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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021phasescrambling-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-21-384-in1k_4_LucyV4	.734
2	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.730
3	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.677
4	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.624
5	dinov2_vitg14_lc	.622
6	convnext_base:clip_laiona_augreg_ft_in1k_384	.621
7	dinov2_vitl14_lc	.619
8	convnext_large_clip_laion2b_finelayers	.613
9	vit_large_patch14_clip_224:laion2b_ft_in1k	.612
10	convnext_large:fb_in22k_ft_in1k	.606
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.600
12	vit_large_patch14_clip_336:laion2b_ft_in1k	.599
13	cvt_cvt-13-384-in1k_4_LucyV4	.596
14	convnext_xxlarge_clip	.588
15	convnext_base_fb_in22k_ft_in1k	.588
16	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.584
17	convnext_large_in22k_supervised	.584
18	vit_large_patch14_clip_224:openai_ft_in1k	.576
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.576
20	resnet_152_v2	.572
21	fixres_resnext101_32x48d_wsl	.567
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.562
23	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.561
24	dinov2_vitg14_reg_lc	.551
25	vit_huge_clip336_laion	.522
26	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.521
27	resnet-152_v2_pytorch	.512
28	SWSL_resnext101_32x8d	.511
29	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.511
30	dinov2_vitl14_lc_res336	.508
31	vision_transformer_vit_large_patch16_224	.506
32	vit_base_patch16_clip_224:openai_ft_in1k	.503
33	convnext_xlarge:fb_in22k_ft_in1k	.499
34	dinov2_vitl14_reg_lc	.479
35	resnext101_32x32d_wsl	.478
36	resnext101_32x48d_wsl	.462
37	convnext_tiny:in12k_ft_in1k	.459
38	resnet_50_v2	.448
39	eva02_large_in22k	.445
40	vit_relpos_base_patch16_clsgap_224:sw_in1k	.442
41	deit3_base_patch16_224_fb_in1k	.433
42	resnext101_32x16d_wsl	.431
43	SWSL_resnet50	.419
44	effnetv2m_custom384	.416
45	CLIP_resnet50	.404
46	CLIP_resnet50_float32	.404
47	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.383
48	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.382
49	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.378
50	pnasnet_large_halfPrecision	.376
51	pnasnet_large	.376
52	convnext_tiny_imagenet_full_seed-0	.372
53	CLIP_ViT-B_32	.367
54	resnet_101_v2	.364
55	ViT_B_16_imagenet1k	.363
56	resnext101_32x8d_wsl	.363
57	pnasnet_large-tf	.361
58	convnext_femto_ols:d1_in1k	.350
59	convnext_base_imagenet_full_seed-0	.347
60	deit3_base_patch16_224_fb_in22k_ft_in1k	.346
61	focalnet_tiny_lrf_in1k	.344
62	cvt_cvt-13-224-in1k_4_LucyV4	.342
63	convnext_large_imagenet_full_seed-0	.339
64	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.338
65	CLIP-RN50	.331
66	resnet50_imagenet_full	.320
67	inception_v4	.312
68	nasnet_large	.308
69	effnetb1_272x240	.307
70	resnet152_imagenet_full	.289
71	convnext_small_imagenet_full_seed-0	.289
72	cvt_cvt-21-224-in1k_4_LucyV4	.289
73	resnet50_st_wzc_seed7_2_323	.287
74	cv_18_dagger_408_pretrained	.286
