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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonIII-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x16d_wsl	.726
2	ViT_L_32_imagenet1k	.644
3	resnext101_32x48d_wsl	.641
4	SWSL_resnet50	.638
5	resnext101_32x32d_wsl	.629
6	vision_transformer_vit_large_patch16_224	.610
7	AdvProp_efficientnet-b4	.597
8	AT_efficientnet-b4	.597
9	convnext_base_fb_in22k_ft_in1k	.596
10	ViT_B_16_imagenet1k	.582
11	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.579
12	fixres_resnext101_32x48d_wsl	.578
13	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.574
14	eva02_large_in22k	.569
15	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.569
16	resnext101_32x8d_wsl	.567
17	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.564
18	cvt_cvt-13-224-in1k_4_LucyV4	.561
19	effnetv2m_custom384	.558
20	convnext_xlarge:fb_in22k_ft_in1k	.556
21	AdvProp_efficientnet-b6	.556
22	convnext_small_imagenet_full_seed-0	.554
23	SWSL_resnext101_32x8d	.551
24	focalnet_tiny_lrf_in1k	.550
25	resnet_101_v2	.548
26	convnext_small_imagenet_100_seed-0	.546
27	resnet_50_v2	.541
28	pnasnet_large-tf	.535
29	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.535
30	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.535
31	convnext_large_clip_laion2b_finelayers	.523
32	convnext_tiny_imagenet_full_seed-0	.521
33	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.518
34	resnet101	.517
35	convnext_base:clip_laiona_augreg_ft_in1k_384	.511
36	convnext_base_fb_in1k	.507
37	AdvProp_efficientnet-b7	.505
38	convnext_large:fb_in22k_ft_in1k	.502
39	ViT_B_32_imagenet1k	.502
40	convnext_xxlarge_clip	.502
41	pnasnet_large_halfPrecision	.501
42	pnasnet_large	.501
43	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.500
44	effnetb1_272x240	.495
45	cvt_cvt-13-384-in1k_4_LucyV4	.494
46	nasnet_large	.490
47	resnet_152_v2	.489
48	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.489
49	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.484
50	convnext_large_in22k_supervised	.483
51	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.480
52	dinov2_vitl14_reg_lc	.479
53	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.477
54	dinov2_vitl14_lc	.477
55	resnet-152_v2_pytorch	.476
56	resnet152_imagenet_full	.474
57	efficientnet-b7	.473
58	vit_relpos_base_patch16_clsgap_224:sw_in1k	.473
59	antialias-resnet152	.470
60	inception_v4	.469
61	resnet50_st_wzc_seed3_2_323	.468
62	AT_efficientnet-b2	.468
63	AdvProp_efficientnet-b2	.468
64	convnext_tiny:in12k_ft_in1k	.468
65	resnet_101_v1	.465
66	vit_large_patch14_clip_224:laion2b_ft_in1k	.464
67	CLIP-RN50	.464
68	resnet-152_v2-tf	.459
69	resnet50_st_wzc_seed6_2_323	.453
70	convnext_large_imagenet_full_seed-0	.453
71	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.453
72	vit_large_patch14_clip_336:laion2b_ft_in1k	.451
73	inception_v4-tf	.445
74	efficientnet_b0	.444
75	nasnet_large-tf	.443
76	CLIP_ViT-B_32	.442
77	deit_base_imagenet_full_seed-0	.441
78	deit_large_imagenet_full_seed-0	.439
79	vit_large_patch14_clip_224:openai_ft_in1k	.438
80	antialiased-rnext101_32x8d	.437
81	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.433
82	resnet_152_v1	.433
83	cvt_cvt-21-384-in1k_4_LucyV4	.432
84	vit_base_patch16_clip_224:openai_ft_in1k	.432
85	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.429
86	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.427
87	convnext_femto_ols:d1_in1k	.424
88	BiT-S-R50x3	.423
89	resnet50_ewconplus_wzc_seed5_2__323	.420
90	convnext_base_imagenet_full_seed-0	.418
91	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.416
