Leaderboard Explore Compare Profile Community Blog FAQ Competition 2024 Report Issue

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	ViT_L_32_imagenet1k	.637
20	resnext101_32x32d_wsl	.637
21	vit_large_patch14_clip_224:laion2b_ft_in1k	.636
22	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.629
23	resnet101	.629
24	effnetv2m_custom384	.621
25	AdvProp_efficientnet-b4	.619
26	AT_efficientnet-b4	.619
27	vit_large_patch14_clip_224:openai_ft_in1k	.619
28	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.615
29	effnetb1_272x240	.614
30	vit_relpos_base_patch16_clsgap_224:sw_in1k	.610
31	resnet152_imagenet_full	.607
32	convnext_base_fb_in22k_ft_in1k	.605
33	deit3_base_patch16_224_fb_in1k	.604
34	resnet50_ewconplus_wzc_2_323	.603
35	focalnet_tiny_lrf_in1k	.603
36	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.593
37	pnasnet_large	.593
38	pnasnet_large_halfPrecision	.592
39	pnasnet_large-tf	.584
40	efficientnet_b0	.583
41	fixres_resnext101_32x48d_wsl	.582
42	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.577
43	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.575
44	vit_large_patch14_clip_336:laion2b_ft_in1k	.574
45	convnext_xlarge:fb_in22k_ft_in1k	.574
46	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.567
47	resnet50_st_wzc_seed5_2_323	.558
48	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.556
49	AdvProp_efficientnet-b6	.553
50	cvt_cvt-13-384-in1k_4_LucyV4	.551
51	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.549
52	resnet_101_v1	.546
53	AT_efficientnet-b2	.541
54	AdvProp_efficientnet-b2	.541
55	cvt_cvt-13-224-in1k_4_LucyV4	.537
56	resnet50_st_wzc_seed6_2_323	.533
57	resnet50_st_wzc_seed7_2_323	.532
58	resnet50_st_wzc_seed4_2_323	.529
59	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
60	vonegrcnn_62e_nobn	.527
61	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.527
62	antialias-resnet152	.524
63	ViT_B_32_imagenet1k	.522
64	resnet50_ewconplus_wzc_seed4_2_323	.521
65	resnet-152_v2-tf	.520
66	inception_v4	.519
67	CLIP_resnet50	.513
68	CLIP_resnet50_float32	.513
69	custom_model_cv_18_dagger_408	.512
70	nasnet_large	.505
71	nasnet_large-tf	.499
72	convnext_tiny_imagenet_full_seed-0	.498
73	vonegrcnn_47e	.497
74	convnext_large:fb_in22k_ft_in1k	.492
75	resnet_50_v2	.491
76	vonegrcnn_52e_full	.487
77	resnet50_ewconplus_wzc_seed5_2__323	.484
78	convnext_femto_ols:d1_in1k	.482
79	deit3_base_patch16_224_fb_in22k_ft_in1k	.482
80	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.481
81	vonegrcnn_62e_nobn	.476
82	resnet50_ewconplus_wzc	.476
83	resnet50_ewconplus_wzc_seed3_2_323	.476
84	convnext_base:clip_laiona_augreg_ft_in1k_384	.472
85	convnext_tiny:in12k_ft_in1k	.471
86	resnet_152_v1	.470
87	cvt_cvt-21-384-in1k_4_LucyV4	.464
88	resnet50_ewconplus_noseed_2_323	.461
89	resnet50_linf_4_robust	.459
90	resnet50_st_wzc_seed3_2_323	.459
91	resnet-152_v2_pytorch	.459
92	resnet-152_v1-tf	.459
93	vonegrcnn_47e	.456
94	efficientnet-b7	.453
95	densenet-201-keras	.448
96	densenet_201_pytorch	.446
97	resnet50_ewconplus_wzc_seed2_2__323	.446
98	inception_v3_pytorch	.445
99	inception_v4-tf	.443
100	resnet50_imagenet_full	.443
101	densenet-201	.443
102	resnet34_imagenet_full	.442
103	resnet50_st_wzc_2_323	.441
104	antialiased-rnext101_32x8d	.436
105	vonegrcnn_52e_full	.435
106	resnet50-SIN_IN_IN	.432
107	resnet_SIN_IN_FT_IN	.432
108	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.431
109	resnet50-VITO-8deg-cc	.430
110	resnet50_st_wzc_noseed_2_323	.429
111	cvt_cvt-21-224-in1k_4_LucyV4	.429
112	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.424
113	convnext_base_fb_in1k	.424
114	resnet50_st_wzc_2	.423
115	resnet50_ewconplus_wzc_seed6_2_323	.420
116	voneresnet-50-non_stochastic	.417
