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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	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
127	inception_v1	.451
128	resnet-101_v1-tf	.450
129	efficientnet-b7	.449
130	resnet_152_v1	.445
131	mobilenet_v2_1-4_224_pytorch	.439
132	mobilenet_v2_1_4_224	.439
133	mobilenet_v1_0.75_224	.438
134	mobilenet_v2_1_0_224	.437
135	deit_large_imagenet_full_seed-0	.434
136	resnet50_linf_4_robust	.421
137	nasnet_mobile	.418
138	ReAlnet03	.417
139	densenet-201-keras	.409
140	resnet50-moclr8deg	.395
141	BiT-S-R50x1	.394
142	custom_model_cv_18_dagger_408	.392
143	Res2Net50_26w_4s	.390
144	resnet50-SIN_IN	.388
145	mobilenet_v2_1.0_192-tf	.384
146	vgg_19	.379
147	densenet-201	.378
148	densenet_201_pytorch	.378
149	cornet_s	.375
150	ReAlnet07	.373
151	mobilenet_v2_0_5_224	.369
152	ReAlnet04	.369
153	ReAlnet01	.365
154	ReAlnet10	.364
155	inception_v1-tf	.362
156	mobilenet_v2_1.0_224-tf	.360
157	mobilenet_v2_1.3_224-tf	.358
158	texture_shape_resnet50_trained_on_SIN	.358
159	efficientnet_b0_imagenet_full	.357
160	CORnet-S	.353
161	mobilenet_v2_1_0_192	.353
162	vgg_16	.352
163	resnet50_linf_8_robust	.349
164	mobilenet_v2_0.5_224-tf	.336
165	mobilenet_v2_1_3_224	.336
166	deit_small_imagenet_full_seed-0	.335
167	resnet-18	.333
168	BiT-S-R152x2	.332
169	nasnet_mobile-tf	.328
170	ReAlnet09	.317
171	mobilenet_v2_0.5_192-tf	.315
172	ReAlnet08	.314
173	texture_shape_alexnet_trained_on_SIN	.310
174	mobilenet_v2_0_75_160	.309
175	ReAlnet02	.304
176	vgg-16-keras	.298
177	resnet50_l2_3_robust	.297
178	mobilenet_v2_1.0_160-tf	.296
179	mobilenet_v2_0_5_192	.296
180	resnet_50_v1_spiking_l4a	.287
181	mobilenet_v2_1_0_160	.287
182	BiT-S-R101x1	.286
183	resnet50_moco_v2	.285
184	ReAlnet05	.280
185	mobilenet_v1_1.0_224	.279
186	mobilenet_v2_0.75_128	.276
187	mobilenet_v1_1.0_160	.270
188	mobilenet_v2_0.75_224-tf	.266
189	AlexNet_SIN	.263
190	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
191	alexnet_random_linf8_perturb	.257
192	mobilenet_v2_0_75_224	.256
193	BiT-S-R152x4	.255
194	mobilenet_v2_0.35_160	.252
195	resnet50-vitoimagevidnet8	.252
196	BiT-S-R101x3	.249
197	alexnet_random_l2_3_perturb	.247
198	alexnet	.246
199	resnet50-SIN	.245
200	AdvProp_efficientnet-b0	.243
201	mobilenet_v2_1_0_128	.240
202	mobilenet_v2_0.5_160	.236
203	xception	.236
204	resnet50_robust_l2_eps1	.231
205	mobilenet_v2_0.75_192-tf	.231
206	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.228
207	ReAlnet06	.220
208	efficientnet_b2_imagenet_full	.214
209	alexnet_l2_3_robust	.214
210	voneresnet-50-robust	.205
211	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
212	mobilenet_v2_0.75_160-tf	.204
213	alexnet_ks_torevert	.203
214	alexnet-baseline	.203
215	alexnet	.203
216	alexnet	.203
217	alexnet	.203
218	alexnet	.203
219	alexnet	.203
220	alexnet	.203
221	alexnet	.203
222	alexnet	.203
223	alexnet	.203
224	squeezenet1_1	.194
225	mobilenet_v1_0.5_224	.194
226	mobilenet_v1_0.75_160	.193
227	mobilenet_v2_0_75_192	.192
228	mobilenet_v2_1.0_128-tf	.186