92	deit_small_imagenet_full_seed-0	.415
93	CLIP_resnet50	.415
94	CLIP_resnet50_float32	.415
95	dinov2_vitg14_lc	.415
96	xception	.412
97	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.412
98	cv_18_dagger_408_pretrained	.400
99	resnet50_ewconplus_wzc_seed6_2_323	.399
100	grcnn_109	.397
101	inception_v3-tf	.395
102	resnet34_imagenet_full	.393
103	vit_large_clip336_openai	.391
104	voneresnet-50-non_stochastic	.391
105	inception_v3	.388
106	Res2Net50_26w_4s	.383
107	grcnn	.380
108	tv_efficientnet-b1	.379
109	resnet101_imagenet_full	.377
110	densenet-121	.373
111	resnet50_ewconplus_wzc_seed2_2__323	.371
112	xception-keras	.369
113	resnet50_linf_4_robust	.368
114	cvt_cvt-21-224-in1k_4_LucyV4	.366
115	resnet-152_v1-tf	.364
116	resnet50_hvs_wzc	.363
117	resnet50_ewconplus_wzc	.363
118	densenet-201	.360
119	densenet_201_pytorch	.360
120	dinov2_vitl14_lc_res336	.359
121	densenet-121-keras	.357
122	resnet50-SIN_IN	.356
123	densenet-201-keras	.353
124	efficientnet_b2_imagenet_full	.353
125	vonegrcnn_52e_full	.352
126	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.350
127	resnet50	.346
128	resnet-50_v2-tf	.343
129	resnet50-moclr8deg	.340
130	vonegrcnn_62e_nobn	.334
131	pnasnet_large_pytorch	.333
132	dinov2_vitg14_reg_lc	.333
133	vonegrcnn_47e	.327
134	custom_model_cv_18_dagger_408	.324
135	blt_vs	.320
136	BiT-S-R152x2	.320
137	AdvProp_efficientnet-b8	.319
138	resnet50_random_linf8_perturb	.319
139	resnet50-VITO-8deg-cc	.319
140	resnet50_imagenet_full	.318
141	ReAlnet02	.317
142	resnet-50_v1-tf	.316
143	BiT-S-R101x3	.315
144	densenet-169	.314
145	resnet50-SIN_IN_IN	.313
146	resnet_SIN_IN_FT_IN	.313
147	nasnet_mobile	.312
148	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.309
149	vit_huge_clip336_laion	.307
150	resnet50_st_wzc_2	.305
151	vonegrcnn_62e_nobn	.304
152	vonegrcnn_52e_full	.302
153	inception_v2-tf	.302
154	mobilenet_v2_1-4_224_pytorch	.299
155	mobilenet_v2_1_4_224	.299
156	densenet-169-keras	.297
157	ReAlnet05	.297
158	alexnet_fov4	.296
159	texture_shape_resnet50_trained_on_SIN	.295
160	resnet_50_v1_spiking	.295
161	vonegrcnn_47e	.294
162	vgg_19	.294
163	resnet-101_v1-tf	.292
164	ReAlnet06	.292
165	resnet-50-pytorch	.291
166	resnet50-sup	.291
167	resnet50_tutorial	.291
168	resnet_50_v1	.291
169	antialiased-r50	.290
170	ReAlnet10	.290
171	BiT-S-R152x4	.287
172	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.286
173	resnet50_random_l2_perturb	.286
174	ReAlnet04	.285
175	voneresnet-50	.284
176	voneresnet-50-robust	.284
177	mobilenet_v2_1_0_160	.283
178	resnet50_moco_v2	.282
179	ReAlnet07	.281
180	inception_v1	.278
181	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.277
182	CORnet-S	.276
183	ReAlnet08	.274
184	ReAlnet01	.273
185	resnet50_l2_3_robust	.273
186	mobilenet_v2_1.4_224-tf	.272
187	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
188	ReAlnet03	.267
189	cornet_s	.263
190	mobilenet_v2_1_3_224	.262
191	convnext_base_fb_in1k_gram_aligned_dinov3	.261
192	mobilenet_v2_1_0_224	.258
193	resnet50_linf_8_robust	.257
194	resnet50-SIN	.256
195	resnet50_byol	.255
196	AlexNet_SIN_fov4	.252
197	mobilenet_v2_1.0_192-tf	.252
198	mobilenet_v2_1.0_224-tf	.251
199	inception_v1-tf	.250
200	resnet18_imagenet_full	.247
201	mobilenet_v2_1_0_192	.245
202	resnet-34	.245
203	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.244
204	vgg_16	.242
205	BiT-S-R50x1	.241
206	mobilenet_v2_1.3_224-tf	.240
207	efficientnet_b1_imagenet_full	.238
208	resnet50_robust_l2_eps1	.233
209	mobilenet_v2_1.0_160-tf	.231
210	eBarlow_lmda_02_1000ep	.230
211	alexnet_random_linf8_perturb	.228
212	ReAlnet09	.226
