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	resnet50-sup	.546
92	resnet-50-pytorch	.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	AT_efficientnet-b4	.521
100	AdvProp_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	resnet_SIN_IN_FT_IN	.492
110	resnet50-SIN_IN_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	vonegrcnn_47e	.482
117	BiT-S-R50x3	.478
118	densenet-169	.474
119	resnet-50_v1-tf	.474
120	convnext_small_imagenet_100_seed-0	.469
121	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
122	densenet-121	.468
123	resnet50_byol	.465
124	voneresnet-50	.463
125	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.458
126	inception_v1	.451
127	resnet-101_v1-tf	.450
128	efficientnet-b7	.449
129	resnet_152_v1	.445
130	mobilenet_v2_1_4_224	.439
131	mobilenet_v2_1-4_224_pytorch	.439
132	mobilenet_v1_0.75_224	.438
133	mobilenet_v2_1_0_224	.437
134	deit_large_imagenet_full_seed-0	.434
135	resnet50_linf_4_robust	.421
136	nasnet_mobile	.418
137	ReAlnet03	.417
138	densenet-201-keras	.409
139	resnet50-moclr8deg	.395
140	BiT-S-R50x1	.394
141	custom_model_cv_18_dagger_408	.392
142	Res2Net50_26w_4s	.390
143	resnet50-SIN_IN	.388
144	mobilenet_v2_1.0_192-tf	.384
145	vgg_19	.379
146	densenet-201	.378
147	densenet_201_pytorch	.378
148	cornet_s	.375
149	ReAlnet07	.373
150	mobilenet_v2_0_5_224	.369
151	ReAlnet04	.369
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	resnet_50_v1_spiking_l4a	.287
180	mobilenet_v2_1_0_160	.287
181	BiT-S-R101x1	.286
182	resnet50_moco_v2	.285
183	ReAlnet05	.280
184	mobilenet_v1_1.0_224	.279
185	mobilenet_v2_0.75_128	.276
186	mobilenet_v1_1.0_160	.270
187	mobilenet_v2_0.75_224-tf	.266
188	AlexNet_SIN	.263
189	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.262
190	alexnet_random_linf8_perturb	.257
191	mobilenet_v2_0_75_224	.256
192	BiT-S-R152x4	.255
193	mobilenet_v2_0.35_160	.252
194	resnet50-vitoimagevidnet8	.252
195	BiT-S-R101x3	.249
196	alexnet_random_l2_3_perturb	.247
197	alexnet	.246
198	resnet50-SIN	.245
199	AdvProp_efficientnet-b0	.243
200	mobilenet_v2_1_0_128	.240
201	mobilenet_v2_0.5_160	.236
202	xception	.236
203	resnet50_robust_l2_eps1	.231
204	mobilenet_v2_0.75_192-tf	.231
205	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.228
206	ReAlnet06	.220
207	efficientnet_b2_imagenet_full	.214
208	alexnet_l2_3_robust	.214
209	voneresnet-50-robust	.205
210	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
211	mobilenet_v2_0.75_160-tf	.204
212	alexnet_ks_torevert	.203
213	alexnet	.203
214	alexnet	.203
215	alexnet	.203
216	alexnet	.203
217	alexnet	.203
218	alexnet	.203
219	alexnet-baseline	.203
220	alexnet	.203
221	alexnet	.203
222	alexnet	.203
223	squeezenet1_1	.194
224	mobilenet_v1_0.5_224	.194
225	mobilenet_v1_0.75_160	.193
226	mobilenet_v2_0_75_192	.192
227	mobilenet_v2_1.0_128-tf	.186
228	resnet50_simclr	.185
229	mobilenet_v2_0.35_192	.185
230	mobilenet_v2_1.0_96	.185
231	mobilenet_v1_1.0_192	.180
232	efficientnet_b1_imagenet_full	.176
233	konkle_alexnetgn_ipcl_ref01_primary_model	.169
234	mobilenet_v2_0.75_96	.166
235	mobilenet_v1_0.5_192	.164
236	mobilenet_v1_0.75_128	.163
237	alexnet_linf_8_robust	.161
238	mobilenet_v1_0.5_160	.154
239	regnet_y_400mf	.150
240	resnet50_robust_l2_eps3	.149
241	resnet-50-robust	.149
242	imagenet_l2_3_0	.149
243	0.5x_resnet-18_LC_w_sh_1_iter	.148
244	AdvProp_efficientnet-b8	.147
245	mobilenet_v1_0.75_192	.146
246	deit_small_imagenet_100_seed-0	.145
247	alexnet_reduced_aliasing_early_checkpoint	.143
248	alexnet_early_checkpoint	.139
249	alexnet_training_seed_01	.139
250	alexnet_training_seed_05	.139
251	alexnet_training_seed_06	.139
252	resnet50-barlow	.139
253	alexnet_robust_correct	.135
254	vonealexnet_gaussian_noise_std4_fixed	.134
255	mobilenet_v2_0.35_128	.129
256	mobilenet_v1_0.5_128	.127
257	mobilenet_v1_0.25_192	.117
258	resnet50_imagenet_100_seed-0	.112
259	artResNet18_1	.109
260	mobilenet_v2_0.35_224	.107
261	mobilenet_v2_0.5_128	.106
262	mobilenet_v2_0.35_96	.102
263	resnet50_imagenet_10_seed-0	.102
264	mobilenet_v1_0.25_160	.098
265	alexnet_training_seed_03	.090
266	resnet50_FractalDB	.089
267	mobilenet_v1_1.0_128	.089
268	convnext_small_imagenet_10_seed-0	.084
269	deit_small_imagenet_10_seed-0	.084
