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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021stylized-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x32d_wsl	.871
2	resnext101_32x16d_wsl	.859
3	resnext101_32x48d_wsl	.851
4	fixres_resnext101_32x48d_wsl	.830
5	SWSL_resnext101_32x8d	.787
6	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.758
7	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.751
8	convnext_base:clip_laiona_augreg_ft_in1k_384	.750
9	convnext_xlarge:fb_in22k_ft_in1k	.746
10	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.737
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.734
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.723
13	vit_base_patch16_clip_224:openai_ft_in1k	.708
14	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.706
15	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.705
16	vision_transformer_vit_large_patch16_224	.705
17	convnext_large:fb_in22k_ft_in1k	.704
18	SWSL_resnet50	.702
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.697
20	vit_large_patch14_clip_224:openai_ft_in1k	.697
21	effnetb1_272x240	.693
22	effnetv2m_custom384	.680
23	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.680
24	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.678
25	cvt_cvt-13-384-in1k_4_LucyV4	.678
26	resnext101_32x8d_wsl	.666
27	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.654
28	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.653
29	CLIP_resnet50	.646
30	CLIP_resnet50_float32	.646
31	nasnet_large	.646
32	cvt_cvt-21-384-in1k_4_LucyV4	.646
33	CLIP_ViT-B_32	.628
34	pnasnet_large_halfPrecision	.627
35	pnasnet_large	.627
36	resnet_101_v2	.623
37	convnext_base_fb_in22k_ft_in1k	.617
38	resnet_152_v2	.603
39	cv_18_dagger_408_pretrained	.600
40	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.590
41	resnet-152_v2-tf	.588
42	nasnet_large-tf	.583
43	pnasnet_large-tf	.579
44	ViT_B_16_imagenet1k	.570
45	deit3_base_patch16_224_fb_in1k	.566
46	resnet_50_v2	.555
47	ViT_L_32_imagenet1k	.538
48	resnet50_ewconplus_wzc_2_323	.521
49	vit_relpos_base_patch16_clsgap_224:sw_in1k	.516
50	xception-keras	.515
51	CLIP-RN50	.511
52	resnet50_ewconplus_wzc_seed7_2_323	.498
53	ViT_B_32_imagenet1k	.494
54	deit3_base_patch16_224_fb_in22k_ft_in1k	.491
55	inception_v4	.488
56	resnet50_st_wzc_seed4_2_323	.486
57	resnet50_ewconplus_wzc_seed3_2_323	.479
58	antialias-resnet152	.467
59	resnet50_st_wzc_seed3_2_323	.464
60	AdvProp_efficientnet-b6	.463
61	resnet152_imagenet_full	.457
62	resnet_152_v1	.456
63	resnet50_ewconplus_wzc	.454
64	xception	.450
65	resnet50-VITO-8deg-cc	.449
66	AdvProp_efficientnet-b4	.443
67	AT_efficientnet-b4	.443
68	resnet-50_v2-tf	.439
69	resnet50_imagenet_full	.438
70	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.438
71	resnet50_st_wzc_seed5_2_323	.437
72	resnet50_ewconplus_wzc_seed5_2__323	.434
73	convnext_base_imagenet_full_seed-0	.433
74	convnext_base_fb_in1k	.430
75	AlexNet_SIN_fov4	.430
76	convnext_small_imagenet_full_seed-0	.429
77	AdvProp_efficientnet-b7	.427
