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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	1.2
2	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.2
3	vit_large_patch14_clip_224:laion2b_ft_in1k	1.2
4	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.1
5	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.1
6	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.1
7	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.1
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.1
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	resnext101_32x16d_wsl	.883
27	convnext_tiny_imagenet_full_seed-0	.870
28	resnet_152_v2	.856
29	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.848
30	CLIP_resnet50	.841
31	CLIP_resnet50_float32	.841
32	CLIP_ViT-B_32	.840
33	resnet_101_v2	.827
34	resnet-152_v2_pytorch	.825
35	focalnet_tiny_lrf_in1k	.822
36	effnetv2m_custom384	.818
37	pnasnet_large	.817
38	pnasnet_large-tf	.810
39	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.793
40	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.781
41	resnet_50_v2	.777
42	resnet50	.773
43	convnext_femto_ols:d1_in1k	.767
44	cvt_cvt-21-384-in1k_4_LucyV4	.763
45	convnext_tiny_sup	.763
46	nasnet_large	.750
47	vision_transformer_vit_large_patch16_224	.749
48	resnet50_random_linf8_perturb	.737
49	convnext_large_imagenet_full_seed-0	.728
50	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.728
51	inception_v4	.718
52	nasnet_large-tf	.706
53	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.704
54	resnet50-VITO-8deg-cc	.701
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.685
56	resnet101_imagenet_full	.681
57	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.675
58	pnasnet_large_pytorch	.674
59	cvt_cvt-13-224-in1k_4_LucyV4	.668
60	inception_v3-tf	.663
61	convnext_small_imagenet_full_seed-0	.660
62	inception_v4-tf	.658
63	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.658
64	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.648
65	antialiased-rnext101_32x8d	.648
66	resnet-152_v2-tf	.647
67	resnet50_imagenet_full	.646
68	resnet34_imagenet_full	.640
69	resnet101	.628
70	cvt_cvt-21-224-in1k_4_LucyV4	.618
71	antialias-resnet152	.615
72	effnetb1_272x240	.614
73	inception_v3	.610
74	AdvProp_efficientnet-b6	.610
75	convnext_base_imagenet_full_seed-0	.608
76	voneresnet-50-non_stochastic	.602
77	xception-keras	.601
78	vonegrcnn_62e_nobn	.584
79	grcnn_109	.578
80	antialiased-r50	.574
81	resnet152_imagenet_full	.560
82	resnet-34	.558
83	vonegrcnn_62e_nobn	.554
84	AT_efficientnet-b2	.553
85	AdvProp_efficientnet-b2	.553
86	resnet_50_v1_spiking	.551
87	resnet_101_v1	.549
88	inception_v3_pytorch	.548
89	resnet50_tutorial	.546
90	resnet_50_v1	.546
91	resnet-50-pytorch	.546
92	resnet50-sup	.546
93	efficientnet_b0	.544
94	resnet50_random_l2_perturb	.535
95	mobilenet_v2_1.4_224-tf	.534
96	densenet-169-keras	.531
97	vonegrcnn_52e_full	.524
98	cv_18_dagger_408_pretrained	.523
99	AdvProp_efficientnet-b4	.521
100	AT_efficientnet-b4	.521
101	inception_v2-tf	.520
102	vonegrcnn_52e_full	.520
103	tv_efficientnet-b1	.510
104	densenet-121-keras	.510
105	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.509
106	vonegrcnn_47e	.500
107	AdvProp_efficientnet-b7	.497
108	resnet-152_v1-tf	.492
109	resnet50-SIN_IN_IN	.492
110	resnet_SIN_IN_FT_IN	.492
111	deit_base_imagenet_full_seed-0	.492