117	convnext_small_imagenet_100_seed-0	.417
118	deit_large_imagenet_full_seed-0	.416
119	resnet_50_v1_spiking	.413
120	resnet101_imagenet_full	.412
121	resnet50_ewconplus_wzc_seed7_2_323	.410
122	CLIP-RN50	.409
123	BiT-S-R50x3	.408
124	convnext_base_imagenet_full_seed-0	.406
125	voneresnet-50-robust	.403
126	xception-keras	.401
127	resnet-50-pytorch	.400
128	resnet50-sup	.400
129	resnet50_tutorial	.400
130	resnet_50_v1	.400
131	resnet50-moclr8deg	.394
132	resnet50_random_linf8_perturb	.393
133	ReAlnet03	.384
134	efficientnet_b2_imagenet_full	.383
135	resnet-50_v2-tf	.378
136	inception_v3-tf	.377
137	convnext_base_fb_in1k_gram_aligned_dinov3	.375
138	resnet50_random_l2_perturb	.371
139	convnext_small_imagenet_full_seed-0	.367
140	resnet-101_v1-tf	.363
141	resnet-50_v1-tf	.359
142	ReAlnet02	.355
143	resnet50-SIN_IN	.355
144	convnext_large_imagenet_full_seed-0	.354
145	alexnet_fov4	.352
146	Res2Net50_26w_4s	.350
147	AdvProp_efficientnet-b8	.349
148	densenet-121-keras	.346
149	resnet50	.344
150	inception_v3	.341
151	deit_small_imagenet_full_seed-0	.338
152	BiT-S-R152x2	.336
153	tv_efficientnet-b1	.336
154	nasnet_mobile	.335
155	ReAlnet04	.334
156	grcnn_109	.333
157	blt_vs	.331
158	ReAlnet07	.329
159	ReAlnet01	.328
160	xception	.327
161	ReAlnet06	.326
162	grcnn	.325
163	inception_v1	.324
164	ReAlnet08	.324
165	mobilenet_v2_1.0_192-tf	.318
166	antialiased-r50	.318
167	ReAlnet05	.317
168	inception_v2-tf	.309
169	densenet-121	.304
170	mobilenet_v2_1_0_192	.301
171	deit_base_imagenet_full_seed-0	.299
172	vonealexnet_gaussian_noise_std4_fixed	.297
173	ReAlnet10	.297
174	CORnet-S	.297
175	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.296
176	resnet18_imagenet_full	.295
177	vgg_19	.295
178	cornet_s	.293
179	resnet50_robust_l2_eps1	.291
180	regnet_y_400mf	.288
181	mobilenet_v1_0.75_224	.287
182	resnet50_linf_8_robust	.287
183	texture_shape_resnet50_trained_on_SIN	.286
184	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.281
185	voneresnet-50	.281
186	alexnet_ks_torevert	.281
187	alexnet	.281
188	ReAlnet09	.277
189	mobilenet_v2_1_0_224	.276
190	densenet-169-keras	.274
191	resnet50_l2_3_robust	.272
192	mobilenet_v1_1.0_192	.269
193	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.264
194	alexnet_random_linf8_perturb	.260
195	resnet50_moco_v2	.256
196	vgg_16	.255
197	mobilenet_v2_0_5_224	.253
198	alexnet_reduced_aliasing_early_checkpoint	.252
199	resnet50_byol	.251
200	alexnet_l2_3_robust	.250
201	efficientnet_b1_imagenet_full	.249
202	mobilenet_v2_1_3_224	.247
203	resnet-18	.245
204	resnet50-SIN	.243
205	resnet-50-robust	.242
206	imagenet_l2_3_0	.242
207	resnet50_robust_l2_eps3	.242
208	densenet-169	.241
209	mobilenet_v2_1_0_128	.239
210	BiT-S-R152x4	.238
211	mobilenet_v2_1_0_160	.235
212	mobilenet_v1_1.0_224	.230
213	mobilenet_v2_1.0_160-tf	.229
214	inception_v1-tf	.223
215	cv_18_dagger_408_pretrained	.223
216	shufflenet_v2_x1_0	.222
217	alexnet_linf_8_robust	.218
218	efficientnet_b0_imagenet_full	.217
219	BiT-S-R101x3	.213
220	resnet50-vitoimagevidnet8	.210
221	alexnet_random_l2_3_perturb	.210
222	mobilenet_v2_1.4_224-tf	.208
223	BiT-S-R50x1	.208
224	resnet-34	.208
225	mobilenet_v2_1.3_224-tf	.206
226	mobilenet_v2_0_75_160	.204
227	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.201
228	mobilenet_v1_1.0_160	.198
229	vgg-16-keras	.197
230	mobilenet_v2_0.5_192-tf	.196
231	resnet50_simclr	.196
232	mobilenet_v2_1.0_224-tf	.194
233	mobilenet_v1_1.0_128	.192
234	BiT-S-R101x1	.188
235	AlexNet_SIN_fov4	.186
236	squeezenet1_1	.182
237	mobilenet_v2_0.75_96	.181
238	mobilenet_v2_0_5_192	.180
239	alexnet_training_seed_05	.180
240	alexnet_training_seed_01	.180
241	alexnet_training_seed_01_fov4	.180
