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	.627
35	resnet_101_v2	.623
36	resnet_152_v2	.603
37	cv_18_dagger_408_pretrained	.600
38	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.590
39	resnet-152_v2-tf	.588
40	nasnet_large-tf	.583
41	pnasnet_large-tf	.579
42	ViT_B_16_imagenet1k	.570
43	resnet_50_v2	.555
44	ViT_L_32_imagenet1k	.538
45	vit_relpos_base_patch16_clsgap_224:sw_in1k	.516
46	xception-keras	.515
47	CLIP-RN50	.511
48	ViT_B_32_imagenet1k	.494
49	inception_v4	.488
50	antialias-resnet152	.467
51	AdvProp_efficientnet-b6	.463
52	resnet152_imagenet_full	.457
53	resnet_152_v1	.456
54	xception	.450
55	resnet50-VITO-8deg-cc	.449
56	AT_efficientnet-b4	.443
57	AdvProp_efficientnet-b4	.443
58	resnet-50_v2-tf	.439
59	resnet50_imagenet_full	.438
60	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.438
61	convnext_base_imagenet_full_seed-0	.433
62	convnext_small_imagenet_full_seed-0	.429
63	AdvProp_efficientnet-b7	.427
64	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.418
65	cvt_cvt-13-224-in1k_4_LucyV4	.417
66	convnext_large_imagenet_full_seed-0	.417
67	resnet101	.417
68	inception_v4-tf	.411
69	convnext_femto_ols:d1_in1k	.409
70	inception_v3	.405
71	resnet101_imagenet_full	.405
72	convnext_tiny_imagenet_full_seed-0	.395
73	efficientnet-b7	.392
74	inception_v3-tf	.391
75	resnet50-SIN_IN	.389
76	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.381
77	grcnn_109	.370
78	efficientnet_b0	.369
79	densenet-201-keras	.365
80	resnet50	.362
81	resnet_101_v1	.358
82	antialiased-rnext101_32x8d	.358
83	resnet50_random_l2_perturb	.351
84	focalnet_tiny_lrf_in1k	.346
85	densenet-201	.342
86	grcnn	.340
87	tv_efficientnet-b1	.324
88	densenet-121	.322
89	texture_shape_resnet50_trained_on_SIN	.321
90	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.320
91	densenet-121-keras	.319
92	custom_model_cv_18_dagger_408	.319
93	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.317
94	ReAlnet01	.315
95	ReAlnet_incorrect	.315
96	ReAlnet03	.315
97	AdvProp_efficientnet-b2	.315
98	AT_efficientnet-b2	.315
99	deit_large_imagenet_full_seed-0	.314
100	resnet50_byol	.314
101	Res2Net50_26w_4s	.313
102	cvt_cvt-21-224-in1k_4_LucyV4	.311
103	ReAlnet09	.307
104	ReAlnet02	.305
105	AdvProp_efficientnet-b8	.303
106	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.302
107	voneresnet-50-non_stochastic	.301
108	ReAlnet04	.300
109	resnet34_imagenet_full	.299
110	cornet_s	.299
111	inception_v1	.298
112	ReAlnet06	.297
113	convnext_tiny:in12k_ft_in1k	.295
114	resnet50-moclr8deg	.295
115	BiT-S-R152x4	.293
116	resnet18_imagenet_full	.292
117	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.290
118	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.289
119	mobilenet_v2_1.4_224-tf	.288
120	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.281
121	antialiased-r50	.281
122	mobilenet_v2_1_0_224	.278