112	resnet-50_v2-tf	.491
113	resnet18_imagenet_full	.491
114	blt_vs	.491
115	grcnn	.482
116	resnet50_ewconplus_wzc	.482
117	vonegrcnn_47e	.482
118	BiT-S-R50x3	.478
119	densenet-169	.474
120	resnet-50_v1-tf	.474
121	convnext_small_imagenet_100_seed-0	.469
122	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
123	densenet-121	.468
124	resnet50_byol	.465
125	voneresnet-50	.463
126	alexnet_fov4	.461
127	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
128	inception_v1	.451
129	resnet-101_v1-tf	.450
130	efficientnet-b7	.449
131	resnet_152_v1	.445
132	mobilenet_v2_1-4_224_pytorch	.439
133	mobilenet_v2_1_4_224	.439
134	mobilenet_v1_0.75_224	.438
135	mobilenet_v2_1_0_224	.437
136	deit_large_imagenet_full_seed-0	.434
137	AlexNet_SIN_fov4	.428
138	resnet50_linf_4_robust	.421
139	nasnet_mobile	.418
140	ReAlnet03	.417
141	densenet-201-keras	.409
142	resnet50-moclr8deg	.395
143	BiT-S-R50x1	.394
144	custom_model_cv_18_dagger_408	.392
145	Res2Net50_26w_4s	.390
146	resnet50-SIN_IN	.388
147	mobilenet_v2_1.0_192-tf	.384
148	vgg_19	.379
149	densenet-201	.378
150	densenet_201_pytorch	.378
151	cornet_s	.375
152	ReAlnet07	.373
153	mobilenet_v2_0_5_224	.369
154	ReAlnet04	.369
155	ReAlnet01	.365
156	ReAlnet10	.364
157	inception_v1-tf	.362
158	mobilenet_v2_1.0_224-tf	.360
159	mobilenet_v2_1.3_224-tf	.358
160	texture_shape_resnet50_trained_on_SIN	.358
161	efficientnet_b0_imagenet_full	.357
162	CORnet-S	.353
163	mobilenet_v2_1_0_192	.353
164	vgg_16	.352
165	resnet50_linf_8_robust	.349
166	mobilenet_v2_0.5_224-tf	.336
167	mobilenet_v2_1_3_224	.336
168	deit_small_imagenet_full_seed-0	.335
169	resnet-18	.333
170	BiT-S-R152x2	.332
171	nasnet_mobile-tf	.328
172	ReAlnet09	.317
173	mobilenet_v2_0.5_192-tf	.315
174	ReAlnet08	.314
175	texture_shape_alexnet_trained_on_SIN	.310
176	mobilenet_v2_0_75_160	.309
177	ReAlnet02	.304
178	vgg-16-keras	.298
179	resnet50_l2_3_robust	.297
180	mobilenet_v2_1.0_160-tf	.296
181	mobilenet_v2_0_5_192	.296
182	resnet_50_v1_spiking_l4a	.287
183	mobilenet_v2_1_0_160	.287
184	BiT-S-R101x1	.286
185	resnet50_moco_v2	.285
186	ReAlnet05	.280
187	mobilenet_v1_1.0_224	.279
188	mobilenet_v2_0.75_128	.276
189	mobilenet_v1_1.0_160	.270
190	mobilenet_v2_0.75_224-tf	.266
191	AlexNet_SIN	.263
192	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
193	alexnet_random_linf8_perturb	.257
194	mobilenet_v2_0_75_224	.256
195	BiT-S-R152x4	.255
196	mobilenet_v2_0.35_160	.252
197	resnet50-vitoimagevidnet8	.252
198	BiT-S-R101x3	.249
199	alexnet_random_l2_3_perturb	.247
200	alexnet	.246
201	resnet50-SIN	.245
202	AdvProp_efficientnet-b0	.243
203	mobilenet_v2_1_0_128	.240
204	mobilenet_v2_0.5_160	.236
205	xception	.236
206	resnet50_robust_l2_eps1	.231
207	mobilenet_v2_0.75_192-tf	.231
208	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.228
209	ReAlnet06	.220
210	efficientnet_b2_imagenet_full	.214
211	alexnet_l2_3_robust	.214
212	voneresnet-50-robust	.205
213	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
214	mobilenet_v2_0.75_160-tf	.204
215	alexnet_ks_torevert	.203
216	alexnet_workflow_trigger	.203
217	alexnet-baseline	.203
218	alexnet	.203
219	alexnet	.203
220	alexnet	.203
221	alexnet	.203
222	alexnet	.203
223	alexnet	.203
224	alexnet	.203
225	alexnet	.203
226	alexnet	.203
227	alexnet	.203
228	squeezenet1_1	.194
229	mobilenet_v1_0.5_224	.194
230	mobilenet_v1_0.75_160	.193
231	mobilenet_v2_0_75_192	.192
