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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021silhouette-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_clip336_laion	1.0
2	vit_large_patch14_clip_224:openai_ft_in1k	1.0
3	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
4	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
5	convnext_xxlarge_clip	1.0
6	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
7	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
9	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
10	eva02_large_in22k	1.0
11	convnext_large_clip_laion2b_finelayers	1.0
12	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
13	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
14	vit_base_patch16_clip_224:openai_ft_in1k	1.0
15	convnext_large:fb_in22k_ft_in1k	1.0
16	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.993
17	resnext101_32x32d_wsl	.990
18	convnext_base:clip_laiona_augreg_ft_in1k_384	.966
19	resnext101_32x8d_wsl	.966
20	convnext_xlarge:fb_in22k_ft_in1k	.959
21	SWSL_resnext101_32x8d	.936
22	vit_large_clip336_openai	.922
23	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.914
24	resnext101_32x48d_wsl	.908
25	CLIP-RN50	.908
26	fixres_resnext101_32x48d_wsl	.904
27	dinov2_vitl14_lc_res336	.901
28	cvt_cvt-13-384-in1k_4_LucyV4	.900
29	convnext_tiny:in12k_ft_in1k	.897
30	dinov2_vitl14_reg_lc	.896
31	SWSL_resnet50	.891
32	vit_relpos_base_patch16_clsgap_224:sw_in1k	.888
33	convnext_base_fb_in22k_ft_in1k	.886
34	resnext101_32x16d_wsl	.883
35	convnext_large_in22k_supervised	.875
36	dinov2_vitg14_lc	.874
37	convnext_tiny_imagenet_full_seed-0	.870
38	deit3_base_patch16_224_fb_in22k_ft_in1k	.860
39	deit3_base_patch16_224_fb_in1k	.859
40	resnet_152_v2	.856
41	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.848
42	CLIP_resnet50	.841
43	CLIP_resnet50_float32	.841
44	CLIP_ViT-B_32	.840
45	resnet_101_v2	.827
46	resnet-152_v2_pytorch	.825
47	focalnet_tiny_lrf_in1k	.822
48	effnetv2m_custom384	.818
49	pnasnet_large_halfPrecision	.817
50	pnasnet_large	.817
51	pnasnet_large-tf	.810
52	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.793
53	seresnext50_32x4d	.785
54	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.781
55	regnety_032	.778
56	resnet_50_v2	.777
57	resnet50	.773
58	convnext_femto_ols:d1_in1k	.767
59	cvt_cvt-21-384-in1k_4_LucyV4	.763
60	convnext_tiny_sup	.763
61	dinov2_vitl14_lc	.754
62	nasnet_large	.750
63	vision_transformer_vit_large_patch16_224	.749
64	dinov2_vitg14_reg_lc	.739
65	resnet50_random_linf8_perturb	.737
66	convnext_large_imagenet_full_seed-0	.728
67	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.728
68	resnet50_st_wzc_seed3_2_323	.725
69	inception_v4	.718
70	nasnet_large-tf	.706
71	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.704
72	resnet50-VITO-8deg-cc	.701
73	resnet50_st_wzc_seed6_2_323	.686
74	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.685
75	resnet101_imagenet_full	.681
76	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.675
77	pnasnet_large_pytorch	.674
