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	AdvProp_efficientnet-b2	.553
85	AT_efficientnet-b2	.553
86	resnet_101_v1	.549
87	inception_v3_pytorch	.548
88	resnet_50_v1	.546
89	resnet50_tutorial	.546
90	resnet50-sup	.546
91	resnet-50-pytorch	.546
92	efficientnet_b0	.544
93	resnet50_random_l2_perturb	.535
94	mobilenet_v2_1.4_224-tf	.534
95	densenet-169-keras	.531
96	vonegrcnn_52e_full	.524
97	cv_18_dagger_408_pretrained	.523
98	AT_efficientnet-b4	.521
99	AdvProp_efficientnet-b4	.521
100	inception_v2-tf	.520
101	vonegrcnn_52e_full	.520
102	tv_efficientnet-b1	.510
103	densenet-121-keras	.510
104	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.509
105	vonegrcnn_47e	.500
106	AdvProp_efficientnet-b7	.497
107	resnet-152_v1-tf	.492
108	resnet50-SIN_IN_IN	.492
109	resnet_SIN_IN_FT_IN	.492
110	deit_base_imagenet_full_seed-0	.492
111	resnet-50_v2-tf	.491
112	resnet18_imagenet_full	.491
113	blt_vs	.491
114	grcnn	.482
115	vonegrcnn_47e	.482
116	BiT-S-R50x3	.478
117	densenet-169	.474
118	resnet-50_v1-tf	.474
119	convnext_small_imagenet_100_seed-0	.469
120	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
121	densenet-121	.468
122	resnet50_byol	.465
123	voneresnet-50	.463
124	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
125	inception_v1	.451
126	resnet-101_v1-tf	.450
127	efficientnet-b7	.449
128	resnet_152_v1	.445
129	mobilenet_v2_1-4_224_pytorch	.439
130	mobilenet_v2_1_4_224	.439
131	mobilenet_v1_0.75_224	.438
132	mobilenet_v2_1_0_224	.437
133	deit_large_imagenet_full_seed-0	.434
134	resnet50_linf_4_robust	.421
135	nasnet_mobile	.418
136	ReAlnet03	.417
137	densenet-201-keras	.409
138	resnet50-moclr8deg	.395
139	BiT-S-R50x1	.394
140	custom_model_cv_18_dagger_408	.392
141	Res2Net50_26w_4s	.390
142	resnet50-SIN_IN	.388
143	mobilenet_v2_1.0_192-tf	.384
144	vgg_19	.379
145	densenet_201_pytorch	.378
146	densenet-201	.378
147	cornet_s	.375
148	ReAlnet07	.373
149	mobilenet_v2_0_5_224	.369
150	ReAlnet04	.369
151	ReAlnet_incorrect	.365
152	ReAlnet01	.365
153	ReAlnet10	.364
154	inception_v1-tf	.362
155	mobilenet_v2_1.0_224-tf	.360
156	mobilenet_v2_1.3_224-tf	.358
157	texture_shape_resnet50_trained_on_SIN	.358
158	efficientnet_b0_imagenet_full	.357
159	CORnet-S	.353
160	mobilenet_v2_1_0_192	.353
161	vgg_16	.352
162	resnet50_linf_8_robust	.349
163	mobilenet_v2_0.5_224-tf	.336
164	mobilenet_v2_1_3_224	.336
165	deit_small_imagenet_full_seed-0	.335
166	resnet-18	.333
167	BiT-S-R152x2	.332
168	nasnet_mobile-tf	.328
169	ReAlnet09	.317
170	mobilenet_v2_0.5_192-tf	.315
171	ReAlnet08	.314
172	texture_shape_alexnet_trained_on_SIN	.310
173	mobilenet_v2_0_75_160	.309
174	ReAlnet02	.304
175	vgg-16-keras	.298
176	resnet50_l2_3_robust	.297
177	mobilenet_v2_1.0_160-tf	.296
178	mobilenet_v2_0_5_192	.296
179	mobilenet_v2_1_0_160	.287
180	BiT-S-R101x1	.286
181	resnet50_moco_v2	.285
182	ReAlnet05	.280
183	mobilenet_v1_1.0_224	.279
184	mobilenet_v2_0.75_128	.276
185	mobilenet_v1_1.0_160	.270
186	mobilenet_v2_0.75_224-tf	.266
187	AlexNet_SIN	.263
188	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
189	alexnet_random_linf8_perturb	.257
190	mobilenet_v2_0_75_224	.256
191	BiT-S-R152x4	.255
192	mobilenet_v2_0.35_160	.252
193	resnet50-vitoimagevidnet8	.252
194	BiT-S-R101x3	.249
195	alexnet_random_l2_3_perturb	.247