270	deit_small_imagenet_1_seed-0	.084
271	shufflenet_v2_x1_0	.083
272	mobilenet_v1_0.25_128	.082
273	convnext_small_imagenet_1_seed-0	.082
274	vgg-19-keras	.081
275	alexnet_training_seed_04	.077
276	alexnet_training_seed_10	.077
277	alexnet_training_seed_02	.077
278	alexnet_training_seed_07	.077
279	alexnet_training_seed_08	.077
280	resnet50-meshes-lt-100-original-scratch	.075
281	imagenet_l2_10_0	.073
282	resnet-50_untrained	.070
283	mobilenet_v2_0.5_96	.066
284	resnet50-simclr-vissl	.065
285	resnet50-meshes-lt-100-original-pretrained	.059
286	ViT_B_32_imagenet1k	.056
287	barlow-twins-resnet50	.054
288	squeezenet1_0	.053
289	ViT_L_32_imagenet1k	.051
290	alexnet_training_seed_09	.049
291	resnet50_imagenet_1_seed-0	.046
292	resnet50-vicregl0p75	.043
293	resnet_50_v1_spiking_l4	.042
294	mobilenet_v1_0.25_224	.041
295	resnet50-vicregl0p9	.033
296	mobilevit_small	.023
297	ViT_B_16_imagenet1k	.023
298	resnet50-vicreg	.012
299	resnet_50_v1_spiking_l3	.004
300	ReAlnet04_cornet
301	yudixie_resnet18_translation_rotation_0_240719
302	yudixie_resnet18_distance_rotation_0_240719
303	yudixie_resnet18_category_class_0_240719
304	yudixie_resnet50_distance_rotation_0_240908
305	ReAlnet05_cornet
306	resnet-18_test_m
307	yudixie_resnet18_random_0_240719
308	yudixie_resnet18_cat_obj_class_all_latents_0_240719
309	yudixie_resnet18_distance_translation_rotation_0_240719
310	yudixie_resnet18_object_class_0_240719
311	ReAlnet06_cornet
312	yudixie_resnet50_translation_reg_0_240908
313	yudixie_resnet50_translation_rotation_0_240908
314	CORnet-Z
315	r3m_resnet18
316	ReAlnet07_cornet
317	resnet18-deepcluster
318	ReAlnet08_cornet
319	resnet18-local_aggregation
320	CORnetZ_CIFAR10
321	VOneCORnet-S
322	omnivore_swinB
323	0.5x_resnet-18_LC
324	ReAlnet09_cornet
325	FrankRobWobv0
326	resnet-18-LC_w_sh_10_iter_conv_init_m
327	resnet-18-LC_w_sh_100_iter_m
328	resnet-18-LC_w_sh_10_iter_m
329	ReAlnet10_cornet
330	resnet-18-LC_w_sh_1_iter_m
331	yudixie_resnet50_distance_translation_rotation_0_240908
332	resnet-18-LC_w_sh_1_iter_conv_init_m
333	BiT-M-R152x2
334	BiT-M-R50x1
335	resnet50_primary_visual_cortex
336	BiT-M-R50x3
337	ViT-B/32
338	resnet50_ImageNet
339	resnet-18-LC_w_sh_100_iter_conv_init_m
340	resnet50_pretrained_with_retinal_waves
341	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
342	resnet50-cifar
343	openclip
344	resnet50
345	yudixie_resnet50_random_0_240908
346	my-model
347	dorinet_cornet_z
348	pixels
349	yudixie_resnet50_imagenet1kpret_0_240312
350	CORnetZ_CIFAR10_bs32_20_04
351	yudixie_resnet50_imagenet1kpret_0_240908
352	v1-pyr-nodown
353	omnivore_swinS
354	yudixie_resnet50_cat_obj_class_all_latents_0_240908
355	yudixie_resnet50_object_class_0_240908
356	unet_entire
357	dcgan
358	resnet-50x2_untrained
359	resnet-18_untrained
360	resnet-18-LC_untrained
361	resnet18-contrastive_predictive
362	r3m_resnet34_nocrop
363	ViT_B_16
364	resnet18-depth_prediction
365	ViT_B_32
366	yudixie_resnet50_category_class_0_240908
367	ViT_L_32
368	omnivore_swinT
369	resnet18-simclr
370	resnet18-autoencoder
371	resnet18-relative_position
372	resnet18_imagenet21kP
373	ConvLSTM
374	yudixie_resnet50_distance_reg_0_240908
375	PredRNN
376	SimVP
377	TAU
378	MIM
379	hmax
380	RN50
381	0.5x_resnet-18_LC_w_sh_100_iter
382	RN50
383	resnet18-colorization
384	briaai_rmbg_1_4
385	yolos_tiny
386	0.5x_resnet-18
387	resnet18-contrastive_multiview
388	ReAlnet01_cornet
389	resnet-18-LC_conv_init_m
390	resnet-18-LC_1st_conv_m
391	resnet18-supervised
392	r3m_resnet50_nocrop
393	resnet-18-LC_m
394	r3m_resnet34
395	yudixie_resnet18_translation_reg_0_240719
396	ReAlnet02_cornet
397	yudixie_resnet18_distance_reg_0_240719
398	yudixie_resnet18_rotation_reg_0_240719
399	yudixie_resnet18_imagenet1kpret_0_240719
400	r3m_resnet50
401	0.5x_resnet-18_LC_w_sh_10_iter
402	resnet18-instance_recognition
403	resnet18_ecoset_full
404	ReAlnet03_cornet
405	resnet34_ecoset_full
406	my-model
407	resnet50_ecoset_full
408	resnet101_ecoset_full
409	yudixie_resnet50_rotation_reg_0_240908
410	resnet152_ecoset_full
411	vggface
412	yudixie_resnet50_distance_translation_0_240908
413	yudixie_resnet18_distance_translation_0_240719

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