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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	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.781
54	resnet_50_v2	.777
55	resnet50	.773
56	convnext_femto_ols:d1_in1k	.767
57	cvt_cvt-21-384-in1k_4_LucyV4	.763
58	convnext_tiny_sup	.763
59	dinov2_vitl14_lc	.754
60	nasnet_large	.750
61	vision_transformer_vit_large_patch16_224	.749
62	dinov2_vitg14_reg_lc	.739
63	resnet50_random_linf8_perturb	.737
64	convnext_large_imagenet_full_seed-0	.728
65	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.728
66	resnet50_st_wzc_seed3_2_323	.725
67	inception_v4	.718
68	nasnet_large-tf	.706
69	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.704
70	resnet50-VITO-8deg-cc	.701
71	resnet50_st_wzc_seed6_2_323	.686
72	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.685
73	resnet101_imagenet_full	.681
74	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.675
75	pnasnet_large_pytorch	.674
76	cvt_cvt-13-224-in1k_4_LucyV4	.668
77	inception_v3-tf	.663
78	convnext_small_imagenet_full_seed-0	.660
79	inception_v4-tf	.658
80	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.658
81	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.648
82	antialiased-rnext101_32x8d	.648
83	resnet-152_v2-tf	.647
84	resnet50_st_wzc_seed7_2_323	.647
85	resnet50_imagenet_full	.646
86	resnet34_imagenet_full	.640
87	resnet101	.628
88	cvt_cvt-21-224-in1k_4_LucyV4	.618
89	antialias-resnet152	.615
90	effnetb1_272x240	.614
91	inception_v3	.610
92	AdvProp_efficientnet-b6	.610
93	convnext_base_imagenet_full_seed-0	.608
94	voneresnet-50-non_stochastic	.602
95	resnet50_ewconplus_wzc_seed5_2__323	.601
96	xception-keras	.601
97	vonegrcnn_62e_nobn	.584
98	convnext_base_fb_in1k	.582
99	grcnn_109	.578
100	antialiased-r50	.574
101	resnet50_st_wzc_2	.562
102	resnet152_imagenet_full	.560
103	resnet-34	.558
104	vonegrcnn_62e_nobn	.554
105	AT_efficientnet-b2	.553
106	AdvProp_efficientnet-b2	.553
107	resnet_50_v1_spiking	.551
108	resnet_101_v1	.549
109	resnet50_hvs_wzc	.549
110	inception_v3_pytorch	.548
111	resnet50_tutorial	.546
112	resnet_50_v1	.546
113	resnet-50-pytorch	.546
114	resnet50-sup	.546
115	efficientnet_b0	.544
116	resnet50_random_l2_perturb	.535
117	mobilenet_v2_1.4_224-tf	.534
118	densenet-169-keras	.531
119	vonegrcnn_52e_full	.524
120	cv_18_dagger_408_pretrained	.523
121	AdvProp_efficientnet-b4	.521
122	AT_efficientnet-b4	.521
123	inception_v2-tf	.520
124	vonegrcnn_52e_full	.520
125	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.511
126	tv_efficientnet-b1	.510
127	densenet-121-keras	.510
128	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.509
129	vonegrcnn_47e	.500
130	AdvProp_efficientnet-b7	.497
131	resnet-152_v1-tf	.492
132	resnet50-SIN_IN_IN	.492
133	resnet_SIN_IN_FT_IN	.492
134	deit_base_imagenet_full_seed-0	.492
135	resnet-50_v2-tf	.491
136	resnet18_imagenet_full	.491
137	blt_vs	.491
138	grcnn	.482
139	resnet50_ewconplus_wzc	.482
140	vonegrcnn_47e	.482
141	BiT-S-R50x3	.478
142	densenet-169	.474
143	resnet-50_v1-tf	.474
144	convnext_small_imagenet_100_seed-0	.469
145	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
146	densenet-121	.468
147	resnet50_byol	.465
148	voneresnet-50	.463
