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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	convnext_large_in22k_supervised	.599
38	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.593
39	pnasnet_large	.593
40	pnasnet_large_halfPrecision	.592
41	convnext_large_clip_laion2b_finelayers	.586
42	pnasnet_large-tf	.584
43	efficientnet_b0	.583
44	fixres_resnext101_32x48d_wsl	.582
45	dinov2_vitg14_lc	.578
46	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.577
47	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.575
48	vit_large_patch14_clip_336:laion2b_ft_in1k	.574
49	convnext_xlarge:fb_in22k_ft_in1k	.574
50	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.567
51	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.556
52	AdvProp_efficientnet-b6	.553
53	cvt_cvt-13-384-in1k_4_LucyV4	.551
54	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.549
55	dinov2_vitl14_lc	.548
56	resnet_101_v1	.546
57	AT_efficientnet-b2	.541
58	AdvProp_efficientnet-b2	.541
59	cvt_cvt-13-224-in1k_4_LucyV4	.537
60	dinov2_vitl14_reg_lc	.536
61	resnet50_st_wzc_seed6_2_323	.533
62	resnet50_st_wzc_seed7_2_323	.532
63	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
64	vonegrcnn_62e_nobn	.527
65	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.527
66	antialias-resnet152	.524
67	ViT_B_32_imagenet1k	.522
68	resnet-152_v2-tf	.520
69	inception_v4	.519
70	vit_huge_clip336_laion	.519
71	CLIP_resnet50	.513
72	CLIP_resnet50_float32	.513
73	custom_model_cv_18_dagger_408	.512
74	nasnet_large	.505
75	nasnet_large-tf	.499
76	convnext_tiny_imagenet_full_seed-0	.498
77	vonegrcnn_47e	.497
78	convnext_large:fb_in22k_ft_in1k	.492
79	resnet_50_v2	.491
80	vonegrcnn_52e_full	.487
81	resnet50_ewconplus_wzc_seed5_2__323	.484
82	convnext_femto_ols:d1_in1k	.482
83	deit3_base_patch16_224_fb_in22k_ft_in1k	.482
84	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.481
85	vonegrcnn_62e_nobn	.476
86	resnet50_ewconplus_wzc	.476
87	convnext_xxlarge_clip	.475
88	convnext_base:clip_laiona_augreg_ft_in1k_384	.472
89	convnext_tiny:in12k_ft_in1k	.471
90	dinov2_vitg14_reg_lc	.471
91	resnet_152_v1	.470
92	cvt_cvt-21-384-in1k_4_LucyV4	.464
93	resnet50_linf_4_robust	.459
94	resnet50_st_wzc_seed3_2_323	.459
95	resnet-152_v2_pytorch	.459
96	resnet-152_v1-tf	.459
97	vonegrcnn_47e	.456
98	efficientnet-b7	.453
99	densenet-201-keras	.448
100	densenet_201_pytorch	.446
101	resnet50_ewconplus_wzc_seed2_2__323	.446
102	inception_v3_pytorch	.445
103	inception_v4-tf	.443
104	resnet50_imagenet_full	.443
105	densenet-201	.443
106	resnet34_imagenet_full	.442
107	dinov2_vitl14_lc_res336	.436
108	antialiased-rnext101_32x8d	.436
109	vonegrcnn_52e_full	.435
110	resnet50-SIN_IN_IN	.432
111	resnet_SIN_IN_FT_IN	.432
112	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.431
113	resnet50-VITO-8deg-cc	.430
114	cvt_cvt-21-224-in1k_4_LucyV4	.429
115	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.424
116	convnext_base_fb_in1k	.424
117	resnet50_st_wzc_2	.423
118	resnet50_ewconplus_wzc_seed6_2_323	.420
119	voneresnet-50-non_stochastic	.417
120	convnext_small_imagenet_100_seed-0	.417
