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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	SWSL_resnet50	.154
65	cvt_cvt-13-224-in1k_4_LucyV4	.151
66	pnasnet_large_pytorch	.150
67	nasnet_large	.149
68	inception_v3	.143
69	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.142
70	CLIP_ViT-B_32	.139
71	inception_v4	.139
72	pnasnet_large-tf	.139
73	inception_v3_pytorch	.139
74	inception_v3-tf	.135
75	densenet-169	.132
76	convnext_small_imagenet_100_seed-0	.131
77	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.127
78	resnet_101_v2	.123
79	resnet50_st_wzc_seed6_2_323	.121
80	resnet-152_v2-tf	.120
81	xception	.118
82	tv_efficientnet-b1	.116
83	densenet-169-keras	.116
84	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.116
85	resnet50_ewconplus_wzc_seed6_2_323	.115
86	ViT_B_16_imagenet1k	.111
87	xception-keras	.110
88	resnet34_imagenet_full	.101
89	resnet_50_v2	.100
90	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.100
91	BiT-S-R50x3	.100
92	alexnet_training_seed_05	.096
93	alexnet_training_seed_01	.096
94	alexnet_training_seed_01_fov4	.096
95	alexnet_training_seed_01_fov12	.096
96	alexnet_training_seed_01_fov16	.096
97	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.095
98	deit_small_imagenet_1_seed-0	.095
99	deit_small_imagenet_10_seed-0	.095
100	resnet50_imagenet_10_seed-0	.095
101	imagenet_l2_10_0	.094
102	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.093
103	BiT-S-R152x2	.093
104	resnet50_FractalDB	.092
105	resnet50_ewconplus_wzc_seed5_2__323	.092
106	resnet50-vicregl0p9	.091
107	barlow-twins-resnet50	.091
108	alexnet_linf_8_robust	.090
109	alexnet_l2_3_robust	.090
110	resnet50_linf_8_robust	.089
111	inception_v4-tf	.089
112	resnet50_ewconplus_wzc_seed2_2__323	.088
113	alexnet_training_seed_07	.088
114	effnetb1_272x240	.088
115	alexnet_robust_correct	.086
116	custom_model_cv_18_dagger_408	.085
117	AdvProp_efficientnet-b4	.083
118	AT_efficientnet-b4	.083
119	mobilenet_v2_0_5_224	.082
120	blt_vs	.081
121	vonealexnet_gaussian_noise_std4_fixed	.081
122	mobilenet_v1_0.5_192	.080
123	resnet50_linf_4_robust	.079
124	densenet-201-keras	.078
125	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.078
126	resnet50_random_l2_perturb	.077
127	mobilenet_v1_0.25_128	.075
128	alexnet_training_seed_02	.075
129	alexnet_training_seed_04	.075
130	alexnet_training_seed_08	.075
131	alexnet_training_seed_10	.075
132	artResNet18_1	.075
133	resnet101	.075
134	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.075
135	resnet_50_v1_spiking_l4a	.074
136	alexnet_fov12	.074
137	BiT-S-R101x3	.074
138	convnext_small_imagenet_10_seed-0	.073
139	resnet50_byol	.073
140	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
141	texture_shape_alexnet_trained_on_SIN	.073
142	alexnet_training_seed_03	.072
143	mobilenet_v1_0.75_160	.072
144	vgg-19-keras	.072
145	nasnet_large-tf	.071
146	resnet50-vicreg	.071
147	alexnet_fov16	.067
148	alexnet_fov4	.067
149	BiT-S-R152x4	.066
150	voneresnet-50-robust	.066
151	vgg-16-keras	.066
152	mobilenet_v1_0.25_192	.065
153	antialias-resnet152	.064
154	mobilenet_v1_1.0_224	.064
