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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	resnet_152_v2	.646
19	focalnet_tiny_lrf_in1k	.646
20	cvt_cvt-13-224-in1k_4_LucyV4	.643
21	pnasnet_large	.640
22	pnasnet_large_halfPrecision	.636
23	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.635
24	convnext_small_imagenet_100_seed-0	.632
25	resnet_50_v2	.630
26	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.626
27	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.625
28	resnext101_32x8d_wsl	.624
29	CLIP_ViT-B_32	.622
30	ViT_L_32_imagenet1k	.617
31	convnext_large:fb_in22k_ft_in1k	.613
32	vit_large_patch14_clip_224:laion2b_ft_in1k	.613
33	SWSL_resnet50	.613
34	resnet_101_v2	.610
35	convnext_base_fb_in22k_ft_in1k	.609
36	convnext_large_clip_laion2b_finelayers	.604
37	vit_large_patch14_clip_224:openai_ft_in1k	.603
38	resnet-152_v2-tf	.602
39	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.597
40	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.590
41	convnext_femto_ols:d1_in1k	.588
42	cvt_cvt-13-384-in1k_4_LucyV4	.582
43	AdvProp_efficientnet-b7	.578
44	pnasnet_large-tf	.577
45	convnext_large_imagenet_full_seed-0	.569
46	convnext_base_fb_in1k	.569
47	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.564
48	AdvProp_efficientnet-b4	.559
49	AT_efficientnet-b4	.559
50	CLIP_resnet50	.558
51	CLIP_resnet50_float32	.558
52	convnext_base:clip_laiona_augreg_ft_in1k_384	.555
53	fixres_resnext101_32x48d_wsl	.549
54	convnext_tiny:in12k_ft_in1k	.547
55	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.546
56	vit_large_clip336_openai	.543
57	effnetv2m_custom384	.540
58	efficientnet_b0	.538
59	cvt_cvt-21-384-in1k_4_LucyV4	.535
60	efficientnet-b7	.534
61	nasnet_large	.534
62	resnet152_imagenet_full	.527
63	resnet_152_v1	.524
64	resnet50-VITO-8deg-cc	.522
65	deit3_base_patch16_224_fb_in22k_ft_in1k	.521
66	nasnet_large-tf	.520
67	dinov2_vitl14_reg_lc	.519
68	cvt_cvt-21-224-in1k_4_LucyV4	.519
69	ViT_B_32_imagenet1k	.518
70	antialias-resnet152	.515
71	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.513
72	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.512
73	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
74	vit_huge_clip336_laion	.505
75	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.503
76	AT_efficientnet-b2	.501
77	AdvProp_efficientnet-b2	.501
78	resnet50_ewconplus_wzc_seed2_2__323	.500
79	deit_large_imagenet_full_seed-0	.496
80	effnetb1_272x240	.495
81	convnext_xlarge:fb_in22k_ft_in1k	.491
82	dinov2_vitg14_lc	.482
83	inception_v4	.482
84	vit_large_patch14_clip_336:laion2b_ft_in1k	.481
85	resnet50_ewconplus_wzc_seed5_2__323	.479
86	CLIP-RN50	.477
87	convnext_base_imagenet_full_seed-0	.475
88	eva02_large_in22k	.473
89	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
90	resnet_101_v1	.470
91	antialiased-rnext101_32x8d	.469
92	convnext_small_imagenet_full_seed-0	.465