242	alexnet_training_seed_01_fov12	.180
243	alexnet_training_seed_01_fov16	.180
244	alexnet_robust_correct	.179
245	vgg-19-keras	.179
246	alexnet_fov12	.175
247	deit_small_imagenet_100_seed-0	.171
248	mobilenet_v2_0.75_128	.169
249	konkle_alexnetgn_ipcl_ref01_primary_model	.168
250	mobilenet_v2_0.75_160-tf	.164
251	convnext_tiny_sup	.161
252	alexnet_early_checkpoint	.161
253	mobilenet_v1_0.25_192	.160
254	0.5x_resnet-18_LC_w_sh_1_iter	.160
255	mobilenet_v2_1-4_224_pytorch	.159
256	mobilenet_v2_1_4_224	.159
257	mobilenet_v2_0.5_224-tf	.156
258	artResNet18_1	.156
259	mobilenet_v2_0.35_160	.150
260	alexnet_fov16	.149
261	mobilenet_v1_0.75_128	.148
262	mobilenet_v2_1.0_128-tf	.143
263	mobilenet_v1_0.75_192	.141
264	resnet50_imagenet_100_seed-0	.140
265	AdvProp_efficientnet-b0	.140
266	mobilenet_v1_0.5_128	.138
267	imagenet_l2_10_0	.132
268	nasnet_mobile-tf	.132
269	mobilenet_v2_1.0_96	.131
270	mobilenet_v2_0.35_96	.131
271	texture_shape_alexnet_trained_on_SIN	.129
272	squeezenet1_0	.126
273	mobilenet_v1_0.5_192	.126
274	mobilenet_v1_0.25_160	.125
275	AlexNet_SIN	.122
276	mobilenet_v2_0.5_160	.120
277	mobilenet_v2_0_75_192	.120
278	mobilenet_v1_0.25_224	.113
279	mobilenet_v2_0.5_128	.113
280	mobilenet_v2_0.75_192-tf	.111
281	mobilenet_v1_0.75_160	.107
282	resnet_50_v1_spiking_l4a	.098
283	mobilenet_v1_0.5_224	.097
284	mobilenet_v2_0_75_224	.097
285	resnet50-meshes-lt-100-original-scratch	.094
286	deit_small_imagenet_1_seed-0	.094
287	deit_small_imagenet_10_seed-0	.094
288	resnet50_imagenet_10_seed-0	.093
289	alexnet_training_seed_06	.089
290	alexnet_training_seed_02	.085
291	alexnet_training_seed_04	.085
292	alexnet_training_seed_08	.085
293	alexnet_training_seed_10	.085
294	convnext_small_imagenet_10_seed-0	.084
295	alexnet_training_seed_07	.084
296	mobilenet_v2_0.5_96	.077
297	resnet50_eMMCR_Vanilla	.077
298	mobilenet_v2_0.35_128	.070
299	AlexNet_SIN_fov12	.065
300	AlexNet_SIN_fov16	.064
301	barlow-twins-resnet50	.063
302	resnet50-vicregl0p9	.059
303	mobilenet_v2_0.75_224-tf	.058
304	resnet50-meshes-lt-100-original-pretrained	.058
305	resnet50_FractalDB	.057
306	mobilenet_v2_0.35_224	.056
307	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.055
308	resnet50-vicregl0p75	.051
309	resnet50-vicreg	.044
310	alexnet_training_seed_09	.042
311	resnet50-barlow	.041
312	mobilenet_v1_0.5_160	.038
313	mobilenet_v2_0.35_192	.036
314	my-model	.029
315	convnext_small_imagenet_1_seed-0	.015
316	resnet_50_v1_spiking_l4	.014
317	mobilenet_v1_0.25_128	.013
318	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.012
319	resnet50-simclr-vissl	.009
320	mobilevit_small	.008
321	resnet50_imagenet_1_seed-0	.008
322	resnet_50_v1_spiking_l3	.004
323	alexnet_training_seed_03	.002
324	resnet-50_untrained	.002
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	BiT-M-R152x2
330	resnet-18-LC_w_sh_1_iter_conv_init_m
331	resnet-18-LC_w_sh_1_iter_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_10_iter_conv_init_m
335	FrankRobWobv0
336	resnet18-local_aggregation
337	0.5x_resnet-18_LC
338	CORnetZ_CIFAR10
339	resnet18-deepcluster
340	r3m_resnet18
341	CORnet-Z
342	resnet18-instance_recognition
343	my-model
344	vggface
345	resnet18-colorization
346	0.5x_resnet-18_LC_w_sh_10_iter
347	r3m_resnet50
348	r3m_resnet34
349	r3m_resnet50_nocrop
350	resnet-18-LC_m
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	resnet18-contrastive_predictive
368	omnivore_swinT
369	resnet-18-LC_untrained
370	resnet-18_untrained
371	resnet-50x2_untrained
372	dcgan
373	v1-pyr-nodown
374	unet_entire
375	CORnetZ_CIFAR10_bs32_20_04
376	pixels
377	dorinet_cornet_z
378	resnet50
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

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