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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonI-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vision_transformer_vit_large_patch16_224	.789
2	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.783
3	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.777
4	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.776
5	resnext101_32x16d_wsl	.731
6	vit_base_patch16_clip_224:openai_ft_in1k	.724
7	SWSL_resnext101_32x8d	.715
8	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.698
9	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.689
10	ViT_B_16_imagenet1k	.677
11	resnext101_32x48d_wsl	.667
12	resnet_101_v2	.663
13	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.661
14	resnext101_32x8d_wsl	.655
15	CLIP_ViT-B_32	.651
16	SWSL_resnet50	.647
17	AdvProp_efficientnet-b7	.646
18	resnet_152_v2	.638
19	eva02_large_in22k	.637
20	ViT_L_32_imagenet1k	.637
21	resnext101_32x32d_wsl	.637
22	vit_large_patch14_clip_224:laion2b_ft_in1k	.636
23	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.629
24	resnet101	.629
25	effnetv2m_custom384	.621
26	AdvProp_efficientnet-b4	.619
27	AT_efficientnet-b4	.619
28	vit_large_patch14_clip_224:openai_ft_in1k	.619
29	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.615
30	effnetb1_272x240	.614
31	vit_relpos_base_patch16_clsgap_224:sw_in1k	.610
32	resnet152_imagenet_full	.607
33	convnext_base_fb_in22k_ft_in1k	.605
34	vit_large_clip336_openai	.605
35	deit3_base_patch16_224_fb_in1k	.604
36	ijepa_vith14_linhead	.603
37	focalnet_tiny_lrf_in1k	.603
38	regnety_032	.601
39	convnext_large_in22k_supervised	.599
40	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.593
41	pnasnet_large	.593
42	pnasnet_large_halfPrecision	.592
43	convnext_large_clip_laion2b_finelayers	.586
44	pnasnet_large-tf	.584
45	efficientnet_b0	.583
46	fixres_resnext101_32x48d_wsl	.582
47	dinov2_vitg14_lc	.578
48	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.577
49	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.575
50	vit_large_patch14_clip_336:laion2b_ft_in1k	.574
51	convnext_xlarge:fb_in22k_ft_in1k	.574
52	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.567
53	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.556
54	AdvProp_efficientnet-b6	.553
55	cvt_cvt-13-384-in1k_4_LucyV4	.551
56	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.549
57	dinov2_vitl14_lc	.548
58	resnet_101_v1	.546
59	AT_efficientnet-b2	.541
60	AdvProp_efficientnet-b2	.541
61	cvt_cvt-13-224-in1k_4_LucyV4	.537
62	dinov2_vitl14_reg_lc	.536
63	resnet50_st_wzc_seed6_2_323	.533
64	resnet50_st_wzc_seed7_2_323	.532
65	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
66	vonegrcnn_62e_nobn	.527
67	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.527
68	antialias-resnet152	.524
69	ViT_B_32_imagenet1k	.522
70	resnet-152_v2-tf	.520
71	inception_v4	.519
72	vit_huge_clip336_laion	.519
73	CLIP_resnet50	.513
74	CLIP_resnet50_float32	.513
75	custom_model_cv_18_dagger_408	.512
76	nasnet_large	.505
77	nasnet_large-tf	.499
78	convnext_tiny_imagenet_full_seed-0	.498
79	vonegrcnn_47e	.497
80	convnext_large:fb_in22k_ft_in1k	.492
81	resnet_50_v2	.491
82	vonegrcnn_52e_full	.487
83	resnet50_ewconplus_wzc_seed5_2__323	.484
