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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021edge-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
2	vit_large_clip336_openai	1.0
3	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
4	eva02_large_in22k	1.0
5	convnext_large_clip_laion2b_finelayers	1.0
6	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
7	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.995
9	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.979
10	vit_huge_clip336_laion	.945
11	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.899
12	dinov2_vitl14_reg_lc	.898
13	CLIP_ViT-B_32	.854
14	dinov2_vitg14_reg_lc	.815
15	vit_large_patch14_clip_224:openai_ft_in1k	.807
16	vit_base_patch16_clip_224:openai_ft_in1k	.753
17	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.744
18	SWSL_resnext101_32x8d	.728
19	resnext101_32x16d_wsl	.719
20	dinov2_vitl14_lc_res336	.705
21	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.699
22	dinov2_vitl14_lc	.670
23	convnext_xlarge:fb_in22k_ft_in1k	.670
24	resnext101_32x32d_wsl	.645
25	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.635
26	dinov2_vitg14_lc	.625
27	resnext101_32x48d_wsl	.619
28	resnext101_32x8d_wsl	.618
29	CLIP-RN50	.608
30	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.543
31	vision_transformer_vit_large_patch16_224	.543
32	convnext_large:fb_in22k_ft_in1k	.531
33	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.519
34	CLIP_resnet50	.516
35	CLIP_resnet50_float32	.516
36	fixres_resnext101_32x48d_wsl	.502
37	convnext_xxlarge_clip	.468
38	convnext_large_in22k_supervised	.466
39	convnext_base_fb_in22k_ft_in1k	.447
40	SWSL_resnet50	.395
41	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.363
42	convnext_large_imagenet_full_seed-0	.285
43	resnet50-VITO-8deg-cc	.264
44	convnext_tiny_imagenet_full_seed-0	.264
45	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.252
46	cvt_cvt-21-384-in1k_4_LucyV4	.243
47	convnext_tiny:in12k_ft_in1k	.232
48	convnext_base_imagenet_full_seed-0	.219
49	AlexNet_SIN_fov4	.200
50	convnext_small_imagenet_full_seed-0	.198
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.195
52	xception-keras	.194
53	vit_relpos_base_patch16_clsgap_224:sw_in1k	.192
54	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.187
55	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.184
56	effnetv2m_custom384	.183
57	inception_v4	.182
58	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.182
59	inception_v3_pytorch	.175
60	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.173
61	cvt_cvt-21-224-in1k_4_LucyV4	.168
62	AT_efficientnet-b2	.163
63	AdvProp_efficientnet-b2	.163
64	cvt_cvt-13-384-in1k_4_LucyV4	.160
65	convnext_femto_ols:d1_in1k	.158
66	pnasnet_large_halfPrecision	.153
67	pnasnet_large	.153
68	cvt_cvt-13-224-in1k_4_LucyV4	.151
69	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.151
70	resnet_152_v2	.151
71	resnet_50_v2	.151
72	resnet50_st_wzc_seed6_2_323	.150
73	inception_v4-tf	.148
74	resnet50_ewconplus_wzc_seed2_2__323	.147