93	deit_base_imagenet_full_seed-0	.465
94	inception_v3_pytorch	.463
95	resnet50_st_wzc_seed6_2_323	.459
96	inception_v3	.458
97	resnet-152_v1-tf	.455
98	grcnn_109	.454
99	tv_efficientnet-b1	.454
100	resnet50-moclr8deg	.453
101	resnet-152_v2_pytorch	.453
102	resnet101	.451
103	convnext_xxlarge_clip	.449
104	resnet50_imagenet_full	.448
105	pnasnet_large_pytorch	.445
106	cv_18_dagger_408_pretrained	.443
107	resnet101_imagenet_full	.433
108	densenet-201-keras	.423
109	inception_v4-tf	.423
110	dinov2_vitl14_lc	.422
111	densenet-201	.421
112	densenet_201_pytorch	.421
113	resnet50_st_wzc_seed3_2_323	.417
114	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
115	resnet-50_v2-tf	.416
116	grcnn	.414
117	dinov2_vitg14_reg_lc	.412
118	alexnet_fov4	.411
119	deit_small_imagenet_full_seed-0	.410
120	dinov2_vitl14_lc_res336	.408
121	resnet50_random_linf8_perturb	.405
122	custom_model_cv_18_dagger_408	.401
123	xception-keras	.399
124	BiT-S-R50x3	.399
125	Res2Net50_26w_4s	.394
126	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.379
127	resnet34_imagenet_full	.379
128	inception_v3-tf	.375
129	resnet50_hvs_wzc	.374
130	BiT-S-R152x2	.371
131	resnet50_random_l2_perturb	.371
132	resnet50_ewconplus_wzc_seed6_2_323	.368
133	ReAlnet03	.366
134	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.365
135	resnet50_moco_v2	.364
136	resnet50_st_wzc_2	.363
137	resnet50	.363
138	cornet_s	.361
139	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.361
140	vonegrcnn_62e_nobn	.359
141	efficientnet_b2_imagenet_full	.359
142	resnet50_linf_4_robust	.358
143	vonegrcnn_47e	.354
144	vonegrcnn_52e_full	.351
145	AdvProp_efficientnet-b8	.350
146	ReAlnet01	.345
147	ReAlnet04	.342
148	resnet50_byol	.337
149	BiT-S-R101x3	.331
150	vonegrcnn_47e	.330
151	resnet50_tutorial	.329
152	resnet_50_v1	.329
153	resnet-50-pytorch	.329
154	resnet50-sup	.329
155	resnet18_imagenet_full	.329
156	resnet50_ewconplus_wzc	.327
157	vonegrcnn_52e_full	.325
158	densenet-121-keras	.320
159	resnet-50_v1-tf	.317
160	vgg_19	.317
161	antialiased-r50	.315
162	alexnet_random_linf8_perturb	.314
163	resnet50-SIN_IN_IN	.313
164	resnet_SIN_IN_FT_IN	.313
165	ReAlnet06	.313
166	densenet-169-keras	.311
167	vonegrcnn_62e_nobn	.311
168	alexnet_reduced_aliasing_early_checkpoint	.308
169	CORnet-S	.304
170	densenet-121	.302
171	convnext_base_fb_in1k_gram_aligned_dinov3	.300
172	ReAlnet05	.299
173	ReAlnet07	.298
174	xception	.298
175	resnet_50_v1_spiking	.297
176	voneresnet-50-robust	.296
177	mobilenet_v2_1.4_224-tf	.296
178	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.294
179	inception_v2-tf	.294
180	voneresnet-50	.289
181	inception_v1-tf	.289
182	mobilenet_v2_1-4_224_pytorch	.287
183	mobilenet_v2_1_4_224	.287
184	voneresnet-50-non_stochastic	.286
185	resnet-101_v1-tf	.285
186	ReAlnet10	.279
187	ReAlnet08	.278
188	ReAlnet02	.274
189	densenet-169	.270
190	vgg_16	.270