229	resnet50_simclr	.185
230	mobilenet_v2_0.35_192	.185
231	mobilenet_v2_1.0_96	.185
232	mobilenet_v1_1.0_192	.180
233	efficientnet_b1_imagenet_full	.176
234	konkle_alexnetgn_ipcl_ref01_primary_model	.169
235	mobilenet_v2_0.75_96	.166
236	mobilenet_v1_0.5_192	.164
237	mobilenet_v1_0.75_128	.163
238	alexnet_linf_8_robust	.161
239	mobilenet_v1_0.5_160	.154
240	regnet_y_400mf	.150
241	resnet-50-robust	.149
242	imagenet_l2_3_0	.149
243	resnet50_robust_l2_eps3	.149
244	0.5x_resnet-18_LC_w_sh_1_iter	.148
245	AdvProp_efficientnet-b8	.147
246	mobilenet_v1_0.75_192	.146
247	deit_small_imagenet_100_seed-0	.145
248	alexnet_reduced_aliasing_early_checkpoint	.143
249	alexnet_early_checkpoint	.139
250	alexnet_training_seed_05	.139
251	alexnet_training_seed_06	.139
252	alexnet_training_seed_01	.139
253	resnet50-barlow	.139
254	alexnet_robust_correct	.135
255	vonealexnet_gaussian_noise_std4_fixed	.134
256	mobilenet_v2_0.35_128	.129
257	mobilenet_v1_0.5_128	.127
258	mobilenet_v1_0.25_192	.117
259	resnet50_imagenet_100_seed-0	.112
260	artResNet18_1	.109
261	mobilenet_v2_0.35_224	.107
262	mobilenet_v2_0.5_128	.106
263	mobilenet_v2_0.35_96	.102
264	resnet50_imagenet_10_seed-0	.102
265	mobilenet_v1_0.25_160	.098
266	alexnet_training_seed_03	.090
267	resnet50_FractalDB	.089
268	mobilenet_v1_1.0_128	.089
269	convnext_small_imagenet_10_seed-0	.084
270	deit_small_imagenet_10_seed-0	.084
271	deit_small_imagenet_1_seed-0	.084
272	shufflenet_v2_x1_0	.083
273	mobilenet_v1_0.25_128	.082
274	convnext_small_imagenet_1_seed-0	.082
275	vgg-19-keras	.081
276	alexnet_training_seed_02	.077
277	alexnet_training_seed_08	.077
278	alexnet_training_seed_04	.077
279	alexnet_training_seed_10	.077
280	alexnet_training_seed_07	.077
281	resnet50-meshes-lt-100-original-scratch	.075
282	imagenet_l2_10_0	.073
283	resnet-50_untrained	.070
284	mobilenet_v2_0.5_96	.066
285	resnet50-simclr-vissl	.065
286	resnet50-meshes-lt-100-original-pretrained	.059
287	ViT_B_32_imagenet1k	.056
288	barlow-twins-resnet50	.054
289	squeezenet1_0	.053
290	resnet50_eMMCR_Vanilla	.052
291	ViT_L_32_imagenet1k	.051
292	alexnet_training_seed_09	.049
293	resnet50_imagenet_1_seed-0	.046
294	resnet50-vicregl0p75	.043
295	resnet_50_v1_spiking_l4	.042
296	mobilenet_v1_0.25_224	.041
297	resnet50-vicregl0p9	.033
298	mobilevit_small	.023
299	ViT_B_16_imagenet1k	.023
300	resnet50-vicreg	.012
301	resnet_50_v1_spiking_l3	.004
302	ReAlnet10_ALL
303	resnet-18-LC_w_sh_100_iter_conv_init_m
304	ViT-B/32
305	BiT-M-R50x3
306	BiT-M-R50x1
307	BiT-M-R152x2
308	resnet-18-LC_w_sh_1_iter_conv_init_m
309	resnet-18-LC_w_sh_1_iter_m
310	resnet-18-LC_w_sh_10_iter_m
311	resnet-18-LC_w_sh_100_iter_m
312	resnet-18-LC_w_sh_10_iter_conv_init_m
313	FrankRobWobv0
314	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-local_aggregation
317	resnet18-deepcluster
318	r3m_resnet18
319	CORnet-Z
320	vggface
321	my-model
322	resnet18-instance_recognition
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	r3m_resnet34
326	resnet-18-LC_m
327	r3m_resnet50_nocrop
328	resnet18-supervised