75	focalnet_tiny_lrf_in1k	.144
76	resnet-152_v2_pytorch	.144
77	inception_v3-tf	.140
78	resnet50_ewconplus_wzc	.139
79	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.139
80	AdvProp_efficientnet-b6	.139
81	efficientnet-b7	.138
82	resnet50_byol	.133
83	pnasnet_large_pytorch	.132
84	resnet50_st_wzc_seed3_2_323	.132
85	convnext_base_fb_in1k	.132
86	antialias-resnet152	.132
87	cv_18_dagger_408_pretrained	.131
88	blt_vs	.128
89	regnety_032	.125
90	alexnet_fov4	.124
91	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.124
92	inception_v3	.124
93	resnet50_st_wzc_2	.124
94	resnet50_ewconplus_wzc_seed5_2__323	.123
95	custom_model_cv_18_dagger_408	.122
96	resnet101	.116
97	resnet-18	.116
98	resnet50_st_wzc_seed7_2_323	.116
99	nasnet_large	.115
100	nasnet_large-tf	.115
101	resnet50-SIN_IN	.115
102	resnet50_random_linf8_perturb	.114
103	convnext_small_imagenet_100_seed-0	.113
104	densenet-201-keras	.113
105	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.113
106	deit_large_imagenet_full_seed-0	.109
107	resnet_101_v2	.109
108	AdvProp_efficientnet-b4	.107
109	squeezenet1_1	.107
110	resnet-152_v2-tf	.107
111	AT_efficientnet-b4	.107
112	vgg_16	.107
113	resnet_101_v1	.107
114	resnet50_imagenet_100_seed-0	.106
115	efficientnet_b0	.106
116	pnasnet_large-tf	.106
117	mobilenet_v2_1_0_160	.106
118	densenet-201	.105
119	densenet_201_pytorch	.105
120	seresnext50_32x4d	.105
121	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.105
122	densenet-121	.104
123	densenet-121-keras	.104
124	mobilenet_v2_1_0_128	.101
125	convnext_tiny_sup	.100
126	resnet50-vitoimagevidnet8	.099
127	inception_v1	.099
128	resnet50_hvs_wzc	.099
129	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.099
130	resnet-50_v2-tf	.098
131	mobilenet_v1_1.0_224	.098
132	tv_efficientnet-b1	.098
133	densenet-169	.098
134	resnet50_random_l2_perturb	.098
135	ReAlnet02	.093
136	AdvProp_efficientnet-b7	.092
137	texture_shape_resnet50_trained_on_SIN	.092
138	resnet-101_v1-tf	.092
139	resnet-34	.092
140	cornet_s	.092
141	alexnet_l2_3_robust	.092
142	alexnet_linf_8_robust	.092
143	ReAlnet10	.092
144	mobilenet_v2_0_5_192	.092
145	nasnet_mobile	.092
146	efficientnet_b0_imagenet_full	.091
147	efficientnet_b2_imagenet_full	.091
148	ReAlnet08	.091
149	resnet18_imagenet_full	.091
150	mobilenet_v2_0_75_160	.091
151	ReAlnet01	.091
152	CORnet-S	.091
153	inception_v1-tf	.091
154	densenet-169-keras	.090
155	resnet50-moclr8deg	.090
156	inception_v2-tf	.085
157	konkle_alexnetgn_ipcl_ref01_primary_model	.085
158	ReAlnet07	.084
159	resnet50_imagenet_full	.084
160	mobilenet_v2_1_0_224	.084
161	vonegrcnn_52e_full	.084
162	BiT-S-R152x2	.084
163	voneresnet-50-non_stochastic	.084
164	mobilenet_v2_0.5_192-tf	.084
165	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
166	mobilenet_v2_0.35_160	.084
167	alexnet_robust_correct	.084
168	vgg_19	.084
169	antialiased-rnext101_32x8d	.084
170	ReAlnet05	.084
171	AlexNet_SIN_fov12	.084
172	resnet50_eMMCR_Vanilla	.084
173	ViT_B_16_imagenet1k	.084
174	ReAlnet06	.083
175	ReAlnet04	.083
