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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021powerequalisation-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	dinov2_vitg14_lc	.759
2	fixres_resnext101_32x48d_wsl	.755
3	dinov2_vitl14_lc	.748
4	vit_large_patch14_clip_224:laion2b_ft_in1k	.718
5	cvt_cvt-13-384-in1k_4_LucyV4	.713
6	vit_large_patch14_clip_224:openai_ft_in1k	.709
7	cvt_cvt-21-384-in1k_4_LucyV4	.707
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.707
9	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.705
10	convnext_xxlarge_clip	.692
11	convnext_xlarge:fb_in22k_ft_in1k	.680
12	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.672
13	convnext_large:fb_in22k_ft_in1k	.670
14	SWSL_resnext101_32x8d	.668
15	dinov2_vitl14_reg_lc	.659
16	vit_large_patch14_clip_336:laion2b_ft_in1k	.651
17	vit_large_clip336_openai	.651
18	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.650
19	vit_base_patch16_clip_224:openai_ft_in1k	.647
20	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.638
21	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.633
22	convnext_large_in22k_supervised	.601
23	dinov2_vitg14_reg_lc	.600
24	resnet_152_v2	.599
25	resnext101_32x16d_wsl	.592
26	convnext_base:clip_laiona_augreg_ft_in1k_384	.586
27	resnext101_32x32d_wsl	.579
28	convnext_base_fb_in22k_ft_in1k	.568
29	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.564
30	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.560
31	resnext101_32x48d_wsl	.548
32	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.540
33	vit_huge_clip336_laion	.526
34	effnetv2m_custom384	.501
35	resnet-152_v2_pytorch	.494
36	convnext_large_clip_laion2b_finelayers	.490
37	SWSL_resnet50	.489
38	dinov2_vitl14_lc_res336	.487
39	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
40	convnext_base_imagenet_full_seed-0	.482
41	vision_transformer_vit_large_patch16_224	.472
42	convnext_tiny:in12k_ft_in1k	.468
43	convnext_large_imagenet_full_seed-0	.467
44	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.462
45	resnet_50_v2	.455
46	resnext101_32x8d_wsl	.453
47	resnet_101_v2	.451
48	convnext_small_imagenet_full_seed-0	.450
49	convnext_tiny_imagenet_full_seed-0	.427
50	CLIP_resnet50	.427
51	CLIP_resnet50_float32	.427
52	cvt_cvt-13-224-in1k_4_LucyV4	.397
53	cv_18_dagger_408_pretrained	.388
54	vit_relpos_base_patch16_clsgap_224:sw_in1k	.382
55	pnasnet_large_halfPrecision	.380
56	pnasnet_large	.380
57	convnext_femto_ols:d1_in1k	.378
58	convnext_base_fb_in1k	.372
59	eva02_large_in22k	.365
60	nasnet_large	.363
61	deit3_base_patch16_224_fb_in22k_ft_in1k	.363
62	ViT_B_16_imagenet1k	.339
63	resnet-152_v2-tf	.335
64	resnet50_st_wzc_seed7_2_323	.328
65	inception_v4	.323
66	focalnet_tiny_lrf_in1k	.317
67	resnet152_imagenet_full	.317
68	CLIP-RN50	.315
69	ViT_L_32_imagenet1k	.307
70	pnasnet_large-tf	.295
71	resnet50-VITO-8deg-cc	.292
72	antialias-resnet152	.289
73	effnetb1_272x240	.289
74	resnet101	.287
75	CLIP_ViT-B_32	.287
76	inception_v3	.280
77	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.278