75	convnext_base_fb_in1k	.286
76	resnet101	.273
77	resnet50-VITO-8deg-cc	.269
78	ViT_L_32_imagenet1k	.268
79	regnety_032	.268
80	inception_v3	.267
81	resnet50_st_wzc_seed6_2_323	.261
82	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.256
83	inception_v3-tf	.251
84	resnet101_imagenet_full	.250
85	AdvProp_efficientnet-b4	.250
86	AT_efficientnet-b4	.250
87	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.249
88	ViT_B_32_imagenet1k	.247
89	resnet-152_v2-tf	.246
90	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.239
91	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.223
92	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.222
93	xception-keras	.222
94	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.219
95	inception_v4-tf	.214
96	resnet_152_v1	.210
97	nasnet_large-tf	.209
98	deit_large_imagenet_full_seed-0	.207
99	antialias-resnet152	.202
100	inception_v3_pytorch	.195
101	resnet34_imagenet_full	.192
102	convnext_small_imagenet_100_seed-0	.189
103	resnet50_ewconplus_wzc_seed5_2__323	.185
104	resnet50_st_wzc_seed3_2_323	.183
105	resnet_101_v1	.183
106	resnet50-moclr8deg	.182
107	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.181
108	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.179
109	resnet50_ewconplus_wzc_seed6_2_323	.177
110	Res2Net50_26w_4s	.177
111	seresnext50_32x4d	.173
112	resnet50	.170
113	AdvProp_efficientnet-b7	.168
114	densenet-201-keras	.165
115	pnasnet_large_pytorch	.162
116	AT_efficientnet-b2	.161
117	AdvProp_efficientnet-b2	.161
118	alexnet_fov4	.161
119	resnet50_random_l2_perturb	.159
120	resnet-50_v2-tf	.158
121	deit_base_imagenet_full_seed-0	.156
122	efficientnet_b0	.154
123	densenet-201	.153
124	densenet_201_pytorch	.153
125	resnet50_random_linf8_perturb	.151
126	xception	.150
127	grcnn_109	.148
128	vonegrcnn_47e	.147
129	deit_small_imagenet_100_seed-0	.146
130	ReAlnet01	.145
131	resnet50_ewconplus_wzc	.145
132	BiT-S-R152x2	.144
133	convnext_base_fb_in1k_gram_aligned_dinov3	.143
134	resnet18_imagenet_full	.142
135	tv_efficientnet-b1	.141
136	alexnet_fov16	.139
137	custom_model_cv_18_dagger_408	.138
138	antialiased-rnext101_32x8d	.137
139	resnet_50_v1_spiking	.134
140	antialiased-r50	.134
141	densenet-169	.133
142	resnet50_st_wzc_2	.133
143	resnet50_ewconplus_wzc_seed2_2__323	.132
144	AdvProp_efficientnet-b6	.130
145	ReAlnet07	.129
146	alexnet_fov12	.129
147	AdvProp_efficientnet-b8	.125
148	inception_v2-tf	.123
149	alexnet_random_l2_3_perturb	.123
150	AlexNet_SIN_fov4	.121
151	densenet-169-keras	.120
152	grcnn	.120
153	resnet50_hvs_wzc	.119
154	alexnet_random_linf8_perturb	.119
155	resnet-50-pytorch	.118
156	resnet50-sup	.118
157	resnet50_tutorial	.118
158	resnet_50_v1	.118
159	ReAlnet03	.116
160	vonegrcnn_62e_nobn	.114
161	resnet-50_untrained	.114
162	deit_small_imagenet_1_seed-0	.114
163	resnet50_imagenet_10_seed-0	.114
164	deit_small_imagenet_10_seed-0	.114
165	alexnet_linf_8_robust	.112
166	ReAlnet05	.112
167	resnet50-vicregl0p9	.111
168	efficientnet-b7	.111
169	vonegrcnn_52e_full	.110
170	vonegrcnn_47e	.110
171	cornet_s	.109
172	ReAlnet06	.108
