_nobn	.053
182	alexnet_training_seed_06	.053
183	AlexNet_SIN_fov16	.053
184	AdvProp_efficientnet-b7	.053
185	mobilenet_v1_0.5_224	.053
186	AlexNet_SIN_fov12	.053
187	vonegrcnn_52e_full	.052
188	vonegrcnn_47e	.052
189	squeezenet1_0	.051
190	convnext_small_imagenet_1_seed-0	.051
191	CORnet-S	.051
192	mobilenet_v1_1.0_128	.051
193	deit_small_imagenet_100_seed-0	.050
194	mobilenet_v2_1_0_224	.050
195	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.050
196	ReAlnet07	.050
197	mobilenet_v2_1.0_224-tf	.050
198	mobilevit_small	.049
199	resnet50-SIN	.049
200	resnet50_eMMCR_Vanilla	.048
201	ReAlnet06	.048
202	resnet152_imagenet_full	.048
203	resnet50_st_wzc_2	.048
204	ReAlnet08	.047
205	cornet_s	.046
206	ReAlnet10	.046
207	resnet-18	.046
208	resnet50_imagenet_full	.045
209	alexnet_ks_torevert	.045
210	alexnet	.045
211	mobilenet_v2_0_75_192	.045
212	resnet50-simclr-vissl	.044
213	resnet50_st_wzc_seed7_2_323	.044
214	resnet50_st_wzc_seed3_2_323	.043
215	vonegrcnn_52e_full	.043
216	nasnet_mobile	.043
217	mobilenet_v2_1_3_224	.043
218	resnet101_imagenet_full	.043
219	efficientnet-b7	.043
220	vonegrcnn_47e	.043
221	mobilenet_v1_0.5_160	.042
222	resnet50-SIN_IN	.042
223	regnet_y_400mf	.042
224	resnet50_moco_v2	.041
225	nasnet_mobile-tf	.041
226	Res2Net50_26w_4s	.040
227	resnet50	.040
228	mobilenet_v1_1.0_160	.040
229	mobilenet_v2_0.75_96	.039
230	mobilenet_v1_0.5_128	.038
231	mobilenet_v1_0.25_224	.038
232	squeezenet1_1	.038
233	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.038
234	alexnet_random_linf8_perturb	.037
235	voneresnet-50	.037
236	vonegrcnn_62e_nobn	.037
237	mobilenet_v2_0.75_224-tf	.036
238	inception_v1	.036
239	deit_large_imagenet_full_seed-0	.036
240	mobilenet_v2_1_0_160	.036
241	mobilenet_v2_1.3_224-tf	.035
242	mobilenet_v2_1_0_192	.035
243	alexnet_early_checkpoint	.035
244	mobilenet_v2_1.0_192-tf	.034
245	texture_shape_resnet50_trained_on_SIN	.033
246	alexnet_training_seed_09	.033
247	mobilenet_v2_0_75_160	.033
248	mobilenet_v2_0.5_96	.033
249	resnet-50_untrained	.033
250	ReAlnet05	.032
251	resnet_152_v1	.031
252	resnet50_imagenet_1_seed-0	.031
253	ReAlnet01	.031
254	resnet-50-pytorch	.030
255	resnet50-sup	.030
256	resnet50_tutorial	.030
257	resnet_50_v1	.030
258	resnet-34	.030
259	inception_v1-tf	.030
260	BiT-S-R50x1	.028
261	resnet50_ewconplus_wzc	.027
262	ReAlnet03	.027
263	resnet_50_v1_spiking_l3	.027
264	resnet_50_v1_spiking	.026
265	ReAlnet02	.026
266	mobilenet_v2_0.35_96	.026
267	ViT_B_32_imagenet1k	.024
268	resnet50_random_linf8_perturb	.024
269	BiT-S-R101x1	.023
270	resnet50-barlow	.023
271	mobilenet_v2_0.75_160-tf	.023
272	mobilenet_v1_0.75_192	.023
273	mobilenet_v2_0.35_128	.022
274	ReAlnet09	.022
275	resnet-50_v1-tf	.022
276	mobilenet_v2_0_75_224	.022
277	antialiased-rnext101_32x8d	.022
278	inception_v2-tf	.022
279	mobilenet_v2_0.5_160	.021
280	mobilenet_v2_1.0_96	.021
281	mobilenet_v1_1.0_192	.020
282	antialiased-r50	.020
283	mobilenet_v2_0.75_192-tf	.019
284	deit_small_imagenet_full_seed-0	.019
285	mobilenet_v2_1.4_224-tf	.019
