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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	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_halfPrecision	.817
38	pnasnet_large	.817
39	pnasnet_large-tf	.810
40	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.793
41	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.781
42	resnet_50_v2	.777
43	resnet50	.773
44	convnext_femto_ols:d1_in1k	.767
45	cvt_cvt-21-384-in1k_4_LucyV4	.763
46	convnext_tiny_sup	.763
47	nasnet_large	.750
48	vision_transformer_vit_large_patch16_224	.749
49	resnet50_random_linf8_perturb	.737
50	convnext_large_imagenet_full_seed-0	.728
51	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.728
52	inception_v4	.718
53	nasnet_large-tf	.706
54	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.704
55	resnet50-VITO-8deg-cc	.701
56	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.685
57	resnet101_imagenet_full	.681
58	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.675
59	pnasnet_large_pytorch	.674
60	cvt_cvt-13-224-in1k_4_LucyV4	.668
61	inception_v3-tf	.663
62	convnext_small_imagenet_full_seed-0	.660
63	inception_v4-tf	.658
64	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.658
65	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.648
66	antialiased-rnext101_32x8d	.648
67	resnet-152_v2-tf	.647
68	resnet50_imagenet_full	.646
69	resnet34_imagenet_full	.640
70	resnet101	.628
71	cvt_cvt-21-224-in1k_4_LucyV4	.618
72	antialias-resnet152	.615
73	effnetb1_272x240	.614
74	inception_v3	.610
75	AdvProp_efficientnet-b6	.610
76	convnext_base_imagenet_full_seed-0	.608
77	voneresnet-50-non_stochastic	.602
78	xception-keras	.601
79	vonegrcnn_62e_nobn	.584
80	grcnn_109	.578
81	antialiased-r50	.574
82	resnet152_imagenet_full	.560
83	resnet-34	.558
84	vonegrcnn_62e_nobn	.554
85	AT_efficientnet-b2	.553
86	AdvProp_efficientnet-b2	.553
87	resnet_50_v1_spiking	.551
88	resnet_101_v1	.549
89	inception_v3_pytorch	.548
90	resnet50_tutorial	.546
91	resnet_50_v1	.546
92	resnet-50-pytorch	.546
93	resnet50-sup	.546
94	efficientnet_b0	.544
95	resnet50_random_l2_perturb	.535
96	mobilenet_v2_1.4_224-tf	.534
97	densenet-169-keras	.531
98	vonegrcnn_52e_full	.524
99	cv_18_dagger_408_pretrained	.523
100	AdvProp_efficientnet-b4	.521
101	AT_efficientnet-b4	.521
102	inception_v2-tf	.520
103	vonegrcnn_52e_full	.520
104	tv_efficientnet-b1	.510
105	densenet-121-keras	.510
106	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.509
107	vonegrcnn_47e	.500
108	AdvProp_efficientnet-b7	.497
109	resnet-152_v1-tf	.492
110	resnet50-SIN_IN_IN	.492
111	resnet_SIN_IN_FT_IN	.492
112	deit_base_imagenet_full_seed-0	.492
113	resnet-50_v2-tf	.491
114	resnet18_imagenet_full	.491
115	blt_vs	.491
116	grcnn	.482
117	resnet50_ewconplus_wzc	.482
118	vonegrcnn_47e	.482
119	BiT-S-R50x3	.478
120	densenet-169	.474
121	resnet-50_v1-tf	.474
122	convnext_small_imagenet_100_seed-0	.469
123	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
124	densenet-121	.468
125	resnet50_byol	.465
126	voneresnet-50	.463
127	alexnet_fov4	.461
128	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
129	inception_v1	.451
130	resnet-101_v1-tf	.450
131	efficientnet-b7	.449
132	resnet_152_v1	.445
133	mobilenet_v2_1-4_224_pytorch	.439
134	mobilenet_v2_1_4_224	.439
135	mobilenet_v1_0.75_224	.438
136	mobilenet_v2_1_0_224	.437
137	deit_large_imagenet_full_seed-0	.434
138	AlexNet_SIN_fov4	.428
139	resnet50_linf_4_robust	.421
140	nasnet_mobile	.418
141	ReAlnet03	.417
142	densenet-201-keras	.409
143	resnet50-moclr8deg	.395
144	BiT-S-R50x1	.394
145	custom_model_cv_18_dagger_408	.392