173	resnet50_moco_v2	.108
174	alexnet_training_seed_05	.107
175	alexnet_training_seed_01	.107
176	alexnet_training_seed_01_fov4	.107
177	alexnet_training_seed_01_fov12	.107
178	alexnet_training_seed_01_fov16	.107
179	vonegrcnn_62e_nobn	.104
180	BiT-S-R50x3	.101
181	convnext_tiny_sup	.101
182	artResNet18_1	.100
183	resnet50_linf_4_robust	.100
184	nasnet_mobile-tf	.097
185	resnet-34	.094
186	resnet-101_v1-tf	.094
187	ReAlnet04	.094
188	ReAlnet10	.094
189	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.093
190	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.091
191	resnet50-barlow	.091
192	resnet50-SIN_IN	.091
193	resnet50-vitoimagevidnet8	.090
194	ReAlnet08	.090
195	deit_small_imagenet_full_seed-0	.089
196	imagenet_l2_10_0	.089
197	alexnet_l2_3_robust	.087
198	eicircuit_resnet18_bn_20260919	.086
199	vonegrcnn_52e_full	.084
200	convnext_small_imagenet_10_seed-0	.083
201	vgg_19	.083
202	alexnet_early_checkpoint	.081
203	densenet-121-keras	.081
204	voneresnet-50-robust	.080
205	alexnet_robust_correct	.080
206	ReAlnet02	.080
207	voneresnet-50	.080
208	resnet50-SIN_IN_IN	.079
209	resnet_SIN_IN_FT_IN	.079
210	mobilenet_v2_1_3_224	.079
211	resnet-18	.077
212	resnet50_imagenet_100_seed-0	.076
213	densenet-121	.075
214	resnet50_robust_l2_eps1	.074
215	resnet50_linf_8_robust	.073
216	vgg-19-keras	.069
217	inception_v1-tf	.069
218	ReAlnet09	.069
219	resnet50-simclr-vissl	.068
220	mobilenet_v2_0.5_96	.067
221	texture_shape_resnet50_trained_on_SIN	.067
222	convnext_small_imagenet_1_seed-0	.066
223	barlow-twins-resnet50	.066
224	resnet-50_v1-tf	.066
225	mobilenet_v2_0.35_96	.063
226	resnet50-vicregl0p75	.060
227	alexnet_training_seed_02	.060
228	alexnet_training_seed_08	.060
229	alexnet_training_seed_04	.060
230	alexnet_training_seed_10	.060
231	alexnet_ks_torevert	.060
232	alexnet	.060
233	resnet50_l2_3_robust	.060
234	voneresnet-50-non_stochastic	.059
235	eBarlow_lmda_02_1000ep	.059
236	resnet50_simclr	.059
237	mobilenet_v1_0.25_128	.057
238	BiT-S-R101x1	.057
239	inception_v1	.055
240	BiT-S-R152x4	.055
241	efficientnet_b2_imagenet_full	.054
242	vgg-16-keras	.052
243	mobilenet_v2_0_5_224	.052
244	CORnet-S	.051
245	resnet50_eMMCR_Vanilla	.051
246	mobilevit_small	.051
247	nasnet_mobile	.051
248	resnet50_byol	.050
249	mobilenet_v2_1_0_224	.049
250	mobilenet_v2_1-4_224_pytorch	.048
251	mobilenet_v2_1_4_224	.048
252	resnet50-SIN	.048
253	mobilenet_v2_0.35_192	.047
254	mobilenet_v2_1.0_224-tf	.047
255	konkle_alexnetgn_ipcl_ref01_primary_model	.045
256	texture_shape_alexnet_trained_on_SIN	.045
257	regnet_y_400mf	.044
258	vgg_16	.044
259	AlexNet_SIN_fov16	.043
260	AlexNet_SIN_fov12	.043
261	alexnet_training_seed_03	.042
262	alexnet_training_seed_07	.041
263	resnet_50_v1_spiking_l3	.041
264	mobilenet_v2_1.0_192-tf	.040
265	mobilenet_v1_1.0_160	.040
266	alexnet_training_seed_06	.037
267	squeezenet1_1	.034
268	mobilenet_v2_0_75_192	.033
269	mobilenet_v1_0.75_128	.033
270	mobilenet_v2_0.5_128	.032
271	mobilenet_v2_1.0_96	.032
272	AdvProp_efficientnet-b0	.031
273	alexnet_reduced_aliasing_early_checkpoint	.031