78	cvt_cvt-13-224-in1k_4_LucyV4	.668
79	inception_v3-tf	.663
80	convnext_small_imagenet_full_seed-0	.660
81	inception_v4-tf	.658
82	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.658
83	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.648
84	antialiased-rnext101_32x8d	.648
85	resnet-152_v2-tf	.647
86	resnet50_st_wzc_seed7_2_323	.647
87	resnet50_imagenet_full	.646
88	resnet34_imagenet_full	.640
89	resnet101	.628
90	cvt_cvt-21-224-in1k_4_LucyV4	.618
91	antialias-resnet152	.615
92	effnetb1_272x240	.614
93	inception_v3	.610
94	AdvProp_efficientnet-b6	.610
95	convnext_base_imagenet_full_seed-0	.608
96	voneresnet-50-non_stochastic	.602
97	resnet50_ewconplus_wzc_seed5_2__323	.601
98	xception-keras	.601
99	vonegrcnn_62e_nobn	.584
100	convnext_base_fb_in1k	.582
101	grcnn_109	.578
102	antialiased-r50	.574
103	resnet50_st_wzc_2	.562
104	resnet152_imagenet_full	.560
105	resnet-34	.558
106	vonegrcnn_62e_nobn	.554
107	AT_efficientnet-b2	.553
108	AdvProp_efficientnet-b2	.553
109	resnet_50_v1_spiking	.551
110	resnet_101_v1	.549
111	resnet50_hvs_wzc	.549
112	inception_v3_pytorch	.548
113	resnet50_tutorial	.546
114	resnet_50_v1	.546
115	resnet-50-pytorch	.546
116	resnet50-sup	.546
117	efficientnet_b0	.544
118	resnet50_random_l2_perturb	.535
119	mobilenet_v2_1.4_224-tf	.534
120	densenet-169-keras	.531
121	vonegrcnn_52e_full	.524
122	cv_18_dagger_408_pretrained	.523
123	AdvProp_efficientnet-b4	.521
124	AT_efficientnet-b4	.521
125	inception_v2-tf	.520
126	vonegrcnn_52e_full	.520
127	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.511
128	tv_efficientnet-b1	.510
129	densenet-121-keras	.510
130	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.509
131	vonegrcnn_47e	.500
132	AdvProp_efficientnet-b7	.497
133	resnet-152_v1-tf	.492
134	resnet50-SIN_IN_IN	.492
135	resnet_SIN_IN_FT_IN	.492
136	deit_base_imagenet_full_seed-0	.492
137	resnet-50_v2-tf	.491
138	resnet18_imagenet_full	.491
139	blt_vs	.491
140	grcnn	.482
141	resnet50_ewconplus_wzc	.482
142	vonegrcnn_47e	.482
143	BiT-S-R50x3	.478
144	densenet-169	.474
145	resnet-50_v1-tf	.474
146	convnext_small_imagenet_100_seed-0	.469
147	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
148	densenet-121	.468
149	resnet50_byol	.465
150	voneresnet-50	.463
151	alexnet_fov4	.461
152	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
153	inception_v1	.451
154	resnet-101_v1-tf	.450
155	efficientnet-b7	.449
156	resnet_152_v1	.445
157	mobilenet_v2_1-4_224_pytorch	.439
158	mobilenet_v2_1_4_224	.439
159	mobilenet_v1_0.75_224	.438
160	mobilenet_v2_1_0_224	.437
161	deit_large_imagenet_full_seed-0	.434
162	AlexNet_SIN_fov4	.428
163	resnet50_linf_4_robust	.421
164	nasnet_mobile	.418
165	ReAlnet03	.417
166	densenet-201-keras	.409
167	resnet50-moclr8deg	.395
168	BiT-S-R50x1	.394
169	custom_model_cv_18_dagger_408	.392
170	Res2Net50_26w_4s	.390
171	resnet50-SIN_IN	.388
172	mobilenet_v2_1.0_192-tf	.384
173	vgg_19	.379
174	densenet-201	.378
175	densenet_201_pytorch	.378
176	cornet_s	.375
177	ReAlnet07	.373
178	mobilenet_v2_0_5_224	.369