155	AdvProp_efficientnet-b8	.064
156	resnet50-moclr8deg	.063
157	mobilenet_v2_0_5_192	.062
158	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.061
159	resnet50-SIN_IN_IN	.060
160	resnet_SIN_IN_FT_IN	.060
161	resnet50_robust_l2_eps1	.060
162	resnet_101_v1	.059
163	mobilenet_v1_0.75_128	.059
164	alexnet_reduced_aliasing_early_checkpoint	.059
165	konkle_alexnetgn_ipcl_ref01_primary_model	.059
166	AdvProp_efficientnet-b0	.056
167	resnet50_l2_3_robust	.056
168	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.056
169	AlexNet_SIN	.055
170	resnet-50-robust	.055
171	imagenet_l2_3_0	.055
172	resnet50_robust_l2_eps3	.055
173	mobilenet_v2_1.0_160-tf	.055
174	mobilenet_v1_0.25_160	.055
175	densenet-201	.054
176	densenet_201_pytorch	.054
177	efficientnet_b0	.054
178	mobilenet_v2_1_0_128	.054
179	vonegrcnn_62e_nobn	.053
180	alexnet_training_seed_06	.053
181	AlexNet_SIN_fov16	.053
182	AdvProp_efficientnet-b7	.053
183	mobilenet_v1_0.5_224	.053
184	AlexNet_SIN_fov12	.053
185	vonegrcnn_52e_full	.052
186	vonegrcnn_47e	.052
187	squeezenet1_0	.051
188	convnext_small_imagenet_1_seed-0	.051
189	CORnet-S	.051
190	mobilenet_v1_1.0_128	.051
191	deit_small_imagenet_100_seed-0	.050
192	mobilenet_v2_1_0_224	.050
193	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.050
194	ReAlnet07	.050
195	mobilenet_v2_1.0_224-tf	.050
196	mobilevit_small	.049
197	resnet50-SIN	.049
198	resnet50_eMMCR_Vanilla	.048
199	ReAlnet06	.048
200	resnet152_imagenet_full	.048
201	resnet50_st_wzc_2	.048
202	ReAlnet08	.047
203	cornet_s	.046
204	ReAlnet10	.046
205	resnet-18	.046
206	resnet50_imagenet_full	.045
207	alexnet_ks_torevert	.045
208	alexnet	.045
209	mobilenet_v2_0_75_192	.045
210	resnet50-simclr-vissl	.044
211	resnet50_st_wzc_seed7_2_323	.044
212	resnet50_st_wzc_seed3_2_323	.043
213	vonegrcnn_52e_full	.043
214	nasnet_mobile	.043
215	mobilenet_v2_1_3_224	.043
216	resnet101_imagenet_full	.043
217	efficientnet-b7	.043
218	vonegrcnn_47e	.043
219	mobilenet_v1_0.5_160	.042
220	resnet50-SIN_IN	.042
221	regnet_y_400mf	.042
222	resnet50_moco_v2	.041
223	nasnet_mobile-tf	.041
224	Res2Net50_26w_4s	.040
225	resnet50	.040
226	mobilenet_v1_1.0_160	.040
227	mobilenet_v2_0.75_96	.039
228	mobilenet_v1_0.5_128	.038
229	mobilenet_v1_0.25_224	.038
230	squeezenet1_1	.038
231	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.038
232	alexnet_random_linf8_perturb	.037
233	voneresnet-50	.037
234	vonegrcnn_62e_nobn	.037
235	mobilenet_v2_0.75_224-tf	.036
236	inception_v1	.036
237	deit_large_imagenet_full_seed-0	.036
238	mobilenet_v2_1_0_160	.036
239	mobilenet_v2_1.3_224-tf	.035
240	mobilenet_v2_1_0_192	.035
241	alexnet_early_checkpoint	.035
242	mobilenet_v2_1.0_192-tf	.034
243	texture_shape_resnet50_trained_on_SIN	.033
244	alexnet_training_seed_09	.033
245	mobilenet_v2_0_75_160	.033
246	mobilenet_v2_0.5_96	.033
247	resnet-50_untrained	.033
248	ReAlnet05	.032
249	resnet_152_v1	.031
250	resnet50_imagenet_1_seed-0	.031
251	ReAlnet01	.031
252	resnet-50-pytorch	.030
253	resnet50-sup	.030
254	resnet50_tutorial	.030
255	resnet_50_v1	.030
256	resnet-34	.030