123	resnet_SIN_IN_FT_IN	.274
124	resnet50-SIN_IN_IN	.274
125	densenet-169	.273
126	resnet50_moco_v2	.272
127	ReAlnet08	.271
128	inception_v2-tf	.270
129	ReAlnet07	.267
130	inception_v1-tf	.266
131	vgg_19	.263
132	ReAlnet10	.263
133	densenet-169-keras	.262
134	resnet50_random_linf8_perturb	.262
135	vonegrcnn_47e	.258
136	nasnet_mobile	.255
137	resnet_50_v1	.254
138	resnet50_tutorial	.254
139	resnet50-sup	.254
140	resnet-50-pytorch	.254
141	resnet50-vitoimagevidnet8	.252
142	ReAlnet05	.251
143	BiT-S-R101x3	.251
144	CORnet-S	.236
145	resnet-50_v1-tf	.233
146	vonegrcnn_62e_nobn	.232
147	BiT-S-R50x3	.230
148	resnet-34	.229
149	vonegrcnn_52e_full	.229
150	resnet50_simclr	.224
151	BiT-S-R152x2	.223
152	voneresnet-50-robust	.219
153	BiT-S-R50x1	.218
154	mobilenet_v2_1_3_224	.216
155	vonegrcnn_62e_nobn	.213
156	mobilenet_v2_1_0_192	.208
157	vonegrcnn_47e	.202
158	efficientnet_b2_imagenet_full	.202
159	mobilenet_v2_1.0_224-tf	.201
160	resnet-18	.199
161	alexnet_training_seed_02	.198
162	alexnet_training_seed_04	.198
163	alexnet_training_seed_10	.198
164	alexnet_training_seed_08	.198
165	resnet-101_v1-tf	.197
166	resnet-152_v1-tf	.195
167	resnet50_linf_4_robust	.195
168	BiT-S-R101x1	.194
169	artResNet18_1	.192
170	vgg_16	.192
171	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.191
172	resnet50-SIN	.187
173	deit_base_imagenet_full_seed-0	.185
174	nasnet_mobile-tf	.185
175	vonegrcnn_52e_full	.184
176	vonealexnet_gaussian_noise_std4_fixed	.182
177	mobilenet_v2_1-4_224_pytorch	.181
178	mobilenet_v2_1_4_224	.181
179	mobilenet_v2_1.3_224-tf	.176
180	alexnet_training_seed_07	.166
181	resnet50-simclr-vissl	.166
182	resnet50_robust_l2_eps1	.163
183	mobilenet_v2_1_0_160	.160
184	efficientnet_b1_imagenet_full	.159
185	resnet50_imagenet_100_seed-0	.157
186	mobilenet_v1_0.75_224	.155
187	voneresnet-50	.154
188	resnet50-vicregl0p9	.146
189	mobilenet_v2_0_5_224	.145
190	AlexNet_SIN	.141
191	alexnet_ks_torevert	.139
192	alexnet	.139
193	alexnet-baseline	.139
194	alexnet	.139
195	alexnet	.139
196	alexnet	.139
197	alexnet	.139
198	alexnet	.139
199	alexnet	.139
200	alexnet	.139
201	alexnet	.139
202	vgg-19-keras	.134
203	texture_shape_alexnet_trained_on_SIN	.133
204	alexnet	.132
205	alexnet_linf_8_robust	.131
206	deit_small_imagenet_full_seed-0	.131
207	blt_vs	.129
208	resnet-50_untrained	.127
209	resnet50-vicregl0p75	.127
210	mobilenet_v2_0.75_128	.123
211	resnet50_l2_3_robust	.121
212	regnet_y_400mf	.121
213	efficientnet_b0_imagenet_full	.120
214	mobilenet_v1_1.0_192	.119
215	resnet50-barlow	.119
216	mobilenet_v2_0_75_192	.116
217	AdvProp_efficientnet-b0	.114
218	alexnet_random_linf8_perturb	.113
219	mobilenet_v2_0.75_224-tf	.111
220	mobilenet_v1_0.75_192	.110
221	mobilenet_v2_0.5_192-tf	.110
222	alexnet_robust_correct	.107
223	alexnet_random_l2_3_perturb	.107
224	mobilenet_v2_1_0_128	.106
225	mobilenet_v1_0.5_192	.106
