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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_large_patch14_clip_224:openai_ft_in1k	1.0
2	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
3	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
4	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
5	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
6	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
7	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
9	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
10	vit_base_patch16_clip_224:openai_ft_in1k	1.0
11	convnext_large:fb_in22k_ft_in1k	1.0
12	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.993
13	resnext101_32x32d_wsl	.990
14	convnext_base:clip_laiona_augreg_ft_in1k_384	.966
15	resnext101_32x8d_wsl	.966
16	convnext_xlarge:fb_in22k_ft_in1k	.959
17	SWSL_resnext101_32x8d	.936
18	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.914
19	resnext101_32x48d_wsl	.908
20	CLIP-RN50	.908
21	fixres_resnext101_32x48d_wsl	.904
22	cvt_cvt-13-384-in1k_4_LucyV4	.900
23	convnext_tiny:in12k_ft_in1k	.897
24	SWSL_resnet50	.891
25	vit_relpos_base_patch16_clsgap_224:sw_in1k	.888
26	convnext_base_fb_in22k_ft_in1k	.886
27	resnext101_32x16d_wsl	.883
28	convnext_tiny_imagenet_full_seed-0	.870
29	deit3_base_patch16_224_fb_in22k_ft_in1k	.860
30	deit3_base_patch16_224_fb_in1k	.859
31	resnet_152_v2	.856
32	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.848
33	CLIP_resnet50	.841
34	CLIP_resnet50_float32	.841
35	CLIP_ViT-B_32	.840
36	resnet_101_v2	.827
37	resnet-152_v2_pytorch	.825
38	focalnet_tiny_lrf_in1k	.822
39	effnetv2m_custom384	.818
40	pnasnet_large_halfPrecision	.817
41	pnasnet_large	.817
42	pnasnet_large-tf	.810
43	resnet50_st_wzc_noseed_2_323	.810
44	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.793
45	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.781
46	resnet_50_v2	.777
47	resnet50	.773
48	convnext_femto_ols:d1_in1k	.767
49	cvt_cvt-21-384-in1k_4_LucyV4	.763
50	convnext_tiny_sup	.763
51	nasnet_large	.750
52	vision_transformer_vit_large_patch16_224	.749
53	resnet50_random_linf8_perturb	.737
54	convnext_large_imagenet_full_seed-0	.728
55	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.728
56	resnet50_st_wzc_seed3_2_323	.725
57	inception_v4	.718
58	nasnet_large-tf	.706
59	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.704
60	resnet50-VITO-8deg-cc	.701
61	resnet50_ewconplus_wzc_seed3_2_323	.693
62	resnet50_ewconplus_wzc_seed7_2_323	.690
63	resnet50_st_wzc_seed6_2_323	.686
64	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.685
65	resnet101_imagenet_full	.681
66	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.675
67	pnasnet_large_pytorch	.674
68	cvt_cvt-13-224-in1k_4_LucyV4	.668
69	inception_v3-tf	.663
70	convnext_small_imagenet_full_seed-0	.660
71	inception_v4-tf	.658
72	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.658
73	resnet50_ewconplus_noseed_2_323	.654
74	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.648