196	alexnet	.246
197	resnet50-SIN	.245
198	AdvProp_efficientnet-b0	.243
199	mobilenet_v2_1_0_128	.240
200	mobilenet_v2_0.5_160	.236
201	xception	.236
202	resnet50_robust_l2_eps1	.231
203	mobilenet_v2_0.75_192-tf	.231
204	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.228
205	ReAlnet06	.220
206	efficientnet_b2_imagenet_full	.214
207	alexnet_l2_3_robust	.214
208	voneresnet-50-robust	.205
209	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
210	mobilenet_v2_0.75_160-tf	.204
211	alexnet_ks_torevert	.203
212	alexnet	.203
213	alexnet-baseline	.203
214	alexnet	.203
215	alexnet	.203
216	alexnet	.203
217	alexnet	.203
218	alexnet	.203
219	alexnet	.203
220	alexnet	.203
221	alexnet	.203
222	squeezenet1_1	.194
223	mobilenet_v1_0.5_224	.194
224	mobilenet_v1_0.75_160	.193
225	mobilenet_v2_0_75_192	.192
226	mobilenet_v2_1.0_128-tf	.186
227	resnet50_simclr	.185
228	mobilenet_v2_0.35_192	.185
229	mobilenet_v2_1.0_96	.185
230	mobilenet_v1_1.0_192	.180
231	efficientnet_b1_imagenet_full	.176
232	konkle_alexnetgn_ipcl_ref01_primary_model	.169
233	mobilenet_v2_0.75_96	.166
234	mobilenet_v1_0.5_192	.164
235	mobilenet_v1_0.75_128	.163
236	alexnet_linf_8_robust	.161
237	mobilenet_v1_0.5_160	.154
238	regnet_y_400mf	.150
239	resnet50_robust_l2_eps3	.149
240	imagenet_l2_3_0	.149
241	resnet-50-robust	.149
242	0.5x_resnet-18_LC_w_sh_1_iter	.148
243	AdvProp_efficientnet-b8	.147
244	mobilenet_v1_0.75_192	.146
245	deit_small_imagenet_100_seed-0	.145
246	alexnet_reduced_aliasing_early_checkpoint	.143
247	alexnet_early_checkpoint	.139
248	alexnet_training_seed_06	.139
249	alexnet_training_seed_05	.139
250	alexnet_training_seed_01	.139
251	resnet50-barlow	.139
252	alexnet_robust_correct	.135
253	vonealexnet_gaussian_noise_std4_fixed	.134
254	mobilenet_v2_0.35_128	.129
255	mobilenet_v1_0.5_128	.127
256	mobilenet_v1_0.25_192	.117
257	resnet50_imagenet_100_seed-0	.112
258	artResNet18_1	.109
259	mobilenet_v2_0.35_224	.107
260	mobilenet_v2_0.5_128	.106
261	mobilenet_v2_0.35_96	.102
262	resnet50_imagenet_10_seed-0	.102
263	mobilenet_v1_0.25_160	.098
264	alexnet_training_seed_03	.090
265	resnet50_FractalDB	.089
266	mobilenet_v1_1.0_128	.089
267	convnext_small_imagenet_10_seed-0	.084
268	deit_small_imagenet_10_seed-0	.084
269	deit_small_imagenet_1_seed-0	.084
270	shufflenet_v2_x1_0	.083
271	mobilenet_v1_0.25_128	.082
272	convnext_small_imagenet_1_seed-0	.082
273	vgg-19-keras	.081
274	alexnet_training_seed_02	.077
275	alexnet_training_seed_10	.077
276	alexnet_training_seed_04	.077
277	alexnet_training_seed_08	.077
278	alexnet_training_seed_07	.077
279	resnet50-meshes-lt-100-original-scratch	.075
280	imagenet_l2_10_0	.073
281	resnet-50_untrained	.070
282	mobilenet_v2_0.5_96	.066
283	resnet50-simclr-vissl	.065
284	resnet50-meshes-lt-100-original-pretrained	.059
285	ViT_B_32_imagenet1k	.056
286	barlow-twins-resnet50	.054
287	squeezenet1_0	.053
288	ViT_L_32_imagenet1k	.051
289	alexnet_training_seed_09	.049
290	resnet50_imagenet_1_seed-0	.046
291	resnet50-vicregl0p75	.043
292	mobilenet_v1_0.25_224	.041
293	resnet50-vicregl0p9	.033
294	mobilevit_small	.023
295	ViT_B_16_imagenet1k	.023
296	resnet50-vicreg	.012
297	yudixie_resnet18_translation_rotation_0_240719	X
298	yudixie_resnet18_distance_rotation_0_240719	X
