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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	mehrer2020_alexnet_training_seed_01_fov4	.138
76	inception_v3-tf	.135
77	densenet-169	.132
78	convnext_small_imagenet_100_seed-0	.131
79	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.127
80	resnet_101_v2	.123
81	resnet50_st_wzc_seed6_2_323	.121
82	resnet-152_v2-tf	.120
83	xception	.118
84	tv_efficientnet-b1	.116
85	densenet-169-keras	.116
86	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.116
87	resnet50_ewconplus_wzc_seed6_2_323	.115
88	ViT_B_16_imagenet1k	.111
89	xception-keras	.110
90	seresnext50_32x4d	.103
91	resnet34_imagenet_full	.101
92	resnet_50_v2	.100
93	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.100
94	BiT-S-R50x3	.100
95	alexnet_training_seed_05	.096
96	alexnet_training_seed_01	.096
97	alexnet_training_seed_01_fov4	.096
98	alexnet_training_seed_01_fov12	.096
99	alexnet_training_seed_01_fov16	.096
100	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.095
101	deit_small_imagenet_1_seed-0	.095
102	deit_small_imagenet_10_seed-0	.095
103	resnet50_imagenet_10_seed-0	.095
104	imagenet_l2_10_0	.094
105	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.093
106	BiT-S-R152x2	.093
107	resnet50_FractalDB	.092
108	resnet50_ewconplus_wzc_seed5_2__323	.092
109	resnet50-vicregl0p9	.091
110	barlow-twins-resnet50	.091
111	alexnet_linf_8_robust	.090
112	alexnet_l2_3_robust	.090
113	resnet50_vicreg_linhead	.090
114	resnet50_linf_8_robust	.089
115	inception_v4-tf	.089
116	resnet50_ewconplus_wzc_seed2_2__323	.088
117	alexnet_training_seed_07	.088
118	effnetb1_272x240	.088
119	alexnet_robust_correct	.086
120	custom_model_cv_18_dagger_408	.085
121	AdvProp_efficientnet-b4	.083
122	AT_efficientnet-b4	.083
123	mobilenet_v2_0_5_224	.082
124	blt_vs	.081
125	vonealexnet_gaussian_noise_std4_fixed	.081
126	mobilenet_v1_0.5_192	.080
127	resnet50_linf_4_robust	.079
128	densenet-201-keras	.078
129	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.078
130	resnet50_random_l2_perturb	.077
131	mobilenet_v1_0.25_128	.075
132	alexnet_training_seed_02	.075
133	alexnet_training_seed_04	.075
134	alexnet_training_seed_08	.075
135	alexnet_training_seed_10	.075
136	artResNet18_1	.075
137	resnet101	.075
138	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.075
139	resnet_50_v1_spiking_l4a	.074
140	alexnet_fov12	.074
141	BiT-S-R101x3	.074
142	convnext_small_imagenet_10_seed-0	.073
143	resnet50_byol	.073
144	mehrer2020_alexnet_training_seed_01_fov12	.073
145	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
146	texture_shape_alexnet_trained_on_SIN	.073
147	mehrer2020_alexnet_training_seed_01_fov16	.073
148	alexnet_training_seed_03	.072
149	mobilenet_v1_0.75_160	.072
150	vgg-19-keras	.072
151	nasnet_large-tf	.071
152	resnet50-vicreg	.071
153	alexnet_fov16	.067
154	alexnet_fov4	.067
155	BiT-S-R152x4	.066
156	voneresnet-50-robust	.066
157	vgg-16-keras	.066
158	mobilenet_v1_0.25_192	.065
159	antialias-resnet152	.064
160	mehrer2020_alexnet_training_seed_04	.064
161	mobilenet_v1_1.0_224	.064
162	AdvProp_efficientnet-b8	.064
163	resnet50-moclr8deg	.063
164	mobilenet_v2_0_5_192	.062
165	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.061
166	resnet50-SIN_IN_IN	.060
167	resnet_SIN_IN_FT_IN	.060
