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