78	convnext_base_fb_in1k_gram_aligned_dinov3	.422
79	resnet50_ewconplus_noseed_2_323	.421
80	resnet50_st_wzc_2_323	.418
81	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.418
82	cvt_cvt-13-224-in1k_4_LucyV4	.417
83	convnext_large_imagenet_full_seed-0	.417
84	resnet101	.417
85	inception_v4-tf	.411
86	resnet50_st_wzc_seed6_2_323	.411
87	convnext_femto_ols:d1_in1k	.409
88	inception_v3	.405
89	resnet101_imagenet_full	.405
90	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.400
91	convnext_tiny_imagenet_full_seed-0	.395
92	efficientnet-b7	.392
93	inception_v3-tf	.391
94	resnet50-SIN_IN	.389
95	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.381
96	grcnn_109	.370
97	efficientnet_b0	.369
98	densenet-201-keras	.365
99	resnet50	.362
100	resnet_101_v1	.358
101	antialiased-rnext101_32x8d	.358
102	resnet50_random_l2_perturb	.351
103	focalnet_tiny_lrf_in1k	.346
104	densenet-201	.342
105	resnet50_ewconplus_wzc_seed6_2_323	.341
106	grcnn	.340
107	resnet50_st_wzc_2	.328
108	tv_efficientnet-b1	.324
109	densenet-121	.322
110	texture_shape_resnet50_trained_on_SIN	.321
111	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.320
112	densenet-121-keras	.319
113	custom_model_cv_18_dagger_408	.319
114	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.317
115	ReAlnet01	.315
116	ReAlnet03	.315
117	AT_efficientnet-b2	.315
118	AdvProp_efficientnet-b2	.315
119	deit_large_imagenet_full_seed-0	.314
120	resnet50_byol	.314
121	Res2Net50_26w_4s	.313
122	cvt_cvt-21-224-in1k_4_LucyV4	.311
123	ReAlnet09	.307
124	ReAlnet02	.305
125	AdvProp_efficientnet-b8	.303
126	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.302
127	voneresnet-50-non_stochastic	.301
128	ReAlnet04	.300
129	resnet34_imagenet_full	.299
130	cornet_s	.299
131	inception_v1	.298
132	ReAlnet06	.297
133	convnext_tiny:in12k_ft_in1k	.295
134	resnet50-moclr8deg	.295
135	BiT-S-R152x4	.293
136	resnet18_imagenet_full	.292
137	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.290
138	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.289
139	mobilenet_v2_1.4_224-tf	.288
140	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.281
141	antialiased-r50	.281
142	mobilenet_v2_1_0_224	.278
143	resnet50-SIN_IN_IN	.274
144	resnet_SIN_IN_FT_IN	.274
145	densenet-169	.273
146	resnet50_moco_v2	.272
147	ReAlnet08	.271
148	inception_v2-tf	.270
149	ReAlnet07	.267
150	inception_v1-tf	.266
151	vgg_19	.263
152	ReAlnet10	.263
153	densenet-169-keras	.262
154	resnet50_random_linf8_perturb	.262
155	vonegrcnn_47e	.258
156	nasnet_mobile	.255
157	resnet50_tutorial	.254
158	resnet_50_v1	.254
159	resnet-50-pytorch	.254
160	resnet50-sup	.254
161	resnet50-vitoimagevidnet8	.252
162	ReAlnet05	.251
163	BiT-S-R101x3	.251
164	alexnet_fov4	.243
165	CORnet-S	.236
166	resnet_50_v1_spiking	.233
167	resnet-50_v1-tf	.233
168	vonegrcnn_62e_nobn	.232
169	BiT-S-R50x3	.230
170	resnet-34	.229
171	vonegrcnn_52e_full	.229
172	resnet50_simclr	.224
173	BiT-S-R152x2	.223
174	voneresnet-50-robust	.219
175	BiT-S-R50x1	.218
176	mobilenet_v2_1_3_224	.216