75	antialiased-rnext101_32x8d	.648
76	resnet-152_v2-tf	.647
77	resnet50_st_wzc_seed7_2_323	.647
78	resnet50_imagenet_full	.646
79	resnet50_ewconplus_wzc_2_323	.645
80	resnet34_imagenet_full	.640
81	resnet101	.628
82	resnet50_st_wzc_seed4_2_323	.627
83	cvt_cvt-21-224-in1k_4_LucyV4	.618
84	antialias-resnet152	.615
85	effnetb1_272x240	.614
86	inception_v3	.610
87	AdvProp_efficientnet-b6	.610
88	convnext_base_imagenet_full_seed-0	.608
89	voneresnet-50-non_stochastic	.602
90	resnet50_ewconplus_wzc_seed5_2__323	.601
91	xception-keras	.601
92	resnet50_st_wzc_2_323	.595
93	vonegrcnn_62e_nobn	.584
94	convnext_base_fb_in1k	.582
95	grcnn_109	.578
96	antialiased-r50	.574
97	resnet50_st_wzc_2	.562
98	resnet152_imagenet_full	.560
99	resnet-34	.558
100	vonegrcnn_62e_nobn	.554
101	AT_efficientnet-b2	.553
102	AdvProp_efficientnet-b2	.553
103	resnet_50_v1_spiking	.551
104	resnet_101_v1	.549
105	inception_v3_pytorch	.548
106	resnet50_tutorial	.546
107	resnet_50_v1	.546
108	resnet-50-pytorch	.546
109	resnet50-sup	.546
110	efficientnet_b0	.544
111	resnet50_random_l2_perturb	.535
112	mobilenet_v2_1.4_224-tf	.534
113	densenet-169-keras	.531
114	vonegrcnn_52e_full	.524
115	cv_18_dagger_408_pretrained	.523
116	AdvProp_efficientnet-b4	.521
117	AT_efficientnet-b4	.521
118	inception_v2-tf	.520
119	vonegrcnn_52e_full	.520
120	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.511
121	tv_efficientnet-b1	.510
122	densenet-121-keras	.510
123	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.509
124	vonegrcnn_47e	.500
125	AdvProp_efficientnet-b7	.497
126	resnet-152_v1-tf	.492
127	resnet50-SIN_IN_IN	.492
128	resnet_SIN_IN_FT_IN	.492
129	deit_base_imagenet_full_seed-0	.492
130	resnet-50_v2-tf	.491
131	resnet18_imagenet_full	.491
132	blt_vs	.491
133	grcnn	.482
134	resnet50_ewconplus_wzc	.482
135	vonegrcnn_47e	.482
136	BiT-S-R50x3	.478
137	densenet-169	.474
138	resnet-50_v1-tf	.474
139	convnext_small_imagenet_100_seed-0	.469
140	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
141	densenet-121	.468
142	resnet50_byol	.465
143	voneresnet-50	.463
144	alexnet_fov4	.461
145	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
146	inception_v1	.451
147	resnet-101_v1-tf	.450
148	efficientnet-b7	.449
149	resnet50_ewconplus_wzc_seed4_2_323	.446
150	resnet_152_v1	.445
151	mobilenet_v2_1-4_224_pytorch	.439
152	mobilenet_v2_1_4_224	.439
153	mobilenet_v1_0.75_224	.438
154	mobilenet_v2_1_0_224	.437
155	deit_large_imagenet_full_seed-0	.434
156	AlexNet_SIN_fov4	.428
157	resnet50_linf_4_robust	.421
158	nasnet_mobile	.418
159	ReAlnet03	.417
160	densenet-201-keras	.409
161	resnet50-moclr8deg	.395
162	BiT-S-R50x1	.394
163	custom_model_cv_18_dagger_408	.392
164	Res2Net50_26w_4s	.390
165	resnet50-SIN_IN	.388
166	mobilenet_v2_1.0_192-tf	.384
167	vgg_19	.379
168	densenet-201	.378
169	densenet_201_pytorch	.378
170	cornet_s	.375
171	ReAlnet07	.373
172	mobilenet_v2_0_5_224	.369
173	ReAlnet04	.369
174	ReAlnet01	.365
175	ReAlnet10	.364
176	inception_v1-tf	.362
177	mobilenet_v2_1.0_224-tf	.360
