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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	inception_v3	.267
80	resnet50_st_wzc_seed6_2_323	.261
81	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.256
82	inception_v3-tf	.251
83	resnet101_imagenet_full	.250
84	AdvProp_efficientnet-b4	.250
85	AT_efficientnet-b4	.250
86	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.249
87	ViT_B_32_imagenet1k	.247
88	resnet-152_v2-tf	.246
89	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.239
90	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.223
91	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.222
92	xception-keras	.222
93	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.219
94	inception_v4-tf	.214
95	resnet_152_v1	.210
96	nasnet_large-tf	.209
97	deit_large_imagenet_full_seed-0	.207
98	antialias-resnet152	.202
99	inception_v3_pytorch	.195
100	resnet34_imagenet_full	.192
101	convnext_small_imagenet_100_seed-0	.189
102	resnet50_ewconplus_wzc_seed5_2__323	.185
103	resnet50_st_wzc_seed3_2_323	.183
104	resnet_101_v1	.183
105	resnet50-moclr8deg	.182
106	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.181
107	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.179
108	resnet50_ewconplus_wzc_seed6_2_323	.177
109	Res2Net50_26w_4s	.177
110	resnet50	.170
111	AdvProp_efficientnet-b7	.168
112	densenet-201-keras	.165
113	pnasnet_large_pytorch	.162
114	AT_efficientnet-b2	.161
115	AdvProp_efficientnet-b2	.161
116	alexnet_fov4	.161
117	resnet50_random_l2_perturb	.159
118	resnet-50_v2-tf	.158
119	deit_base_imagenet_full_seed-0	.156
120	efficientnet_b0	.154
121	densenet-201	.153
122	densenet_201_pytorch	.153
123	resnet50_random_linf8_perturb	.151
124	xception	.150
125	grcnn_109	.148
126	vonegrcnn_47e	.147
127	deit_small_imagenet_100_seed-0	.146
128	ReAlnet01	.145
129	resnet50_ewconplus_wzc	.145
130	BiT-S-R152x2	.144
131	convnext_base_fb_in1k_gram_aligned_dinov3	.143
132	resnet18_imagenet_full	.142
133	tv_efficientnet-b1	.141
134	alexnet_fov16	.139
135	custom_model_cv_18_dagger_408	.138
136	antialiased-rnext101_32x8d	.137
137	resnet_50_v1_spiking	.134
138	antialiased-r50	.134
139	densenet-169	.133
140	resnet50_st_wzc_2	.133
141	resnet50_ewconplus_wzc_seed2_2__323	.132
142	AdvProp_efficientnet-b6	.130
143	ReAlnet07	.129
144	alexnet_fov12	.129
145	AdvProp_efficientnet-b8	.125
146	inception_v2-tf	.123
147	alexnet_random_l2_3_perturb	.123
148	AlexNet_SIN_fov4	.121
149	densenet-169-keras	.120
150	grcnn	.120
151	resnet50_hvs_wzc	.119
152	alexnet_random_linf8_perturb	.119
153	resnet-50-pytorch	.118
154	resnet50-sup	.118
155	resnet50_tutorial	.118
156	resnet_50_v1	.118
157	ReAlnet03	.116
158	vonegrcnn_62e_nobn	.114
159	resnet-50_untrained	.114
160	deit_small_imagenet_1_seed-0	.114
161	resnet50_imagenet_10_seed-0	.114
162	deit_small_imagenet_10_seed-0	.114
163	alexnet_linf_8_robust	.112
164	ReAlnet05	.112
165	resnet50-vicregl0p9	.111
166	efficientnet-b7	.111
167	vonegrcnn_52e_full	.110
168	vonegrcnn_47e	.110