179	ReAlnet04	.369
180	ReAlnet01	.365
181	ReAlnet10	.364
182	inception_v1-tf	.362
183	mobilenet_v2_1.0_224-tf	.360
184	mobilenet_v2_1.3_224-tf	.358
185	texture_shape_resnet50_trained_on_SIN	.358
186	efficientnet_b0_imagenet_full	.357
187	CORnet-S	.353
188	mobilenet_v2_1_0_192	.353
189	vgg_16	.352
190	resnet50_linf_8_robust	.349
191	mobilenet_v2_0.5_224-tf	.336
192	mobilenet_v2_1_3_224	.336
193	deit_small_imagenet_full_seed-0	.335
194	resnet-18	.333
195	BiT-S-R152x2	.332
196	nasnet_mobile-tf	.328
197	ReAlnet09	.317
198	mobilenet_v2_0.5_192-tf	.315
199	ReAlnet08	.314
200	texture_shape_alexnet_trained_on_SIN	.310
201	mobilenet_v2_0_75_160	.309
202	ReAlnet02	.304
203	vgg-16-keras	.298
204	resnet50_l2_3_robust	.297
205	mobilenet_v2_1.0_160-tf	.296
206	mobilenet_v2_0_5_192	.296
207	resnet_50_v1_spiking_l4a	.287
208	mobilenet_v2_1_0_160	.287
209	BiT-S-R101x1	.286
210	resnet50_moco_v2	.285
211	ReAlnet05	.280
212	mobilenet_v1_1.0_224	.279
213	mobilenet_v2_0.75_128	.276
214	convnext_base_fb_in1k_gram_aligned_dinov3	.271
215	mobilenet_v1_1.0_160	.270
216	mobilenet_v2_0.75_224-tf	.266
217	AlexNet_SIN	.263
218	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
219	alexnet_random_linf8_perturb	.257
220	mobilenet_v2_0_75_224	.256
221	BiT-S-R152x4	.255
222	mobilenet_v2_0.35_160	.252
223	resnet50-vitoimagevidnet8	.252
224	BiT-S-R101x3	.249
225	alexnet_random_l2_3_perturb	.247
226	resnet50-SIN	.245
227	AdvProp_efficientnet-b0	.243
228	mobilenet_v2_1_0_128	.240
229	mobilenet_v2_0.5_160	.236
230	xception	.236
231	resnet50_robust_l2_eps1	.231
232	mobilenet_v2_0.75_192-tf	.231
233	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.228
234	ReAlnet06	.220
235	efficientnet_b2_imagenet_full	.214
236	alexnet_l2_3_robust	.214
237	voneresnet-50-robust	.205
238	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
239	mobilenet_v2_0.75_160-tf	.204
240	alexnet_ks_torevert	.203
241	alexnet	.203
242	squeezenet1_1	.194
243	mobilenet_v1_0.5_224	.194
244	mobilenet_v1_0.75_160	.193
245	mobilenet_v2_0_75_192	.192
246	mobilenet_v2_1.0_128-tf	.186
247	resnet50_simclr	.185
248	mobilenet_v2_0.35_192	.185
249	mobilenet_v2_1.0_96	.185
250	mobilenet_v1_1.0_192	.180
251	efficientnet_b1_imagenet_full	.176
252	konkle_alexnetgn_ipcl_ref01_primary_model	.169
253	eBarlow_lmda_02_1000ep	.167
254	mobilenet_v2_0.75_96	.166
255	mobilenet_v1_0.5_192	.164
256	mobilenet_v1_0.75_128	.163
257	alexnet_linf_8_robust	.161
258	AlexNet_SIN_fov12	.159
259	mobilenet_v1_0.5_160	.154
260	regnet_y_400mf	.150
261	resnet-50-robust	.149
262	imagenet_l2_3_0	.149
263	resnet50_robust_l2_eps3	.149
264	0.5x_resnet-18_LC_w_sh_1_iter	.148
265	AdvProp_efficientnet-b8	.147
266	mobilenet_v1_0.75_192	.146
267	deit_small_imagenet_100_seed-0	.145
268	alexnet_reduced_aliasing_early_checkpoint	.143
269	alexnet_early_checkpoint	.139
270	alexnet_training_seed_05	.139
271	alexnet_training_seed_06	.139
272	alexnet_training_seed_01	.139
273	alexnet_training_seed_01_fov4	.139
274	alexnet_training_seed_01_fov12	.139
275	alexnet_training_seed_01_fov16	.139