286	resnet_50_v1_spiking_l4	.019
287	mobilenet_v2_0.5_224-tf	.018
288	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.016
289	resnet50-vicregl0p75	.016
290	resnet50_imagenet_100_seed-0	.015
291	efficientnet_b0_imagenet_full	.015
292	resnet18_imagenet_full	.015
293	eicircuit_resnet18_bn_20260919	.015
294	resnet-152_v1-tf	.015
295	densenet-121-keras	.015
296	grcnn_109	.015
297	mobilenet_v2_1-4_224_pytorch	.015
298	mobilenet_v2_1_4_224	.015
299	0.5x_resnet-18_LC_w_sh_1_iter	.014
300	AdvProp_efficientnet-b6	.014
301	mobilenet_v1_0.75_224	.014
302	deit_base_imagenet_full_seed-0	.013
303	resnet50_simclr	.013
304	mobilenet_v2_0.5_192-tf	.013
305	mobilenet_v2_0.35_160	.012
306	mobilenet_v2_1.0_128-tf	.012
307	AT_efficientnet-b2	.010
308	AdvProp_efficientnet-b2	.010
309	mobilenet_v2_0.35_192	.010
310	vgg_16	.010
311	mobilenet_v2_0.35_224	.010
312	mobilenet_v2_0.5_128	.010
313	grcnn	.009
314	resnet-50_v2-tf	.007
315	resnet50_hvs_wzc	.006
316	mobilenet_v2_0.75_128	.005
317	resnet-101_v1-tf	.004
318	ReAlnet04	.004
319	alexnet_random_l2_3_perturb	.004
320	shufflenet_v2_x1_0	.004
321	vgg_19	.003
322	densenet-121	.003
323	resnet50-vitoimagevidnet8	.003
324	efficientnet_b2_imagenet_full	.002
325	ViT_L_32_imagenet1k	.002
326	efficientnet_b1_imagenet_full	.001
327	voneresnet-50-non_stochastic	.000
328	resnet-18-LC_w_sh_100_iter_conv_init_m
329	ViT-B/32
330	BiT-M-R50x3
331	BiT-M-R50x1
332	BiT-M-R152x2
333	resnet-18-LC_w_sh_1_iter_conv_init_m
334	resnet-18-LC_w_sh_10_iter_m
335	resnet-18-LC_w_sh_100_iter_m
336	resnet-18-LC_w_sh_10_iter_conv_init_m
337	resnet-18-LC_w_sh_1_iter_m
338	FrankRobWobv0
339	0.5x_resnet-18_LC
340	CORnetZ_CIFAR10
341	resnet18-local_aggregation
342	resnet18-deepcluster
343	r3m_resnet18
344	CORnet-Z
345	resnet18-instance_recognition
346	vggface
347	my-model
348	0.5x_resnet-18_LC_w_sh_10_iter
349	r3m_resnet50
350	resnet18-colorization
351	r3m_resnet34
352	resnet-18-LC_m
353	r3m_resnet50_nocrop
354	resnet18-supervised
355	resnet-18-LC_1st_conv_m
356	resnet-18-LC_conv_init_m
357	resnet18-contrastive_multiview
358	0.5x_resnet-18
359	RN50
360	0.5x_resnet-18_LC_w_sh_100_iter
361	RN50
362	resnet18-relative_position
363	resnet18-autoencoder
364	resnet18-simclr
365	ViT_L_32
366	ViT_B_32
367	resnet18-depth_prediction
368	ViT_B_16
369	r3m_resnet34_nocrop
370	resnet18-contrastive_predictive
371	omnivore_swinT
372	resnet-18-LC_untrained
373	resnet-18_untrained
374	resnet-50x2_untrained
375	dcgan
376	v1-pyr-nodown
377	unet_entire
378	CORnetZ_CIFAR10_bs32_20_04
379	pixels
380	dorinet_cornet_z
381	my-model
382	openclip
383	resnet50-cifar
384	resnet50_pretrained_with_retinal_waves
385	resnet50_ImageNet
386	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
387	ReAlnet07_ALL
388	yudixie_resnet50_distance_translation_rotation_0_240908
389	yudixie_resnet50_imagenet1kpret_0_240312
390	yudixie_resnet50_random_0_240908
391	yudixie_resnet50_imagenet1kpret_0_240908
392	yudixie_resnet50_cat_obj_class_all_latents_0_240908
393	yudixie_resnet50_object_class_0_240908