178	mobilenet_v2_1.3_224-tf	.358
179	texture_shape_resnet50_trained_on_SIN	.358
180	efficientnet_b0_imagenet_full	.357
181	CORnet-S	.353
182	mobilenet_v2_1_0_192	.353
183	vgg_16	.352
184	resnet50_linf_8_robust	.349
185	mobilenet_v2_0.5_224-tf	.336
186	mobilenet_v2_1_3_224	.336
187	deit_small_imagenet_full_seed-0	.335
188	resnet-18	.333
189	BiT-S-R152x2	.332
190	nasnet_mobile-tf	.328
191	ReAlnet09	.317
192	mobilenet_v2_0.5_192-tf	.315
193	ReAlnet08	.314
194	texture_shape_alexnet_trained_on_SIN	.310
195	mobilenet_v2_0_75_160	.309
196	ReAlnet02	.304
197	vgg-16-keras	.298
198	resnet50_l2_3_robust	.297
199	mobilenet_v2_1.0_160-tf	.296
200	mobilenet_v2_0_5_192	.296
201	resnet_50_v1_spiking_l4a	.287
202	mobilenet_v2_1_0_160	.287
203	BiT-S-R101x1	.286
204	resnet50_moco_v2	.285
205	ReAlnet05	.280
206	mobilenet_v1_1.0_224	.279
207	mobilenet_v2_0.75_128	.276
208	convnext_base_fb_in1k_gram_aligned_dinov3	.271
209	mobilenet_v1_1.0_160	.270
210	mobilenet_v2_0.75_224-tf	.266
211	AlexNet_SIN	.263
212	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
213	alexnet_random_linf8_perturb	.257
214	mobilenet_v2_0_75_224	.256
215	BiT-S-R152x4	.255
216	mobilenet_v2_0.35_160	.252
217	resnet50-vitoimagevidnet8	.252
218	BiT-S-R101x3	.249
219	alexnet_random_l2_3_perturb	.247
220	resnet50-SIN	.245
221	AdvProp_efficientnet-b0	.243
222	mobilenet_v2_1_0_128	.240
223	mobilenet_v2_0.5_160	.236
224	xception	.236
225	resnet50_robust_l2_eps1	.231
226	mobilenet_v2_0.75_192-tf	.231
227	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.228
228	ReAlnet06	.220
229	efficientnet_b2_imagenet_full	.214
230	alexnet_l2_3_robust	.214
231	voneresnet-50-robust	.205
232	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
233	mobilenet_v2_0.75_160-tf	.204
234	alexnet_ks_torevert	.203
235	alexnet	.203
236	squeezenet1_1	.194
237	mobilenet_v1_0.5_224	.194
238	mobilenet_v1_0.75_160	.193
239	mobilenet_v2_0_75_192	.192
240	mobilenet_v2_1.0_128-tf	.186
241	resnet50_simclr	.185
242	mobilenet_v2_0.35_192	.185
243	mobilenet_v2_1.0_96	.185
244	mobilenet_v1_1.0_192	.180
245	efficientnet_b1_imagenet_full	.176
246	konkle_alexnetgn_ipcl_ref01_primary_model	.169
247	eBarlow_lmda_02_1000ep	.167
248	mobilenet_v2_0.75_96	.166
249	mobilenet_v1_0.5_192	.164
250	mobilenet_v1_0.75_128	.163
251	alexnet_linf_8_robust	.161
252	AlexNet_SIN_fov12	.159
253	mobilenet_v1_0.5_160	.154
254	regnet_y_400mf	.150
255	resnet-50-robust	.149
256	imagenet_l2_3_0	.149
257	resnet50_robust_l2_eps3	.149
258	0.5x_resnet-18_LC_w_sh_1_iter	.148
259	AdvProp_efficientnet-b8	.147
260	mobilenet_v1_0.75_192	.146
261	deit_small_imagenet_100_seed-0	.145
262	alexnet_reduced_aliasing_early_checkpoint	.143
263	alexnet_early_checkpoint	.139
264	alexnet_training_seed_05	.139
265	alexnet_training_seed_06	.139
266	alexnet_training_seed_01	.139
267	alexnet_training_seed_01_fov4	.139
268	alexnet_training_seed_01_fov12	.139
269	alexnet_training_seed_01_fov16	.139
270	resnet50-barlow	.139
271	alexnet_robust_correct	.135