176	ReAlnet03	.083
177	mobilenet_v1_0.25_160	.079
178	0.5x_resnet-18_LC_w_sh_1_iter	.078
179	antialiased-r50	.078
180	vonegrcnn_62e_nobn	.077
181	mobilenet_v2_0_75_224	.077
182	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.077
183	resnet50_ewconplus_wzc_seed6_2_323	.077
184	mobilenet_v1_1.0_192	.077
185	mobilenet_v2_0.35_192	.077
186	mobilenet_v1_0.5_128	.077
187	resnet50	.077
188	vonealexnet_gaussian_noise_std4_fixed	.077
189	vonegrcnn_47e	.076
190	deit_base_imagenet_full_seed-0	.076
191	xception	.076
192	mobilenet_v2_1.4_224-tf	.076
193	BiT-S-R50x1	.076
194	BiT-S-R152x4	.076
195	mobilenet_v2_0.5_128	.076
196	alexnet_early_checkpoint	.076
197	alexnet_reduced_aliasing_early_checkpoint	.076
198	Res2Net50_26w_4s	.076
199	mobilenet_v1_1.0_160	.075
200	ReAlnet09	.075
201	mobilenet_v2_1_3_224	.075
202	grcnn	.075
203	mobilenet_v1_0.75_128	.074
204	resnet101_imagenet_full	.074
205	mobilenet_v2_0.75_96	.074
206	effnetb1_272x240	.074
207	convnext_base_fb_in1k_gram_aligned_dinov3	.072
208	resnet34_imagenet_full	.070
209	mobilenet_v2_0_5_224	.070
210	vonegrcnn_52e_full	.070
211	mobilenet_v2_1.0_128-tf	.070
212	resnet50-SIN_IN_IN	.069
213	resnet_SIN_IN_FT_IN	.069
214	mobilenet_v2_1.0_224-tf	.069
215	AdvProp_efficientnet-b8	.069
216	alexnet	.069
217	shufflenet_v2_x1_0	.069
218	alexnet_ks_torevert	.069
219	resnet-50-pytorch	.068
220	BiT-S-R50x3	.068
221	resnet-152_v1-tf	.068
222	resnet50-sup	.068
223	mobilenet_v1_0.5_192	.068
224	ViT_L_32_imagenet1k	.068
225	resnet50_tutorial	.068
226	grcnn_109	.068
227	resnet_50_v1	.068
228	BiT-S-R101x3	.066
229	efficientnet_b1_imagenet_full	.064
230	squeezenet1_0	.063
231	mobilenet_v2_0.75_224-tf	.063
232	mobilenet_v1_0.25_224	.063
233	resnet50_moco_v2	.063
234	voneresnet-50	.062
235	vonegrcnn_62e_nobn	.062
236	resnet50-SIN	.062
237	ViT_B_32_imagenet1k	.062
238	vgg-16-keras	.062
239	mobilenet_v2_0.35_128	.062
240	regnet_y_400mf	.062
241	mobilenet_v2_1_0_192	.062
242	vonegrcnn_47e	.062
243	mobilenet_v1_0.5_224	.062
244	mobilenet_v1_0.75_160	.062
245	resnet152_imagenet_full	.062
246	mobilenet_v2_1-4_224_pytorch	.061
247	deit_small_imagenet_full_seed-0	.061
248	mobilenet_v2_1_4_224	.061
249	resnet_152_v1	.060
250	resnet50_imagenet_1_seed-0	.059
251	resnet-50_untrained	.059
252	alexnet_random_l2_3_perturb	.056
253	mobilenet_v2_0_75_192	.056
254	voneresnet-50-robust	.055
255	mobilenet_v2_0.75_192-tf	.055
256	texture_shape_alexnet_trained_on_SIN	.055
257	mobilenet_v2_0.5_224-tf	.055
258	mobilenet_v2_1.0_160-tf	.055
259	nasnet_mobile-tf	.055
260	BiT-S-R101x1	.055
261	mobilenet_v2_1.3_224-tf	.055
262	resnet-50-robust	.055
263	vgg-19-keras	.055
264	mobilenet_v2_1.0_96	.055
265	mobilenet_v1_0.75_192	.055
266	mobilenet_v1_1.0_128	.055
267	mobilenet_v2_0.75_128	.055
268	mobilenet_v2_0.35_96	.055
269	AdvProp_efficientnet-b0	.055
270	mobilenet_v2_0.5_96	.055
271	resnet50_l2_3_robust	.055
272	resnet50_linf_4_robust	.055
273	resnet50_linf_8_robust	.055
274	resnet50_FractalDB	.055
275	imagenet_l2_3_0	.055
276	imagenet_l2_10_0	.055
