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