232	mobilenet_v2_1.0_128-tf	.186
233	resnet50_simclr	.185
234	mobilenet_v2_0.35_192	.185
235	mobilenet_v2_1.0_96	.185
236	mobilenet_v1_1.0_192	.180
237	efficientnet_b1_imagenet_full	.176
238	konkle_alexnetgn_ipcl_ref01_primary_model	.169
239	mobilenet_v2_0.75_96	.166
240	mobilenet_v1_0.5_192	.164
241	mobilenet_v1_0.75_128	.163
242	alexnet_linf_8_robust	.161
243	AlexNet_SIN_fov12	.159
244	mobilenet_v1_0.5_160	.154
245	regnet_y_400mf	.150
246	resnet-50-robust	.149
247	imagenet_l2_3_0	.149
248	resnet50_robust_l2_eps3	.149
249	0.5x_resnet-18_LC_w_sh_1_iter	.148
250	AdvProp_efficientnet-b8	.147
251	mobilenet_v1_0.75_192	.146
252	deit_small_imagenet_100_seed-0	.145
253	alexnet_reduced_aliasing_early_checkpoint	.143
254	alexnet_early_checkpoint	.139
255	alexnet_training_seed_05	.139
256	alexnet_training_seed_06	.139
257	alexnet_training_seed_01	.139
258	alexnet_training_seed_01_fov4	.139
259	alexnet_training_seed_01_fov12	.139
260	alexnet_training_seed_01_fov16	.139
261	resnet50-barlow	.139
262	alexnet_robust_correct	.135
263	vonealexnet_gaussian_noise_std4_fixed	.134
264	mobilenet_v2_0.35_128	.129
265	mobilenet_v1_0.5_128	.127
266	alexnet_fov12	.118
267	AlexNet_SIN_fov16	.117
268	mobilenet_v1_0.25_192	.117
269	resnet50_imagenet_100_seed-0	.112
270	artResNet18_1	.109
271	mobilenet_v2_0.35_224	.107
272	mobilenet_v2_0.5_128	.106
273	mobilenet_v2_0.35_96	.102
274	resnet50_imagenet_10_seed-0	.102
275	mobilenet_v1_0.25_160	.098
276	alexnet_training_seed_03	.090
277	resnet50_FractalDB	.089
278	mobilenet_v1_1.0_128	.089
279	convnext_small_imagenet_10_seed-0	.084
280	deit_small_imagenet_10_seed-0	.084
281	deit_small_imagenet_1_seed-0	.084
282	shufflenet_v2_x1_0	.083
283	mobilenet_v1_0.25_128	.082
284	convnext_small_imagenet_1_seed-0	.082
285	vgg-19-keras	.081
286	alexnet_training_seed_02	.077
287	alexnet_training_seed_08	.077
288	alexnet_training_seed_04	.077
289	alexnet_training_seed_10	.077
290	alexnet_training_seed_07	.077
291	resnet50-meshes-lt-100-original-scratch	.075
292	imagenet_l2_10_0	.073
293	resnet-50_untrained	.070
294	mobilenet_v2_0.5_96	.066
295	resnet50-simclr-vissl	.065
296	resnet50-meshes-lt-100-original-pretrained	.059
297	ViT_B_32_imagenet1k	.056
298	barlow-twins-resnet50	.054
299	squeezenet1_0	.053
300	resnet50_eMMCR_Vanilla	.052
301	ViT_L_32_imagenet1k	.051
302	alexnet_fov16	.050
303	alexnet_training_seed_09	.049
304	resnet50_imagenet_1_seed-0	.046
305	resnet50-vicregl0p75	.043
306	resnet_50_v1_spiking_l4	.042
307	mobilenet_v1_0.25_224	.041
308	resnet50-vicregl0p9	.033
309	mobilevit_small	.023
310	ViT_B_16_imagenet1k	.023
311	resnet50-vicreg	.012
312	resnet_50_v1_spiking_l3	.004
313	ReAlnet10_ALL
314	resnet-18-LC_w_sh_100_iter_conv_init_m
315	ViT-B/32
316	BiT-M-R50x3
317	BiT-M-R50x1
318	BiT-M-R152x2
319	resnet-18-LC_w_sh_1_iter_conv_init_m
320	resnet-18-LC_w_sh_1_iter_m
321	resnet-18-LC_w_sh_10_iter_m
322	resnet-18-LC_w_sh_100_iter_m
323	resnet-18-LC_w_sh_10_iter_conv_init_m
324	FrankRobWobv0
325	0.5x_resnet-18_LC
326	CORnetZ_CIFAR10
327	resnet18-local_aggregation
328	resnet18-deepcluster
329	r3m_resnet18
330	CORnet-Z
331	vggface
332	my-model
333	resnet18-instance_recognition
334	0.5x_resnet-18_LC_w_sh_10_iter
335	r3m_resnet50
336	r3m_resnet34
337	resnet-18-LC_m
338	r3m_resnet50_nocrop
339	resnet18-supervised
340	resnet-18-LC_1st_conv_m
341	resnet-18-LC_conv_init_m