226	alexnet_l2_3_robust	.103
227	mobilenet_v1_1.0_224	.103
228	vgg-16-keras	.102
229	resnet50_imagenet_10_seed-0	.100
230	mobilenet_v2_1.0_192-tf	.098
231	mobilenet_v2_0_75_160	.097
232	konkle_alexnetgn_ipcl_ref01_primary_model	.097
233	alexnet_reduced_aliasing_early_checkpoint	.097
234	imagenet_l2_10_0	.097
235	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.096
236	resnet50_linf_8_robust	.094
237	mobilenet_v1_1.0_160	.094
238	alexnet_training_seed_05	.093
239	alexnet_training_seed_01	.093
240	mobilenet_v2_1.0_160-tf	.092
241	mobilenet_v2_0.5_224-tf	.090
242	mobilenet_v2_0_75_224	.089
243	convnext_small_imagenet_1_seed-0	.088
244	mobilenet_v2_0.35_96	.088
245	mobilenet_v1_0.25_128	.087
246	mobilenet_v2_0_5_192	.087
247	resnet50_imagenet_1_seed-0	.086
248	resnet-50-robust	.084
249	resnet50_robust_l2_eps3	.084
250	imagenet_l2_3_0	.084
251	convnext_tiny_sup	.080
252	mobilenet_v2_0.35_224	.079
253	mobilenet_v2_0.35_160	.078
254	mobilenet_v2_1.0_96	.078
255	mobilenet_v2_0.75_96	.074
256	alexnet_early_checkpoint	.070
257	squeezenet1_1	.066
258	mobilenet_v2_0.5_128	.065
259	convnext_small_imagenet_100_seed-0	.064
260	mobilenet_v2_0.75_192-tf	.064
261	mobilevit_small	.064
262	mobilenet_v2_0.75_160-tf	.062
263	mobilenet_v2_0.5_96	.061
264	mobilenet_v1_0.25_224	.060
265	resnet50_FractalDB	.057
266	deit_small_imagenet_100_seed-0	.057
267	mobilenet_v2_0.5_160	.049
268	deit_small_imagenet_1_seed-0	.047
269	shufflenet_v2_x1_0	.046
270	alexnet_training_seed_09	.045
271	deit_small_imagenet_10_seed-0	.045
272	resnet50-meshes-lt-100-original-pretrained	.044
273	alexnet_training_seed_03	.044
274	resnet50-meshes-lt-100-original-scratch	.044
275	mobilenet_v2_0.35_192	.040
276	mobilenet_v1_0.25_160	.040
277	convnext_small_imagenet_10_seed-0	.040
278	mobilenet_v1_1.0_128	.038
279	mobilenet_v2_1.0_128-tf	.037
280	mobilenet_v1_0.5_160	.028
281	squeezenet1_0	.026
282	mobilenet_v2_0.35_128	.026
283	0.5x_resnet-18_LC_w_sh_1_iter	.025
284	resnet50-vicreg	.016
285	mobilenet_v1_0.25_192	.015
286	mobilenet_v1_0.5_128	.015
287	mobilenet_v1_0.5_224	.015
288	mobilenet_v1_0.75_160	.014
289	barlow-twins-resnet50	.012
290	mobilenet_v1_0.75_128	.012
291	alexnet_training_seed_06	.008
292	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.006
293	yudixie_resnet50_rotation_reg_0_240908	X
294	yudixie_resnet50_translation_rotation_0_240908	X
295	resnet18-deepcluster	X
296	ReAlnet07_cornet	X
297	resnet18-local_aggregation	X
298	CORnetZ_CIFAR10	X
299	0.5x_resnet-18_LC	X
300	ReAlnet08_cornet	X
301	omnivore_swinB	X
302	FrankRobWobv0	X
303	resnet-18-LC_w_sh_10_iter_conv_init_m	X
304	resnet-18-LC_w_sh_100_iter_m	X
305	resnet-18-LC_w_sh_10_iter_m	X
306	omnivore_swinS	X
307	yudixie_resnet50_distance_translation_rotation_0_240908	X
308	ReAlnet10_cornet	X
309	resnet-18-LC_w_sh_1_iter_m	X
310	resnet-18-LC_w_sh_1_iter_conv_init_m	X