149	alexnet_fov4	.461
150	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
151	inception_v1	.451
152	resnet-101_v1-tf	.450
153	efficientnet-b7	.449
154	resnet_152_v1	.445
155	mobilenet_v2_1-4_224_pytorch	.439
156	mobilenet_v2_1_4_224	.439
157	mobilenet_v1_0.75_224	.438
158	mobilenet_v2_1_0_224	.437
159	deit_large_imagenet_full_seed-0	.434
160	AlexNet_SIN_fov4	.428
161	resnet50_linf_4_robust	.421
162	nasnet_mobile	.418
163	ReAlnet03	.417
164	densenet-201-keras	.409
165	resnet50-moclr8deg	.395
166	BiT-S-R50x1	.394
167	custom_model_cv_18_dagger_408	.392
168	Res2Net50_26w_4s	.390
169	resnet50-SIN_IN	.388
170	mobilenet_v2_1.0_192-tf	.384
171	vgg_19	.379
172	densenet-201	.378
173	densenet_201_pytorch	.378
174	cornet_s	.375
175	ReAlnet07	.373
176	mobilenet_v2_0_5_224	.369
177	ReAlnet04	.369
178	ReAlnet01	.365
179	ReAlnet10	.364
180	inception_v1-tf	.362
181	mobilenet_v2_1.0_224-tf	.360
182	mobilenet_v2_1.3_224-tf	.358
183	texture_shape_resnet50_trained_on_SIN	.358
184	efficientnet_b0_imagenet_full	.357
185	CORnet-S	.353
186	mobilenet_v2_1_0_192	.353
187	vgg_16	.352
188	resnet50_linf_8_robust	.349
189	mobilenet_v2_0.5_224-tf	.336
190	mobilenet_v2_1_3_224	.336
191	deit_small_imagenet_full_seed-0	.335
192	resnet-18	.333
193	BiT-S-R152x2	.332
194	nasnet_mobile-tf	.328
195	ReAlnet09	.317
196	mobilenet_v2_0.5_192-tf	.315
197	ReAlnet08	.314
198	texture_shape_alexnet_trained_on_SIN	.310
199	mobilenet_v2_0_75_160	.309
200	ReAlnet02	.304
201	vgg-16-keras	.298
202	resnet50_l2_3_robust	.297
203	mobilenet_v2_1.0_160-tf	.296
204	mobilenet_v2_0_5_192	.296
205	resnet_50_v1_spiking_l4a	.287
206	mobilenet_v2_1_0_160	.287
207	BiT-S-R101x1	.286
208	resnet50_moco_v2	.285
209	ReAlnet05	.280
210	mobilenet_v1_1.0_224	.279
211	mobilenet_v2_0.75_128	.276
212	convnext_base_fb_in1k_gram_aligned_dinov3	.271
213	mobilenet_v1_1.0_160	.270
214	mobilenet_v2_0.75_224-tf	.266
215	AlexNet_SIN	.263
216	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
217	alexnet_random_linf8_perturb	.257
218	mobilenet_v2_0_75_224	.256
219	BiT-S-R152x4	.255
220	mobilenet_v2_0.35_160	.252
221	resnet50-vitoimagevidnet8	.252
222	BiT-S-R101x3	.249
223	alexnet_random_l2_3_perturb	.247
224	resnet50-SIN	.245
225	AdvProp_efficientnet-b0	.243
226	mobilenet_v2_1_0_128	.240
227	mobilenet_v2_0.5_160	.236
228	xception	.236
229	resnet50_robust_l2_eps1	.231
230	mobilenet_v2_0.75_192-tf	.231
231	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.228
232	ReAlnet06	.220
233	efficientnet_b2_imagenet_full	.214
234	alexnet_l2_3_robust	.214
235	voneresnet-50-robust	.205
236	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
237	mobilenet_v2_0.75_160-tf	.204
238	alexnet_ks_torevert	.203
239	alexnet	.203
240	squeezenet1_1	.194
241	mobilenet_v1_0.5_224	.194
242	mobilenet_v1_0.75_160	.193
243	mobilenet_v2_0_75_192	.192
244	mobilenet_v2_1.0_128-tf	.186
245	resnet50_simclr	.185
246	mobilenet_v2_0.35_192	.185
247	mobilenet_v2_1.0_96	.185
248	mobilenet_v1_1.0_192	.180
249	efficientnet_b1_imagenet_full	.176
250	konkle_alexnetgn_ipcl_ref01_primary_model	.169
251	eBarlow_lmda_02_1000ep	.167
252	mobilenet_v2_0.75_96	.166
253	mobilenet_v1_0.5_192	.164
254	mobilenet_v1_0.75_128	.163
255	alexnet_linf_8_robust	.161