257	inception_v1-tf	.030
258	BiT-S-R50x1	.028
259	resnet50_ewconplus_wzc	.027
260	ReAlnet03	.027
261	resnet_50_v1_spiking_l3	.027
262	resnet_50_v1_spiking	.026
263	ReAlnet02	.026
264	mobilenet_v2_0.35_96	.026
265	ViT_B_32_imagenet1k	.024
266	resnet50_random_linf8_perturb	.024
267	BiT-S-R101x1	.023
268	resnet50-barlow	.023
269	mobilenet_v2_0.75_160-tf	.023
270	mobilenet_v1_0.75_192	.023
271	mobilenet_v2_0.35_128	.022
272	ReAlnet09	.022
273	resnet-50_v1-tf	.022
274	mobilenet_v2_0_75_224	.022
275	antialiased-rnext101_32x8d	.022
276	inception_v2-tf	.022
277	mobilenet_v2_0.5_160	.021
278	mobilenet_v2_1.0_96	.021
279	mobilenet_v1_1.0_192	.020
280	antialiased-r50	.020
281	mobilenet_v2_0.75_192-tf	.019
282	deit_small_imagenet_full_seed-0	.019
283	mobilenet_v2_1.4_224-tf	.019
284	resnet_50_v1_spiking_l4	.019
285	mobilenet_v2_0.5_224-tf	.018
286	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.016
287	resnet50-vicregl0p75	.016
288	resnet50_imagenet_100_seed-0	.015
289	efficientnet_b0_imagenet_full	.015
290	resnet18_imagenet_full	.015
291	resnet-152_v1-tf	.015
292	densenet-121-keras	.015
293	grcnn_109	.015
294	mobilenet_v2_1-4_224_pytorch	.015
295	mobilenet_v2_1_4_224	.015
296	0.5x_resnet-18_LC_w_sh_1_iter	.014
297	AdvProp_efficientnet-b6	.014
298	mobilenet_v1_0.75_224	.014
299	deit_base_imagenet_full_seed-0	.013
300	resnet50_simclr	.013
301	mobilenet_v2_0.5_192-tf	.013
302	mobilenet_v2_0.35_160	.012
303	mobilenet_v2_1.0_128-tf	.012
304	AT_efficientnet-b2	.010
305	AdvProp_efficientnet-b2	.010
306	mobilenet_v2_0.35_192	.010
307	vgg_16	.010
308	mobilenet_v2_0.35_224	.010
309	mobilenet_v2_0.5_128	.010
310	grcnn	.009
311	resnet-50_v2-tf	.007
312	resnet50_hvs_wzc	.006
313	mobilenet_v2_0.75_128	.005
314	resnet-101_v1-tf	.004
315	ReAlnet04	.004
316	alexnet_random_l2_3_perturb	.004
317	shufflenet_v2_x1_0	.004
318	vgg_19	.003
319	densenet-121	.003
320	resnet50-vitoimagevidnet8	.003
321	efficientnet_b2_imagenet_full	.002
322	ViT_L_32_imagenet1k	.002
323	efficientnet_b1_imagenet_full	.001
324	voneresnet-50-non_stochastic	.000
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	BiT-M-R152x2
330	resnet-18-LC_w_sh_1_iter_conv_init_m
331	resnet-18-LC_w_sh_10_iter_m
332	resnet-18-LC_w_sh_100_iter_m
333	resnet-18-LC_w_sh_10_iter_conv_init_m
334	resnet-18-LC_w_sh_1_iter_m
335	FrankRobWobv0
336	0.5x_resnet-18_LC
337	CORnetZ_CIFAR10
338	resnet18-local_aggregation
339	resnet18-deepcluster
340	r3m_resnet18
341	CORnet-Z
342	resnet18-instance_recognition
343	vggface
344	my-model
345	0.5x_resnet-18_LC_w_sh_10_iter
346	r3m_resnet50
347	resnet18-colorization
348	r3m_resnet34
349	resnet-18-LC_m
350	r3m_resnet50_nocrop
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	resnet18-contrastive_predictive
368	omnivore_swinT
369	resnet-18-LC_untrained
370	resnet-18_untrained
371	resnet-50x2_untrained
372	dcgan
373	v1-pyr-nodown
374	unet_entire
375	CORnetZ_CIFAR10_bs32_20_04
376	pixels
377	dorinet_cornet_z
378	my-model
379	openclip
380	resnet50-cifar
381	resnet50_pretrained_with_retinal_waves
382	resnet50_ImageNet
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	ReAlnet07_ALL
