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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonII-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	SWSL_resnext101_32x8d	.732
2	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.703
3	ViT_B_16_imagenet1k	.699
4	vision_transformer_vit_large_patch16_224	.696
5	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.692
6	vit_base_patch16_clip_224:openai_ft_in1k	.680
7	vit_relpos_base_patch16_clsgap_224:sw_in1k	.674
8	convnext_tiny_imagenet_full_seed-0	.665
9	resnext101_32x48d_wsl	.664
10	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.662
11	resnext101_32x16d_wsl	.662
12	deit3_base_patch16_224_fb_in1k	.661
13	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.659
14	AdvProp_efficientnet-b6	.656
15	convnext_large_in22k_supervised	.654
16	resnext101_32x32d_wsl	.650
17	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.650
18	regnety_032	.647
19	resnet_152_v2	.646
20	focalnet_tiny_lrf_in1k	.646
21	cvt_cvt-13-224-in1k_4_LucyV4	.643
22	pnasnet_large	.640
23	pnasnet_large_halfPrecision	.636
24	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.635
25	convnext_small_imagenet_100_seed-0	.632
26	resnet_50_v2	.630
27	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.626
28	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.625
29	resnext101_32x8d_wsl	.624
30	CLIP_ViT-B_32	.622
31	ViT_L_32_imagenet1k	.617
32	convnext_large:fb_in22k_ft_in1k	.613
33	vit_large_patch14_clip_224:laion2b_ft_in1k	.613
34	SWSL_resnet50	.613
35	resnet_101_v2	.610
36	convnext_base_fb_in22k_ft_in1k	.609
37	convnext_large_clip_laion2b_finelayers	.604
38	vit_large_patch14_clip_224:openai_ft_in1k	.603
39	resnet-152_v2-tf	.602
40	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.597
41	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.590
42	convnext_femto_ols:d1_in1k	.588
43	cvt_cvt-13-384-in1k_4_LucyV4	.582
44	AdvProp_efficientnet-b7	.578
45	pnasnet_large-tf	.577
46	convnext_large_imagenet_full_seed-0	.569
47	convnext_base_fb_in1k	.569
48	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.564
49	AdvProp_efficientnet-b4	.559
50	AT_efficientnet-b4	.559
51	CLIP_resnet50	.558
52	CLIP_resnet50_float32	.558
53	convnext_base:clip_laiona_augreg_ft_in1k_384	.555
54	seresnext50_32x4d	.553
55	fixres_resnext101_32x48d_wsl	.549
56	convnext_tiny:in12k_ft_in1k	.547
57	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.546
58	vit_large_clip336_openai	.543
59	effnetv2m_custom384	.540
60	efficientnet_b0	.538
61	cvt_cvt-21-384-in1k_4_LucyV4	.535
62	efficientnet-b7	.534
63	nasnet_large	.534
64	resnet152_imagenet_full	.527
65	resnet_152_v1	.524
66	resnet50-VITO-8deg-cc	.522
67	deit3_base_patch16_224_fb_in22k_ft_in1k	.521
68	nasnet_large-tf	.520
69	dinov2_vitl14_reg_lc	.519
70	cvt_cvt-21-224-in1k_4_LucyV4	.519
71	ViT_B_32_imagenet1k	.518
72	antialias-resnet152	.515
73	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.513
74	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.512
75	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
76	vit_huge_clip336_laion	.505
77	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.503
78	AT_efficientnet-b2	.501
79	AdvProp_efficientnet-b2	.501
80	resnet50_ewconplus_wzc_seed2_2__323	.500
81	deit_large_imagenet_full_seed-0	.496
82	effnetb1_272x240	.495
83	convnext_xlarge:fb_in22k_ft_in1k	.491
84	dinov2_vitg14_lc	.482
85	inception_v4	.482
86	vit_large_patch14_clip_336:laion2b_ft_in1k	.481
87	resnet50_ewconplus_wzc_seed5_2__323	.479
88	CLIP-RN50	.477
89	convnext_base_imagenet_full_seed-0	.475
90	eva02_large_in22k	.473
91	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