256	AlexNet_SIN_fov12	.159
257	mobilenet_v1_0.5_160	.154
258	regnet_y_400mf	.150
259	resnet-50-robust	.149
260	imagenet_l2_3_0	.149
261	resnet50_robust_l2_eps3	.149
262	0.5x_resnet-18_LC_w_sh_1_iter	.148
263	AdvProp_efficientnet-b8	.147
264	mobilenet_v1_0.75_192	.146
265	deit_small_imagenet_100_seed-0	.145
266	alexnet_reduced_aliasing_early_checkpoint	.143
267	alexnet_early_checkpoint	.139
268	alexnet_training_seed_05	.139
269	alexnet_training_seed_06	.139
270	alexnet_training_seed_01	.139
271	alexnet_training_seed_01_fov4	.139
272	alexnet_training_seed_01_fov12	.139
273	alexnet_training_seed_01_fov16	.139
274	resnet50-barlow	.139
275	alexnet_robust_correct	.135
276	vonealexnet_gaussian_noise_std4_fixed	.134
277	mobilenet_v2_0.35_128	.129
278	mobilenet_v1_0.5_128	.127
279	alexnet_fov12	.118
280	AlexNet_SIN_fov16	.117
281	mobilenet_v1_0.25_192	.117
282	resnet50_imagenet_100_seed-0	.112
283	artResNet18_1	.109
284	mobilenet_v2_0.35_224	.107
285	mobilenet_v2_0.5_128	.106
286	mobilenet_v2_0.35_96	.102
287	resnet50_imagenet_10_seed-0	.102
288	mobilenet_v1_0.25_160	.098
289	alexnet_training_seed_03	.090
290	resnet50_FractalDB	.089
291	mobilenet_v1_1.0_128	.089
292	convnext_small_imagenet_10_seed-0	.084
293	deit_small_imagenet_10_seed-0	.084
294	deit_small_imagenet_1_seed-0	.084
295	shufflenet_v2_x1_0	.083
296	mobilenet_v1_0.25_128	.082
297	convnext_small_imagenet_1_seed-0	.082
298	vgg-19-keras	.081
299	alexnet_training_seed_02	.077
300	alexnet_training_seed_08	.077
301	alexnet_training_seed_04	.077
302	alexnet_training_seed_10	.077
303	alexnet_training_seed_07	.077
304	imagenet_l2_10_0	.073
305	resnet-50_untrained	.070
306	mobilenet_v2_0.5_96	.066
307	resnet50-simclr-vissl	.065
308	ViT_B_32_imagenet1k	.056
309	barlow-twins-resnet50	.054
310	squeezenet1_0	.053
311	resnet50_eMMCR_Vanilla	.052
312	ViT_L_32_imagenet1k	.051
313	alexnet_fov16	.050
314	alexnet_training_seed_09	.049
315	resnet50_imagenet_1_seed-0	.046
316	resnet50-vicregl0p75	.043
317	resnet_50_v1_spiking_l4	.042
318	mobilenet_v1_0.25_224	.041
319	resnet50-vicregl0p9	.033
320	mobilevit_small	.023
321	ViT_B_16_imagenet1k	.023
322	resnet50-vicreg	.012
323	resnet_50_v1_spiking_l3	.004
324	ReAlnet10_ALL
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	BiT-M-R152x2
330	resnet-18-LC_w_sh_1_iter_conv_init_m
331	resnet-18-LC_w_sh_1_iter_m
332	resnet-18-LC_w_sh_10_iter_m
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_10_iter_conv_init_m
335	FrankRobWobv0
336	0.5x_resnet-18_LC
337	CORnetZ_CIFAR10
338	resnet18-local_aggregation
339	resnet18-deepcluster
340	r3m_resnet18
341	CORnet-Z
342	vggface
343	my-model
344	resnet18-instance_recognition
345	0.5x_resnet-18_LC_w_sh_10_iter
346	r3m_resnet50
347	r3m_resnet34
348	resnet-18-LC_m
349	r3m_resnet50_nocrop
350	resnet18-supervised
351	resnet-18-LC_1st_conv_m
352	resnet-18-LC_conv_init_m
353	resnet18-contrastive_multiview
354	0.5x_resnet-18
355	resnet18-colorization
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	omnivore_swinT
363	ViT_L_32
364	ViT_B_32
365	resnet18-depth_prediction
366	ViT_B_16
367	r3m_resnet34_nocrop
368	resnet18-contrastive_predictive
369	resnet-18-LC_untrained
370	resnet-18_untrained
371	resnet-50x2_untrained