84	convnext_femto_ols:d1_in1k	.482
85	deit3_base_patch16_224_fb_in22k_ft_in1k	.482
86	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.481
87	vonegrcnn_62e_nobn	.476
88	resnet50_ewconplus_wzc	.476
89	convnext_xxlarge_clip	.475
90	convnext_base:clip_laiona_augreg_ft_in1k_384	.472
91	convnext_tiny:in12k_ft_in1k	.471
92	dinov2_vitg14_reg_lc	.471
93	resnet_152_v1	.470
94	cvt_cvt-21-384-in1k_4_LucyV4	.464
95	resnet50_linf_4_robust	.459
96	resnet50_st_wzc_seed3_2_323	.459
97	resnet-152_v2_pytorch	.459
98	resnet-152_v1-tf	.459
99	vonegrcnn_47e	.456
100	efficientnet-b7	.453
101	densenet-201-keras	.448
102	densenet_201_pytorch	.446
103	resnet50_ewconplus_wzc_seed2_2__323	.446
104	inception_v3_pytorch	.445
105	inception_v4-tf	.443
106	resnet50_imagenet_full	.443
107	densenet-201	.443
108	resnet34_imagenet_full	.442
109	dinov2_vitl14_lc_res336	.436
110	antialiased-rnext101_32x8d	.436
111	vonegrcnn_52e_full	.435
112	resnet50-SIN_IN_IN	.432
113	resnet_SIN_IN_FT_IN	.432
114	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.431
115	resnet50-VITO-8deg-cc	.430
116	seresnext50_32x4d	.429
117	cvt_cvt-21-224-in1k_4_LucyV4	.429
118	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.424
119	convnext_base_fb_in1k	.424
120	resnet50_st_wzc_2	.423
121	resnet50_ewconplus_wzc_seed6_2_323	.420
122	voneresnet-50-non_stochastic	.417
123	convnext_small_imagenet_100_seed-0	.417
124	deit_large_imagenet_full_seed-0	.416
125	resnet_50_v1_spiking	.413
126	resnet101_imagenet_full	.412
127	CLIP-RN50	.409
128	BiT-S-R50x3	.408
129	convnext_base_imagenet_full_seed-0	.406
130	voneresnet-50-robust	.403
131	xception-keras	.401
132	resnet-50-pytorch	.400
133	resnet50-sup	.400
134	resnet50_tutorial	.400
135	resnet_50_v1	.400
136	resnet50-moclr8deg	.394
137	resnet50_random_linf8_perturb	.393
138	ReAlnet03	.384
139	efficientnet_b2_imagenet_full	.383
140	resnet-50_v2-tf	.378
141	inception_v3-tf	.377
142	convnext_base_fb_in1k_gram_aligned_dinov3	.375
143	resnet50_random_l2_perturb	.371
144	convnext_small_imagenet_full_seed-0	.367
145	resnet-101_v1-tf	.363
146	resnet-50_v1-tf	.359
147	resnet50_hvs_wzc	.358
148	ReAlnet02	.355
149	resnet50-SIN_IN	.355
150	convnext_large_imagenet_full_seed-0	.354
151	alexnet_fov4	.352
152	Res2Net50_26w_4s	.350
153	AdvProp_efficientnet-b8	.349
154	densenet-121-keras	.346
155	resnet50	.344
156	inception_v3	.341
157	deit_small_imagenet_full_seed-0	.338
158	BiT-S-R152x2	.336
159	tv_efficientnet-b1	.336
160	nasnet_mobile	.335
161	ReAlnet04	.334
162	grcnn_109	.333
163	resnet50_barlowtwins_linhead	.332
164	blt_vs	.331
165	ReAlnet07	.329
166	ReAlnet01	.328
167	xception	.327
168	ReAlnet06	.326
169	grcnn	.325
170	inception_v1	.324
171	ReAlnet08	.324
172	mobilenet_v2_1.0_192-tf	.318
173	antialiased-r50	.318
174	ReAlnet05	.317
175	inception_v2-tf	.309
176	densenet-121	.304
177	mehrer2020_alexnet_training_seed_01_fov4	.302
178	eicircuit_resnet18_bn_20260919	.301
179	mobilenet_v2_1_0_192	.301
180	deit_base_imagenet_full_seed-0	.299
181	vonealexnet_gaussian_noise_std4_fixed	.297
182	ReAlnet10	.297
183	CORnet-S	.297
184	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.296
185	resnet18_imagenet_full	.295
186	vgg_19	.295
187	cornet_s	.293
188	resnet50_robust_l2_eps1	.291