92	resnet_101_v1	.470
93	antialiased-rnext101_32x8d	.469
94	convnext_small_imagenet_full_seed-0	.465
95	deit_base_imagenet_full_seed-0	.465
96	inception_v3_pytorch	.463
97	resnet50_st_wzc_seed6_2_323	.459
98	inception_v3	.458
99	resnet-152_v1-tf	.455
100	grcnn_109	.454
101	tv_efficientnet-b1	.454
102	resnet50-moclr8deg	.453
103	resnet-152_v2_pytorch	.453
104	resnet101	.451
105	convnext_xxlarge_clip	.449
106	resnet50_imagenet_full	.448
107	pnasnet_large_pytorch	.445
108	cv_18_dagger_408_pretrained	.443
109	resnet101_imagenet_full	.433
110	densenet-201-keras	.423
111	inception_v4-tf	.423
112	dinov2_vitl14_lc	.422
113	densenet-201	.421
114	densenet_201_pytorch	.421
115	resnet50_st_wzc_seed3_2_323	.417
116	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
117	resnet-50_v2-tf	.416
118	grcnn	.414
119	dinov2_vitg14_reg_lc	.412
120	alexnet_fov4	.411
121	deit_small_imagenet_full_seed-0	.410
122	dinov2_vitl14_lc_res336	.408
123	resnet50_random_linf8_perturb	.405
124	custom_model_cv_18_dagger_408	.401
125	xception-keras	.399
126	BiT-S-R50x3	.399
127	Res2Net50_26w_4s	.394
128	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.379
129	resnet34_imagenet_full	.379
130	inception_v3-tf	.375
131	resnet50_hvs_wzc	.374
132	BiT-S-R152x2	.371
133	resnet50_random_l2_perturb	.371
134	resnet50_ewconplus_wzc_seed6_2_323	.368
135	ReAlnet03	.366
136	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.365
137	resnet50_moco_v2	.364
138	resnet50_st_wzc_2	.363
139	resnet50	.363
140	cornet_s	.361
141	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.361
142	vonegrcnn_62e_nobn	.359
143	efficientnet_b2_imagenet_full	.359
144	resnet50_linf_4_robust	.358
145	vonegrcnn_47e	.354
146	vonegrcnn_52e_full	.351
147	AdvProp_efficientnet-b8	.350
148	ReAlnet01	.345
149	ReAlnet04	.342
150	resnet50_byol	.337
151	BiT-S-R101x3	.331
152	vonegrcnn_47e	.330
153	resnet50_tutorial	.329
154	resnet_50_v1	.329
155	resnet-50-pytorch	.329
156	resnet50-sup	.329
157	resnet18_imagenet_full	.329
158	resnet50_ewconplus_wzc	.327
159	vonegrcnn_52e_full	.325
160	densenet-121-keras	.320
161	resnet-50_v1-tf	.317
162	vgg_19	.317
163	antialiased-r50	.315
164	alexnet_random_linf8_perturb	.314
165	resnet50-SIN_IN_IN	.313
166	resnet_SIN_IN_FT_IN	.313
167	ReAlnet06	.313
168	densenet-169-keras	.311
169	vonegrcnn_62e_nobn	.311
170	alexnet_reduced_aliasing_early_checkpoint	.308
171	CORnet-S	.304
172	densenet-121	.302
173	convnext_base_fb_in1k_gram_aligned_dinov3	.300
174	ReAlnet05	.299
175	ReAlnet07	.298
176	xception	.298
177	resnet_50_v1_spiking	.297
178	voneresnet-50-robust	.296
179	mobilenet_v2_1.4_224-tf	.296
180	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.294
181	inception_v2-tf	.294
182	voneresnet-50	.289
183	inception_v1-tf	.289
184	mobilenet_v2_1-4_224_pytorch	.287
185	mobilenet_v2_1_4_224	.287
186	voneresnet-50-non_stochastic	.286
187	resnet-101_v1-tf	.285
188	ReAlnet10	.279
189	ReAlnet08	.278
190	ReAlnet02	.274
191	densenet-169	.270
192	vgg_16	.270
193	mobilenet_v2_1_0_224	.267
194	resnet50_l2_3_robust	.266
195	mobilenet_v2_1_0_160	.266
196	ReAlnet09	.265
197	mobilenet_v2_1_3_224	.260
198	resnet50-SIN_IN	.258
199	BiT-S-R152x4	.257
200	BiT-S-R50x1	.256
201	alexnet_l2_3_robust	.255
202	resnet50-vitoimagevidnet8	.254
203	alexnet	.252
204	alexnet_ks_torevert	.252
205	nasnet_mobile	.250
206	blt_vs	.247
207	alexnet_linf_8_robust	.246
208	eicircuit_resnet18_bn_20260919	.239
209	alexnet_robust_correct	.236
210	vonealexnet_gaussian_noise_std4_fixed	.235
211	inception_v1	.235
212	alexnet_fov12	.232
213	mobilenet_v2_0_5_224	.226
214	resnet-50-robust	.226