276	resnet50-barlow	.139
277	alexnet_robust_correct	.135
278	vonealexnet_gaussian_noise_std4_fixed	.134
279	mobilenet_v2_0.35_128	.129
280	mobilenet_v1_0.5_128	.127
281	alexnet_fov12	.118
282	AlexNet_SIN_fov16	.117
283	mobilenet_v1_0.25_192	.117
284	resnet50_imagenet_100_seed-0	.112
285	artResNet18_1	.109
286	mobilenet_v2_0.35_224	.107
287	mobilenet_v2_0.5_128	.106
288	mobilenet_v2_0.35_96	.102
289	resnet50_imagenet_10_seed-0	.102
290	mobilenet_v1_0.25_160	.098
291	alexnet_training_seed_03	.090
292	resnet50_FractalDB	.089
293	mobilenet_v1_1.0_128	.089
294	convnext_small_imagenet_10_seed-0	.084
295	deit_small_imagenet_10_seed-0	.084
296	deit_small_imagenet_1_seed-0	.084
297	shufflenet_v2_x1_0	.083
298	mobilenet_v1_0.25_128	.082
299	convnext_small_imagenet_1_seed-0	.082
300	vgg-19-keras	.081
301	alexnet_training_seed_02	.077
302	alexnet_training_seed_08	.077
303	alexnet_training_seed_04	.077
304	alexnet_training_seed_10	.077
305	alexnet_training_seed_07	.077
306	imagenet_l2_10_0	.073
307	resnet-50_untrained	.070
308	mobilenet_v2_0.5_96	.066
309	resnet50-simclr-vissl	.065
310	ViT_B_32_imagenet1k	.056
311	barlow-twins-resnet50	.054
312	squeezenet1_0	.053
313	resnet50_eMMCR_Vanilla	.052
314	ViT_L_32_imagenet1k	.051
315	alexnet_fov16	.050
316	alexnet_training_seed_09	.049
317	resnet50_imagenet_1_seed-0	.046
318	resnet50-vicregl0p75	.043
319	resnet_50_v1_spiking_l4	.042
320	mobilenet_v1_0.25_224	.041
321	resnet50-vicregl0p9	.033
322	mobilevit_small	.023
323	ViT_B_16_imagenet1k	.023
324	resnet50-vicreg	.012
325	resnet_50_v1_spiking_l3	.004
326	ReAlnet10_ALL
327	resnet-18-LC_w_sh_100_iter_conv_init_m
328	ViT-B/32
329	BiT-M-R50x3
330	BiT-M-R50x1
331	BiT-M-R152x2
332	resnet-18-LC_w_sh_1_iter_conv_init_m
333	resnet-18-LC_w_sh_1_iter_m
334	resnet-18-LC_w_sh_10_iter_m
335	resnet-18-LC_w_sh_100_iter_m
336	resnet-18-LC_w_sh_10_iter_conv_init_m
337	FrankRobWobv0
338	0.5x_resnet-18_LC
339	CORnetZ_CIFAR10
340	resnet18-local_aggregation
341	resnet18-deepcluster
342	r3m_resnet18
343	CORnet-Z
344	vggface
345	my-model
346	resnet18-instance_recognition
347	0.5x_resnet-18_LC_w_sh_10_iter
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	resnet18-colorization
358	RN50
359	0.5x_resnet-18_LC_w_sh_100_iter
360	RN50
361	resnet18-relative_position
362	resnet18-autoencoder
363	resnet18-simclr
364	omnivore_swinT
365	ViT_L_32
366	ViT_B_32
367	resnet18-depth_prediction
368	ViT_B_16
369	r3m_resnet34_nocrop
370	resnet18-contrastive_predictive
371	resnet-18-LC_untrained
372	resnet-18_untrained
373	resnet-50x2_untrained
374	dcgan
375	unet_entire
376	omnivore_swinS
377	v1-pyr-nodown
378	CORnetZ_CIFAR10_bs32_20_04
379	pixels
380	dorinet_cornet_z
381	my-model
382	openclip
383	resnet50-cifar
384	resnet50_pretrained_with_retinal_waves
385	resnet50_ImageNet
386	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
387	yudixie_resnet50_random_0_240908
388	yudixie_resnet50_imagenet1kpret_0_240312
389	yudixie_resnet50_imagenet1kpret_0_240908
390	yudixie_resnet50_cat_obj_class_all_latents_0_240908