168	resnet50_robust_l2_eps1	.060
169	resnet_101_v1	.059
170	mobilenet_v1_0.75_128	.059
171	alexnet_reduced_aliasing_early_checkpoint	.059
172	konkle_alexnetgn_ipcl_ref01_primary_model	.059
173	resnet50_simsiam_linhead	.057
174	AdvProp_efficientnet-b0	.056
175	resnet50_l2_3_robust	.056
176	mehrer2020_alexnet_training_seed_05	.056
177	resnet50_barlowtwins_linhead	.056
178	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.056
179	AlexNet_SIN	.055
180	resnet-50-robust	.055
181	imagenet_l2_3_0	.055
182	resnet50_robust_l2_eps3	.055
183	resnet50_simclr_linhead_01norm	.055
184	mobilenet_v2_1.0_160-tf	.055
185	mobilenet_v1_0.25_160	.055
186	densenet-201	.054
187	densenet_201_pytorch	.054
188	efficientnet_b0	.054
189	mobilenet_v2_1_0_128	.054
190	vonegrcnn_62e_nobn	.053
191	alexnet_training_seed_06	.053
192	AlexNet_SIN_fov16	.053
193	AdvProp_efficientnet-b7	.053
194	mobilenet_v1_0.5_224	.053
195	AlexNet_SIN_fov12	.053
196	vonegrcnn_52e_full	.052
197	mehrer2020_alexnet_training_seed_02	.052
198	vonegrcnn_47e	.052
199	squeezenet1_0	.051
200	convnext_small_imagenet_1_seed-0	.051
201	CORnet-S	.051
202	mobilenet_v1_1.0_128	.051
203	deit_small_imagenet_100_seed-0	.050
204	mobilenet_v2_1_0_224	.050
205	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.050
206	ReAlnet07	.050
207	mobilenet_v2_1.0_224-tf	.050
208	mobilevit_small	.049
209	resnet50-SIN	.049
210	resnet50_eMMCR_Vanilla	.048
211	ReAlnet06	.048
212	resnet152_imagenet_full	.048
213	resnet50_st_wzc_2	.048
214	ReAlnet08	.047
215	cornet_s	.046
216	ReAlnet10	.046
217	mehrer2020_alexnet_training_seed_03	.046
218	resnet50_byol_linhead	.046
219	resnet-18	.046
220	resnet50_imagenet_full	.045
221	alexnet_ks_torevert	.045
222	alexnet	.045
223	mobilenet_v2_0_75_192	.045
224	resnet50-simclr-vissl	.044
225	resnet50_st_wzc_seed7_2_323	.044
226	resnet50_st_wzc_seed3_2_323	.043
227	vonegrcnn_52e_full	.043
228	nasnet_mobile	.043
229	mobilenet_v2_1_3_224	.043
230	resnet101_imagenet_full	.043
231	efficientnet-b7	.043
232	vonegrcnn_47e	.043
233	mobilenet_v1_0.5_160	.042
234	resnet50-SIN_IN	.042
235	regnet_y_400mf	.042
236	resnet50_moco_v2	.041
237	nasnet_mobile-tf	.041
238	Res2Net50_26w_4s	.040
239	resnet50	.040
240	mobilenet_v1_1.0_160	.040
241	mobilenet_v2_0.75_96	.039
242	mobilenet_v1_0.5_128	.038
243	mobilenet_v1_0.25_224	.038
244	squeezenet1_1	.038
245	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.038
246	alexnet_random_linf8_perturb	.037
247	voneresnet-50	.037
248	vonegrcnn_62e_nobn	.037
249	mobilenet_v2_0.75_224-tf	.036
250	inception_v1	.036
251	mehrer2020_alexnet_training_seed_01	.036
252	deit_large_imagenet_full_seed-0	.036
253	mobilenet_v2_1_0_160	.036
254	mobilenet_v2_1.3_224-tf	.035
255	mobilenet_v2_1_0_192	.035
256	alexnet_early_checkpoint	.035
257	mobilenet_v2_1.0_192-tf	.034
258	texture_shape_resnet50_trained_on_SIN	.033
259	alexnet_training_seed_09	.033
260	mobilenet_v2_0_75_160	.033
261	mobilenet_v2_0.5_96	.033
262	resnet-50_untrained	.033
263	ReAlnet05	.032
264	resnet_152_v1	.031
265	resnet50_imagenet_1_seed-0	.031
266	ReAlnet01	.031
267	resnet-50-pytorch	.030
268	resnet50-sup	.030
269	resnet50_tutorial	.030
270	resnet_50_v1	.030
271	resnet-34	.030
272	inception_v1-tf	.030
273	BiT-S-R50x1	.028
274	resnet50_ewconplus_wzc	.027
275	ReAlnet03	.027
276	resnet_50_v1_spiking_l3	.027