215	imagenet_l2_3_0	.226
216	resnet50_robust_l2_eps3	.226
217	mobilenet_v2_1.0_160-tf	.225
218	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.225
219	deit_small_imagenet_100_seed-0	.224
220	efficientnet_b0_imagenet_full	.224
221	konkle_alexnetgn_ipcl_ref01_primary_model	.223
222	convnext_tiny_sup	.223
223	resnet-18	.216
224	resnet-34	.216
225	alexnet_random_l2_3_perturb	.215
226	resnet50_robust_l2_eps1	.206
227	mobilenet_v2_1_0_192	.206
228	vgg-16-keras	.204
229	mobilenet_v2_1.3_224-tf	.198
230	mobilenet_v1_1.0_224	.196
231	resnet50_linf_8_robust	.195
232	BiT-S-R101x1	.191
233	regnet_y_400mf	.190
234	efficientnet_b1_imagenet_full	.189
235	mobilenet_v2_1.0_192-tf	.189
236	resnet50_imagenet_10_seed-0	.182
237	deit_small_imagenet_10_seed-0	.182
238	mobilenet_v1_0.75_224	.180
239	mobilenet_v2_1.0_224-tf	.180
240	resnet50-barlow	.179
241	resnet50-vicreg	.178
242	texture_shape_resnet50_trained_on_SIN	.178
243	deit_small_imagenet_1_seed-0	.174
244	alexnet_fov16	.174
245	resnet50_eMMCR_Vanilla	.173
246	nasnet_mobile-tf	.169
247	mobilenet_v2_0_5_192	.166
248	convnext_small_imagenet_10_seed-0	.161
249	mobilenet_v2_0.5_192-tf	.161
250	resnet50_simclr	.158
251	shufflenet_v2_x1_0	.158
252	mobilenet_v2_0_75_160	.155
253	mobilenet_v1_1.0_192	.154
254	alexnet_early_checkpoint	.151
255	imagenet_l2_10_0	.149
256	mobilenet_v1_0.75_128	.145
257	mobilenet_v2_0.5_224-tf	.143
258	alexnet_training_seed_05	.143
259	alexnet_training_seed_01	.143
260	alexnet_training_seed_01_fov4	.143
261	alexnet_training_seed_01_fov16	.143
262	alexnet_training_seed_01_fov12	.143
263	0.5x_resnet-18_LC_w_sh_1_iter	.142
264	mobilenet_v2_0.35_160	.137
265	vgg-19-keras	.136
266	resnet50-SIN	.134
267	mobilenet_v2_0.5_96	.133
268	AlexNet_SIN_fov4	.130
269	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.128
270	texture_shape_alexnet_trained_on_SIN	.124
271	mobilenet_v2_0.75_192-tf	.124
272	AdvProp_efficientnet-b0	.118
273	resnet50_imagenet_100_seed-0	.118
274	mobilenet_v1_0.75_192	.116
275	AlexNet_SIN	.115
276	mobilenet_v2_0.75_224-tf	.114
277	mobilenet_v2_0.5_160	.114
278	mobilenet_v1_0.25_160	.112
279	mobilenet_v2_0_75_192	.110
280	mobilenet_v2_0.35_128	.109
281	mobilenet_v2_0_75_224	.107
282	mobilenet_v2_0.75_128	.105
283	mobilenet_v2_1_0_128	.105
284	mobilenet_v2_0.35_96	.104
285	mobilenet_v1_0.75_160	.100
286	alexnet_training_seed_06	.098
287	mobilenet_v1_1.0_160	.098
288	mobilenet_v1_0.5_192	.096
289	mobilenet_v2_1.0_96	.089
290	mobilenet_v2_1.0_128-tf	.088
291	mobilenet_v2_0.75_160-tf	.086
292	mobilenet_v1_0.5_224	.086
293	mobilenet_v1_0.25_192	.086
294	mobilenet_v2_0.35_192	.078
295	squeezenet1_1	.075
296	mobilenet_v1_1.0_128	.071
297	my-model	.065
298	mobilenet_v2_0.75_96	.065
299	resnet_50_v1_spiking_l4a	.061
300	mobilenet_v2_0.35_224	.061
301	squeezenet1_0	.058
302	resnet50-simclr-vissl	.058
303	resnet50-vicregl0p9	.057
304	AlexNet_SIN_fov12	.053
305	mobilevit_small	.052
306	alexnet_training_seed_02	.049
307	alexnet_training_seed_04	.049
308	alexnet_training_seed_08	.049
309	alexnet_training_seed_10	.049
310	resnet50-vicregl0p75	.048
311	mobilenet_v1_0.25_128	.046
312	mobilenet_v2_0.5_128	.042
313	resnet-50_untrained	.041
314	mobilenet_v1_0.5_160	.040
315	mobilenet_v1_0.25_224	.039
316	convnext_small_imagenet_1_seed-0	.032
317	alexnet_training_seed_03	.030
318	resnet_50_v1_spiking_l3	.026
319	resnet50_FractalDB	.024
320	alexnet_training_seed_07	.024
321	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.024
322	resnet_50_v1_spiking_l4	.020
323	alexnet_training_seed_09	.013
324	resnet50_imagenet_1_seed-0	.010
325	mobilenet_v1_0.5_128	.007
326	artResNet18_1	.006