272	vonealexnet_gaussian_noise_std4_fixed	.134
273	mobilenet_v2_0.35_128	.129
274	mobilenet_v1_0.5_128	.127
275	alexnet_fov12	.118
276	AlexNet_SIN_fov16	.117
277	mobilenet_v1_0.25_192	.117
278	resnet50_imagenet_100_seed-0	.112
279	artResNet18_1	.109
280	mobilenet_v2_0.35_224	.107
281	mobilenet_v2_0.5_128	.106
282	mobilenet_v2_0.35_96	.102
283	resnet50_imagenet_10_seed-0	.102
284	mobilenet_v1_0.25_160	.098
285	alexnet_training_seed_03	.090
286	resnet50_FractalDB	.089
287	mobilenet_v1_1.0_128	.089
288	convnext_small_imagenet_10_seed-0	.084
289	deit_small_imagenet_10_seed-0	.084
290	deit_small_imagenet_1_seed-0	.084
291	shufflenet_v2_x1_0	.083
292	mobilenet_v1_0.25_128	.082
293	convnext_small_imagenet_1_seed-0	.082
294	vgg-19-keras	.081
295	alexnet_training_seed_02	.077
296	alexnet_training_seed_08	.077
297	alexnet_training_seed_04	.077
298	alexnet_training_seed_10	.077
299	alexnet_training_seed_07	.077
300	resnet50-meshes-lt-100-original-scratch	.075
301	imagenet_l2_10_0	.073
302	resnet-50_untrained	.070
303	mobilenet_v2_0.5_96	.066
304	resnet50-simclr-vissl	.065
305	resnet50-meshes-lt-100-original-pretrained	.059
306	ViT_B_32_imagenet1k	.056
307	barlow-twins-resnet50	.054
308	squeezenet1_0	.053
309	resnet50_eMMCR_Vanilla	.052
310	ViT_L_32_imagenet1k	.051
311	alexnet_fov16	.050
312	alexnet_training_seed_09	.049
313	resnet50_imagenet_1_seed-0	.046
314	resnet50-vicregl0p75	.043
315	resnet_50_v1_spiking_l4	.042
316	mobilenet_v1_0.25_224	.041
317	resnet50-vicregl0p9	.033
318	mobilevit_small	.023
319	ViT_B_16_imagenet1k	.023
320	resnet50-vicreg	.012
321	resnet_50_v1_spiking_l3	.004
322	ReAlnet10_ALL
323	resnet-18-LC_w_sh_100_iter_conv_init_m
324	ViT-B/32
325	BiT-M-R50x3
326	BiT-M-R50x1
327	BiT-M-R152x2
328	resnet-18-LC_w_sh_1_iter_conv_init_m
329	resnet-18-LC_w_sh_1_iter_m
330	resnet-18-LC_w_sh_10_iter_m
331	resnet-18-LC_w_sh_100_iter_m
332	resnet-18-LC_w_sh_10_iter_conv_init_m
333	FrankRobWobv0
334	0.5x_resnet-18_LC
335	CORnetZ_CIFAR10
336	resnet18-local_aggregation
337	resnet18-deepcluster
338	r3m_resnet18
339	CORnet-Z
340	vggface
341	my-model
342	resnet18-instance_recognition
343	0.5x_resnet-18_LC_w_sh_10_iter
344	r3m_resnet50
345	r3m_resnet34
346	resnet-18-LC_m
347	r3m_resnet50_nocrop
348	resnet18-supervised
349	resnet-18-LC_1st_conv_m
350	resnet-18-LC_conv_init_m
351	resnet18-contrastive_multiview
352	0.5x_resnet-18
353	resnet18-colorization
354	RN50
355	0.5x_resnet-18_LC_w_sh_100_iter
356	RN50
357	resnet18-relative_position
358	resnet18-autoencoder
359	resnet18-simclr
360	omnivore_swinT
361	ViT_L_32
362	ViT_B_32
363	resnet18-depth_prediction
364	ViT_B_16
365	r3m_resnet34_nocrop
366	resnet18-contrastive_predictive
367	resnet-18-LC_untrained
368	resnet-18_untrained
369	resnet-50x2_untrained
370	dcgan
371	unet_entire
372	omnivore_swinS
373	v1-pyr-nodown
374	CORnetZ_CIFAR10_bs32_20_04
375	pixels
376	dorinet_cornet_z
377	my-model
378	resnet50
379	openclip
380	resnet50-cifar
381	resnet50_pretrained_with_retinal_waves
382	resnet50_ImageNet
