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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	vit_large_patch14_clip_336:laion2b_ft_in1k	.722
10	fixres_resnext101_32x48d_wsl	.716
11	convnext_base:clip_laiona_augreg_ft_in1k_384	.709
12	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.652
13	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.592
14	convnext_base_fb_in22k_ft_in1k	.584
15	CLIP_resnet50	.550
16	CLIP_resnet50_float32	.550
17	vit_base_patch16_clip_224:openai_ft_in1k	.506
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.493
19	convnext_tiny:in12k_ft_in1k	.457
20	convnext_tiny_imagenet_full_seed-0	.442
21	CLIP-RN50	.422
22	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.419
23	effnetv2m_custom384	.410
24	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.404
25	SWSL_resnext101_32x8d	.380
26	resnext101_32x48d_wsl	.367
27	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.364
28	convnext_large_imagenet_full_seed-0	.355
29	convnext_small_imagenet_full_seed-0	.330
30	convnext_base_fb_in1k	.327
31	cvt_cvt-13-384-in1k_4_LucyV4	.305
32	resnet_152_v2	.303
33	resnext101_32x16d_wsl	.294
34	cv_18_dagger_408_pretrained	.293
35	resnext101_32x32d_wsl	.291
36	convnext_base_imagenet_full_seed-0	.287
37	resnet50-VITO-8deg-cc	.285
38	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.262
39	vision_transformer_vit_large_patch16_224	.248
40	convnext_tiny_sup	.226
41	focalnet_tiny_lrf_in1k	.220
42	resnet-152_v2_pytorch	.202
43	cvt_cvt-21-384-in1k_4_LucyV4	.200
44	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.196
45	cvt_cvt-21-224-in1k_4_LucyV4	.195
46	vit_relpos_base_patch16_clsgap_224:sw_in1k	.189
47	pnasnet_large_halfPrecision	.177
48	pnasnet_large	.177
49	resnext101_32x8d_wsl	.176
50	convnext_base_fb_in1k_gram_aligned_dinov3	.175
51	convnext_femto_ols:d1_in1k	.175
52	AlexNet_SIN_fov4	.169
53	resnet50_st_wzc_noseed_2_323	.155
54	SWSL_resnet50	.154
55	cvt_cvt-13-224-in1k_4_LucyV4	.151
56	pnasnet_large_pytorch	.150
57	nasnet_large	.149
58	inception_v3	.143
59	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.142
60	CLIP_ViT-B_32	.139
61	inception_v4	.139
62	pnasnet_large-tf	.139
63	inception_v3_pytorch	.139
64	resnet50_ewconplus_wzc_seed7_2_323	.138
65	inception_v3-tf	.135
66	densenet-169	.132
67	convnext_small_imagenet_100_seed-0	.131
68	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.127
69	resnet_101_v2	.123
70	resnet50_st_wzc_seed6_2_323	.121
71	resnet-152_v2-tf	.120
72	xception	.118
73	tv_efficientnet-b1	.116
74	densenet-169-keras	.116
75	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.116
76	resnet50_ewconplus_wzc_seed6_2_323	.115
77	ViT_B_16_imagenet1k	.111
78	xception-keras	.110
79	resnet50_ewconplus_noseed_2_323	.104
80	resnet50_ewconplus_wzc_seed3_2_323	.103
81	resnet50_st_wzc_seed4_2_323	.102
82	resnet50_ewconplus_wzc_seed4_2_323	.101
83	resnet34_imagenet_full	.101
84	resnet_50_v2	.100
85	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.100
86	BiT-S-R50x3	.100
87	alexnet_training_seed_05	.096
88	alexnet_training_seed_01	.096
89	alexnet_training_seed_01_fov4	.096
90	alexnet_training_seed_01_fov12	.096
91	alexnet_training_seed_01_fov16	.096
92	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.095
93	deit_small_imagenet_1_seed-0	.095
94	deit_small_imagenet_10_seed-0	.095
95	resnet50_imagenet_10_seed-0	.095
96	imagenet_l2_10_0	.094
97	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.093
98	BiT-S-R152x2	.093
99	resnet50_FractalDB	.092
100	resnet50_ewconplus_wzc_seed5_2__323	.092
101	resnet50-vicregl0p9	.091
102	barlow-twins-resnet50	.091
103	alexnet_linf_8_robust	.090
104	alexnet_l2_3_robust	.090
105	resnet50_linf_8_robust	.089
106	inception_v4-tf	.089
107	resnet50_ewconplus_wzc_seed2_2__323	.088
108	alexnet_training_seed_07	.088
109	effnetb1_272x240	.088
110	alexnet_robust_correct	.086
111	custom_model_cv_18_dagger_408	.085
112	AdvProp_efficientnet-b4	.083
113	AT_efficientnet-b4	.083
114	mobilenet_v2_0_5_224	.082
115	blt_vs	.081
116	vonealexnet_gaussian_noise_std4_fixed	.081
117	resnet50_st_wzc_seed5_2_323	.080
118	mobilenet_v1_0.5_192	.080
119	resnet50_linf_4_robust	.079
120	densenet-201-keras	.078
121	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.078
122	resnet50_random_l2_perturb	.077
123	resnet50_st_wzc_2_323	.076
124	mobilenet_v1_0.25_128	.075
125	resnet50-meshes-lt-100-original-scratch	.075
126	alexnet_training_seed_02	.075