327	AlexNet_SIN_fov16	.003
328	barlow-twins-resnet50	.002
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_10_iter_m
336	resnet-18-LC_w_sh_100_iter_m
337	resnet-18-LC_w_sh_1_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	0.5x_resnet-18_LC_w_sh_10_iter
350	resnet18-colorization
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	yudixie_resnet50_imagenet1kpret_0_240908
380	CORnetZ_CIFAR10_bs32_20_04
381	pixels
382	dorinet_cornet_z
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_cat_obj_class_all_latents_0_240908
390	yudixie_resnet50_imagenet1kpret_0_240312
391	yudixie_resnet50_object_class_0_240908
392	yudixie_resnet50_category_class_0_240908
393	resnet18_imagenet21kP
394	yudixie_resnet50_distance_reg_0_240908
395	ConvLSTM
396	PredRNN
397	SimVP
398	TAU
399	MIM
400	hmax
401	ReAlnet01_cornet
402	briaai_rmbg_1_4
403	vit_base_patch16_dinov3-lvd1689m
404	yolos_tiny
405	yudixie_resnet18_translation_reg_0_240719
406	compact_resnet50_V4
407	yudixie_resnet18_distance_reg_0_240719
408	ReAlnet03_cornet
409	yudixie_resnet18_rotation_reg_0_240719
410	yudixie_resnet18_imagenet1kpret_0_240719
411	ReAlnet02_cornet
412	eBarlow_lmda_02_1000ep
413	resnet18_ecoset_full
414	vit_base_patch14_dinov2-lvd142m
415	compact_resnet50_IT
416	VOneCORnet-S
417	resnet50_ecoset_full
418	ReAlnet01_cornet_fov4
419	vit_base_patch14_reg4_dinov2-lvd142m
420	yudixie_resnet50_distance_rotation_0_240908
421	compact_resnet50_robust_V4
422	yudixie_resnet50_rotation_reg_0_240908
423	resnet152_ecoset_full
424	yudixie_resnet50_distance_translation_0_240908
425	yudixie_resnet18_distance_translation_0_240719
426	ReAlnet01_cornet_fov12
427	yudixie_resnet18_translation_rotation_0_240719
428	yudixie_resnet18_distance_rotation_0_240719
429	compact_vgg19_V4
430	yudixie_resnet18_category_class_0_240719
431	ReAlnet05_cornet
432	resnet-18_test_m
433	yudixie_resnet18_cat_obj_class_all_latents_0_240719
434	yudixie_resnet18_distance_translation_rotation_0_240719
435	yudixie_resnet18_object_class_0_240719
436	SeLaVi-AVE
437	ReAlnet06_cornet
438	compact_vgg19_IT
439	r3d_18
440	ReAlnet01_cornet_fov16
441	yudixie_resnet50_translation_reg_0_240908
442	yudixie_resnet50_translation_rotation_0_240908
443	ReAlnet01_ALL
444	ReAlnet07_cornet
445	mc3_18
446	s3d
447	mvit_v1_b
448	ReAlnet08_cornet
449	ReAlnet02_ALL
450	ReAlnet03_ALL
451	compact_cornets_V4
452	ReAlnet04_ALL
453	omnivore_swinB
454	SeLaVi-Kinetics400
455	SeLaVi-VGG-Sound
456	compact_cornets_IT
457	ReAlnet05_ALL
458	SeLaVi-Kinetics-Sound
459	ReAlnet06_ALL
460	omnivore_swinS
461	r2plus1d_18
462	ReAlnet10_cornet
463	mvit_v2_s
464	ReAlnet07_ALL
465	compact_resnet50_robust_IT
466	resnet101_ecoset_full
467	resnet34_ecoset_full
468	yudixie_resnet50_distance_translation_rotation_0_240908
469	ReAlnet08_ALL
470	yudixie_resnet18_random_0_240719
471	ReAlnet04_cornet
472	ReAlnet09_ALL
473	ReAlnet09_cornet
474	resnet50_primary_visual_cortex
475	ReAlnet10_ALL
476	resnet18_fair
477	predictive_coding_pc
478	yudixie_wbh_object_class_scaling_0415_run_0
479	yudixie_wbh_object_class_scaling_0415_run_3
480	yudixie_wbh_object_class_scaling_0415_imagenet
481	yudixie_wbh_object_class_scaling_0415_run_2
482	yudixie_wbh_object_class_scaling_0415_run_1
483	deepeisnn_ei_cifar10_vgg19
484	deepeisnn_ei_cifar10_resnet18
485	deepeisnn_ei_imagenet_resnet18
486	dinov2_vitl14_reg_lc_featread
487	gabor_filter_entropy_4x4
488	gabor_filter_entropy_4x4_v2
489	gabor_filter_entropy_4x4_v2_forcedCosine
490	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

0.45.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonII

Metric: error_consistency