177	vonegrcnn_62e_nobn	.213
178	mobilenet_v2_1_0_192	.208
179	vonegrcnn_47e	.202
180	efficientnet_b2_imagenet_full	.202
181	mobilenet_v2_1.0_224-tf	.201
182	resnet-18	.199
183	alexnet_training_seed_02	.198
184	alexnet_training_seed_04	.198
185	alexnet_training_seed_08	.198
186	alexnet_training_seed_10	.198
187	resnet-101_v1-tf	.197
188	resnet-152_v1-tf	.195
189	resnet50_linf_4_robust	.195
190	BiT-S-R101x1	.194
191	artResNet18_1	.192
192	vgg_16	.192
193	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.191
194	resnet50-SIN	.187
195	deit_base_imagenet_full_seed-0	.185
196	nasnet_mobile-tf	.185
197	vonegrcnn_52e_full	.184
198	vonealexnet_gaussian_noise_std4_fixed	.182
199	mobilenet_v2_1-4_224_pytorch	.181
200	mobilenet_v2_1_4_224	.181
201	mobilenet_v2_1.3_224-tf	.176
202	alexnet_training_seed_07	.166
203	resnet50-simclr-vissl	.166
204	resnet50_robust_l2_eps1	.163
205	mobilenet_v2_1_0_160	.160
206	resnet50_eMMCR_Vanilla	.159
207	efficientnet_b1_imagenet_full	.159
208	resnet50_imagenet_100_seed-0	.157
209	mobilenet_v1_0.75_224	.155
210	voneresnet-50	.154
211	resnet50-vicregl0p9	.146
212	mobilenet_v2_0_5_224	.145
213	AlexNet_SIN	.141
214	alexnet_ks_torevert	.139
215	alexnet	.139
216	vgg-19-keras	.134
217	texture_shape_alexnet_trained_on_SIN	.133
218	alexnet_linf_8_robust	.131
219	deit_small_imagenet_full_seed-0	.131
220	blt_vs	.129
221	resnet-50_untrained	.127
222	resnet50-vicregl0p75	.127
223	mobilenet_v2_0.75_128	.123
224	resnet50_l2_3_robust	.121
225	regnet_y_400mf	.121
226	efficientnet_b0_imagenet_full	.120
227	mobilenet_v1_1.0_192	.119
228	resnet50-barlow	.119
229	mobilenet_v2_0_75_192	.116
230	AdvProp_efficientnet-b0	.114
231	alexnet_random_linf8_perturb	.113
232	mobilenet_v2_0.75_224-tf	.111
233	mobilenet_v1_0.75_192	.110
234	mobilenet_v2_0.5_192-tf	.110
235	alexnet_robust_correct	.107
236	alexnet_random_l2_3_perturb	.107
237	mobilenet_v2_1_0_128	.106
238	mobilenet_v1_0.5_192	.106
239	alexnet_l2_3_robust	.103
240	mobilenet_v1_1.0_224	.103
241	vgg-16-keras	.102
242	resnet50_imagenet_10_seed-0	.100
243	mobilenet_v2_1.0_192-tf	.098
244	mobilenet_v2_0_75_160	.097
245	konkle_alexnetgn_ipcl_ref01_primary_model	.097
246	alexnet_reduced_aliasing_early_checkpoint	.097
247	imagenet_l2_10_0	.097
248	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.096
249	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.096
250	resnet50_linf_8_robust	.094
251	mobilenet_v1_1.0_160	.094
252	alexnet_training_seed_05	.093
253	alexnet_training_seed_01	.093
254	alexnet_training_seed_01_fov4	.093
255	alexnet_training_seed_01_fov12	.093
256	alexnet_training_seed_01_fov16	.093
257	mobilenet_v2_1.0_160-tf	.092
258	mobilenet_v2_0.5_224-tf	.090
259	mobilenet_v2_0_75_224	.089
260	convnext_small_imagenet_1_seed-0	.088
261	mobilenet_v2_0.35_96	.088
262	mobilenet_v1_0.25_128	.087
263	mobilenet_v2_0_5_192	.087
264	resnet50_imagenet_1_seed-0	.086
265	resnet-50-robust	.084
266	imagenet_l2_3_0	.084
267	resnet50_robust_l2_eps3	.084
268	convnext_tiny_sup	.080
269	mobilenet_v2_0.35_224	.079
270	alexnet_fov12	.078