391	yudixie_resnet50_object_class_0_240908
392	yudixie_resnet50_category_class_0_240908
393	resnet18_imagenet21kP
394	ConvLSTM
395	yudixie_resnet50_distance_reg_0_240908
396	PredRNN
397	SimVP
398	TAU
399	MIM
400	hmax
401	briaai_rmbg_1_4
402	yolos_tiny
403	vit_base_patch14_reg4_dinov2-lvd142m
404	ReAlnet01_cornet
405	yudixie_resnet18_translation_reg_0_240719
406	compact_resnet50_V4
407	ReAlnet02_cornet
408	yudixie_resnet18_distance_reg_0_240719
409	yudixie_resnet18_imagenet1kpret_0_240719
410	vit_base_patch16_dinov3-lvd1689m
411	resnet18_ecoset_full
412	ReAlnet03_cornet
413	resnet34_ecoset_full
414	ReAlnet01_cornet_fov4
415	resnet50_ecoset_full
416	compact_resnet50_IT
417	compact_resnet50_robust_V4
418	yudixie_resnet50_rotation_reg_0_240908
419	ReAlnet01_cornet_fov12
420	yudixie_resnet50_distance_translation_0_240908
421	yudixie_resnet18_distance_translation_0_240719
422	ReAlnet04_cornet
423	yudixie_resnet18_distance_rotation_0_240719
424	yudixie_resnet18_category_class_0_240719
425	yudixie_resnet50_distance_rotation_0_240908
426	compact_vgg19_V4
427	vit_base_patch14_dinov2-lvd142m
428	resnet-18_test_m
429	yudixie_resnet18_random_0_240719
430	SeLaVi-AVE
431	r3d_18
432	ReAlnet06_cornet
433	ReAlnet01_cornet_fov16
434	compact_vgg19_IT
435	yudixie_resnet50_translation_reg_0_240908
436	yudixie_resnet50_translation_rotation_0_240908
437	ReAlnet01_ALL
438	mc3_18
439	ReAlnet07_cornet
440	s3d
441	mvit_v1_b
442	ReAlnet08_cornet
443	yudixie_resnet18_rotation_reg_0_240719
444	ReAlnet02_ALL
445	VOneCORnet-S
446	ReAlnet03_ALL
447	omnivore_swinB
448	compact_cornets_V4
449	ReAlnet04_ALL
450	SeLaVi-Kinetics400
451	ReAlnet09_cornet
452	compact_cornets_IT
453	SeLaVi-VGG-Sound
454	ReAlnet05_ALL
455	resnet152_ecoset_full
456	ReAlnet06_ALL
457	r2plus1d_18
458	mvit_v2_s
459	SeLaVi-Kinetics-Sound
460	compact_resnet50_robust_IT
461	ReAlnet10_cornet
462	resnet101_ecoset_full
463	ReAlnet07_ALL
464	yudixie_resnet18_object_class_0_240719
465	yudixie_resnet18_cat_obj_class_all_latents_0_240719
466	yudixie_resnet50_distance_translation_rotation_0_240908
467	yudixie_resnet18_distance_translation_rotation_0_240719
468	yudixie_resnet18_translation_rotation_0_240719
469	ReAlnet05_cornet
470	ReAlnet09_ALL
471	resnet50_primary_visual_cortex
472	ReAlnet08_ALL
473	resnet18_fair
474	pr1_stl10_disentangled_alpha04
475	predictive_coding_pc
476	yudixie_wbh_object_class_scaling_0415_run_0
477	yudixie_wbh_object_class_scaling_0415_run_3
478	yudixie_wbh_object_class_scaling_0415_imagenet
479	yudixie_wbh_object_class_scaling_0415_run_2
480	yudixie_wbh_object_class_scaling_0415_run_1
481	deepeisnn_ei_cifar10_vgg19
482	deepeisnn_ei_cifar10_resnet18
483	deepeisnn_ei_imagenet_resnet18
484	dinov2_vitl14_reg_lc_featread
485	gabor_filter_entropy_4x4
486	gabor_filter_mean_abs_4x4_v2
487	gabor_filter_entropy_4x4_v2
488	gabor_filter_entropy_4x4_v2_forcedCosine

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

0.48.

Note that scores are relative to this ceiling.

Data: Geirhos2021silhouette

Metric: error_consistency