78	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.269
79	deit_large_imagenet_full_seed-0	.269
80	resnet50_random_linf8_perturb	.260
81	resnet_152_v1	.260
82	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.257
83	nasnet_large-tf	.248
84	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.246
85	xception-keras	.245
86	inception_v3-tf	.237
87	resnet_101_v1	.234
88	resnet50_st_wzc_seed6_2_323	.231
89	cvt_cvt-21-224-in1k_4_LucyV4	.228
90	resnet50_random_l2_perturb	.227
91	inception_v3_pytorch	.222
92	resnet101_imagenet_full	.221
93	resnet50_ewconplus_wzc_seed5_2__323	.219
94	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.218
95	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.217
96	ViT_B_32_imagenet1k	.217
97	resnet50_imagenet_full	.216
98	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.214
99	inception_v4-tf	.209
100	convnext_base_fb_in1k_gram_aligned_dinov3	.206
101	resnet50_st_wzc_seed3_2_323	.203
102	grcnn_109	.201
103	resnet50_ewconplus_wzc_seed2_2__323	.197
104	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.197
105	vgg_19	.195
106	Res2Net50_26w_4s	.193
107	AdvProp_efficientnet-b7	.190
108	AdvProp_efficientnet-b4	.189
109	AT_efficientnet-b4	.189
110	AdvProp_efficientnet-b6	.187
111	AT_efficientnet-b2	.186
112	AdvProp_efficientnet-b2	.186
113	resnet50	.185
114	xception	.184
115	resnet50_linf_4_robust	.182
116	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.182
117	efficientnet_b0	.181
118	resnet_50_v1_spiking	.181
119	resnet50_ewconplus_wzc	.174
120	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.173
121	AlexNet_SIN_fov4	.172
122	deit_base_imagenet_full_seed-0	.169
123	convnext_small_imagenet_100_seed-0	.167
124	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.164
125	resnet50_st_wzc_2	.163
126	resnet50-moclr8deg	.159
127	resnet-50_v2-tf	.155
128	antialiased-rnext101_32x8d	.149
129	densenet-121-keras	.147
130	custom_model_cv_18_dagger_408	.145
131	inception_v2-tf	.145
132	alexnet_random_linf8_perturb	.140
133	resnet50_hvs_wzc	.136
134	resnet50_byol	.134
135	pnasnet_large_pytorch	.133
136	resnet50-SIN_IN_IN	.132
137	resnet_SIN_IN_FT_IN	.132
138	alexnet_fov4	.132
139	BiT-S-R50x3	.128
140	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.127
141	antialiased-r50	.127
142	resnet34_imagenet_full	.126
143	grcnn	.125
144	densenet-201	.125
145	densenet_201_pytorch	.125
146	voneresnet-50-robust	.122
147	ReAlnet03	.121
148	densenet-121	.119
149	resnet50_linf_8_robust	.119
150	ReAlnet04	.118
151	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.116
152	ReAlnet08	.115
153	efficientnet-b7	.114
154	vgg_16	.113
155	densenet-201-keras	.112
156	voneresnet-50-non_stochastic	.108
157	resnet-50_v1-tf	.107
158	vonealexnet_gaussian_noise_std4_fixed	.105
159	ReAlnet01	.105
160	inception_v1	.100
161	alexnet_l2_3_robust	.098
162	alexnet_random_l2_3_perturb	.098
163	resnet50_tutorial	.098
164	resnet_50_v1	.098
165	resnet-50-pytorch	.098
166	resnet50-sup	.098
167	alexnet_ks_torevert	.097
168	alexnet	.097
169	ReAlnet05	.096
170	densenet-169	.096
171	resnet50_l2_3_robust	.095
172	resnet-152_v1-tf	.094