191	mobilenet_v2_1_0_224	.267
192	resnet50_l2_3_robust	.266
193	mobilenet_v2_1_0_160	.266
194	ReAlnet09	.265
195	mobilenet_v2_1_3_224	.260
196	resnet50-SIN_IN	.258
197	BiT-S-R152x4	.257
198	BiT-S-R50x1	.256
199	alexnet_l2_3_robust	.255
200	resnet50-vitoimagevidnet8	.254
201	alexnet	.252
202	alexnet_ks_torevert	.252
203	nasnet_mobile	.250
204	blt_vs	.247
205	alexnet_linf_8_robust	.246
206	alexnet_robust_correct	.236
207	vonealexnet_gaussian_noise_std4_fixed	.235
208	inception_v1	.235
209	alexnet_fov12	.232
210	mobilenet_v2_0_5_224	.226
211	resnet-50-robust	.226
212	imagenet_l2_3_0	.226
213	resnet50_robust_l2_eps3	.226
214	mobilenet_v2_1.0_160-tf	.225
215	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.225
216	deit_small_imagenet_100_seed-0	.224
217	efficientnet_b0_imagenet_full	.224
218	konkle_alexnetgn_ipcl_ref01_primary_model	.223
219	convnext_tiny_sup	.223
220	resnet-18	.216
221	resnet-34	.216
222	alexnet_random_l2_3_perturb	.215
223	resnet50_robust_l2_eps1	.206
224	mobilenet_v2_1_0_192	.206
225	vgg-16-keras	.204
226	mobilenet_v2_1.3_224-tf	.198
227	mobilenet_v1_1.0_224	.196
228	resnet50_linf_8_robust	.195
229	BiT-S-R101x1	.191
230	regnet_y_400mf	.190
231	efficientnet_b1_imagenet_full	.189
232	mobilenet_v2_1.0_192-tf	.189
233	resnet50_imagenet_10_seed-0	.182
234	deit_small_imagenet_10_seed-0	.182
235	mobilenet_v1_0.75_224	.180
236	mobilenet_v2_1.0_224-tf	.180
237	resnet50-barlow	.179
238	resnet50-vicreg	.178
239	texture_shape_resnet50_trained_on_SIN	.178
240	deit_small_imagenet_1_seed-0	.174
241	alexnet_fov16	.174
242	resnet50_eMMCR_Vanilla	.173
243	nasnet_mobile-tf	.169
244	mobilenet_v2_0_5_192	.166
245	convnext_small_imagenet_10_seed-0	.161
246	mobilenet_v2_0.5_192-tf	.161
247	resnet50_simclr	.158
248	shufflenet_v2_x1_0	.158
249	mobilenet_v2_0_75_160	.155
250	mobilenet_v1_1.0_192	.154
251	alexnet_early_checkpoint	.151
252	imagenet_l2_10_0	.149
253	mobilenet_v1_0.75_128	.145
254	mobilenet_v2_0.5_224-tf	.143
255	alexnet_training_seed_05	.143
256	alexnet_training_seed_01	.143
257	alexnet_training_seed_01_fov4	.143
258	alexnet_training_seed_01_fov16	.143
259	alexnet_training_seed_01_fov12	.143
260	0.5x_resnet-18_LC_w_sh_1_iter	.142
261	mobilenet_v2_0.35_160	.137
262	vgg-19-keras	.136
263	resnet50-SIN	.134
264	mobilenet_v2_0.5_96	.133
265	AlexNet_SIN_fov4	.130
266	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.128
267	texture_shape_alexnet_trained_on_SIN	.124
268	mobilenet_v2_0.75_192-tf	.124
269	AdvProp_efficientnet-b0	.118
270	resnet50_imagenet_100_seed-0	.118
271	mobilenet_v1_0.75_192	.116
272	AlexNet_SIN	.115
273	mobilenet_v2_0.75_224-tf	.114
274	mobilenet_v2_0.5_160	.114
275	mobilenet_v1_0.25_160	.112
276	mobilenet_v2_0_75_192	.110
277	mobilenet_v2_0.35_128	.109
278	mobilenet_v2_0_75_224	.107
279	mobilenet_v2_0.75_128	.105
280	mobilenet_v2_1_0_128	.105
281	mobilenet_v2_0.35_96	.104
282	mobilenet_v1_0.75_160	.100