127	alexnet_training_seed_04	.075
128	alexnet_training_seed_08	.075
129	alexnet_training_seed_10	.075
130	artResNet18_1	.075
131	resnet101	.075
132	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.075
133	resnet_50_v1_spiking_l4a	.074
134	alexnet_fov12	.074
135	BiT-S-R101x3	.074
136	convnext_small_imagenet_10_seed-0	.073
137	resnet50_byol	.073
138	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
139	texture_shape_alexnet_trained_on_SIN	.073
140	alexnet_training_seed_03	.072
141	mobilenet_v1_0.75_160	.072
142	vgg-19-keras	.072
143	nasnet_large-tf	.071
144	resnet50-vicreg	.071
145	alexnet_fov16	.067
146	alexnet_fov4	.067
147	BiT-S-R152x4	.066
148	voneresnet-50-robust	.066
149	resnet50_ewconplus_wzc_2_323	.066
150	vgg-16-keras	.066
151	mobilenet_v1_0.25_192	.065
152	antialias-resnet152	.064
153	mobilenet_v1_1.0_224	.064
154	AdvProp_efficientnet-b8	.064
155	resnet50-moclr8deg	.063
156	mobilenet_v2_0_5_192	.062
157	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.061
158	resnet50-SIN_IN_IN	.060
159	resnet_SIN_IN_FT_IN	.060
160	resnet50_robust_l2_eps1	.060
161	resnet_101_v1	.059
162	mobilenet_v1_0.75_128	.059
163	alexnet_reduced_aliasing_early_checkpoint	.059
164	konkle_alexnetgn_ipcl_ref01_primary_model	.059
165	AdvProp_efficientnet-b0	.056
166	resnet50_l2_3_robust	.056
167	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.056
168	AlexNet_SIN	.055
169	resnet-50-robust	.055
170	imagenet_l2_3_0	.055
171	resnet50_robust_l2_eps3	.055
172	mobilenet_v2_1.0_160-tf	.055
173	mobilenet_v1_0.25_160	.055
174	densenet-201	.054
175	densenet_201_pytorch	.054
176	efficientnet_b0	.054
177	mobilenet_v2_1_0_128	.054
178	vonegrcnn_62e_nobn	.053
179	alexnet_training_seed_06	.053
180	AlexNet_SIN_fov16	.053
181	AdvProp_efficientnet-b7	.053
182	mobilenet_v1_0.5_224	.053
183	AlexNet_SIN_fov12	.053
184	vonegrcnn_52e_full	.052
185	vonegrcnn_47e	.052
186	squeezenet1_0	.051
187	convnext_small_imagenet_1_seed-0	.051
188	CORnet-S	.051
189	mobilenet_v1_1.0_128	.051
190	deit_small_imagenet_100_seed-0	.050
191	mobilenet_v2_1_0_224	.050
192	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.050
193	ReAlnet07	.050
194	mobilenet_v2_1.0_224-tf	.050
195	mobilevit_small	.049
196	resnet50-SIN	.049
197	resnet50_eMMCR_Vanilla	.048
198	ReAlnet06	.048
199	resnet152_imagenet_full	.048
200	resnet50_st_wzc_2	.048
201	ReAlnet08	.047
202	cornet_s	.046
203	ReAlnet10	.046
204	resnet-18	.046
205	resnet50_imagenet_full	.045
206	alexnet_ks_torevert	.045
207	alexnet	.045
208	mobilenet_v2_0_75_192	.045
209	resnet50-simclr-vissl	.044
210	resnet50_st_wzc_seed7_2_323	.044
211	resnet50-meshes-lt-100-original-pretrained	.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	mobilenet_v2_0.75_128	.005
313	resnet-101_v1-tf	.004
314	ReAlnet04	.004
315	alexnet_random_l2_3_perturb	.004
316	shufflenet_v2_x1_0	.004
317	vgg_19	.003
318	densenet-121	.003
319	resnet50-vitoimagevidnet8	.003
320	efficientnet_b2_imagenet_full	.002
321	ViT_L_32_imagenet1k	.002
322	efficientnet_b1_imagenet_full	.001
323	voneresnet-50-non_stochastic	.000
324	resnet-18-LC_w_sh_100_iter_conv_init_m
325	ViT-B/32
326	BiT-M-R50x3
327	BiT-M-R50x1
328	BiT-M-R152x2
329	resnet-18-LC_w_sh_1_iter_conv_init_m
330	resnet-18-LC_w_sh_10_iter_m
331	resnet-18-LC_w_sh_100_iter_m
332	resnet-18-LC_w_sh_10_iter_conv_init_m
333	resnet-18-LC_w_sh_1_iter_m
334	FrankRobWobv0
335	0.5x_resnet-18_LC
336	CORnetZ_CIFAR10
337	resnet18-local_aggregation
338	resnet18-deepcluster
339	r3m_resnet18
340	CORnet-Z
341	resnet18-instance_recognition
342	vggface
343	my-model
344	0.5x_resnet-18_LC_w_sh_10_iter
345	r3m_resnet50
346	resnet18-colorization
347	r3m_resnet34
348	resnet-18-LC_m
349	r3m_resnet50_nocrop
350	resnet18-supervised
351	resnet-18-LC_1st_conv_m
352	resnet-18-LC_conv_init_m
353	resnet18-contrastive_multiview
354	0.5x_resnet-18
355	RN50
356	0.5x_resnet-18_LC_w_sh_100_iter
357	RN50
358	resnet18-relative_position
359	resnet18-autoencoder
360	resnet18-simclr
361	ViT_L_32
362	ViT_B_32
363	resnet18-depth_prediction
364	ViT_B_16
365	r3m_resnet34_nocrop
366	resnet18-contrastive_predictive
367	omnivore_swinT
368	resnet-18-LC_untrained
369	resnet-18_untrained
370	resnet-50x2_untrained
371	dcgan
372	v1-pyr-nodown
373	unet_entire
374	CORnetZ_CIFAR10_bs32_20_04
375	pixels
376	dorinet_cornet_z
377	my-model
378	resnet50
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

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