277	resnet50_robust_l2_eps3	.055
278	resnet50_robust_l2_eps1	.055
279	eicircuit_resnet18_bn_20260919	.055
280	AlexNet_SIN	.055
281	alexnet_fov12	.055
282	alexnet_fov16	.055
283	AlexNet_SIN_fov16	.055
284	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.055
285	mobilenet_v2_0.75_160-tf	.053
286	mobilenet_v1_0.25_192	.053
287	mobilenet_v2_0.35_224	.053
288	mobilenet_v2_0.5_160	.053
289	eBarlow_lmda_02_1000ep	.053
290	mobilevit_small	.050
291	resnet50_simclr	.046
292	mobilenet_v1_0.75_224	.046
293	vonealexnet	.044
294	resnet50-barlow	.042
295	convnext_small_imagenet_1_seed-0	.042
296	resnet-50_v1-tf	.039
297	resnet_50_v1_spiking_l4a	.039
298	alexnet_random_linf8_perturb	.038
299	resnet50-vicregl0p9	.037
300	alexnet_training_seed_03	.036
301	resnet_50_v1_spiking	.035
302	mobilenet_v2_1.0_192-tf	.034
303	alexnet_training_seed_09	.034
304	mobilenet_v1_0.25_128	.032
305	alexnet_training_seed_02	.032
306	alexnet_training_seed_08	.032
307	alexnet_training_seed_04	.032
308	alexnet_training_seed_10	.032
309	alexnet_training_seed_07	.032
310	mobilenet_v1_0.5_160	.032
311	alexnet_training_seed_05	.024
312	alexnet_training_seed_01	.024
313	alexnet_training_seed_01_fov4	.024
314	alexnet_training_seed_01_fov12	.024
315	alexnet_training_seed_01_fov16	.024
316	barlow-twins-resnet50	.024
317	resnet50-vicreg	.024
318	resnet_50_v1_spiking_l4	.023
319	deit_small_imagenet_10_seed-0	.021
320	resnet50_imagenet_10_seed-0	.021
321	convnext_small_imagenet_10_seed-0	.021
322	deit_small_imagenet_1_seed-0	.021
323	artResNet18_1	.021
324	resnet_50_v1_spiking_l3	.015
325	deit_small_imagenet_100_seed-0	.014
326	resnet50-simclr-vissl	.011
327	resnet50-vicregl0p75	.010
328	alexnet_training_seed_06	.007
329	resnet-18-LC_w_sh_100_iter_conv_init_m
330	ViT-B/32
331	BiT-M-R50x3
332	BiT-M-R50x1
333	BiT-M-R152x2
334	resnet-18-LC_w_sh_1_iter_conv_init_m
335	resnet-18-LC_w_sh_1_iter_m
336	resnet-18-LC_w_sh_10_iter_m
337	resnet-18-LC_w_sh_100_iter_m
338	resnet-18-LC_w_sh_10_iter_conv_init_m
339	FrankRobWobv0
340	resnet18-local_aggregation
341	0.5x_resnet-18_LC
342	CORnetZ_CIFAR10
343	resnet18-deepcluster
344	r3m_resnet18
345	CORnet-Z
346	resnet18-instance_recognition
347	my-model
348	vggface
349	resnet18-colorization
350	0.5x_resnet-18_LC_w_sh_10_iter
351	r3m_resnet50
352	r3m_resnet34
353	resnet-18-LC_m
354	r3m_resnet50_nocrop
355	resnet18-supervised
356	resnet-18-LC_1st_conv_m
357	resnet-18-LC_conv_init_m
358	resnet18-contrastive_multiview
359	0.5x_resnet-18
360	RN50
361	0.5x_resnet-18_LC_w_sh_100_iter
362	RN50
363	resnet18-relative_position
364	resnet18-autoencoder
365	resnet18-simclr
366	ViT_L_32
367	ViT_B_32
368	resnet18-depth_prediction
369	ViT_B_16
370	r3m_resnet34_nocrop
371	resnet18-contrastive_predictive
372	omnivore_swinT
373	resnet-18-LC_untrained
374	resnet-18_untrained
375	resnet-50x2_untrained
376	dcgan
377	v1-pyr-nodown
378	unet_entire
379	CORnetZ_CIFAR10_bs32_20_04
380	pixels
381	dorinet_cornet_z
382	my-model
383	openclip
384	resnet50-cifar
385	resnet50_pretrained_with_retinal_waves
386	resnet50_ImageNet