277	resnet_50_v1_spiking	.026
278	ReAlnet02	.026
279	mobilenet_v2_0.35_96	.026
280	ViT_B_32_imagenet1k	.024
281	resnet50_random_linf8_perturb	.024
282	BiT-S-R101x1	.023
283	resnet50-barlow	.023
284	mobilenet_v2_0.75_160-tf	.023
285	mobilenet_v1_0.75_192	.023
286	mobilenet_v2_0.35_128	.022
287	ReAlnet09	.022
288	resnet-50_v1-tf	.022
289	mobilenet_v2_0_75_224	.022
290	antialiased-rnext101_32x8d	.022
291	inception_v2-tf	.022
292	mobilenet_v2_0.5_160	.021
293	mobilenet_v2_1.0_96	.021
294	mobilenet_v1_1.0_192	.020
295	antialiased-r50	.020
296	mobilenet_v2_0.75_192-tf	.019
297	deit_small_imagenet_full_seed-0	.019
298	mobilenet_v2_1.4_224-tf	.019
299	resnet_50_v1_spiking_l4	.019
300	mobilenet_v2_0.5_224-tf	.018
301	ijepa_vith14_linhead	.016
302	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.016
303	resnet50-vicregl0p75	.016
304	resnet50_imagenet_100_seed-0	.015
305	efficientnet_b0_imagenet_full	.015
306	resnet18_imagenet_full	.015
307	eicircuit_resnet18_bn_20260919	.015
308	resnet-152_v1-tf	.015
309	densenet-121-keras	.015
310	grcnn_109	.015
311	mobilenet_v2_1-4_224_pytorch	.015
312	mobilenet_v2_1_4_224	.015
313	0.5x_resnet-18_LC_w_sh_1_iter	.014
314	AdvProp_efficientnet-b6	.014
315	mobilenet_v1_0.75_224	.014
316	deit_base_imagenet_full_seed-0	.013
317	resnet50_simclr	.013
318	mobilenet_v2_0.5_192-tf	.013
319	mobilenet_v2_0.35_160	.012
320	mobilenet_v2_1.0_128-tf	.012
321	AT_efficientnet-b2	.010
322	AdvProp_efficientnet-b2	.010
323	mobilenet_v2_0.35_192	.010
324	vgg_16	.010
325	mobilenet_v2_0.35_224	.010
326	mobilenet_v2_0.5_128	.010
327	grcnn	.009
328	resnet-50_v2-tf	.007
329	resnet50_hvs_wzc	.006
330	mobilenet_v2_0.75_128	.005
331	resnet-101_v1-tf	.004
332	ReAlnet04	.004
333	alexnet_random_l2_3_perturb	.004
334	shufflenet_v2_x1_0	.004
335	vgg_19	.003
336	densenet-121	.003
337	resnet50-vitoimagevidnet8	.003
338	efficientnet_b2_imagenet_full	.002
339	ViT_L_32_imagenet1k	.002
340	efficientnet_b1_imagenet_full	.001
341	voneresnet-50-non_stochastic	.000
342	resnet-18-LC_w_sh_100_iter_conv_init_m
343	ViT-B/32
344	BiT-M-R50x3
345	BiT-M-R50x1
346	BiT-M-R152x2
347	resnet-18-LC_w_sh_1_iter_conv_init_m
348	resnet-18-LC_w_sh_10_iter_m
349	resnet-18-LC_w_sh_100_iter_m
350	resnet-18-LC_w_sh_10_iter_conv_init_m
351	resnet-18-LC_w_sh_1_iter_m
352	FrankRobWobv0
353	0.5x_resnet-18_LC
354	CORnetZ_CIFAR10
355	resnet18-local_aggregation
356	resnet18-deepcluster
357	r3m_resnet18
358	CORnet-Z
359	resnet18-instance_recognition
360	vggface
361	my-model
362	0.5x_resnet-18_LC_w_sh_10_iter
363	r3m_resnet50
364	resnet18-colorization
365	r3m_resnet34
366	resnet-18-LC_m
367	r3m_resnet50_nocrop
368	resnet18-supervised
369	resnet-18-LC_1st_conv_m
370	resnet-18-LC_conv_init_m
371	resnet18-contrastive_multiview
372	0.5x_resnet-18
373	RN50
374	0.5x_resnet-18_LC_w_sh_100_iter
375	RN50
376	resnet18-relative_position
377	resnet18-autoencoder
378	resnet18-simclr
379	ViT_L_32
380	ViT_B_32
381	resnet18-depth_prediction
382	ViT_B_16
383	r3m_resnet34_nocrop
384	resnet18-contrastive_predictive
385	omnivore_swinT
386	resnet-18-LC_untrained
387	resnet-18_untrained
388	resnet-50x2_untrained
389	dcgan
390	v1-pyr-nodown
391	unet_entire
392	CORnetZ_CIFAR10_bs32_20_04
393	pixels
394	dorinet_cornet_z
395	my-model
396	openclip
397	resnet50-cifar