394	yudixie_resnet50_category_class_0_240908
395	resnet18_imagenet21kP
396	ConvLSTM
397	PredRNN
398	yudixie_resnet50_distance_reg_0_240908
399	SimVP
400	TAU
401	MIM
402	hmax
403	vit_base_patch16_dinov3-lvd1689m
404	ReAlnet01_cornet
405	briaai_rmbg_1_4
406	compact_resnet50_V4
407	yudixie_resnet18_translation_reg_0_240719
408	yudixie_resnet18_distance_reg_0_240719
409	yolos_tiny
410	yudixie_resnet18_rotation_reg_0_240719
411	ReAlnet02_cornet
412	yudixie_resnet18_imagenet1kpret_0_240719
413	eBarlow_lmda_02_1000ep
414	ReAlnet03_cornet
415	compact_resnet50_IT
416	vit_base_patch14_dinov2-lvd142m
417	resnet18_ecoset_full
418	resnet34_ecoset_full
419	ReAlnet01_cornet_fov4
420	VOneCORnet-S
421	resnet101_ecoset_full
422	resnet152_ecoset_full
423	yudixie_resnet50_rotation_reg_0_240908
424	yudixie_resnet50_distance_translation_0_240908
425	yudixie_resnet50_distance_rotation_0_240908
426	compact_vgg19_V4
427	yudixie_resnet18_distance_translation_0_240719
428	yudixie_resnet18_translation_rotation_0_240719
429	vit_base_patch14_reg4_dinov2-lvd142m
430	yudixie_resnet18_category_class_0_240719
431	yudixie_resnet18_random_0_240719
432	yudixie_resnet18_cat_obj_class_all_latents_0_240719
433	resnet-18_test_m
434	SeLaVi-AVE
435	yudixie_resnet18_distance_translation_rotation_0_240719
436	ReAlnet01_cornet_fov12
437	yudixie_resnet18_object_class_0_240719
438	compact_resnet50_robust_V4
439	compact_vgg19_IT
440	r3d_18
441	ReAlnet06_cornet
442	yudixie_resnet50_translation_reg_0_240908
443	omnivore_swinS
444	mc3_18
445	yudixie_resnet50_translation_rotation_0_240908
446	ReAlnet07_cornet
447	s3d
448	ReAlnet02_ALL
449	mvit_v1_b
450	ReAlnet04_ALL
451	ReAlnet01_cornet_fov16
452	compact_cornets_V4
453	ReAlnet08_cornet
454	ReAlnet03_ALL
455	omnivore_swinB
456	compact_cornets_IT
457	SeLaVi-Kinetics400
458	ReAlnet05_ALL
459	ReAlnet09_cornet
460	SeLaVi-VGG-Sound
461	ReAlnet06_ALL
462	SeLaVi-Kinetics-Sound
463	compact_resnet50_robust_IT
464	r2plus1d_18
465	mvit_v2_s
466	ReAlnet10_cornet
467	resnet50_ecoset_full
468	ReAlnet08_ALL
469	yudixie_resnet18_distance_rotation_0_240719
470	ReAlnet09_ALL
471	ReAlnet04_cornet
472	ReAlnet05_cornet
473	resnet50_primary_visual_cortex
474	ReAlnet10_ALL
475	ReAlnet01_ALL
476	resnet18_fair
477	pr1_stl10_disentangled_alpha04
478	deit3_base_patch16_224_fb_in22k_ft_in1k
479	yudixie_wbh_object_class_scaling_0415_run_0
480	yudixie_wbh_object_class_scaling_0415_run_3
481	yudixie_wbh_object_class_scaling_0415_imagenet
482	yudixie_wbh_object_class_scaling_0415_run_2
483	yudixie_wbh_object_class_scaling_0415_run_1
484	deepeisnn_ei_cifar10_vgg19
485	deepeisnn_ei_cifar10_resnet18
486	deepeisnn_ei_imagenet_resnet18
487	dinov2_vitl14_reg_lc_featread
488	gabor_filter_entropy_4x4
489	gabor_filter_entropy_4x4_v2
490	gabor_filter_entropy_4x4_v2_forcedCosine
491	gabor_filter_mean_abs_4x4_v2

Benchmark bibtex

@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}
        }

Ceiling

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021highpass

Metric: error_consistency