311	BiT-M-R152x2	X
312	BiT-M-R50x1	X
313	BiT-M-R50x3	X
314	ViT-B/32	X
315	resnet-18-LC_w_sh_100_iter_conv_init_m	X
316	ReAlnet09_cornet	X
317	openclip	X
318	resnet50-cifar	X
319	resnet50_pretrained_with_retinal_waves	X
320	resnet50_ImageNet	X
321	resnet50	X
322	my-model	X
323	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
324	dorinet_cornet_z	X
325	pixels	X
326	CORnetZ_CIFAR10_bs32_20_04	X
327	v1-pyr-nodown	X
328	unet_entire	X
329	dcgan	X
330	resnet-50x2_untrained	X
331	resnet-18_untrained	X
332	resnet-18-LC_untrained	X
333	resnet18-contrastive_predictive	X
334	yudixie_resnet50_imagenet1kpret_0_240908	X
335	hmax	X
336	r3m_resnet34_nocrop	X
337	ViT_B_16	X
338	resnet18-depth_prediction	X
339	ViT_B_32	X
340	yudixie_resnet50_random_0_240908	X
341	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
342	omnivore_swinT	X
343	ViT_L_32	X
344	yudixie_resnet50_imagenet1kpret_0_240312	X
345	resnet18-simclr	X
346	resnet18-autoencoder	X
347	resnet18-relative_position	X
348	yudixie_resnet50_object_class_0_240908	X
349	resnet18-colorization	X
350	RN50	X
351	0.5x_resnet-18_LC_w_sh_100_iter	X
352	RN50	X
353	0.5x_resnet-18	X
354	yudixie_resnet50_category_class_0_240908	X
355	resnet18-contrastive_multiview	X
356	resnet-18-LC_conv_init_m	X
357	resnet-18-LC_1st_conv_m	X
358	resnet18-supervised	X
359	r3m_resnet50_nocrop	X
360	yudixie_resnet50_distance_reg_0_240908	X
361	ConvLSTM	X
362	PredRNN	X
363	SimVP	X
364	TAU	X
365	MIM	X
366	resnet-18-LC_m	X
367	r3m_resnet34	X
368	resnet-152_v2_pytorch	X
369	densenet_201_pytorch	X
370	pnasnet_large_pytorch	X
371	inception_v3_pytorch	X
372	ReAlnet01_cornet	X
373	r3m_resnet50	X
374	0.5x_resnet-18_LC_w_sh_10_iter	X
375	resnet18-instance_recognition	X
376	my-model	X
377	vggface	X
378	yudixie_resnet18_translation_reg_0_240719	X
379	yudixie_resnet18_distance_reg_0_240719	X
380	yudixie_resnet18_rotation_reg_0_240719	X
381	ReAlnet03_cornet	X
382	yudixie_resnet18_imagenet1kpret_0_240719	X
383	resnet18_ecoset_full	X
384	ReAlnet02_cornet	X
385	resnet34_ecoset_full	X
386	VOneCORnet-S	X
387	resnet50_ecoset_full	X
388	CORnet-Z	X
389	resnet101_ecoset_full	X
390	ReAlnet04_cornet	X
391	resnet152_ecoset_full	X
392	yudixie_resnet18_distance_translation_0_240719	X
393	yudixie_resnet50_distance_translation_0_240908	X
394	yudixie_resnet18_translation_rotation_0_240719	X
395	yudixie_resnet18_distance_rotation_0_240719	X
396	yudixie_resnet18_category_class_0_240719	X
397	ReAlnet05_cornet	X
398	yudixie_resnet50_distance_rotation_0_240908	X
399	yudixie_resnet18_random_0_240719	X
400	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
401	resnet-18_test_m	X
402	yudixie_resnet18_distance_translation_rotation_0_240719	X
403	yudixie_resnet18_object_class_0_240719	X
404	r3m_resnet18	X
405	ReAlnet06_cornet	X
406	yudixie_resnet50_translation_reg_0_240908	X

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