213	efficientnet_b0_imagenet_full	.221
214	mobilenet_v1_0.75_224	.220
215	resnet-50-robust	.219
216	imagenet_l2_3_0	.219
217	resnet50_robust_l2_eps3	.219
218	resnet-18	.215
219	mobilenet_v1_1.0_224	.214
220	mobilenet_v2_0_5_224	.213
221	resnet50-vitoimagevidnet8	.212
222	regnet_y_400mf	.203
223	vonealexnet_gaussian_noise_std4_fixed	.200
224	mobilenet_v2_0.75_128	.196
225	alexnet_random_l2_3_perturb	.190
226	mobilenet_v2_0_75_160	.189
227	alexnet_reduced_aliasing_early_checkpoint	.174
228	mobilenet_v2_0.5_192-tf	.173
229	mobilenet_v2_0.75_160-tf	.171
230	shufflenet_v2_x1_0	.168
231	nasnet_mobile-tf	.168
232	alexnet_ks_torevert	.167
233	alexnet	.167
234	resnet50_simclr	.162
235	mobilenet_v1_0.75_192	.161
236	mobilenet_v2_1_0_128	.157
237	deit_small_imagenet_100_seed-0	.154
238	BiT-S-R101x1	.154
239	vgg-19-keras	.151
240	AdvProp_efficientnet-b0	.151
241	alexnet_robust_correct	.146
242	mobilenet_v1_1.0_192	.145
243	mobilenet_v1_0.75_160	.142
244	convnext_tiny_sup	.138
245	mobilenet_v2_0_5_192	.137
246	mobilenet_v2_0.35_192	.136
247	alexnet_fov16	.135
248	alexnet_l2_3_robust	.132
249	vgg-16-keras	.130
250	resnet50-barlow	.129
251	alexnet_training_seed_05	.129
252	alexnet_training_seed_01	.129
253	alexnet_training_seed_01_fov4	.129
254	alexnet_training_seed_01_fov12	.129
255	alexnet_training_seed_01_fov16	.129
256	alexnet_linf_8_robust	.127
257	mobilenet_v2_0_75_192	.125
258	alexnet_early_checkpoint	.124
259	mobilenet_v2_0.35_96	.124
260	mobilenet_v1_1.0_128	.124
261	mobilenet_v1_1.0_160	.123
262	resnet50_eMMCR_Vanilla	.123
263	0.5x_resnet-18_LC_w_sh_1_iter	.121
264	resnet50-simclr-vissl	.121
265	mobilenet_v1_0.5_224	.117
266	mobilenet_v2_0_75_224	.113
267	resnet50_imagenet_10_seed-0	.112
268	deit_small_imagenet_10_seed-0	.112
269	deit_small_imagenet_1_seed-0	.112
270	alexnet_fov12	.112
271	mobilenet_v1_0.5_128	.112
272	mobilenet_v1_0.5_192	.108
273	squeezenet1_1	.105
274	konkle_alexnetgn_ipcl_ref01_primary_model	.105
275	mobilenet_v1_0.75_128	.104
276	resnet50-vicreg	.103
277	mobilenet_v2_1.0_128-tf	.101
278	resnet50_imagenet_100_seed-0	.099
279	mobilenet_v2_0.5_96	.097
280	mobilenet_v2_0.5_160	.096
281	mobilenet_v2_0.35_224	.095
282	mobilenet_v2_1.0_96	.094
283	AlexNet_SIN	.093
284	mobilenet_v1_0.25_224	.093
285	mobilenet_v2_0.75_96	.093
286	mobilenet_v1_0.5_160	.091
287	convnext_small_imagenet_10_seed-0	.091
288	mobilenet_v2_0.75_192-tf	.090
289	mobilenet_v2_0.75_224-tf	.090
290	imagenet_l2_10_0	.089
291	resnet_50_v1_spiking_l4a	.089
292	mobilenet_v2_0.35_128	.085
293	mobilenet_v1_0.25_160	.081
294	mobilenet_v2_0.5_224-tf	.080
295	mobilenet_v2_0.35_160	.079
296	texture_shape_alexnet_trained_on_SIN	.078
297	alexnet_training_seed_06	.076
298	squeezenet1_0	.071
299	mobilenet_v1_0.25_192	.069
300	resnet50-vicregl0p75	.063
301	resnet_50_v1_spiking_l4	.062
302	resnet_50_v1_spiking_l3	.057
303	mobilenet_v2_0.5_128	.056
304	AlexNet_SIN_fov12	.042
305	my-model	.040
306	alexnet_training_seed_02	.037
307	alexnet_training_seed_04	.037
308	alexnet_training_seed_08	.037
309	alexnet_training_seed_10	.037
310	artResNet18_1	.037
311	resnet50-vicregl0p9	.036
312	alexnet_training_seed_09	.034
313	mobilenet_v1_0.25_128	.033
314	alexnet_training_seed_07	.033
315	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.028
316	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.026
317	resnet50_FractalDB	.023
318	resnet-50_untrained	.018
319	AlexNet_SIN_fov16	.010
320	resnet50_imagenet_1_seed-0	.010
321	convnext_small_imagenet_1_seed-0	.007
322	barlow-twins-resnet50	.003
323	alexnet_training_seed_03	.001
324	mobilevit_small	.000