283	alexnet_training_seed_06	.098
284	mobilenet_v1_1.0_160	.098
285	mobilenet_v1_0.5_192	.096
286	mobilenet_v2_1.0_96	.089
287	mobilenet_v2_1.0_128-tf	.088
288	mobilenet_v2_0.75_160-tf	.086
289	mobilenet_v1_0.5_224	.086
290	mobilenet_v1_0.25_192	.086
291	mobilenet_v2_0.35_192	.078
292	squeezenet1_1	.075
293	mobilenet_v1_1.0_128	.071
294	my-model	.065
295	mobilenet_v2_0.75_96	.065
296	resnet_50_v1_spiking_l4a	.061
297	mobilenet_v2_0.35_224	.061
298	squeezenet1_0	.058
299	resnet50-simclr-vissl	.058
300	resnet50-vicregl0p9	.057
301	AlexNet_SIN_fov12	.053
302	mobilevit_small	.052
303	alexnet_training_seed_02	.049
304	alexnet_training_seed_04	.049
305	alexnet_training_seed_08	.049
306	alexnet_training_seed_10	.049
307	resnet50-vicregl0p75	.048
308	mobilenet_v1_0.25_128	.046
309	mobilenet_v2_0.5_128	.042
310	resnet-50_untrained	.041
311	mobilenet_v1_0.5_160	.040
312	mobilenet_v1_0.25_224	.039
313	convnext_small_imagenet_1_seed-0	.032
314	alexnet_training_seed_03	.030
315	resnet_50_v1_spiking_l3	.026
316	resnet50_FractalDB	.024
317	alexnet_training_seed_07	.024
318	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.024
319	resnet_50_v1_spiking_l4	.020
320	alexnet_training_seed_09	.013
321	resnet50_imagenet_1_seed-0	.010
322	mobilenet_v1_0.5_128	.007
323	artResNet18_1	.006
324	AlexNet_SIN_fov16	.003
325	barlow-twins-resnet50	.002
326	resnet-18-LC_w_sh_100_iter_conv_init_m
327	ViT-B/32
328	BiT-M-R50x3
329	BiT-M-R50x1
330	BiT-M-R152x2
331	resnet-18-LC_w_sh_1_iter_conv_init_m
332	resnet-18-LC_w_sh_10_iter_m
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_1_iter_m
335	resnet-18-LC_w_sh_10_iter_conv_init_m
336	FrankRobWobv0
337	resnet18-local_aggregation
338	0.5x_resnet-18_LC
339	CORnetZ_CIFAR10
340	resnet18-deepcluster
341	r3m_resnet18
342	CORnet-Z
343	resnet18-instance_recognition
344	my-model
345	vggface
346	0.5x_resnet-18_LC_w_sh_10_iter
347	resnet18-colorization
348	r3m_resnet50
349	r3m_resnet34
350	resnet-18-LC_m
351	r3m_resnet50_nocrop
352	resnet18-supervised
353	resnet-18-LC_1st_conv_m
354	resnet-18-LC_conv_init_m
355	resnet18-contrastive_multiview
356	0.5x_resnet-18
357	RN50
358	0.5x_resnet-18_LC_w_sh_100_iter
359	RN50
360	resnet18-relative_position
361	resnet18-autoencoder
362	resnet18-simclr
363	ViT_L_32
364	ViT_B_32
365	resnet18-depth_prediction
366	ViT_B_16
367	r3m_resnet34_nocrop
368	resnet18-contrastive_predictive
369	omnivore_swinT
370	resnet-18-LC_untrained
371	resnet-18_untrained
372	resnet-50x2_untrained
373	dcgan
374	v1-pyr-nodown
375	unet_entire
376	yudixie_resnet50_imagenet1kpret_0_240908
377	CORnetZ_CIFAR10_bs32_20_04
378	pixels
379	dorinet_cornet_z
380	openclip
381	resnet50-cifar
382	resnet50_pretrained_with_retinal_waves
383	resnet50_ImageNet
384	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
385	yudixie_resnet50_random_0_240908
386	yudixie_resnet50_cat_obj_class_all_latents_0_240908