385	yudixie_resnet50_distance_translation_rotation_0_240908
386	yudixie_resnet50_imagenet1kpret_0_240312
387	yudixie_resnet50_random_0_240908
388	yudixie_resnet50_imagenet1kpret_0_240908
389	yudixie_resnet50_cat_obj_class_all_latents_0_240908
390	yudixie_resnet50_object_class_0_240908
391	yudixie_resnet50_category_class_0_240908
392	resnet18_imagenet21kP
393	ConvLSTM
394	PredRNN
395	yudixie_resnet50_distance_reg_0_240908
396	SimVP
397	TAU
398	MIM
399	hmax
400	vit_base_patch16_dinov3-lvd1689m
401	ReAlnet01_cornet
402	briaai_rmbg_1_4
403	compact_resnet50_V4
404	yudixie_resnet18_translation_reg_0_240719
405	yudixie_resnet18_distance_reg_0_240719
406	yolos_tiny
407	yudixie_resnet18_rotation_reg_0_240719
408	ReAlnet02_cornet
409	yudixie_resnet18_imagenet1kpret_0_240719
410	eBarlow_lmda_02_1000ep
411	ReAlnet03_cornet
412	compact_resnet50_IT
413	vit_base_patch14_dinov2-lvd142m
414	resnet18_ecoset_full
415	resnet34_ecoset_full
416	ReAlnet01_cornet_fov4
417	VOneCORnet-S
418	resnet101_ecoset_full
419	resnet152_ecoset_full
420	yudixie_resnet50_rotation_reg_0_240908
421	yudixie_resnet50_distance_translation_0_240908
422	yudixie_resnet50_distance_rotation_0_240908
423	compact_vgg19_V4
424	yudixie_resnet18_distance_translation_0_240719
425	yudixie_resnet18_translation_rotation_0_240719
426	aughost_fair_resnet18
427	vit_base_patch14_reg4_dinov2-lvd142m
428	yudixie_resnet18_category_class_0_240719
429	yudixie_resnet18_random_0_240719
430	yudixie_resnet18_cat_obj_class_all_latents_0_240719
431	resnet-18_test_m
432	SeLaVi-AVE
433	yudixie_resnet18_distance_translation_rotation_0_240719
434	ReAlnet01_cornet_fov12
435	yudixie_resnet18_object_class_0_240719
436	compact_resnet50_robust_V4
437	compact_vgg19_IT
438	r3d_18
439	ReAlnet06_cornet
440	yudixie_resnet50_translation_reg_0_240908
441	omnivore_swinS
442	mc3_18
443	yudixie_resnet50_translation_rotation_0_240908
444	ReAlnet07_cornet
445	s3d
446	ReAlnet02_ALL
447	mvit_v1_b
448	ReAlnet04_ALL
449	ReAlnet01_cornet_fov16
450	compact_cornets_V4
451	ReAlnet08_cornet
452	ReAlnet03_ALL
453	omnivore_swinB
454	compact_cornets_IT
455	SeLaVi-Kinetics400
456	ReAlnet05_ALL
457	ReAlnet09_cornet
458	SeLaVi-VGG-Sound
459	ReAlnet06_ALL
460	SeLaVi-Kinetics-Sound
461	compact_resnet50_robust_IT
462	r2plus1d_18
463	mvit_v2_s
464	ReAlnet10_cornet
465	resnet50_ecoset_full
466	ReAlnet08_ALL
467	yudixie_resnet18_distance_rotation_0_240719
468	ReAlnet09_ALL
469	ReAlnet04_cornet
470	ReAlnet05_cornet
471	resnet50_primary_visual_cortex
472	ReAlnet10_ALL
473	ReAlnet01_ALL
474	resnet18_fair
475	pr1_stl10_disentangled_alpha04
476	deit3_base_patch16_224_fb_in22k_ft_in1k
477	yudixie_wbh_object_class_scaling_0415_run_0
478	yudixie_wbh_object_class_scaling_0415_run_3
479	yudixie_wbh_object_class_scaling_0415_imagenet
480	yudixie_wbh_object_class_scaling_0415_run_2
481	yudixie_wbh_object_class_scaling_0415_run_1
482	deepeisnn_ei_cifar10_vgg19
483	deepeisnn_ei_cifar10_resnet18
484	deepeisnn_ei_imagenet_resnet18
485	dinov2_vitl14_reg_lc_featread

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