121	deit_large_imagenet_full_seed-0	.416
122	resnet_50_v1_spiking	.413
123	resnet101_imagenet_full	.412
124	CLIP-RN50	.409
125	BiT-S-R50x3	.408
126	convnext_base_imagenet_full_seed-0	.406
127	voneresnet-50-robust	.403
128	xception-keras	.401
129	resnet-50-pytorch	.400
130	resnet50-sup	.400
131	resnet50_tutorial	.400
132	resnet_50_v1	.400
133	resnet50-moclr8deg	.394
134	resnet50_random_linf8_perturb	.393
135	ReAlnet03	.384
136	efficientnet_b2_imagenet_full	.383
137	resnet-50_v2-tf	.378
138	inception_v3-tf	.377
139	convnext_base_fb_in1k_gram_aligned_dinov3	.375
140	resnet50_random_l2_perturb	.371
141	convnext_small_imagenet_full_seed-0	.367
142	resnet-101_v1-tf	.363
143	resnet-50_v1-tf	.359
144	resnet50_hvs_wzc	.358
145	ReAlnet02	.355
146	resnet50-SIN_IN	.355
147	convnext_large_imagenet_full_seed-0	.354
148	alexnet_fov4	.352
149	Res2Net50_26w_4s	.350
150	AdvProp_efficientnet-b8	.349
151	densenet-121-keras	.346
152	resnet50	.344
153	inception_v3	.341
154	deit_small_imagenet_full_seed-0	.338
155	BiT-S-R152x2	.336
156	tv_efficientnet-b1	.336
157	nasnet_mobile	.335
158	ReAlnet04	.334
159	grcnn_109	.333
160	blt_vs	.331
161	ReAlnet07	.329
162	ReAlnet01	.328
163	xception	.327
164	ReAlnet06	.326
165	grcnn	.325
166	inception_v1	.324
167	ReAlnet08	.324
168	mobilenet_v2_1.0_192-tf	.318
169	antialiased-r50	.318
170	ReAlnet05	.317
171	inception_v2-tf	.309
172	densenet-121	.304
173	mobilenet_v2_1_0_192	.301
174	deit_base_imagenet_full_seed-0	.299
175	vonealexnet_gaussian_noise_std4_fixed	.297
176	ReAlnet10	.297
177	CORnet-S	.297
178	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.296
179	resnet18_imagenet_full	.295
180	vgg_19	.295
181	cornet_s	.293
182	resnet50_robust_l2_eps1	.291
183	regnet_y_400mf	.288
184	mobilenet_v1_0.75_224	.287
185	resnet50_linf_8_robust	.287
186	texture_shape_resnet50_trained_on_SIN	.286
187	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.281
188	voneresnet-50	.281
189	alexnet_ks_torevert	.281
190	alexnet	.281
191	ReAlnet09	.277
192	mobilenet_v2_1_0_224	.276
193	densenet-169-keras	.274
194	resnet50_l2_3_robust	.272
195	mobilenet_v1_1.0_192	.269
196	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.264
197	alexnet_random_linf8_perturb	.260
198	resnet50_moco_v2	.256
199	vgg_16	.255
200	mobilenet_v2_0_5_224	.253
201	alexnet_reduced_aliasing_early_checkpoint	.252
202	resnet50_byol	.251
203	alexnet_l2_3_robust	.250
204	efficientnet_b1_imagenet_full	.249
205	mobilenet_v2_1_3_224	.247
206	resnet-18	.245
207	resnet50-SIN	.243
208	resnet-50-robust	.242
209	imagenet_l2_3_0	.242
210	resnet50_robust_l2_eps3	.242
211	densenet-169	.241
212	mobilenet_v2_1_0_128	.239
213	BiT-S-R152x4	.238
214	mobilenet_v2_1_0_160	.235
215	mobilenet_v1_1.0_224	.230
216	mobilenet_v2_1.0_160-tf	.229
217	inception_v1-tf	.223
218	cv_18_dagger_408_pretrained	.223
219	shufflenet_v2_x1_0	.222
220	alexnet_linf_8_robust	.218
221	efficientnet_b0_imagenet_full	.217
222	BiT-S-R101x3	.213
223	resnet50-vitoimagevidnet8	.210
224	alexnet_random_l2_3_perturb	.210
225	mobilenet_v2_1.4_224-tf	.208
226	BiT-S-R50x1	.208
227	resnet-34	.208
228	mobilenet_v2_1.3_224-tf	.206
229	mobilenet_v2_0_75_160	.204