146	Res2Net50_26w_4s	.390
147	resnet50-SIN_IN	.388
148	mobilenet_v2_1.0_192-tf	.384
149	vgg_19	.379
150	densenet-201	.378
151	densenet_201_pytorch	.378
152	cornet_s	.375
153	ReAlnet07	.373
154	mobilenet_v2_0_5_224	.369
155	ReAlnet04	.369
156	ReAlnet01	.365
157	ReAlnet10	.364
158	inception_v1-tf	.362
159	mobilenet_v2_1.0_224-tf	.360
160	mobilenet_v2_1.3_224-tf	.358
161	texture_shape_resnet50_trained_on_SIN	.358
162	efficientnet_b0_imagenet_full	.357
163	CORnet-S	.353
164	mobilenet_v2_1_0_192	.353
165	vgg_16	.352
166	resnet50_linf_8_robust	.349
167	mobilenet_v2_0.5_224-tf	.336
168	mobilenet_v2_1_3_224	.336
169	deit_small_imagenet_full_seed-0	.335
170	resnet-18	.333
171	BiT-S-R152x2	.332
172	nasnet_mobile-tf	.328
173	ReAlnet09	.317
174	mobilenet_v2_0.5_192-tf	.315
175	ReAlnet08	.314
176	texture_shape_alexnet_trained_on_SIN	.310
177	mobilenet_v2_0_75_160	.309
178	ReAlnet02	.304
179	vgg-16-keras	.298
180	resnet50_l2_3_robust	.297
181	mobilenet_v2_1.0_160-tf	.296
182	mobilenet_v2_0_5_192	.296
183	resnet_50_v1_spiking_l4a	.287
184	mobilenet_v2_1_0_160	.287
185	BiT-S-R101x1	.286
186	resnet50_moco_v2	.285
187	ReAlnet05	.280
188	mobilenet_v1_1.0_224	.279
189	mobilenet_v2_0.75_128	.276
190	mobilenet_v1_1.0_160	.270
191	mobilenet_v2_0.75_224-tf	.266
192	AlexNet_SIN	.263
193	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
194	alexnet_random_linf8_perturb	.257
195	mobilenet_v2_0_75_224	.256
196	BiT-S-R152x4	.255
197	mobilenet_v2_0.35_160	.252
198	resnet50-vitoimagevidnet8	.252
199	BiT-S-R101x3	.249
200	alexnet_random_l2_3_perturb	.247
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	.203
217	squeezenet1_1	.194
218	mobilenet_v1_0.5_224	.194
219	mobilenet_v1_0.75_160	.193
220	mobilenet_v2_0_75_192	.192
221	mobilenet_v2_1.0_128-tf	.186
222	resnet50_simclr	.185
223	mobilenet_v2_0.35_192	.185
224	mobilenet_v2_1.0_96	.185
225	mobilenet_v1_1.0_192	.180
226	efficientnet_b1_imagenet_full	.176
227	konkle_alexnetgn_ipcl_ref01_primary_model	.169
228	eBarlow_lmda_02_1000ep	.167
229	mobilenet_v2_0.75_96	.166
230	mobilenet_v1_0.5_192	.164
231	mobilenet_v1_0.75_128	.163
232	alexnet_linf_8_robust	.161
233	AlexNet_SIN_fov12	.159
234	mobilenet_v1_0.5_160	.154
235	regnet_y_400mf	.150
236	resnet-50-robust	.149
237	imagenet_l2_3_0	.149
238	resnet50_robust_l2_eps3	.149
239	0.5x_resnet-18_LC_w_sh_1_iter	.148
240	AdvProp_efficientnet-b8	.147
241	mobilenet_v1_0.75_192	.146
242	deit_small_imagenet_100_seed-0	.145
243	alexnet_reduced_aliasing_early_checkpoint	.143
244	alexnet_early_checkpoint	.139
245	alexnet_training_seed_05	.139
246	alexnet_training_seed_06	.139
247	alexnet_training_seed_01	.139
248	alexnet_training_seed_01_fov4	.139
249	alexnet_training_seed_01_fov12	.139
250	alexnet_training_seed_01_fov16	.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	alexnet_fov12	.118
257	AlexNet_SIN_fov16	.117
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_fov16	.050
293	alexnet_training_seed_09	.049
294	resnet50_imagenet_1_seed-0	.046
295	resnet50-vicregl0p75	.043
296	resnet_50_v1_spiking_l4	.042
297	mobilenet_v1_0.25_224	.041
298	resnet50-vicregl0p9	.033
299	mobilevit_small	.023
300	ViT_B_16_imagenet1k	.023
301	resnet50-vicreg	.012
302	resnet_50_v1_spiking_l3	.004
303	ReAlnet10_ALL
304	resnet-18-LC_w_sh_100_iter_conv_init_m
305	ViT-B/32
306	BiT-M-R50x3
307	BiT-M-R50x1
308	BiT-M-R152x2
309	resnet-18-LC_w_sh_1_iter_conv_init_m
310	resnet-18-LC_w_sh_1_iter_m
311	resnet-18-LC_w_sh_10_iter_m
312	resnet-18-LC_w_sh_100_iter_m
313	resnet-18-LC_w_sh_10_iter_conv_init_m
314	FrankRobWobv0
315	0.5x_resnet-18_LC
316	CORnetZ_CIFAR10
317	resnet18-local_aggregation