274	mobilenet_v2_0.75_160-tf	.030
275	blt_vs	.030
276	mobilenet_v2_0.75_96	.029
277	mobilenet_v2_1_0_192	.029
278	resnet-50-robust	.029
279	imagenet_l2_3_0	.029
280	resnet50_robust_l2_eps3	.029
281	alexnet_training_seed_09	.028
282	mobilenet_v2_1.3_224-tf	.028
283	mobilenet_v2_0.35_224	.027
284	AlexNet_SIN	.027
285	mobilenet_v1_0.75_160	.025
286	mobilenet_v1_0.25_224	.024
287	resnet-152_v1-tf	.023
288	mobilenet_v1_1.0_128	.023
289	resnet_50_v1_spiking_l4	.023
290	resnet50_imagenet_1_seed-0	.022
291	vonealexnet_gaussian_noise_std4_fixed	.022
292	vonealexnet	.022
293	mobilenet_v2_0_75_224	.020
294	mobilenet_v1_0.75_192	.019
295	mobilenet_v2_1_0_128	.019
296	efficientnet_b0_imagenet_full	.019
297	mobilenet_v2_0_75_160	.018
298	resnet_50_v1_spiking_l4a	.018
299	resnet50-vicreg	.017
300	mobilenet_v2_1.0_160-tf	.017
301	BiT-S-R50x1	.016
302	shufflenet_v2_x1_0	.016
303	mobilenet_v1_0.5_128	.015
304	0.5x_resnet-18_LC_w_sh_1_iter	.014
305	mobilenet_v2_0_5_192	.013
306	squeezenet1_0	.013
307	mobilenet_v2_0.75_128	.012
308	resnet50_FractalDB	.012
309	mobilenet_v1_0.25_160	.011
310	mobilenet_v1_1.0_224	.010
311	mobilenet_v2_0.5_192-tf	.009
312	mobilenet_v1_0.5_192	.008
313	BiT-S-R101x3	.007
314	mobilenet_v2_1_0_160	.007
315	mobilenet_v2_1.4_224-tf	.006
316	efficientnet_b1_imagenet_full	.006
317	mobilenet_v2_0.75_224-tf	.006
318	mobilenet_v1_0.5_160	.005
319	mobilenet_v1_1.0_192	.005
320	mobilenet_v2_0.35_128	.005
321	mobilenet_v1_0.75_224	.003
322	mobilenet_v2_0.5_224-tf	.003
323	mobilenet_v2_0.35_160	.002
324	mobilenet_v2_0.75_192-tf	.001
325	mobilenet_v2_0.5_160	.001
326	mobilenet_v1_0.5_224	.001
327	mobilenet_v1_0.25_192	.001
328	mobilenet_v2_1.0_128-tf	.000
329	resnet-18-LC_w_sh_100_iter_conv_init_m
330	ViT-B/32
331	BiT-M-R50x3
332	BiT-M-R50x1
333	BiT-M-R152x2
334	resnet-18-LC_w_sh_1_iter_conv_init_m
335	resnet-18-LC_w_sh_10_iter_m
336	resnet-18-LC_w_sh_100_iter_m
337	resnet-18-LC_w_sh_10_iter_conv_init_m
338	resnet-18-LC_w_sh_1_iter_m
339	FrankRobWobv0
340	CORnetZ_CIFAR10
341	0.5x_resnet-18_LC
342	resnet18-local_aggregation
343	resnet18-deepcluster
344	r3m_resnet18
345	CORnet-Z
346	resnet18-instance_recognition
347	vggface
348	my-model
349	0.5x_resnet-18_LC_w_sh_10_iter
350	r3m_resnet50
351	r3m_resnet34
352	resnet18-colorization
353	resnet-18-LC_m
354	r3m_resnet50_nocrop
355	resnet18-supervised
356	resnet-18-LC_1st_conv_m
357	resnet-18-LC_conv_init_m
358	resnet18-contrastive_multiview
359	0.5x_resnet-18
360	RN50
361	0.5x_resnet-18_LC_w_sh_100_iter
362	RN50
363	resnet18-relative_position
364	resnet18-autoencoder
365	resnet18-simclr
366	ViT_L_32
367	ViT_B_32
368	resnet18-depth_prediction
369	ViT_B_16
370	r3m_resnet34_nocrop
371	resnet18-contrastive_predictive
372	omnivore_swinT
373	resnet-18-LC_untrained
374	resnet-18_untrained
375	resnet-50x2_untrained
376	dcgan
377	unet_entire
378	v1-pyr-nodown
379	CORnetZ_CIFAR10_bs32_20_04
380	pixels
381	dorinet_cornet_z
382	my-model
383	openclip
384	resnet50-cifar
385	resnet50_pretrained_with_retinal_waves
386	resnet50_ImageNet
387	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