173	resnet50_moco_v2	.094
174	densenet-169-keras	.094
175	ReAlnet02	.093
176	ReAlnet07	.091
177	tv_efficientnet-b1	.090
178	cornet_s	.089
179	convnext_tiny_sup	.089
180	ReAlnet06	.089
181	vonegrcnn_62e_nobn	.088
182	vonegrcnn_62e_nobn	.088
183	alexnet_reduced_aliasing_early_checkpoint	.088
184	nasnet_mobile-tf	.086
185	alexnet_early_checkpoint	.085
186	deit_small_imagenet_full_seed-0	.084
187	BiT-S-R152x2	.083
188	ReAlnet10	.083
189	vonegrcnn_52e_full	.083
190	alexnet_training_seed_05	.083
191	alexnet_training_seed_01	.083
192	alexnet_training_seed_01_fov4	.083
193	alexnet_training_seed_01_fov12	.083
194	alexnet_training_seed_01_fov16	.083
195	resnet50-vitoimagevidnet8	.082
196	inception_v1-tf	.078
197	barlow-twins-resnet50	.078
198	ReAlnet09	.078
199	resnet-101_v1-tf	.076
200	resnet50_robust_l2_eps1	.075
201	vonegrcnn_47e	.075
202	AdvProp_efficientnet-b8	.073
203	resnet-34	.070
204	vonegrcnn_52e_full	.070
205	alexnet_fov12	.069
206	squeezenet1_1	.068
207	resnet18_imagenet_full	.065
208	vonegrcnn_47e	.065
209	mobilenet_v2_1.0_160-tf	.065
210	alexnet_robust_correct	.064
211	artResNet18_1	.064
212	resnet50-SIN_IN	.063
213	resnet-50-robust	.062
214	imagenet_l2_3_0	.062
215	resnet50_robust_l2_eps3	.062
216	konkle_alexnetgn_ipcl_ref01_primary_model	.062
217	mobilenet_v2_1_0_192	.060
218	resnet50_simclr	.059
219	mobilenet_v2_1_0_160	.059
220	eBarlow_lmda_02_1000ep	.059
221	BiT-S-R152x4	.057
222	alexnet_training_seed_02	.053
223	alexnet_training_seed_04	.053
224	alexnet_training_seed_08	.053
225	alexnet_training_seed_10	.053
226	vgg-19-keras	.053
227	CORnet-S	.052
228	efficientnet_b2_imagenet_full	.050
229	mobilenet_v1_0.25_128	.048
230	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.048
231	mobilenet_v2_1.0_224-tf	.047
232	vgg-16-keras	.047
233	mobilenet_v2_0_5_192	.046
234	voneresnet-50	.045
235	texture_shape_resnet50_trained_on_SIN	.044
236	mobilenet_v2_0.5_96	.043
237	mobilenet_v1_0.5_160	.041
238	convnext_small_imagenet_1_seed-0	.041
239	AlexNet_SIN_fov12	.040
240	deit_small_imagenet_100_seed-0	.040
241	mobilenet_v2_1_0_224	.040
242	resnet50_imagenet_100_seed-0	.040
243	alexnet_fov16	.040
244	resnet50-SIN	.040
245	mobilenet_v1_0.5_128	.039
246	BiT-S-R101x3	.039
247	resnet50-barlow	.038
248	alexnet_training_seed_07	.038
249	alexnet_training_seed_09	.038
250	AdvProp_efficientnet-b0	.038
251	0.5x_resnet-18_LC_w_sh_1_iter	.038
252	BiT-S-R50x1	.037
253	mobilenet_v2_0.35_192	.037
254	texture_shape_alexnet_trained_on_SIN	.037
255	alexnet_linf_8_robust	.037
256	mobilenet_v2_1-4_224_pytorch	.036
257	mobilenet_v2_1_4_224	.036
258	resnet50_FractalDB	.036
259	BiT-S-R101x1	.035
260	mobilenet_v1_0.75_192	.035
261	mobilenet_v1_0.5_224	.034
262	mobilenet_v2_0.5_128	.034
263	mobilenet_v2_0_75_224	.034
264	mobilenet_v1_0.25_192	.033
265	mobilenet_v2_0.35_224	.033
266	resnet50_imagenet_10_seed-0	.033
267	AlexNet_SIN	.032
268	nasnet_mobile	.032
269	efficientnet_b0_imagenet_full	.031
270	mobilenet_v2_1.0_96	.031
271	deit_small_imagenet_10_seed-0	.031
272	deit_small_imagenet_1_seed-0	.031
273	convnext_small_imagenet_10_seed-0	.031