189	regnet_y_400mf	.288
190	mobilenet_v1_0.75_224	.287
191	resnet50_linf_8_robust	.287
192	texture_shape_resnet50_trained_on_SIN	.286
193	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.281
194	voneresnet-50	.281
195	alexnet_ks_torevert	.281
196	alexnet	.281
197	ReAlnet09	.277
198	mobilenet_v2_1_0_224	.276
199	resnet50_byol_linhead	.274
200	densenet-169-keras	.274
201	resnet50_vicreg_linhead	.273
202	resnet50_l2_3_robust	.272
203	mobilenet_v1_1.0_192	.269
204	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.264
205	alexnet_random_linf8_perturb	.260
206	resnet50_moco_v2	.256
207	vgg_16	.255
208	mobilenet_v2_0_5_224	.253
209	alexnet_reduced_aliasing_early_checkpoint	.252
210	resnet50_byol	.251
211	alexnet_l2_3_robust	.250
212	efficientnet_b1_imagenet_full	.249
213	mobilenet_v2_1_3_224	.247
214	resnet-18	.245
215	resnet50-SIN	.243
216	resnet-50-robust	.242
217	imagenet_l2_3_0	.242
218	resnet50_robust_l2_eps3	.242
219	densenet-169	.241
220	mobilenet_v2_1_0_128	.239
221	BiT-S-R152x4	.238
222	mobilenet_v2_1_0_160	.235
223	mobilenet_v1_1.0_224	.230
224	mobilenet_v2_1.0_160-tf	.229
225	inception_v1-tf	.223
226	cv_18_dagger_408_pretrained	.223
227	shufflenet_v2_x1_0	.222
228	alexnet_linf_8_robust	.218
229	efficientnet_b0_imagenet_full	.217
230	BiT-S-R101x3	.213
231	resnet50-vitoimagevidnet8	.210
232	alexnet_random_l2_3_perturb	.210
233	mobilenet_v2_1.4_224-tf	.208
234	BiT-S-R50x1	.208
235	resnet-34	.208
236	mobilenet_v2_1.3_224-tf	.206
237	mobilenet_v2_0_75_160	.204
238	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.201
239	resnet50_simclr_linhead_01norm	.198
240	mobilenet_v1_1.0_160	.198
241	vgg-16-keras	.197
242	mobilenet_v2_0.5_192-tf	.196
243	resnet50_simclr	.196
244	mobilenet_v2_1.0_224-tf	.194
245	mobilenet_v1_1.0_128	.192
246	resnet50_simsiam_linhead	.192
247	BiT-S-R101x1	.188
248	AlexNet_SIN_fov4	.186
249	squeezenet1_1	.182
250	mobilenet_v2_0.75_96	.181
251	mobilenet_v2_0_5_192	.180
252	alexnet_training_seed_05	.180
253	alexnet_training_seed_01	.180
254	alexnet_training_seed_01_fov4	.180
255	alexnet_training_seed_01_fov12	.180
256	alexnet_training_seed_01_fov16	.180
257	alexnet_robust_correct	.179
258	vgg-19-keras	.179
259	mehrer2020_alexnet_training_seed_04	.176
260	alexnet_fov12	.175
261	deit_small_imagenet_100_seed-0	.171
262	mehrer2020_alexnet_training_seed_02	.171
263	mehrer2020_alexnet_training_seed_01_fov16	.170
264	mobilenet_v2_0.75_128	.169
265	konkle_alexnetgn_ipcl_ref01_primary_model	.168
266	mehrer2020_alexnet_training_seed_01	.167
267	mobilenet_v2_0.75_160-tf	.164
268	mehrer2020_alexnet_training_seed_03	.161
269	convnext_tiny_sup	.161
270	alexnet_early_checkpoint	.161
271	mobilenet_v1_0.25_192	.160
272	0.5x_resnet-18_LC_w_sh_1_iter	.160
273	mobilenet_v2_1-4_224_pytorch	.159
274	mobilenet_v2_1_4_224	.159
275	mobilenet_v2_0.5_224-tf	.156
276	artResNet18_1	.156
277	mobilenet_v2_0.35_160	.150
278	alexnet_fov16	.149
279	mobilenet_v1_0.75_128	.148
280	mobilenet_v2_1.0_128-tf	.143
281	mehrer2020_alexnet_training_seed_01_fov12	.142
282	mobilenet_v1_0.75_192	.141
283	resnet50_imagenet_100_seed-0	.140
284	AdvProp_efficientnet-b0	.140
285	mobilenet_v1_0.5_128	.138
286	imagenet_l2_10_0	.132
287	nasnet_mobile-tf	.132