388	yudixie_resnet50_random_0_240908
389	ReAlnet10_ALL
390	yudixie_resnet50_imagenet1kpret_0_240908
391	yudixie_resnet50_cat_obj_class_all_latents_0_240908
392	yudixie_resnet50_object_class_0_240908
393	resnet18_imagenet21kP
394	yudixie_resnet50_distance_reg_0_240908
395	ConvLSTM
396	SimVP
397	TAU
398	MIM
399	ReAlnet01_cornet
400	briaai_rmbg_1_4
401	yolos_tiny
402	yudixie_resnet50_imagenet1kpret_0_240312
403	compact_resnet50_V4
404	yudixie_resnet18_rotation_reg_0_240719
405	ReAlnet03_cornet
406	ReAlnet02_cornet
407	vit_base_patch16_dinov3-lvd1689m
408	resnet18_ecoset_full
409	resnet34_ecoset_full
410	resnet50_ecoset_full
411	VOneCORnet-S
412	vit_base_patch14_dinov2-lvd142m
413	vit_base_patch14_reg4_dinov2-lvd142m
414	ReAlnet01_cornet_fov12
415	yudixie_resnet50_distance_rotation_0_240908
416	yudixie_resnet50_rotation_reg_0_240908
417	compact_resnet50_robust_V4
418	resnet152_ecoset_full
419	yudixie_resnet18_distance_translation_0_240719
420	ReAlnet04_cornet
421	yudixie_resnet50_distance_translation_0_240908
422	yudixie_resnet18_translation_rotation_0_240719
423	yudixie_resnet18_distance_rotation_0_240719
424	compact_vgg19_V4
425	yudixie_resnet18_category_class_0_240719
426	compact_resnet50_IT
427	ReAlnet05_cornet
428	yudixie_resnet18_random_0_240719
429	resnet-18_test_m
430	yudixie_resnet18_cat_obj_class_all_latents_0_240719
431	ReAlnet01_cornet_fov4
432	SeLaVi-AVE
433	yudixie_resnet18_distance_translation_rotation_0_240719
434	yudixie_resnet18_object_class_0_240719
435	yudixie_resnet18_translation_reg_0_240719
436	hmax
437	ReAlnet01_cornet_fov16
438	r3d_18
439	ReAlnet06_cornet
440	yudixie_resnet50_translation_reg_0_240908
441	compact_vgg19_IT
442	yudixie_resnet18_distance_reg_0_240719
443	mc3_18
444	ReAlnet01_ALL
445	yudixie_resnet50_translation_rotation_0_240908
446	s3d
447	mvit_v1_b
448	ReAlnet08_cornet
449	ReAlnet03_ALL
450	ReAlnet02_ALL
451	omnivore_swinB
452	ReAlnet04_ALL
453	SeLaVi-Kinetics400
454	ReAlnet09_cornet
455	compact_cornets_V4
456	SeLaVi-VGG-Sound
457	compact_cornets_IT
458	ReAlnet05_ALL
459	SeLaVi-Kinetics-Sound
460	omnivore_swinS
461	ReAlnet06_ALL
462	mvit_v2_s
463	ReAlnet10_cornet
464	r2plus1d_18
465	compact_resnet50_robust_IT
466	resnet101_ecoset_full
467	ReAlnet07_ALL
468	yudixie_resnet50_distance_translation_rotation_0_240908
469	ReAlnet08_ALL
470	yudixie_resnet18_imagenet1kpret_0_240719
471	ReAlnet07_cornet
472	ReAlnet09_ALL
473	resnet50_primary_visual_cortex
474	yudixie_resnet50_category_class_0_240908
475	resnet18_fair
476	pr1_stl10_disentangled_alpha04
477	predictive_coding_pc
478	yudixie_wbh_object_class_scaling_0415_run_0
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
485	vit_large_clip336_openai
486	persistence_mem
487	persistence_memory
488	gabor_filter_entropy_4x4
489	gabor_filter_entropy_4x4_v2_forcedCosine
490	gabor_filter_entropy_4x4_v2
491	gabor_filter_mean_abs_4x4_v2

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

0.45.

Note that scores are relative to this ceiling.

Data: Geirhos2021phasescrambling

Metric: error_consistency