230	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.201
231	mobilenet_v1_1.0_160	.198
232	vgg-16-keras	.197
233	mobilenet_v2_0.5_192-tf	.196
234	resnet50_simclr	.196
235	mobilenet_v2_1.0_224-tf	.194
236	mobilenet_v1_1.0_128	.192
237	BiT-S-R101x1	.188
238	AlexNet_SIN_fov4	.186
239	squeezenet1_1	.182
240	mobilenet_v2_0.75_96	.181
241	mobilenet_v2_0_5_192	.180
242	alexnet_training_seed_05	.180
243	alexnet_training_seed_01	.180
244	alexnet_training_seed_01_fov4	.180
245	alexnet_training_seed_01_fov12	.180
246	alexnet_training_seed_01_fov16	.180
247	alexnet_robust_correct	.179
248	vgg-19-keras	.179
249	alexnet_fov12	.175
250	deit_small_imagenet_100_seed-0	.171
251	mobilenet_v2_0.75_128	.169
252	konkle_alexnetgn_ipcl_ref01_primary_model	.168
253	mobilenet_v2_0.75_160-tf	.164
254	convnext_tiny_sup	.161
255	alexnet_early_checkpoint	.161
256	mobilenet_v1_0.25_192	.160
257	0.5x_resnet-18_LC_w_sh_1_iter	.160
258	mobilenet_v2_1-4_224_pytorch	.159
259	mobilenet_v2_1_4_224	.159
260	mobilenet_v2_0.5_224-tf	.156
261	artResNet18_1	.156
262	mobilenet_v2_0.35_160	.150
263	alexnet_fov16	.149
264	mobilenet_v1_0.75_128	.148
265	mobilenet_v2_1.0_128-tf	.143
266	mobilenet_v1_0.75_192	.141
267	resnet50_imagenet_100_seed-0	.140
268	AdvProp_efficientnet-b0	.140
269	mobilenet_v1_0.5_128	.138
270	imagenet_l2_10_0	.132
271	nasnet_mobile-tf	.132
272	mobilenet_v2_1.0_96	.131
273	mobilenet_v2_0.35_96	.131
274	texture_shape_alexnet_trained_on_SIN	.129
275	squeezenet1_0	.126
276	mobilenet_v1_0.5_192	.126
277	mobilenet_v1_0.25_160	.125
278	AlexNet_SIN	.122
279	mobilenet_v2_0.5_160	.120
280	mobilenet_v2_0_75_192	.120
281	mobilenet_v1_0.25_224	.113
282	mobilenet_v2_0.5_128	.113
283	mobilenet_v2_0.75_192-tf	.111
284	mobilenet_v1_0.75_160	.107
285	resnet_50_v1_spiking_l4a	.098
286	mobilenet_v1_0.5_224	.097
287	mobilenet_v2_0_75_224	.097
288	deit_small_imagenet_1_seed-0	.094
289	deit_small_imagenet_10_seed-0	.094
290	resnet50_imagenet_10_seed-0	.093
291	alexnet_training_seed_06	.089
292	alexnet_training_seed_02	.085
293	alexnet_training_seed_04	.085
294	alexnet_training_seed_08	.085
295	alexnet_training_seed_10	.085
296	convnext_small_imagenet_10_seed-0	.084
297	alexnet_training_seed_07	.084
298	mobilenet_v2_0.5_96	.077
299	resnet50_eMMCR_Vanilla	.077
300	mobilenet_v2_0.35_128	.070
301	AlexNet_SIN_fov12	.065
302	AlexNet_SIN_fov16	.064
303	barlow-twins-resnet50	.063
304	resnet50-vicregl0p9	.059
305	mobilenet_v2_0.75_224-tf	.058
306	resnet50_FractalDB	.057
307	mobilenet_v2_0.35_224	.056
308	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.055
309	resnet50-vicregl0p75	.051
310	resnet50-vicreg	.044
311	alexnet_training_seed_09	.042
312	resnet50-barlow	.041
313	mobilenet_v1_0.5_160	.038
314	mobilenet_v2_0.35_192	.036
315	my-model	.029
316	convnext_small_imagenet_1_seed-0	.015
317	resnet_50_v1_spiking_l4	.014
318	mobilenet_v1_0.25_128	.013
319	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.012
320	resnet50-simclr-vissl	.009
321	mobilevit_small	.008
322	resnet50_imagenet_1_seed-0	.008
323	resnet_50_v1_spiking_l3	.004
324	alexnet_training_seed_03	.002