288	mobilenet_v2_1.0_96	.131
289	mobilenet_v2_0.35_96	.131
290	texture_shape_alexnet_trained_on_SIN	.129
291	squeezenet1_0	.126
292	mobilenet_v1_0.5_192	.126
293	mobilenet_v1_0.25_160	.125
294	AlexNet_SIN	.122
295	mobilenet_v2_0.5_160	.120
296	mobilenet_v2_0_75_192	.120
297	mobilenet_v1_0.25_224	.113
298	mehrer2020_alexnet_training_seed_05	.113
299	mobilenet_v2_0.5_128	.113
300	mobilenet_v2_0.75_192-tf	.111
301	mobilenet_v1_0.75_160	.107
302	resnet_50_v1_spiking_l4a	.098
303	mobilenet_v1_0.5_224	.097
304	mobilenet_v2_0_75_224	.097
305	deit_small_imagenet_1_seed-0	.094
306	deit_small_imagenet_10_seed-0	.094
307	resnet50_imagenet_10_seed-0	.093
308	alexnet_training_seed_06	.089
309	alexnet_training_seed_02	.085
310	alexnet_training_seed_04	.085
311	alexnet_training_seed_08	.085
312	alexnet_training_seed_10	.085
313	convnext_small_imagenet_10_seed-0	.084
314	alexnet_training_seed_07	.084
315	mobilenet_v2_0.5_96	.077
316	resnet50_eMMCR_Vanilla	.077
317	mobilenet_v2_0.35_128	.070
318	AlexNet_SIN_fov12	.065
319	AlexNet_SIN_fov16	.064
320	barlow-twins-resnet50	.063
321	resnet50-vicregl0p9	.059
322	mobilenet_v2_0.75_224-tf	.058
323	resnet50_FractalDB	.057
324	mobilenet_v2_0.35_224	.056
325	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.055
326	resnet50-vicregl0p75	.051
327	resnet50-vicreg	.044
328	alexnet_training_seed_09	.042
329	resnet50-barlow	.041
330	mobilenet_v1_0.5_160	.038
331	mobilenet_v2_0.35_192	.036
332	my-model	.029
333	convnext_small_imagenet_1_seed-0	.015
334	resnet_50_v1_spiking_l4	.014
335	mobilenet_v1_0.25_128	.013
336	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.012
337	resnet50-simclr-vissl	.009
338	mobilevit_small	.008
339	resnet50_imagenet_1_seed-0	.008
340	resnet_50_v1_spiking_l3	.004
341	alexnet_training_seed_03	.002
342	resnet-50_untrained	.002
343	resnet-18-LC_w_sh_100_iter_conv_init_m
344	ViT-B/32
345	BiT-M-R50x3
346	BiT-M-R50x1
347	BiT-M-R152x2
348	resnet-18-LC_w_sh_1_iter_conv_init_m
349	resnet-18-LC_w_sh_1_iter_m
350	resnet-18-LC_w_sh_10_iter_m
351	resnet-18-LC_w_sh_100_iter_m
352	resnet-18-LC_w_sh_10_iter_conv_init_m
353	FrankRobWobv0
354	resnet18-local_aggregation
355	0.5x_resnet-18_LC
356	CORnetZ_CIFAR10
357	resnet18-deepcluster
358	r3m_resnet18
359	CORnet-Z
360	resnet18-instance_recognition
361	my-model
362	vggface
363	resnet18-colorization
364	0.5x_resnet-18_LC_w_sh_10_iter
365	r3m_resnet50
366	r3m_resnet34
367	r3m_resnet50_nocrop
368	resnet-18-LC_m
369	resnet18-supervised
370	resnet-18-LC_1st_conv_m
371	resnet-18-LC_conv_init_m
372	resnet18-contrastive_multiview
373	0.5x_resnet-18
374	RN50
375	0.5x_resnet-18_LC_w_sh_100_iter
376	RN50
377	resnet18-relative_position
378	resnet18-autoencoder
379	resnet18-simclr
380	ViT_L_32
381	ViT_B_32
382	resnet18-depth_prediction
383	ViT_B_16
384	r3m_resnet34_nocrop
385	resnet18-contrastive_predictive
386	omnivore_swinT
387	resnet-18-LC_untrained
388	resnet-18_untrained
389	resnet-50x2_untrained
390	dcgan
391	v1-pyr-nodown
392	unet_entire
393	CORnetZ_CIFAR10_bs32_20_04
394	pixels
395	dorinet_cornet_z
396	openclip
397	resnet50-cifar
398	resnet50_pretrained_with_retinal_waves
399	resnet50_ImageNet
400	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
401	yudixie_resnet50_random_0_240908
