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

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