274	AlexNet_SIN_fov16	.031
275	resnet50-vicregl0p9	.030
276	resnet50-vicregl0p75	.029
277	mobilenet_v2_0.5_192-tf	.028
278	resnet50-vicreg	.027
279	mobilenet_v2_0.75_96	.027
280	mobilenet_v2_1_3_224	.027
281	mobilenet_v2_1.0_128-tf	.026
282	resnet-50_untrained	.024
283	mobilenet_v1_0.25_224	.024
284	mobilenet_v2_0.75_224-tf	.023
285	blt_vs	.023
286	mobilenet_v2_0.35_128	.023
287	mobilenet_v1_0.5_192	.021
288	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.021
289	mobilenet_v2_1.4_224-tf	.020
290	mobilenet_v2_0.35_160	.020
291	mobilenet_v2_0_75_160	.019
292	mobilenet_v2_0.75_128	.019
293	mobilenet_v2_0_75_192	.019
294	mobilenet_v2_0.5_160	.018
295	imagenet_l2_10_0	.018
296	mobilenet_v2_1.0_192-tf	.017
297	mobilenet_v1_0.75_160	.016
298	squeezenet1_0	.015
299	shufflenet_v2_x1_0	.015
300	mobilenet_v2_0.35_96	.014
301	alexnet_training_seed_06	.014
302	mobilenet_v2_0.5_224-tf	.014
303	mobilenet_v1_0.75_128	.014
304	resnet50_eMMCR_Vanilla	.013
305	resnet_50_v1_spiking_l4a	.012
306	resnet50_imagenet_1_seed-0	.012
307	mobilenet_v2_0.75_160-tf	.011
308	resnet_50_v1_spiking_l4	.011
309	resnet_50_v1_spiking_l3	.011
310	alexnet_training_seed_03	.010
311	mobilenet_v2_0_5_224	.010
312	mobilevit_small	.010
313	mobilenet_v1_1.0_192	.009
314	resnet50-simclr-vissl	.008
315	mobilenet_v1_0.25_160	.007
316	mobilenet_v1_1.0_128	.006
317	mobilenet_v1_1.0_160	.006
318	mobilenet_v1_1.0_224	.005
319	resnet-18	.004
320	mobilenet_v2_1_0_128	.003
321	mobilenet_v2_1.3_224-tf	.003
322	efficientnet_b1_imagenet_full	.003
323	regnet_y_400mf	.002
324	mobilenet_v2_0.75_192-tf	.002
325	mobilenet_v1_0.75_224	.001
326	my-model	.000
327	resnet-18-LC_w_sh_100_iter_conv_init_m
328	ViT-B/32
329	BiT-M-R50x3
330	BiT-M-R50x1
331	BiT-M-R152x2
332	resnet-18-LC_w_sh_1_iter_conv_init_m
333	resnet-18-LC_w_sh_10_iter_m
334	resnet-18-LC_w_sh_100_iter_m
335	resnet-18-LC_w_sh_10_iter_conv_init_m
336	resnet-18-LC_w_sh_1_iter_m
337	FrankRobWobv0
338	0.5x_resnet-18_LC
339	CORnetZ_CIFAR10
340	resnet18-local_aggregation
341	resnet18-deepcluster
342	r3m_resnet18
343	CORnet-Z
344	resnet18-instance_recognition
345	vggface
346	my-model
347	0.5x_resnet-18_LC_w_sh_10_iter
348	r3m_resnet50
349	r3m_resnet34
350	resnet18-colorization
351	resnet-18-LC_m
352	r3m_resnet50_nocrop
353	resnet18-supervised
354	resnet-18-LC_1st_conv_m
355	resnet-18-LC_conv_init_m
356	resnet18-contrastive_multiview
357	0.5x_resnet-18
358	RN50
359	0.5x_resnet-18_LC_w_sh_100_iter
360	RN50
361	resnet18-relative_position
362	resnet18-autoencoder
363	resnet18-simclr
364	ViT_L_32
365	ViT_B_32
366	resnet18-depth_prediction
367	ViT_B_16
368	r3m_resnet34_nocrop
369	resnet18-contrastive_predictive
370	omnivore_swinT
371	resnet-18-LC_untrained
372	resnet-18_untrained
373	resnet-50x2_untrained
374	dcgan
375	unet_entire
376	v1-pyr-nodown
377	CORnetZ_CIFAR10_bs32_20_04
378	pixels
379	dorinet_cornet_z
380	openclip
381	resnet50-cifar
382	resnet50_pretrained_with_retinal_waves
383	resnet50_ImageNet
384	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
385	yudixie_resnet50_random_0_240908
