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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-baseline	.203
217	alexnet	.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	squeezenet1_1	.194
228	mobilenet_v1_0.5_224	.194
229	mobilenet_v1_0.75_160	.193
230	mobilenet_v2_0_75_192	.192
231	mobilenet_v2_1.0_128-tf	.186
232	resnet50_simclr	.185
233	mobilenet_v2_0.35_192	.185
234	mobilenet_v2_1.0_96	.185
235	mobilenet_v1_1.0_192	.180
236	efficientnet_b1_imagenet_full	.176
237	konkle_alexnetgn_ipcl_ref01_primary_model	.169
238	mobilenet_v2_0.75_96	.166
239	mobilenet_v1_0.5_192	.164
240	mobilenet_v1_0.75_128	.163
241	alexnet_linf_8_robust	.161
242	AlexNet_SIN_fov12	.159
243	mobilenet_v1_0.5_160	.154
244	regnet_y_400mf	.150
245	resnet-50-robust	.149
246	imagenet_l2_3_0	.149
247	resnet50_robust_l2_eps3	.149
248	0.5x_resnet-18_LC_w_sh_1_iter	.148
249	AdvProp_efficientnet-b8	.147
250	mobilenet_v1_0.75_192	.146
251	deit_small_imagenet_100_seed-0	.145
252	alexnet_reduced_aliasing_early_checkpoint	.143
253	alexnet_early_checkpoint	.139
254	alexnet_training_seed_05	.139
255	alexnet_training_seed_06	.139
256	alexnet_training_seed_01	.139
257	alexnet_training_seed_01_fov4	.139
258	alexnet_training_seed_01_fov12	.139
259	alexnet_training_seed_01_fov16	.139
260	resnet50-barlow	.139
261	alexnet_robust_correct	.135
262	vonealexnet_gaussian_noise_std4_fixed	.134
263	mobilenet_v2_0.35_128	.129
264	mobilenet_v1_0.5_128	.127
265	alexnet_fov12	.118
266	AlexNet_SIN_fov16	.117
267	mobilenet_v1_0.25_192	.117
268	resnet50_imagenet_100_seed-0	.112
269	artResNet18_1	.109
270	mobilenet_v2_0.35_224	.107
271	mobilenet_v2_0.5_128	.106
272	mobilenet_v2_0.35_96	.102
273	resnet50_imagenet_10_seed-0	.102
274	mobilenet_v1_0.25_160	.098
275	alexnet_training_seed_03	.090
276	resnet50_FractalDB	.089
277	mobilenet_v1_1.0_128	.089
278	convnext_small_imagenet_10_seed-0	.084
279	deit_small_imagenet_10_seed-0	.084
280	deit_small_imagenet_1_seed-0	.084
281	shufflenet_v2_x1_0	.083
282	mobilenet_v1_0.25_128	.082
283	convnext_small_imagenet_1_seed-0	.082
284	vgg-19-keras	.081
285	alexnet_training_seed_02	.077
286	alexnet_training_seed_08	.077
287	alexnet_training_seed_04	.077
288	alexnet_training_seed_10	.077
289	alexnet_training_seed_07	.077
290	resnet50-meshes-lt-100-original-scratch	.075
291	imagenet_l2_10_0	.073
292	resnet-50_untrained	.070
293	mobilenet_v2_0.5_96	.066
294	resnet50-simclr-vissl	.065
295	resnet50-meshes-lt-100-original-pretrained	.059
296	ViT_B_32_imagenet1k	.056
297	barlow-twins-resnet50	.054
298	squeezenet1_0	.053
299	resnet50_eMMCR_Vanilla	.052
300	ViT_L_32_imagenet1k	.051
301	alexnet_fov16	.050
302	alexnet_training_seed_09	.049
303	resnet50_imagenet_1_seed-0	.046
304	resnet50-vicregl0p75	.043
305	resnet_50_v1_spiking_l4	.042
306	mobilenet_v1_0.25_224	.041
307	resnet50-vicregl0p9	.033
308	mobilevit_small	.023
309	ViT_B_16_imagenet1k	.023
310	resnet50-vicreg	.012
311	resnet_50_v1_spiking_l3	.004
312	ReAlnet10_ALL
313	resnet-18-LC_w_sh_100_iter_conv_init_m
314	ViT-B/32
315	BiT-M-R50x3
316	BiT-M-R50x1
317	BiT-M-R152x2
318	resnet-18-LC_w_sh_1_iter_conv_init_m
319	resnet-18-LC_w_sh_1_iter_m
320	resnet-18-LC_w_sh_10_iter_m
321	resnet-18-LC_w_sh_100_iter_m
322	resnet-18-LC_w_sh_10_iter_conv_init_m
323	FrankRobWobv0
324	0.5x_resnet-18_LC
325	CORnetZ_CIFAR10
326	resnet18-local_aggregation
327	resnet18-deepcluster
328	r3m_resnet18