169	cornet_s	.109
170	ReAlnet06	.108
171	resnet50_moco_v2	.108
172	alexnet_training_seed_05	.107
173	alexnet_training_seed_01	.107
174	alexnet_training_seed_01_fov4	.107
175	alexnet_training_seed_01_fov12	.107
176	alexnet_training_seed_01_fov16	.107
177	vonegrcnn_62e_nobn	.104
178	BiT-S-R50x3	.101
179	convnext_tiny_sup	.101
180	artResNet18_1	.100
181	resnet50_linf_4_robust	.100
182	nasnet_mobile-tf	.097
183	resnet-34	.094
184	resnet-101_v1-tf	.094
185	ReAlnet04	.094
186	ReAlnet10	.094
187	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.093
188	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.091
189	resnet50-barlow	.091
190	resnet50-SIN_IN	.091
191	resnet50-vitoimagevidnet8	.090
192	ReAlnet08	.090
193	deit_small_imagenet_full_seed-0	.089
194	imagenet_l2_10_0	.089
195	alexnet_l2_3_robust	.087
196	vonegrcnn_52e_full	.084
197	convnext_small_imagenet_10_seed-0	.083
198	vgg_19	.083
199	alexnet_early_checkpoint	.081
200	densenet-121-keras	.081
201	voneresnet-50-robust	.080
202	alexnet_robust_correct	.080
203	ReAlnet02	.080
204	voneresnet-50	.080
205	resnet50-SIN_IN_IN	.079
206	resnet_SIN_IN_FT_IN	.079
207	mobilenet_v2_1_3_224	.079
208	resnet-18	.077
209	resnet50_imagenet_100_seed-0	.076
210	densenet-121	.075
211	resnet50_robust_l2_eps1	.074
212	resnet50_linf_8_robust	.073
213	vgg-19-keras	.069
214	inception_v1-tf	.069
215	ReAlnet09	.069
216	resnet50-simclr-vissl	.068
217	mobilenet_v2_0.5_96	.067
218	texture_shape_resnet50_trained_on_SIN	.067
219	convnext_small_imagenet_1_seed-0	.066
220	barlow-twins-resnet50	.066
221	resnet-50_v1-tf	.066
222	mobilenet_v2_0.35_96	.063
223	resnet50-vicregl0p75	.060
224	alexnet_training_seed_02	.060
225	alexnet_training_seed_08	.060
226	alexnet_training_seed_04	.060
227	alexnet_training_seed_10	.060
228	alexnet_ks_torevert	.060
229	alexnet	.060
230	resnet50_l2_3_robust	.060
231	voneresnet-50-non_stochastic	.059
232	eBarlow_lmda_02_1000ep	.059
233	resnet50_simclr	.059
234	mobilenet_v1_0.25_128	.057
235	BiT-S-R101x1	.057
236	inception_v1	.055
237	BiT-S-R152x4	.055
238	efficientnet_b2_imagenet_full	.054
239	vgg-16-keras	.052
240	mobilenet_v2_0_5_224	.052
241	CORnet-S	.051
242	resnet50_eMMCR_Vanilla	.051
243	mobilevit_small	.051
244	nasnet_mobile	.051
245	resnet50_byol	.050
246	mobilenet_v2_1_0_224	.049
247	mobilenet_v2_1-4_224_pytorch	.048
248	mobilenet_v2_1_4_224	.048
249	resnet50-SIN	.048
250	mobilenet_v2_0.35_192	.047
251	mobilenet_v2_1.0_224-tf	.047
252	konkle_alexnetgn_ipcl_ref01_primary_model	.045
253	texture_shape_alexnet_trained_on_SIN	.045
254	regnet_y_400mf	.044
255	vgg_16	.044
256	AlexNet_SIN_fov16	.043
257	AlexNet_SIN_fov12	.043
258	alexnet_training_seed_03	.042
259	alexnet_training_seed_07	.041
260	resnet_50_v1_spiking_l3	.041
261	mobilenet_v2_1.0_192-tf	.040
262	mobilenet_v1_1.0_160	.040
263	alexnet_training_seed_06	.037
264	squeezenet1_1	.034
265	mobilenet_v2_0_75_192	.033
266	mobilenet_v1_0.75_128	.033
267	mobilenet_v2_0.5_128	.032
268	mobilenet_v2_1.0_96	.032