386	yudixie_resnet50_cat_obj_class_all_latents_0_240908
387	yudixie_resnet50_imagenet1kpret_0_240908
388	yudixie_resnet50_imagenet1kpret_0_240312
389	yudixie_resnet50_object_class_0_240908
390	yudixie_resnet50_category_class_0_240908
391	resnet18_imagenet21kP
392	yudixie_resnet50_distance_reg_0_240908
393	ConvLSTM
394	PredRNN
395	SimVP
396	TAU
397	MIM
398	hmax
399	briaai_rmbg_1_4
400	ReAlnet01_cornet
401	compact_resnet50_V4
402	yudixie_resnet18_translation_reg_0_240719
403	yolos_tiny
404	ReAlnet03_cornet
405	yudixie_resnet18_rotation_reg_0_240719
406	yudixie_resnet18_imagenet1kpret_0_240719
407	ReAlnet02_cornet
408	vit_base_patch14_dinov2-lvd142m
409	resnet18_ecoset_full
410	compact_resnet50_IT
411	resnet50_ecoset_full
412	VOneCORnet-S
413	vit_base_patch14_reg4_dinov2-lvd142m
414	ReAlnet01_cornet_fov12
415	resnet101_ecoset_full
416	yudixie_resnet50_distance_translation_0_240908
417	resnet152_ecoset_full
418	yudixie_resnet18_distance_translation_0_240719
419	ReAlnet01_cornet_fov4
420	yudixie_resnet18_translation_rotation_0_240719
421	compact_vgg19_V4
422	aughost_fair_resnet18
423	yudixie_resnet18_distance_rotation_0_240719
424	yudixie_resnet18_category_class_0_240719
425	resnet-18_test_m
426	yudixie_resnet18_cat_obj_class_all_latents_0_240719
427	yudixie_resnet50_rotation_reg_0_240908
428	yudixie_resnet50_distance_rotation_0_240908
429	yudixie_resnet18_object_class_0_240719
430	SeLaVi-AVE
431	ReAlnet05_cornet
432	ReAlnet06_cornet
433	compact_resnet50_robust_V4
434	compact_vgg19_IT
435	yudixie_resnet50_translation_reg_0_240908
436	r3d_18
437	ReAlnet01_ALL
438	yudixie_resnet18_distance_reg_0_240719
439	yudixie_resnet50_translation_rotation_0_240908
440	omnivore_swinB
441	mc3_18
442	ReAlnet07_cornet
443	s3d
444	ReAlnet02_ALL
445	ReAlnet01_cornet_fov16
446	mvit_v1_b
447	ReAlnet08_cornet
448	ReAlnet04_ALL
449	compact_cornets_V4
450	ReAlnet03_ALL
451	ReAlnet09_cornet
452	SeLaVi-Kinetics400
453	ReAlnet05_ALL
454	compact_cornets_IT
455	SeLaVi-VGG-Sound
456	vit_base_patch16_dinov3-lvd1689m
457	ReAlnet06_ALL
458	SeLaVi-Kinetics-Sound
459	mvit_v2_s
460	omnivore_swinS
461	r2plus1d_18
462	compact_resnet50_robust_IT
463	yudixie_resnet50_distance_translation_rotation_0_240908
464	ReAlnet10_cornet
465	ReAlnet07_ALL
466	resnet34_ecoset_full
467	ReAlnet08_ALL
468	yudixie_resnet18_random_0_240719
469	yudixie_resnet18_distance_translation_rotation_0_240719
470	ReAlnet09_ALL
471	ReAlnet04_cornet
472	resnet50_primary_visual_cortex
473	ReAlnet10_ALL
474	resnet18_fair
475	pr1_stl10_disentangled_alpha04
476	yudixie_wbh_object_class_scaling_0415_run_0
477	yudixie_wbh_object_class_scaling_0415_run_3
478	yudixie_wbh_object_class_scaling_0415_imagenet
479	yudixie_wbh_object_class_scaling_0415_run_2
480	yudixie_wbh_object_class_scaling_0415_run_1
481	deepeisnn_ei_cifar10_vgg19
482	deepeisnn_ei_cifar10_resnet18
483	deepeisnn_ei_imagenet_resnet18
484	dinov2_vitl14_reg_lc_featread

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

0.51.

Note that scores are relative to this ceiling.

Data: Geirhos2021powerequalisation

Metric: error_consistency