299	yudixie_resnet18_category_class_0_240719	X
300	yudixie_resnet50_distance_rotation_0_240908	X
301	ReAlnet05_cornet	X
302	resnet-18_test_m	X
303	yudixie_resnet18_random_0_240719	X
304	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
305	yudixie_resnet18_distance_translation_rotation_0_240719	X
306	yudixie_resnet18_object_class_0_240719	X
307	CORnet-Z	X
308	ReAlnet06_cornet	X
309	yudixie_resnet50_translation_reg_0_240908	X
310	yudixie_resnet50_translation_rotation_0_240908	X
311	r3m_resnet18	X
312	ReAlnet07_cornet	X
313	resnet18-deepcluster	X
314	ReAlnet08_cornet	X
315	resnet18-local_aggregation	X
316	CORnetZ_CIFAR10	X
317	VOneCORnet-S	X
318	omnivore_swinB	X
319	0.5x_resnet-18_LC	X
320	ReAlnet09_cornet	X
321	FrankRobWobv0	X
322	resnet-18-LC_w_sh_10_iter_conv_init_m	X
323	resnet-18-LC_w_sh_100_iter_m	X
324	resnet-18-LC_w_sh_10_iter_m	X
325	ReAlnet10_cornet	X
326	resnet-18-LC_w_sh_1_iter_m	X
327	resnet-18-LC_w_sh_1_iter_conv_init_m	X
328	yudixie_resnet50_distance_translation_rotation_0_240908	X
329	BiT-M-R152x2	X
330	BiT-M-R50x1	X
331	BiT-M-R50x3	X
332	ViT-B/32	X
333	resnet50-cifar	X
334	resnet50_pretrained_with_retinal_waves	X
335	resnet50_ImageNet	X
336	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
337	resnet-18-LC_w_sh_100_iter_conv_init_m	X
338	openclip	X
339	resnet50	X
340	my-model	X
341	dorinet_cornet_z	X
342	pixels	X
343	yudixie_resnet50_random_0_240908	X
344	CORnetZ_CIFAR10_bs32_20_04	X
345	v1-pyr-nodown	X
346	yudixie_resnet50_imagenet1kpret_0_240312	X
347	omnivore_swinS	X
348	yudixie_resnet50_imagenet1kpret_0_240908	X
349	unet_entire	X
350	dcgan	X
351	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
352	yudixie_resnet50_object_class_0_240908	X
353	resnet-50x2_untrained	X
354	resnet-18_untrained	X
355	resnet-18-LC_untrained	X
356	resnet18-contrastive_predictive	X
357	r3m_resnet34_nocrop	X
358	ViT_B_16	X
359	resnet18-depth_prediction	X
360	ViT_B_32	X
361	ViT_L_32	X
362	yudixie_resnet50_category_class_0_240908	X
363	omnivore_swinT	X
364	resnet18-simclr	X
365	resnet18-autoencoder	X
366	resnet18-relative_position	X
367	resnet18_imagenet21kP	X
368	RN50	X
369	ConvLSTM	X
370	yudixie_resnet50_distance_reg_0_240908	X
371	PredRNN	X
372	SimVP	X
373	TAU	X
374	MIM	X
375	hmax	X
376	0.5x_resnet-18_LC_w_sh_100_iter	X
377	RN50	X
378	resnet18-colorization	X
379	0.5x_resnet-18	X
380	resnet18-contrastive_multiview	X
381	resnet-18-LC_conv_init_m	X
382	ReAlnet01_cornet	X
383	resnet-18-LC_1st_conv_m	X
384	resnet18-supervised	X
385	r3m_resnet50_nocrop	X
386	resnet-18-LC_m	X
387	r3m_resnet34	X
388	yudixie_resnet18_translation_reg_0_240719	X
389	ReAlnet02_cornet	X
390	yudixie_resnet18_distance_reg_0_240719	X
391	yudixie_resnet18_rotation_reg_0_240719	X
392	yudixie_resnet18_imagenet1kpret_0_240719	X
393	r3m_resnet50	X
394	0.5x_resnet-18_LC_w_sh_10_iter	X
395	resnet18-instance_recognition	X
396	my-model	X
397	resnet18_ecoset_full	X
398	ReAlnet03_cornet	X
399	resnet34_ecoset_full	X
400	vggface	X
401	resnet50_ecoset_full	X
402	resnet101_ecoset_full	X
403	yudixie_resnet50_rotation_reg_0_240908	X
404	resnet152_ecoset_full	X
405	yudixie_resnet50_distance_translation_0_240908	X
406	yudixie_resnet18_distance_translation_0_240719	X
407	ReAlnet04_cornet	X

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