387	yudixie_resnet50_imagenet1kpret_0_240312
388	yudixie_resnet50_object_class_0_240908
389	yudixie_resnet50_category_class_0_240908
390	resnet18_imagenet21kP
391	yudixie_resnet50_distance_reg_0_240908
392	ConvLSTM
393	PredRNN
394	SimVP
395	TAU
396	MIM
397	hmax
398	ReAlnet01_cornet
399	briaai_rmbg_1_4
400	vit_base_patch16_dinov3-lvd1689m
401	yolos_tiny
402	yudixie_resnet18_translation_reg_0_240719
403	compact_resnet50_V4
404	yudixie_resnet18_distance_reg_0_240719
405	ReAlnet03_cornet
406	yudixie_resnet18_rotation_reg_0_240719
407	yudixie_resnet18_imagenet1kpret_0_240719
408	ReAlnet02_cornet
409	eBarlow_lmda_02_1000ep
410	resnet18_ecoset_full
411	vit_base_patch14_dinov2-lvd142m
412	compact_resnet50_IT
413	VOneCORnet-S
414	resnet50_ecoset_full
415	ReAlnet01_cornet_fov4
416	vit_base_patch14_reg4_dinov2-lvd142m
417	yudixie_resnet50_distance_rotation_0_240908
418	compact_resnet50_robust_V4
419	aughost_fair_resnet18
420	yudixie_resnet50_rotation_reg_0_240908
421	resnet152_ecoset_full
422	yudixie_resnet50_distance_translation_0_240908
423	yudixie_resnet18_distance_translation_0_240719
424	ReAlnet01_cornet_fov12
425	yudixie_resnet18_translation_rotation_0_240719
426	yudixie_resnet18_distance_rotation_0_240719
427	compact_vgg19_V4
428	yudixie_resnet18_category_class_0_240719
429	ReAlnet05_cornet
430	resnet-18_test_m
431	yudixie_resnet18_cat_obj_class_all_latents_0_240719
432	yudixie_resnet18_distance_translation_rotation_0_240719
433	yudixie_resnet18_object_class_0_240719
434	SeLaVi-AVE
435	ReAlnet06_cornet
436	compact_vgg19_IT
437	r3d_18
438	ReAlnet01_cornet_fov16
439	yudixie_resnet50_translation_reg_0_240908
440	yudixie_resnet50_translation_rotation_0_240908
441	ReAlnet01_ALL
442	ReAlnet07_cornet
443	mc3_18
444	s3d
445	mvit_v1_b
446	ReAlnet08_cornet
447	ReAlnet02_ALL
448	ReAlnet03_ALL
449	compact_cornets_V4
450	ReAlnet04_ALL
451	omnivore_swinB
452	SeLaVi-Kinetics400
453	SeLaVi-VGG-Sound
454	compact_cornets_IT
455	ReAlnet05_ALL
456	SeLaVi-Kinetics-Sound
457	ReAlnet06_ALL
458	omnivore_swinS
459	r2plus1d_18
460	ReAlnet10_cornet
461	mvit_v2_s
462	ReAlnet07_ALL
463	compact_resnet50_robust_IT
464	resnet101_ecoset_full
465	resnet34_ecoset_full
466	yudixie_resnet50_distance_translation_rotation_0_240908
467	ReAlnet08_ALL
468	yudixie_resnet18_random_0_240719
469	ReAlnet04_cornet
470	ReAlnet09_ALL
471	ReAlnet09_cornet
472	resnet50_primary_visual_cortex
473	ReAlnet10_ALL
474	resnet18_fair
475	predictive_coding_pc
476	yudixie_wbh_object_class_scaling_0415_run_0
477	yudixie_wbh_object_class_scaling_0415_run_3
478	yudixie_wbh_object_class_scaling_0415_imagenet
479	yudixie_wbh_object_class_scaling_0415_run_2
480	yudixie_wbh_object_class_scaling_0415_run_1
481	deepeisnn_ei_cifar10_vgg19
482	deepeisnn_ei_cifar10_resnet18
483	deepeisnn_ei_imagenet_resnet18
484	dinov2_vitl14_reg_lc_featread

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