271	mobilenet_v2_0.35_160	.078
272	mobilenet_v2_1.0_96	.078
273	resnet_50_v1_spiking_l3	.075
274	AlexNet_SIN_fov12	.075
275	mobilenet_v2_0.75_96	.074
276	alexnet_early_checkpoint	.070
277	alexnet_fov16	.067
278	squeezenet1_1	.066
279	mobilenet_v2_0.5_128	.065
280	convnext_small_imagenet_100_seed-0	.064
281	mobilenet_v2_0.75_192-tf	.064
282	mobilevit_small	.064
283	mobilenet_v2_0.75_160-tf	.062
284	mobilenet_v2_0.5_96	.061
285	mobilenet_v1_0.25_224	.060
286	AlexNet_SIN_fov16	.060
287	resnet50_FractalDB	.057
288	deit_small_imagenet_100_seed-0	.057
289	mobilenet_v2_0.5_160	.049
290	deit_small_imagenet_1_seed-0	.047
291	shufflenet_v2_x1_0	.046
292	alexnet_training_seed_09	.045
293	deit_small_imagenet_10_seed-0	.045
294	resnet50-meshes-lt-100-original-pretrained	.044
295	alexnet_training_seed_03	.044
296	resnet50-meshes-lt-100-original-scratch	.044
297	mobilenet_v2_0.35_192	.040
298	mobilenet_v1_0.25_160	.040
299	convnext_small_imagenet_10_seed-0	.040
300	mobilenet_v1_1.0_128	.038
301	mobilenet_v2_1.0_128-tf	.037
302	mobilenet_v1_0.5_160	.028
303	squeezenet1_0	.026
304	mobilenet_v2_0.35_128	.026
305	0.5x_resnet-18_LC_w_sh_1_iter	.025
306	resnet_50_v1_spiking_l4	.016
307	resnet50-vicreg	.016
308	mobilenet_v1_0.25_192	.015
309	mobilenet_v1_0.5_128	.015
310	mobilenet_v1_0.5_224	.015
311	mobilenet_v1_0.75_160	.014
312	barlow-twins-resnet50	.012
313	mobilenet_v1_0.75_128	.012
314	alexnet_training_seed_06	.008
315	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.006
316	resnet_50_v1_spiking_l4a	.005
317	resnet-18-LC_w_sh_100_iter_conv_init_m
318	ViT-B/32
319	BiT-M-R50x3
320	BiT-M-R50x1
321	BiT-M-R152x2
322	resnet-18-LC_w_sh_1_iter_conv_init_m
323	resnet-18-LC_w_sh_1_iter_m
324	resnet-18-LC_w_sh_10_iter_m
325	resnet-18-LC_w_sh_100_iter_m
326	resnet-18-LC_w_sh_10_iter_conv_init_m
327	FrankRobWobv0
328	0.5x_resnet-18_LC
329	CORnetZ_CIFAR10
330	resnet18-local_aggregation
331	resnet18-deepcluster
332	r3m_resnet18
333	CORnet-Z
334	vggface
335	my-model
336	resnet18-instance_recognition
337	0.5x_resnet-18_LC_w_sh_10_iter
338	r3m_resnet50
339	r3m_resnet34
340	resnet-18-LC_m
341	r3m_resnet50_nocrop
342	resnet18-supervised
343	resnet-18-LC_1st_conv_m
344	resnet-18-LC_conv_init_m
345	resnet18-contrastive_multiview
346	0.5x_resnet-18
347	RN50
348	0.5x_resnet-18_LC_w_sh_100_iter
349	RN50
350	resnet18-colorization
351	resnet18-relative_position
352	resnet18-autoencoder
353	resnet18-simclr
354	ViT_L_32
355	omnivore_swinT
356	ViT_B_32
357	resnet18-depth_prediction
358	ViT_B_16
359	r3m_resnet34_nocrop
360	hmax
361	resnet18-contrastive_predictive
362	resnet-18-LC_untrained
363	resnet-18_untrained
364	resnet-50x2_untrained
365	dcgan
366	unet_entire
367	v1-pyr-nodown
368	CORnetZ_CIFAR10_bs32_20_04
369	pixels
370	dorinet_cornet_z
371	my-model
372	resnet50
373	openclip
374	resnet50-cifar
375	resnet50_pretrained_with_retinal_waves
376	resnet50_ImageNet
377	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
378	yudixie_resnet50_imagenet1kpret_0_240908
379	yudixie_resnet50_random_0_240908