387	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
388	yudixie_resnet50_random_0_240908
389	yudixie_resnet50_imagenet1kpret_0_240908
390	yudixie_resnet50_object_class_0_240908
391	yudixie_resnet50_category_class_0_240908
392	resnet18_imagenet21kP
393	yudixie_resnet50_distance_reg_0_240908
394	ConvLSTM
395	PredRNN
396	SimVP
397	TAU
398	MIM
399	vit_base_patch16_dinov3-lvd1689m
400	ReAlnet01_cornet
401	briaai_rmbg_1_4
402	yudixie_resnet18_translation_reg_0_240719
403	yudixie_resnet18_distance_reg_0_240719
404	yudixie_resnet18_rotation_reg_0_240719
405	yolos_tiny
406	yudixie_resnet18_imagenet1kpret_0_240719
407	ReAlnet02_cornet
408	compact_resnet50_V4
409	vit_base_patch14_dinov2-lvd142m
410	vit_base_patch14_reg4_dinov2-lvd142m
411	ReAlnet03_cornet
412	compact_resnet50_IT
413	ReAlnet01_cornet_fov4
414	resnet101_ecoset_full
415	ReAlnet01_cornet_fov12
416	compact_resnet50_robust_V4
417	yudixie_resnet50_rotation_reg_0_240908
418	resnet152_ecoset_full
419	yudixie_resnet50_distance_rotation_0_240908
420	yudixie_resnet50_distance_translation_0_240908
421	yudixie_resnet18_translation_rotation_0_240719
422	yudixie_resnet18_distance_rotation_0_240719
423	compact_vgg19_V4
424	yudixie_resnet18_category_class_0_240719
425	ReAlnet05_cornet
426	resnet-18_test_m
427	SeLaVi-AVE
428	yudixie_resnet18_distance_translation_rotation_0_240719
429	yudixie_resnet18_object_class_0_240719
430	hmax
431	ReAlnet06_cornet
432	r3d_18
433	compact_vgg19_IT
434	ReAlnet01_cornet_fov16
435	yudixie_resnet50_translation_reg_0_240908
436	mc3_18
437	ReAlnet01_ALL
438	yudixie_resnet50_translation_rotation_0_240908
439	s3d
440	ReAlnet07_cornet
441	mvit_v1_b
442	ReAlnet02_ALL
443	ReAlnet08_cornet
444	ReAlnet03_ALL
445	compact_cornets_V4
446	VOneCORnet-S
447	ReAlnet04_ALL
448	omnivore_swinB
449	ReAlnet09_cornet
450	SeLaVi-VGG-Sound
451	compact_cornets_IT
452	ReAlnet05_ALL
453	SeLaVi-Kinetics-Sound
454	SeLaVi-Kinetics400
455	r2plus1d_18
456	ReAlnet06_ALL
457	omnivore_swinS
458	mvit_v2_s
459	compact_resnet50_robust_IT
460	ReAlnet10_cornet
461	resnet18_ecoset_full
462	resnet34_ecoset_full
463	ReAlnet07_ALL
464	resnet50_ecoset_full
465	yudixie_resnet18_cat_obj_class_all_latents_0_240719
466	yudixie_resnet18_random_0_240719
467	ReAlnet08_ALL
468	yudixie_resnet18_distance_translation_0_240719
469	yudixie_resnet50_imagenet1kpret_0_240312
470	ReAlnet04_cornet
471	yudixie_resnet50_distance_translation_rotation_0_240908
472	yudixie_resnet50_cat_obj_class_all_latents_0_240908
473	resnet50_primary_visual_cortex
474	ReAlnet09_ALL
475	ReAlnet10_ALL
476	resnet18_fair
477	pr1_stl10_disentangled_alpha04
478	predictive_coding_pc
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	persistence_mem
489	persistence_memory
490	gabor_filter_entropy_4x4
491	gabor_filter_entropy_4x4_v2
492	gabor_filter_mean_abs_4x4_v2
493	gabor_filter_entropy_4x4_v2_forcedCosine

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

0.32.

Note that scores are relative to this ceiling.

Data: Geirhos2021edge

Metric: error_consistency