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_10_iter_m
332	resnet-18_test_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	0.5x_resnet-18_LC
338	CORnetZ_CIFAR10
339	resnet18-local_aggregation
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	yudixie_resnet50_random_0_240908
366	resnet18-depth_prediction
367	ViT_B_16
368	r3m_resnet34_nocrop
369	resnet18-contrastive_predictive
370	omnivore_swinT
371	resnet-18-LC_untrained
372	resnet-18_untrained
373	resnet-50x2_untrained
374	dcgan
375	v1-pyr-nodown
376	unet_entire
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_imagenet1kpret_0_240908
386	yudixie_resnet50_imagenet1kpret_0_240312
387	yudixie_resnet50_object_class_0_240908
388	yudixie_resnet50_cat_obj_class_all_latents_0_240908
389	yudixie_resnet50_category_class_0_240908
390	inception_v3_pytorch
391	resnet18_imagenet21kP
392	yudixie_resnet50_distance_reg_0_240908
393	ConvLSTM
394	PredRNN
395	TAU
396	MIM
397	hmax
398	briaai_rmbg_1_4
399	ReAlnet01_cornet
400	vit_base_patch16_dinov3-lvd1689m
401	ReAlnet02_cornet
402	yudixie_resnet18_distance_reg_0_240719
403	compact_resnet50_V4
404	yudixie_resnet18_rotation_reg_0_240719
405	ReAlnet03_cornet
406	vit_base_patch14_dinov2-lvd142m
407	resnet34_ecoset_full
408	VOneCORnet-S
409	resnet50_ecoset_full
410	ReAlnet01_cornet_fov4
411	vit_base_patch14_reg4_dinov2-lvd142m
412	yudixie_resnet50_distance_translation_0_240908
413	compact_resnet50_robust_V4
414	yudixie_resnet18_distance_translation_0_240719
415	aughost_fair_resnet18
416	yudixie_resnet18_translation_rotation_0_240719
417	compact_resnet50_IT
418	yudixie_resnet18_cat_obj_class_all_latents_0_240719
419	ReAlnet05_cornet
420	yudixie_resnet18_translation_reg_0_240719
421	ReAlnet01_cornet_fov12
422	yudixie_resnet50_rotation_reg_0_240908
423	yudixie_resnet18_distance_translation_rotation_0_240719
424	yudixie_resnet18_object_class_0_240719
425	SeLaVi-AVE
426	yudixie_resnet18_imagenet1kpret_0_240719
427	r3d_18
428	compact_vgg19_V4
429	yudixie_resnet50_translation_reg_0_240908
430	ReAlnet06_cornet
431	ReAlnet01_cornet_fov16
432	compact_vgg19_IT
433	yudixie_resnet50_translation_rotation_0_240908
434	mc3_18
435	ReAlnet01_ALL
436	ReAlnet07_cornet
437	s3d
438	mvit_v1_b
439	ReAlnet08_cornet
440	ReAlnet02_ALL
441	compact_cornets_V4
442	omnivore_swinB
443	ReAlnet04_ALL
444	SeLaVi-Kinetics400
445	ReAlnet09_cornet
446	SeLaVi-VGG-Sound
447	compact_cornets_IT
448	ReAlnet05_ALL
449	SeLaVi-Kinetics-Sound
450	omnivore_swinS
451	resnet152_ecoset_full
452	ReAlnet06_ALL
453	r2plus1d_18
454	mvit_v2_s
455	compact_resnet50_robust_IT
456	ReAlnet10_cornet
457	ReAlnet07_ALL
458	resnet18_ecoset_full
459	resnet101_ecoset_full
460	yudixie_resnet18_category_class_0_240719
461	yudixie_resnet50_distance_translation_rotation_0_240908
462	ReAlnet08_ALL
463	yudixie_resnet18_random_0_240719
464	yudixie_resnet18_distance_rotation_0_240719
465	yudixie_resnet50_distance_rotation_0_240908
466	ReAlnet04_cornet
467	ReAlnet09_ALL
468	resnet50_primary_visual_cortex
469	yolos_tiny
470	ReAlnet10_ALL
471	ReAlnet03_ALL
472	resnet18_fair
473	pr1_stl10_disentangled_alpha04
474	predictive_coding_pc
475	yudixie_wbh_object_class_scaling_0415_run_0
476	yudixie_wbh_object_class_scaling_0415_run_3
477	yudixie_wbh_object_class_scaling_0415_imagenet
478	yudixie_wbh_object_class_scaling_0415_run_2
479	yudixie_wbh_object_class_scaling_0415_run_1
480	deepeisnn_ei_cifar10_vgg19
481	deepeisnn_ei_cifar10_resnet18
482	deepeisnn_ei_imagenet_resnet18
483	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

0.46.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonIII

Metric: error_consistency