329	CORnet-Z
330	vggface
331	my-model
332	resnet18-instance_recognition
333	0.5x_resnet-18_LC_w_sh_10_iter
334	r3m_resnet50
335	r3m_resnet34
336	resnet-18-LC_m
337	r3m_resnet50_nocrop
338	resnet18-supervised
339	resnet-18-LC_1st_conv_m
340	resnet-18-LC_conv_init_m
341	resnet18-contrastive_multiview
342	0.5x_resnet-18
343	resnet18-colorization
344	RN50
345	0.5x_resnet-18_LC_w_sh_100_iter
346	RN50
347	resnet18-relative_position
348	resnet18-autoencoder
349	resnet18-simclr
350	omnivore_swinT
351	ViT_L_32
352	ViT_B_32
353	resnet18-depth_prediction
354	ViT_B_16
355	r3m_resnet34_nocrop
356	resnet18-contrastive_predictive
357	resnet-18-LC_untrained
358	resnet-18_untrained
359	resnet-50x2_untrained
360	dcgan
361	unet_entire
362	omnivore_swinS
363	v1-pyr-nodown
364	CORnetZ_CIFAR10_bs32_20_04
365	pixels
366	dorinet_cornet_z
367	my-model
368	resnet50
369	openclip
370	resnet50-cifar
371	resnet50_pretrained_with_retinal_waves
372	resnet50_ImageNet
373	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
374	yudixie_resnet50_random_0_240908
375	yudixie_resnet50_imagenet1kpret_0_240312
376	yudixie_resnet50_imagenet1kpret_0_240908
377	yudixie_resnet50_cat_obj_class_all_latents_0_240908
378	yudixie_resnet50_object_class_0_240908
379	yudixie_resnet50_category_class_0_240908
380	resnet18_imagenet21kP
381	ConvLSTM
382	yudixie_resnet50_distance_reg_0_240908
383	PredRNN
384	SimVP
385	TAU
386	MIM
387	hmax
388	briaai_rmbg_1_4
389	yolos_tiny
390	ReAlnet01_cornet
391	yudixie_resnet18_translation_reg_0_240719
392	compact_resnet50_V4
393	ReAlnet02_cornet
394	yudixie_resnet18_distance_reg_0_240719
395	yudixie_resnet18_imagenet1kpret_0_240719
396	resnet18_ecoset_full
397	ReAlnet03_cornet
398	resnet34_ecoset_full
399	ReAlnet01_cornet_fov4
400	resnet50_ecoset_full
401	compact_resnet50_IT
402	compact_resnet50_robust_V4
403	yudixie_resnet50_rotation_reg_0_240908
404	ReAlnet01_cornet_fov12
405	yudixie_resnet50_distance_translation_0_240908
406	yudixie_resnet18_distance_translation_0_240719
407	ReAlnet04_cornet
408	yudixie_resnet18_distance_rotation_0_240719
409	yudixie_resnet18_category_class_0_240719
410	yudixie_resnet50_distance_rotation_0_240908
411	compact_vgg19_V4
412	resnet-18_test_m
413	yudixie_resnet18_random_0_240719
414	SeLaVi-AVE
415	r3d_18
416	ReAlnet06_cornet
417	ReAlnet01_cornet_fov16
418	compact_vgg19_IT
419	yudixie_resnet50_translation_reg_0_240908
420	yudixie_resnet50_translation_rotation_0_240908
421	ReAlnet01_ALL
422	mc3_18
423	ReAlnet07_cornet
424	s3d
425	mvit_v1_b
426	ReAlnet08_cornet
427	yudixie_resnet18_rotation_reg_0_240719
428	ReAlnet02_ALL
429	VOneCORnet-S
430	ReAlnet03_ALL
431	omnivore_swinB
432	compact_cornets_V4
433	ReAlnet04_ALL
434	SeLaVi-Kinetics400
435	ReAlnet09_cornet
436	compact_cornets_IT
437	SeLaVi-VGG-Sound
438	ReAlnet05_ALL
439	resnet152_ecoset_full
440	ReAlnet06_ALL
441	r2plus1d_18
442	mvit_v2_s
443	SeLaVi-Kinetics-Sound
444	compact_resnet50_robust_IT
445	ReAlnet10_cornet
446	resnet101_ecoset_full
447	ReAlnet07_ALL
448	yudixie_resnet18_object_class_0_240719
449	yudixie_resnet18_cat_obj_class_all_latents_0_240719
450	yudixie_resnet50_distance_translation_rotation_0_240908
451	yudixie_resnet18_distance_translation_rotation_0_240719
452	yudixie_resnet18_translation_rotation_0_240719
453	ReAlnet05_cornet
454	ReAlnet09_ALL
455	resnet50_primary_visual_cortex
456	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