380	yudixie_resnet50_imagenet1kpret_0_240312
381	yudixie_resnet50_object_class_0_240908
382	yudixie_resnet50_category_class_0_240908
383	yudixie_resnet50_distance_reg_0_240908
384	ConvLSTM
385	PredRNN
386	SimVP
387	TAU
388	MIM
389	resnet-152_v2_pytorch
390	briaai_rmbg_1_4
391	densenet_201_pytorch
392	pnasnet_large_pytorch
393	inception_v3_pytorch
394	compact_resnet50_V4
395	yudixie_resnet18_translation_reg_0_240719
396	yudixie_resnet18_distance_reg_0_240719
397	yolos_tiny
398	yudixie_resnet18_rotation_reg_0_240719
399	ReAlnet03_cornet
400	yudixie_resnet18_imagenet1kpret_0_240719
401	eBarlow_lmda_02_1000ep
402	vit_base_patch14_reg4_dinov2-lvd142m
403	vit_base_patch16_dinov3-lvd1689m
404	resnet18_ecoset_full
405	ReAlnet02_cornet
406	resnet34_ecoset_full
407	compact_resnet50_IT
408	VOneCORnet-S
409	resnet50_ecoset_full
410	ReAlnet01_cornet_fov4
411	compact_resnet50_robust_V4
412	resnet101_ecoset_full
413	ReAlnet04_cornet
414	resnet152_ecoset_full
415	vit_base_patch14_dinov2-lvd142m
416	yudixie_resnet50_distance_translation_0_240908
417	ReAlnet01_cornet_fov12
418	yudixie_resnet18_translation_rotation_0_240719
419	aughost_fair_resnet18
420	yudixie_resnet18_distance_rotation_0_240719
421	ReAlnet05_cornet
422	yudixie_resnet50_distance_rotation_0_240908
423	yudixie_resnet18_cat_obj_class_all_latents_0_240719
424	compact_vgg19_V4
425	resnet-18_test_m
426	SeLaVi-AVE
427	yudixie_resnet18_distance_translation_rotation_0_240719
428	yudixie_resnet18_object_class_0_240719
429	ReAlnet06_cornet
430	r3d_18
431	yudixie_resnet50_translation_reg_0_240908
432	compact_vgg19_IT
433	yudixie_resnet50_rotation_reg_0_240908
434	yudixie_resnet50_translation_rotation_0_240908
435	ReAlnet01_ALL
436	mc3_18
437	ReAlnet07_cornet
438	s3d
439	ReAlnet08_cornet
440	mvit_v1_b
441	ReAlnet01_cornet_fov16
442	ReAlnet02_ALL
443	compact_cornets_IT
444	omnivore_swinB
445	resnet18_imagenet21kP
446	ReAlnet03_ALL
447	compact_cornets_V4
448	ReAlnet09_cornet
449	ReAlnet04_ALL
450	SeLaVi-Kinetics400
451	SeLaVi-VGG-Sound
452	ReAlnet05_ALL
453	r2plus1d_18
454	omnivore_swinS
455	ReAlnet06_ALL
456	ReAlnet10_cornet
457	SeLaVi-Kinetics-Sound
458	mvit_v2_s
459	compact_resnet50_robust_IT
460	ReAlnet07_ALL
461	yudixie_resnet18_category_class_0_240719
462	ReAlnet08_ALL
463	yudixie_resnet18_distance_translation_0_240719
464	yudixie_resnet18_random_0_240719
465	ReAlnet01_cornet
466	resnet50_primary_visual_cortex
467	ReAlnet09_ALL
468	yudixie_resnet50_distance_translation_rotation_0_240908
469	yudixie_resnet50_cat_obj_class_all_latents_0_240908
470	ReAlnet10_ALL
471	resnet18_fair
472	pr1_stl10_disentangled_alpha04
473	predictive_coding_pc
474	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3
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_run_2
478	yudixie_wbh_object_class_scaling_0415_run_1
479	deepeisnn_ei_cifar10_vgg19
480	deepeisnn_ei_cifar10_resnet18
481	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

0.50.

Note that scores are relative to this ceiling.

Data: Geirhos2021stylized

Metric: error_consistency