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	yudixie_resnet50_random_0_240908
385	yudixie_resnet50_imagenet1kpret_0_240312
386	yudixie_resnet50_imagenet1kpret_0_240908
387	yudixie_resnet50_cat_obj_class_all_latents_0_240908
388	yudixie_resnet50_object_class_0_240908
389	yudixie_resnet50_category_class_0_240908
390	resnet18_imagenet21kP
391	ConvLSTM
392	yudixie_resnet50_distance_reg_0_240908
393	PredRNN
394	SimVP
395	TAU
396	MIM
397	hmax
398	briaai_rmbg_1_4
399	yolos_tiny
400	vit_base_patch14_reg4_dinov2-lvd142m
401	ReAlnet01_cornet
402	yudixie_resnet18_translation_reg_0_240719
403	compact_resnet50_V4
404	ReAlnet02_cornet
405	yudixie_resnet18_distance_reg_0_240719
406	yudixie_resnet18_imagenet1kpret_0_240719
407	vit_base_patch16_dinov3-lvd1689m
408	resnet18_ecoset_full
409	ReAlnet03_cornet
410	resnet34_ecoset_full
411	ReAlnet01_cornet_fov4
412	resnet50_ecoset_full
413	compact_resnet50_IT
414	compact_resnet50_robust_V4
415	yudixie_resnet50_rotation_reg_0_240908
416	ReAlnet01_cornet_fov12
417	yudixie_resnet50_distance_translation_0_240908
418	yudixie_resnet18_distance_translation_0_240719
419	ReAlnet04_cornet
420	yudixie_resnet18_distance_rotation_0_240719
421	yudixie_resnet18_category_class_0_240719
422	yudixie_resnet50_distance_rotation_0_240908
423	compact_vgg19_V4
424	vit_base_patch14_dinov2-lvd142m
425	resnet-18_test_m
426	yudixie_resnet18_random_0_240719
427	SeLaVi-AVE
428	r3d_18
429	ReAlnet06_cornet
430	ReAlnet01_cornet_fov16
431	compact_vgg19_IT
432	yudixie_resnet50_translation_reg_0_240908
433	yudixie_resnet50_translation_rotation_0_240908
434	ReAlnet01_ALL
435	mc3_18
436	ReAlnet07_cornet
437	s3d
438	mvit_v1_b
439	ReAlnet08_cornet
440	yudixie_resnet18_rotation_reg_0_240719
441	ReAlnet02_ALL
442	aughost_fair_resnet18
443	VOneCORnet-S
444	ReAlnet03_ALL
445	omnivore_swinB
446	compact_cornets_V4
447	ReAlnet04_ALL
448	SeLaVi-Kinetics400
449	ReAlnet09_cornet
450	compact_cornets_IT
451	SeLaVi-VGG-Sound
452	ReAlnet05_ALL
453	resnet152_ecoset_full
454	ReAlnet06_ALL
455	r2plus1d_18
456	mvit_v2_s
457	SeLaVi-Kinetics-Sound
458	compact_resnet50_robust_IT
459	ReAlnet10_cornet
460	resnet101_ecoset_full
461	ReAlnet07_ALL
462	yudixie_resnet18_object_class_0_240719
463	yudixie_resnet18_cat_obj_class_all_latents_0_240719
464	yudixie_resnet50_distance_translation_rotation_0_240908
465	yudixie_resnet18_distance_translation_rotation_0_240719
466	yudixie_resnet18_translation_rotation_0_240719
467	ReAlnet05_cornet
468	ReAlnet09_ALL
469	resnet50_primary_visual_cortex
470	ReAlnet08_ALL
471	resnet18_fair
472	pr1_stl10_disentangled_alpha04
473	predictive_coding_pc
474	yudixie_wbh_object_class_scaling_0415_run_0
475	yudixie_wbh_object_class_scaling_0415_run_3
476	yudixie_wbh_object_class_scaling_0415_imagenet
477	yudixie_wbh_object_class_scaling_0415_run_2
478	yudixie_wbh_object_class_scaling_0415_run_1
479	deepeisnn_ei_cifar10_vgg19
480	deepeisnn_ei_cifar10_resnet18
481	deepeisnn_ei_imagenet_resnet18

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