269	AdvProp_efficientnet-b0	.031
270	alexnet_reduced_aliasing_early_checkpoint	.031
271	mobilenet_v2_0.75_160-tf	.030
272	blt_vs	.030
273	mobilenet_v2_0.75_96	.029
274	mobilenet_v2_1_0_192	.029
275	resnet-50-robust	.029
276	imagenet_l2_3_0	.029
277	resnet50_robust_l2_eps3	.029
278	alexnet_training_seed_09	.028
279	mobilenet_v2_1.3_224-tf	.028
280	mobilenet_v2_0.35_224	.027
281	AlexNet_SIN	.027
282	mobilenet_v1_0.75_160	.025
283	mobilenet_v1_0.25_224	.024
284	resnet-152_v1-tf	.023
285	mobilenet_v1_1.0_128	.023
286	resnet_50_v1_spiking_l4	.023
287	resnet50_imagenet_1_seed-0	.022
288	vonealexnet_gaussian_noise_std4_fixed	.022
289	vonealexnet	.022
290	mobilenet_v2_0_75_224	.020
291	mobilenet_v1_0.75_192	.019
292	mobilenet_v2_1_0_128	.019
293	efficientnet_b0_imagenet_full	.019
294	mobilenet_v2_0_75_160	.018
295	resnet_50_v1_spiking_l4a	.018
296	resnet50-vicreg	.017
297	mobilenet_v2_1.0_160-tf	.017
298	BiT-S-R50x1	.016
299	shufflenet_v2_x1_0	.016
300	mobilenet_v1_0.5_128	.015
301	0.5x_resnet-18_LC_w_sh_1_iter	.014
302	mobilenet_v2_0_5_192	.013
303	squeezenet1_0	.013
304	mobilenet_v2_0.75_128	.012
305	resnet50_FractalDB	.012
306	mobilenet_v1_0.25_160	.011
307	mobilenet_v1_1.0_224	.010
308	mobilenet_v2_0.5_192-tf	.009
309	mobilenet_v1_0.5_192	.008
310	BiT-S-R101x3	.007
311	mobilenet_v2_1_0_160	.007
312	mobilenet_v2_1.4_224-tf	.006
313	efficientnet_b1_imagenet_full	.006
314	mobilenet_v2_0.75_224-tf	.006
315	mobilenet_v1_0.5_160	.005
316	mobilenet_v1_1.0_192	.005
317	mobilenet_v2_0.35_128	.005
318	mobilenet_v1_0.75_224	.003
319	mobilenet_v2_0.5_224-tf	.003
320	mobilenet_v2_0.35_160	.002
321	mobilenet_v2_0.75_192-tf	.001
322	mobilenet_v2_0.5_160	.001
323	mobilenet_v1_0.5_224	.001
324	mobilenet_v1_0.25_192	.001
325	mobilenet_v2_1.0_128-tf	.000
326	resnet-18-LC_w_sh_100_iter_conv_init_m
327	ViT-B/32
328	BiT-M-R50x3
329	BiT-M-R50x1
330	BiT-M-R152x2
331	resnet-18-LC_w_sh_1_iter_conv_init_m
332	resnet-18-LC_w_sh_10_iter_m
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_10_iter_conv_init_m
335	resnet-18-LC_w_sh_1_iter_m
336	FrankRobWobv0
337	CORnetZ_CIFAR10
338	0.5x_resnet-18_LC
339	resnet18-local_aggregation
340	resnet18-deepcluster
341	r3m_resnet18
342	CORnet-Z
343	resnet18-instance_recognition
344	vggface
345	my-model
346	0.5x_resnet-18_LC_w_sh_10_iter
347	r3m_resnet50
348	r3m_resnet34
349	resnet18-colorization
350	resnet-18-LC_m
351	r3m_resnet50_nocrop
352	resnet18-supervised
353	resnet-18-LC_1st_conv_m
354	resnet-18-LC_conv_init_m
355	resnet18-contrastive_multiview
356	0.5x_resnet-18
357	RN50
358	0.5x_resnet-18_LC_w_sh_100_iter
359	RN50
360	resnet18-relative_position
361	resnet18-autoencoder
362	resnet18-simclr
363	ViT_L_32
364	ViT_B_32
365	resnet18-depth_prediction
366	ViT_B_16
367	r3m_resnet34_nocrop
368	resnet18-contrastive_predictive
369	omnivore_swinT
370	resnet-18-LC_untrained
371	resnet-18_untrained
372	resnet-50x2_untrained
373	dcgan
374	unet_entire
375	v1-pyr-nodown
376	CORnetZ_CIFAR10_bs32_20_04
377	pixels
378	dorinet_cornet_z