325	resnet-50_untrained	.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_1_iter_m
333	resnet-18-LC_w_sh_10_iter_m
334	resnet-18-LC_w_sh_100_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	resnet18-colorization
347	0.5x_resnet-18_LC_w_sh_10_iter
348	r3m_resnet50
349	r3m_resnet34
350	r3m_resnet50_nocrop
351	resnet-18-LC_m
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	CORnetZ_CIFAR10_bs32_20_04
377	pixels
378	dorinet_cornet_z
379	openclip
380	resnet50-cifar
381	resnet50_pretrained_with_retinal_waves
382	resnet50_ImageNet
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	yudixie_resnet50_random_0_240908
385	yudixie_resnet50_imagenet1kpret_0_240908
386	yudixie_resnet50_imagenet1kpret_0_240312
387	yudixie_resnet50_cat_obj_class_all_latents_0_240908
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	pnasnet_large_pytorch
399	ReAlnet01_cornet
400	yolos_tiny
401	yudixie_resnet18_translation_reg_0_240719
402	compact_resnet50_V4
403	briaai_rmbg_1_4
404	yudixie_resnet18_distance_reg_0_240719
405	yudixie_resnet18_rotation_reg_0_240719
406	ReAlnet03_cornet
407	eBarlow_lmda_02_1000ep
408	ReAlnet02_cornet
409	resnet18_ecoset_full
410	vit_base_patch14_dinov2-lvd142m
411	compact_resnet50_IT
412	ReAlnet01_cornet_fov4
413	resnet34_ecoset_full
414	VOneCORnet-S
415	resnet50_ecoset_full
416	vit_base_patch14_reg4_dinov2-lvd142m
417	resnet101_ecoset_full
418	compact_vgg19_V4
419	yudixie_resnet50_distance_rotation_0_240908
420	yudixie_resnet50_rotation_reg_0_240908
421	yudixie_resnet50_distance_translation_0_240908
422	resnet152_ecoset_full
423	yudixie_resnet18_distance_translation_0_240719
424	aughost_fair_resnet18
425	yudixie_resnet18_translation_rotation_0_240719
426	yudixie_resnet18_distance_rotation_0_240719
427	yudixie_resnet18_category_class_0_240719
428	ReAlnet05_cornet
429	yudixie_resnet18_random_0_240719
430	compact_resnet50_robust_V4
431	yudixie_resnet18_cat_obj_class_all_latents_0_240719
432	resnet-18_test_m
433	yudixie_resnet18_distance_translation_rotation_0_240719
434	ReAlnet01_cornet_fov12
435	yudixie_resnet18_object_class_0_240719
436	SeLaVi-AVE
437	yudixie_resnet18_imagenet1kpret_0_240719
438	r3d_18
439	compact_vgg19_IT
440	ReAlnet06_cornet
441	ReAlnet01_cornet_fov16
442	yudixie_resnet50_translation_reg_0_240908
443	ReAlnet01_ALL
444	mc3_18
445	yudixie_resnet50_translation_rotation_0_240908
446	ReAlnet07_cornet
447	ReAlnet02_ALL
448	s3d
449	mvit_v1_b
450	ReAlnet04_ALL
451	ReAlnet08_cornet
452	compact_cornets_V4
453	ReAlnet03_ALL
454	compact_cornets_IT
455	omnivore_swinB
456	SeLaVi-Kinetics400
457	ReAlnet09_cornet
458	ReAlnet05_ALL
459	vit_base_patch16_dinov3-lvd1689m
460	SeLaVi-VGG-Sound
461	SeLaVi-Kinetics-Sound
462	ReAlnet06_ALL
463	compact_resnet50_robust_IT
464	omnivore_swinS
465	ReAlnet07_ALL
466	mvit_v2_s
467	r2plus1d_18
468	ReAlnet10_cornet
469	yudixie_resnet50_distance_translation_rotation_0_240908
470	ReAlnet08_ALL
471	ReAlnet09_ALL
472	ReAlnet04_cornet
473	resnet50_primary_visual_cortex
474	ReAlnet10_ALL
475	resnet18_fair
476	pr1_stl10_disentangled_alpha04
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

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