402	yudixie_resnet50_imagenet1kpret_0_240908
403	yudixie_resnet50_imagenet1kpret_0_240312
404	yudixie_resnet50_cat_obj_class_all_latents_0_240908
405	yudixie_resnet50_object_class_0_240908
406	yudixie_resnet50_category_class_0_240908
407	resnet18_imagenet21kP
408	yudixie_resnet50_distance_reg_0_240908
409	ConvLSTM
410	PredRNN
411	SimVP
412	TAU
413	MIM
414	hmax
415	pnasnet_large_pytorch
416	ReAlnet01_cornet
417	yolos_tiny
418	yudixie_resnet18_translation_reg_0_240719
419	compact_resnet50_V4
420	briaai_rmbg_1_4
421	yudixie_resnet18_distance_reg_0_240719
422	yudixie_resnet18_rotation_reg_0_240719
423	ReAlnet03_cornet
424	eBarlow_lmda_02_1000ep
425	ReAlnet02_cornet
426	resnet18_ecoset_full
427	vit_base_patch14_dinov2-lvd142m
428	compact_resnet50_IT
429	ReAlnet01_cornet_fov4
430	VOneCORnet-S
431	resnet50_ecoset_full
432	vit_base_patch14_reg4_dinov2-lvd142m
433	compact_vgg19_V4
434	yudixie_resnet50_distance_rotation_0_240908
435	yudixie_resnet50_rotation_reg_0_240908
436	yudixie_resnet50_distance_translation_0_240908
437	resnet152_ecoset_full
438	yudixie_resnet18_distance_translation_0_240719
439	yudixie_resnet18_translation_rotation_0_240719
440	yudixie_resnet18_distance_rotation_0_240719
441	yudixie_resnet18_category_class_0_240719
442	yudixie_resnet18_random_0_240719
443	compact_resnet50_robust_V4
444	yudixie_resnet18_cat_obj_class_all_latents_0_240719
445	resnet-18_test_m
446	yudixie_resnet18_distance_translation_rotation_0_240719
447	yudixie_resnet18_object_class_0_240719
448	SeLaVi-AVE
449	yudixie_resnet18_imagenet1kpret_0_240719
450	r3d_18
451	compact_vgg19_IT
452	ReAlnet06_cornet
453	ReAlnet01_cornet_fov16
454	yudixie_resnet50_translation_reg_0_240908
455	ReAlnet01_ALL
456	mc3_18
457	yudixie_resnet50_translation_rotation_0_240908
458	ReAlnet07_cornet
459	ReAlnet02_ALL
460	s3d
461	mvit_v1_b
462	ReAlnet04_ALL
463	compact_cornets_V4
464	ReAlnet03_ALL
465	compact_cornets_IT
466	omnivore_swinB
467	SeLaVi-Kinetics400
468	ReAlnet09_cornet
469	ReAlnet05_ALL
470	vit_base_patch16_dinov3-lvd1689m
471	SeLaVi-VGG-Sound
472	SeLaVi-Kinetics-Sound
473	ReAlnet06_ALL
474	compact_resnet50_robust_IT
475	omnivore_swinS
476	ReAlnet07_ALL
477	mvit_v2_s
478	r2plus1d_18
479	ReAlnet10_cornet
480	yudixie_resnet50_distance_translation_rotation_0_240908
481	ReAlnet08_ALL
482	ReAlnet09_ALL
483	ReAlnet04_cornet
484	resnet50_primary_visual_cortex
485	ReAlnet10_ALL
486	ReAlnet01_cornet_fov12
487	ReAlnet05_cornet
488	ReAlnet08_cornet
489	resnet101_ecoset_full
490	resnet34_ecoset_full
491	resnet18_fair
492	pr1_stl10_disentangled_alpha04
493	predictive_coding_pc
494	yudixie_wbh_object_class_scaling_0415_run_0
495	yudixie_wbh_object_class_scaling_0415_run_3
496	yudixie_wbh_object_class_scaling_0415_imagenet
497	yudixie_wbh_object_class_scaling_0415_run_2
498	yudixie_wbh_object_class_scaling_0415_run_1
499	deepeisnn_ei_cifar10_vgg19
500	deepeisnn_ei_cifar10_resnet18
501	deepeisnn_ei_imagenet_resnet18
502	dinov2_vitl14_reg_lc_featread
503	gabor_filter_entropy_4x4
504	gabor_filter_entropy_4x4_v2
505	gabor_filter_entropy_4x4_v2_forcedCosine
506	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

0.39.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonI

Metric: error_consistency