379	my-model
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	ReAlnet10_ALL
387	yudixie_resnet50_imagenet1kpret_0_240908
388	yudixie_resnet50_cat_obj_class_all_latents_0_240908
389	yudixie_resnet50_object_class_0_240908
390	resnet18_imagenet21kP
391	yudixie_resnet50_distance_reg_0_240908
392	ConvLSTM
393	SimVP
394	TAU
395	MIM
396	ReAlnet01_cornet
397	briaai_rmbg_1_4
398	yolos_tiny
399	yudixie_resnet50_imagenet1kpret_0_240312
400	compact_resnet50_V4
401	yudixie_resnet18_rotation_reg_0_240719
402	ReAlnet03_cornet
403	ReAlnet02_cornet
404	vit_base_patch16_dinov3-lvd1689m
405	resnet18_ecoset_full
406	resnet34_ecoset_full
407	resnet50_ecoset_full
408	VOneCORnet-S
409	vit_base_patch14_dinov2-lvd142m
410	vit_base_patch14_reg4_dinov2-lvd142m
411	ReAlnet01_cornet_fov12
412	yudixie_resnet50_distance_rotation_0_240908
413	yudixie_resnet50_rotation_reg_0_240908
414	compact_resnet50_robust_V4
415	resnet152_ecoset_full
416	yudixie_resnet18_distance_translation_0_240719
417	ReAlnet04_cornet
418	yudixie_resnet50_distance_translation_0_240908
419	yudixie_resnet18_translation_rotation_0_240719
420	yudixie_resnet18_distance_rotation_0_240719
421	compact_vgg19_V4
422	yudixie_resnet18_category_class_0_240719
423	compact_resnet50_IT
424	ReAlnet05_cornet
425	yudixie_resnet18_random_0_240719
426	resnet-18_test_m
427	yudixie_resnet18_cat_obj_class_all_latents_0_240719
428	ReAlnet01_cornet_fov4
429	SeLaVi-AVE
430	yudixie_resnet18_distance_translation_rotation_0_240719
431	yudixie_resnet18_object_class_0_240719
432	yudixie_resnet18_translation_reg_0_240719
433	hmax
434	ReAlnet01_cornet_fov16
435	r3d_18
436	ReAlnet06_cornet
437	yudixie_resnet50_translation_reg_0_240908
438	compact_vgg19_IT
439	yudixie_resnet18_distance_reg_0_240719
440	mc3_18
441	ReAlnet01_ALL
442	yudixie_resnet50_translation_rotation_0_240908
443	s3d
444	mvit_v1_b
445	ReAlnet08_cornet
446	ReAlnet03_ALL
447	ReAlnet02_ALL
448	omnivore_swinB
449	ReAlnet04_ALL
450	SeLaVi-Kinetics400
451	ReAlnet09_cornet
452	compact_cornets_V4
453	SeLaVi-VGG-Sound
454	compact_cornets_IT
455	ReAlnet05_ALL
456	SeLaVi-Kinetics-Sound
457	omnivore_swinS
458	ReAlnet06_ALL
459	mvit_v2_s
460	ReAlnet10_cornet
461	r2plus1d_18
462	compact_resnet50_robust_IT
463	resnet101_ecoset_full
464	ReAlnet07_ALL
465	yudixie_resnet50_distance_translation_rotation_0_240908
466	ReAlnet08_ALL
467	yudixie_resnet18_imagenet1kpret_0_240719
468	ReAlnet07_cornet
469	ReAlnet09_ALL
470	resnet50_primary_visual_cortex
471	yudixie_resnet50_category_class_0_240908
472	resnet18_fair
473	pr1_stl10_disentangled_alpha04
474	predictive_coding_pc
475	yudixie_wbh_object_class_scaling_0415_run_0
476	yudixie_wbh_object_class_scaling_0415_run_2
477	yudixie_wbh_object_class_scaling_0415_run_1
478	deepeisnn_ei_cifar10_vgg19
479	deepeisnn_ei_cifar10_resnet18
480	deepeisnn_ei_imagenet_resnet18
481	dinov2_vitl14_reg_lc_featread
482	vit_large_clip336_openai
483	persistence_mem
484	persistence_memory

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