372	dcgan
373	unet_entire
374	omnivore_swinS
375	v1-pyr-nodown
376	CORnetZ_CIFAR10_bs32_20_04
377	pixels
378	dorinet_cornet_z
379	my-model
380	openclip
381	resnet50-cifar
382	resnet50_pretrained_with_retinal_waves
383	resnet50_ImageNet
384	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
385	yudixie_resnet50_random_0_240908
386	yudixie_resnet50_imagenet1kpret_0_240312
387	yudixie_resnet50_imagenet1kpret_0_240908
388	yudixie_resnet50_cat_obj_class_all_latents_0_240908
389	yudixie_resnet50_object_class_0_240908
390	yudixie_resnet50_category_class_0_240908
391	resnet18_imagenet21kP
392	ConvLSTM
393	yudixie_resnet50_distance_reg_0_240908
394	PredRNN
395	SimVP
396	TAU
397	MIM
398	hmax
399	briaai_rmbg_1_4
400	yolos_tiny
401	vit_base_patch14_reg4_dinov2-lvd142m
402	ReAlnet01_cornet
403	yudixie_resnet18_translation_reg_0_240719
404	compact_resnet50_V4
405	ReAlnet02_cornet
406	yudixie_resnet18_distance_reg_0_240719
407	yudixie_resnet18_imagenet1kpret_0_240719
408	vit_base_patch16_dinov3-lvd1689m
409	resnet18_ecoset_full
410	ReAlnet03_cornet
411	resnet34_ecoset_full
412	ReAlnet01_cornet_fov4
413	resnet50_ecoset_full
414	compact_resnet50_IT
415	compact_resnet50_robust_V4
416	yudixie_resnet50_rotation_reg_0_240908
417	ReAlnet01_cornet_fov12
418	yudixie_resnet50_distance_translation_0_240908
419	yudixie_resnet18_distance_translation_0_240719
420	ReAlnet04_cornet
421	yudixie_resnet18_distance_rotation_0_240719
422	yudixie_resnet18_category_class_0_240719
423	yudixie_resnet50_distance_rotation_0_240908
424	compact_vgg19_V4
425	vit_base_patch14_dinov2-lvd142m
426	resnet-18_test_m
427	yudixie_resnet18_random_0_240719
428	SeLaVi-AVE
429	r3d_18
430	ReAlnet06_cornet
431	ReAlnet01_cornet_fov16
432	compact_vgg19_IT
433	yudixie_resnet50_translation_reg_0_240908
434	yudixie_resnet50_translation_rotation_0_240908
435	ReAlnet01_ALL
436	mc3_18
437	ReAlnet07_cornet
438	s3d
439	mvit_v1_b
440	ReAlnet08_cornet
441	yudixie_resnet18_rotation_reg_0_240719
442	ReAlnet02_ALL
443	aughost_fair_resnet18
444	VOneCORnet-S
445	ReAlnet03_ALL
446	omnivore_swinB
447	compact_cornets_V4
448	ReAlnet04_ALL
449	SeLaVi-Kinetics400
450	ReAlnet09_cornet
451	compact_cornets_IT
452	SeLaVi-VGG-Sound
453	ReAlnet05_ALL
454	resnet152_ecoset_full
455	ReAlnet06_ALL
456	r2plus1d_18
457	mvit_v2_s
458	SeLaVi-Kinetics-Sound
459	compact_resnet50_robust_IT
460	ReAlnet10_cornet
461	resnet101_ecoset_full
462	ReAlnet07_ALL
463	yudixie_resnet18_object_class_0_240719
464	yudixie_resnet18_cat_obj_class_all_latents_0_240719
465	yudixie_resnet50_distance_translation_rotation_0_240908
466	yudixie_resnet18_distance_translation_rotation_0_240719
467	yudixie_resnet18_translation_rotation_0_240719
468	ReAlnet05_cornet
469	ReAlnet09_ALL
470	resnet50_primary_visual_cortex
471	ReAlnet08_ALL
472	resnet18_fair
473	pr1_stl10_disentangled_alpha04
474	predictive_coding_pc
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_imagenet
478	yudixie_wbh_object_class_scaling_0415_run_2
479	yudixie_wbh_object_class_scaling_0415_run_1
480	deepeisnn_ei_cifar10_vgg19
481	deepeisnn_ei_cifar10_resnet18
482	deepeisnn_ei_imagenet_resnet18
483	dinov2_vitl14_reg_lc_featread

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