342	resnet18-contrastive_multiview
343	0.5x_resnet-18
344	resnet18-colorization
345	RN50
346	0.5x_resnet-18_LC_w_sh_100_iter
347	RN50
348	resnet18-relative_position
349	resnet18-autoencoder
350	resnet18-simclr
351	omnivore_swinT
352	ViT_L_32
353	ViT_B_32
354	resnet18-depth_prediction
355	ViT_B_16
356	r3m_resnet34_nocrop
357	resnet18-contrastive_predictive
358	resnet-18-LC_untrained
359	resnet-18_untrained
360	resnet-50x2_untrained
361	dcgan
362	unet_entire
363	omnivore_swinS
364	v1-pyr-nodown
365	CORnetZ_CIFAR10_bs32_20_04
366	pixels
367	dorinet_cornet_z
368	my-model
369	resnet50
370	openclip
371	resnet50-cifar
372	resnet50_pretrained_with_retinal_waves
373	resnet50_ImageNet
374	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
375	yudixie_resnet50_random_0_240908
376	yudixie_resnet50_imagenet1kpret_0_240312
377	yudixie_resnet50_imagenet1kpret_0_240908
378	yudixie_resnet50_cat_obj_class_all_latents_0_240908
379	yudixie_resnet50_object_class_0_240908
380	yudixie_resnet50_category_class_0_240908
381	resnet18_imagenet21kP
382	ConvLSTM
383	yudixie_resnet50_distance_reg_0_240908
384	PredRNN
385	SimVP
386	TAU
387	MIM
388	hmax
389	briaai_rmbg_1_4
390	yolos_tiny
391	ReAlnet01_cornet
392	vit_base_patch16_dinov3:lvd1689m
393	yudixie_resnet18_translation_reg_0_240719
394	compact_resnet50_V4
395	ReAlnet02_cornet
396	yudixie_resnet18_distance_reg_0_240719
397	yudixie_resnet18_imagenet1kpret_0_240719
398	resnet18_ecoset_full
399	ReAlnet03_cornet
400	resnet34_ecoset_full
401	ReAlnet01_cornet_fov4
402	resnet50_ecoset_full
403	compact_resnet50_IT
404	compact_resnet50_robust_V4
405	yudixie_resnet50_rotation_reg_0_240908
406	ReAlnet01_cornet_fov12
407	yudixie_resnet50_distance_translation_0_240908
408	yudixie_resnet18_distance_translation_0_240719
409	ReAlnet04_cornet
410	yudixie_resnet18_distance_rotation_0_240719
411	yudixie_resnet18_category_class_0_240719
412	yudixie_resnet50_distance_rotation_0_240908
413	compact_vgg19_V4
414	resnet-18_test_m
415	yudixie_resnet18_random_0_240719
416	SeLaVi-AVE
417	r3d_18
418	ReAlnet06_cornet
419	ReAlnet01_cornet_fov16
420	compact_vgg19_IT
421	yudixie_resnet50_translation_reg_0_240908
422	yudixie_resnet50_translation_rotation_0_240908
423	ReAlnet01_ALL
424	mc3_18
425	ReAlnet07_cornet
426	s3d
427	vit_base_patch14_dinov2:lvd142m
428	mvit_v1_b
429	ReAlnet08_cornet
430	yudixie_resnet18_rotation_reg_0_240719
431	ReAlnet02_ALL
432	VOneCORnet-S
433	ReAlnet03_ALL
434	omnivore_swinB
435	compact_cornets_V4
436	ReAlnet04_ALL
437	SeLaVi-Kinetics400
438	ReAlnet09_cornet
439	compact_cornets_IT
440	SeLaVi-VGG-Sound
441	ReAlnet05_ALL
442	vit_base_patch14_reg4_dinov2:lvd142m
443	resnet152_ecoset_full
444	ReAlnet06_ALL
445	r2plus1d_18
446	mvit_v2_s
447	SeLaVi-Kinetics-Sound
448	compact_resnet50_robust_IT
449	ReAlnet10_cornet
450	resnet101_ecoset_full
451	ReAlnet07_ALL
452	yudixie_resnet18_object_class_0_240719
453	yudixie_resnet18_cat_obj_class_all_latents_0_240719
454	yudixie_resnet50_distance_translation_rotation_0_240908
455	yudixie_resnet18_distance_translation_rotation_0_240719
456	yudixie_resnet18_translation_rotation_0_240719
457	ReAlnet05_cornet
458	ReAlnet09_ALL
459	resnet50_primary_visual_cortex
460	ReAlnet08_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

0.48.

Note that scores are relative to this ceiling.

Data: Geirhos2021silhouette

Metric: error_consistency