398	resnet50_pretrained_with_retinal_waves
399	resnet50_ImageNet
400	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
401	ReAlnet07_ALL
402	yudixie_resnet50_distance_translation_rotation_0_240908
403	yudixie_resnet50_imagenet1kpret_0_240312
404	yudixie_resnet50_random_0_240908
405	yudixie_resnet50_imagenet1kpret_0_240908
406	yudixie_resnet50_cat_obj_class_all_latents_0_240908
407	yudixie_resnet50_object_class_0_240908
408	yudixie_resnet50_category_class_0_240908
409	resnet18_imagenet21kP
410	ConvLSTM
411	PredRNN
412	yudixie_resnet50_distance_reg_0_240908
413	SimVP
414	TAU
415	MIM
416	hmax
417	vit_base_patch16_dinov3-lvd1689m
418	ReAlnet01_cornet
419	briaai_rmbg_1_4
420	compact_resnet50_V4
421	yudixie_resnet18_translation_reg_0_240719
422	yudixie_resnet18_distance_reg_0_240719
423	yolos_tiny
424	yudixie_resnet18_rotation_reg_0_240719
425	ReAlnet02_cornet
426	yudixie_resnet18_imagenet1kpret_0_240719
427	eBarlow_lmda_02_1000ep
428	ReAlnet03_cornet
429	compact_resnet50_IT
430	vit_base_patch14_dinov2-lvd142m
431	resnet18_ecoset_full
432	resnet34_ecoset_full
433	ReAlnet01_cornet_fov4
434	VOneCORnet-S
435	resnet101_ecoset_full
436	resnet152_ecoset_full
437	yudixie_resnet50_rotation_reg_0_240908
438	yudixie_resnet50_distance_translation_0_240908
439	yudixie_resnet50_distance_rotation_0_240908
440	compact_vgg19_V4
441	yudixie_resnet18_distance_translation_0_240719
442	yudixie_resnet18_translation_rotation_0_240719
443	vit_base_patch14_reg4_dinov2-lvd142m
444	yudixie_resnet18_category_class_0_240719
445	yudixie_resnet18_random_0_240719
446	yudixie_resnet18_cat_obj_class_all_latents_0_240719
447	resnet-18_test_m
448	SeLaVi-AVE
449	yudixie_resnet18_distance_translation_rotation_0_240719
450	yudixie_resnet18_object_class_0_240719
451	compact_resnet50_robust_V4
452	compact_vgg19_IT
453	r3d_18
454	yudixie_resnet50_translation_reg_0_240908
455	omnivore_swinS
456	mc3_18
457	yudixie_resnet50_translation_rotation_0_240908
458	ReAlnet07_cornet
459	s3d
460	ReAlnet02_ALL
461	mvit_v1_b
462	ReAlnet04_ALL
463	ReAlnet01_cornet_fov16
464	compact_cornets_V4
465	ReAlnet08_cornet
466	ReAlnet03_ALL
467	omnivore_swinB
468	compact_cornets_IT
469	SeLaVi-Kinetics400
470	ReAlnet05_ALL
471	ReAlnet09_cornet
472	SeLaVi-VGG-Sound
473	ReAlnet06_ALL
474	SeLaVi-Kinetics-Sound
475	compact_resnet50_robust_IT
476	r2plus1d_18
477	mvit_v2_s
478	ReAlnet10_cornet
479	resnet50_ecoset_full
480	ReAlnet08_ALL
481	yudixie_resnet18_distance_rotation_0_240719
482	ReAlnet09_ALL
483	ReAlnet04_cornet
484	ReAlnet05_cornet
485	resnet50_primary_visual_cortex
486	ReAlnet10_ALL
487	ReAlnet01_ALL
488	ReAlnet01_cornet_fov12
489	ReAlnet06_cornet
490	resnet18_fair
491	pr1_stl10_disentangled_alpha04
492	deit3_base_patch16_224_fb_in22k_ft_in1k
493	yudixie_wbh_object_class_scaling_0415_run_0
494	yudixie_wbh_object_class_scaling_0415_run_3
495	yudixie_wbh_object_class_scaling_0415_imagenet
496	yudixie_wbh_object_class_scaling_0415_run_2
497	yudixie_wbh_object_class_scaling_0415_run_1
498	deepeisnn_ei_cifar10_vgg19
499	deepeisnn_ei_cifar10_resnet18
500	deepeisnn_ei_imagenet_resnet18
501	dinov2_vitl14_reg_lc_featread
502	gabor_filter_entropy_4x4
503	gabor_filter_entropy_4x4_v2
504	gabor_filter_entropy_4x4_v2_forcedCosine
505	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