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	resnet18-colorization
334	RN50
335	0.5x_resnet-18_LC_w_sh_100_iter
336	RN50
337	resnet18-relative_position
338	resnet18-autoencoder
339	resnet18-simclr
340	omnivore_swinT
341	ViT_L_32
342	ViT_B_32
343	resnet18-depth_prediction
344	ViT_B_16
345	r3m_resnet34_nocrop
346	resnet18-contrastive_predictive
347	resnet-18-LC_untrained
348	resnet-18_untrained
349	resnet-50x2_untrained
350	dcgan
351	unet_entire
352	omnivore_swinS
353	v1-pyr-nodown
354	CORnetZ_CIFAR10_bs32_20_04
355	pixels
356	dorinet_cornet_z
357	my-model
358	resnet50
359	openclip
360	resnet50-cifar
361	resnet50_pretrained_with_retinal_waves
362	resnet50_ImageNet
363	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
364	yudixie_resnet50_random_0_240908
365	yudixie_resnet50_imagenet1kpret_0_240312
366	yudixie_resnet50_imagenet1kpret_0_240908
367	yudixie_resnet50_cat_obj_class_all_latents_0_240908
368	yudixie_resnet50_object_class_0_240908
369	yudixie_resnet50_category_class_0_240908
370	resnet18_imagenet21kP
371	ConvLSTM
372	yudixie_resnet50_distance_reg_0_240908
373	PredRNN
374	SimVP
375	TAU
376	MIM
377	hmax
378	briaai_rmbg_1_4
379	yolos_tiny
380	ReAlnet01_cornet
381	yudixie_resnet18_translation_reg_0_240719
382	compact_resnet50_V4
383	ReAlnet02_cornet
384	yudixie_resnet18_distance_reg_0_240719
385	yudixie_resnet18_imagenet1kpret_0_240719
386	resnet18_ecoset_full
387	ReAlnet03_cornet
388	resnet34_ecoset_full
389	resnet50_ecoset_full
390	compact_resnet50_IT
391	compact_resnet50_robust_V4
392	yudixie_resnet50_rotation_reg_0_240908
393	yudixie_resnet50_distance_translation_0_240908
394	yudixie_resnet18_distance_translation_0_240719
395	ReAlnet04_cornet
396	yudixie_resnet18_distance_rotation_0_240719
397	yudixie_resnet18_category_class_0_240719
398	yudixie_resnet50_distance_rotation_0_240908
399	compact_vgg19_V4
400	resnet-18_test_m
401	yudixie_resnet18_random_0_240719
402	SeLaVi-AVE
403	r3d_18
404	ReAlnet06_cornet
405	compact_vgg19_IT
406	yudixie_resnet50_translation_reg_0_240908
407	yudixie_resnet50_translation_rotation_0_240908
408	ReAlnet01_ALL
409	mc3_18
410	ReAlnet07_cornet
411	s3d
412	mvit_v1_b
413	ReAlnet08_cornet
414	yudixie_resnet18_rotation_reg_0_240719
415	ReAlnet02_ALL
416	VOneCORnet-S
417	ReAlnet03_ALL
418	omnivore_swinB
419	compact_cornets_V4
420	ReAlnet04_ALL
421	SeLaVi-Kinetics400
422	ReAlnet09_cornet
423	compact_cornets_IT
424	SeLaVi-VGG-Sound
425	ReAlnet05_ALL
426	resnet152_ecoset_full
427	ReAlnet06_ALL
428	r2plus1d_18
429	mvit_v2_s
430	SeLaVi-Kinetics-Sound
431	compact_resnet50_robust_IT
432	ReAlnet10_cornet
433	resnet101_ecoset_full
434	ReAlnet07_ALL
435	yudixie_resnet18_object_class_0_240719
436	yudixie_resnet18_cat_obj_class_all_latents_0_240719
437	yudixie_resnet50_distance_translation_rotation_0_240908
438	yudixie_resnet18_distance_translation_rotation_0_240719
439	yudixie_resnet18_translation_rotation_0_240719
440	ReAlnet05_cornet
441	ReAlnet09_ALL
442	resnet50_primary_visual_cortex
443	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