318	resnet18-deepcluster
319	r3m_resnet18
320	CORnet-Z
321	vggface
322	my-model
323	resnet18-instance_recognition
324	0.5x_resnet-18_LC_w_sh_10_iter
325	r3m_resnet50
326	r3m_resnet34
327	resnet-18-LC_m
328	r3m_resnet50_nocrop
329	resnet18-supervised
330	resnet-18-LC_1st_conv_m
331	resnet-18-LC_conv_init_m
332	resnet18-contrastive_multiview
333	0.5x_resnet-18
334	resnet18-colorization
335	RN50
336	0.5x_resnet-18_LC_w_sh_100_iter
337	RN50
338	resnet18-relative_position
339	resnet18-autoencoder
340	resnet18-simclr
341	omnivore_swinT
342	ViT_L_32
343	ViT_B_32
344	resnet18-depth_prediction
345	ViT_B_16
346	r3m_resnet34_nocrop
347	resnet18-contrastive_predictive
348	resnet-18-LC_untrained
349	resnet-18_untrained
350	resnet-50x2_untrained
351	dcgan
352	unet_entire
353	omnivore_swinS
354	v1-pyr-nodown
355	CORnetZ_CIFAR10_bs32_20_04
356	pixels
357	dorinet_cornet_z
358	my-model
359	resnet50
360	openclip
361	resnet50-cifar
362	resnet50_pretrained_with_retinal_waves
363	resnet50_ImageNet
364	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
365	yudixie_resnet50_random_0_240908
366	yudixie_resnet50_imagenet1kpret_0_240312
367	yudixie_resnet50_imagenet1kpret_0_240908
368	yudixie_resnet50_cat_obj_class_all_latents_0_240908
369	yudixie_resnet50_object_class_0_240908
370	yudixie_resnet50_category_class_0_240908
371	resnet18_imagenet21kP
372	ConvLSTM
373	yudixie_resnet50_distance_reg_0_240908
374	PredRNN
375	SimVP
376	TAU
377	MIM
378	hmax
379	briaai_rmbg_1_4
380	yolos_tiny
381	vit_base_patch14_reg4_dinov2-lvd142m
382	ReAlnet01_cornet
383	yudixie_resnet18_translation_reg_0_240719
384	compact_resnet50_V4
385	ReAlnet02_cornet
386	yudixie_resnet18_distance_reg_0_240719
387	yudixie_resnet18_imagenet1kpret_0_240719
388	vit_base_patch16_dinov3-lvd1689m
389	resnet18_ecoset_full
390	ReAlnet03_cornet
391	resnet34_ecoset_full
392	ReAlnet01_cornet_fov4
393	resnet50_ecoset_full
394	compact_resnet50_IT
395	compact_resnet50_robust_V4
396	yudixie_resnet50_rotation_reg_0_240908
397	ReAlnet01_cornet_fov12
398	yudixie_resnet50_distance_translation_0_240908
399	yudixie_resnet18_distance_translation_0_240719
400	ReAlnet04_cornet
401	yudixie_resnet18_distance_rotation_0_240719
402	yudixie_resnet18_category_class_0_240719
403	yudixie_resnet50_distance_rotation_0_240908
404	compact_vgg19_V4
405	vit_base_patch14_dinov2-lvd142m
406	resnet-18_test_m
407	yudixie_resnet18_random_0_240719
408	SeLaVi-AVE
409	r3d_18
410	ReAlnet06_cornet
411	ReAlnet01_cornet_fov16
412	compact_vgg19_IT
413	yudixie_resnet50_translation_reg_0_240908
414	yudixie_resnet50_translation_rotation_0_240908
415	ReAlnet01_ALL
416	mc3_18
417	ReAlnet07_cornet
418	s3d
419	mvit_v1_b
420	ReAlnet08_cornet
421	yudixie_resnet18_rotation_reg_0_240719
422	ReAlnet02_ALL
423	aughost_fair_resnet18
424	VOneCORnet-S
425	ReAlnet03_ALL
426	omnivore_swinB
427	compact_cornets_V4
428	ReAlnet04_ALL
429	SeLaVi-Kinetics400
430	ReAlnet09_cornet
431	compact_cornets_IT
432	SeLaVi-VGG-Sound
433	ReAlnet05_ALL
434	resnet152_ecoset_full
435	ReAlnet06_ALL
436	r2plus1d_18
437	mvit_v2_s
438	SeLaVi-Kinetics-Sound
439	compact_resnet50_robust_IT
440	ReAlnet10_cornet
441	resnet101_ecoset_full
442	ReAlnet07_ALL
443	yudixie_resnet18_object_class_0_240719
444	yudixie_resnet18_cat_obj_class_all_latents_0_240719
445	yudixie_resnet50_distance_translation_rotation_0_240908
446	yudixie_resnet18_distance_translation_rotation_0_240719
447	yudixie_resnet18_translation_rotation_0_240719
448	ReAlnet05_cornet
449	ReAlnet09_ALL
450	resnet50_primary_visual_cortex
451	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