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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	convnext_base:clip_laiona_augreg_ft_in1k_384	.621
6	vit_large_patch14_clip_224:laion2b_ft_in1k	.612
7	convnext_large:fb_in22k_ft_in1k	.606
8	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.600
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.599
10	cvt_cvt-13-384-in1k_4_LucyV4	.596
11	convnext_base_fb_in22k_ft_in1k	.588
12	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.584
13	vit_large_patch14_clip_224:openai_ft_in1k	.576
14	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.576
15	resnet_152_v2	.572
16	fixres_resnext101_32x48d_wsl	.567
17	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.562
18	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.561
19	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.521
20	resnet-152_v2_pytorch	.512
21	SWSL_resnext101_32x8d	.511
22	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.511
23	vision_transformer_vit_large_patch16_224	.506
24	vit_base_patch16_clip_224:openai_ft_in1k	.503
25	convnext_xlarge:fb_in22k_ft_in1k	.499
26	resnext101_32x32d_wsl	.478
27	resnext101_32x48d_wsl	.462
28	convnext_tiny:in12k_ft_in1k	.459
29	resnet_50_v2	.448
30	vit_relpos_base_patch16_clsgap_224:sw_in1k	.442
31	deit3_base_patch16_224_fb_in1k	.433
32	resnext101_32x16d_wsl	.431
33	SWSL_resnet50	.419
34	effnetv2m_custom384	.416
35	CLIP_resnet50	.404
36	CLIP_resnet50_float32	.404
37	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.383
38	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.382
39	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.378
40	pnasnet_large_halfPrecision	.376
41	pnasnet_large	.376
42	convnext_tiny_imagenet_full_seed-0	.372
43	CLIP_ViT-B_32	.367
44	resnet_101_v2	.364
45	ViT_B_16_imagenet1k	.363
46	resnext101_32x8d_wsl	.363
47	pnasnet_large-tf	.361
48	convnext_femto_ols:d1_in1k	.350
49	convnext_base_imagenet_full_seed-0	.347
50	deit3_base_patch16_224_fb_in22k_ft_in1k	.346
51	focalnet_tiny_lrf_in1k	.344
52	cvt_cvt-13-224-in1k_4_LucyV4	.342
53	convnext_large_imagenet_full_seed-0	.339
54	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.338
55	CLIP-RN50	.331
56	resnet50_imagenet_full	.320
57	inception_v4	.312
58	nasnet_large	.308
59	effnetb1_272x240	.307
60	resnet152_imagenet_full	.289
61	convnext_small_imagenet_full_seed-0	.289
62	cvt_cvt-21-224-in1k_4_LucyV4	.289
63	resnet50_st_wzc_seed7_2_323	.287
64	cv_18_dagger_408_pretrained	.286
65	convnext_base_fb_in1k	.286
66	resnet101	.273
67	resnet50_st_wzc_seed5_2_323	.272
68	resnet50-VITO-8deg-cc	.269
69	ViT_L_32_imagenet1k	.268
70	inception_v3	.267
71	resnet50_st_wzc_seed6_2_323	.261
72	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.256
73	inception_v3-tf	.251
74	resnet101_imagenet_full	.250
75	AdvProp_efficientnet-b4	.250
76	AT_efficientnet-b4	.250
77	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.249
78	ViT_B_32_imagenet1k	.247
79	resnet-152_v2-tf	.246
80	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.239
81	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.223
82	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.222
83	xception-keras	.222
84	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.219
85	resnet50_ewconplus_noseed_2_323	.218
86	inception_v4-tf	.214
87	resnet_152_v1	.210
88	nasnet_large-tf	.209
89	deit_large_imagenet_full_seed-0	.207
90	resnet50_st_wzc_noseed_2_323	.207
91	antialias-resnet152	.202
92	inception_v3_pytorch	.195
93	resnet50_st_wzc_seed4_2_323	.193
94	resnet34_imagenet_full	.192
95	convnext_small_imagenet_100_seed-0	.189
96	resnet50_st_wzc_2_323	.188
97	resnet50_ewconplus_wzc_seed5_2__323	.185
98	resnet50_st_wzc_seed3_2_323	.183
99	resnet_101_v1	.183
100	resnet50-moclr8deg	.182
101	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.181
102	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.179
103	resnet50_ewconplus_wzc_seed6_2_323	.177
104	Res2Net50_26w_4s	.177
105	resnet50	.170
106	AdvProp_efficientnet-b7	.168
107	densenet-201-keras	.165
108	pnasnet_large_pytorch	.162
109	AT_efficientnet-b2	.161
110	AdvProp_efficientnet-b2	.161
111	alexnet_fov4	.161
112	resnet50_random_l2_perturb	.159
113	resnet-50_v2-tf	.158
114	deit_base_imagenet_full_seed-0	.156
115	efficientnet_b0	.154
116	densenet-201	.153
117	densenet_201_pytorch	.153
118	resnet50_random_linf8_perturb	.151
119	xception	.150
120	grcnn_109	.148
121	vonegrcnn_47e	.147
122	deit_small_imagenet_100_seed-0	.146
123	ReAlnet01	.145
124	resnet50_ewconplus_wzc	.145
125	resnet50_ewconplus_wzc_seed4_2_323	.145
126	BiT-S-R152x2	.144
127	convnext_base_fb_in1k_gram_aligned_dinov3	.143
128	resnet18_imagenet_full	.142
129	tv_efficientnet-b1	.141
130	alexnet_fov16	.139
131	custom_model_cv_18_dagger_408	.138
132	antialiased-rnext101_32x8d	.137
133	resnet_50_v1_spiking	.134
134	antialiased-r50	.134
135	densenet-169	.133
136	resnet50_st_wzc_2	.133
137	resnet50_ewconplus_wzc_seed2_2__323	.132
138	AdvProp_efficientnet-b6	.130
139	ReAlnet07	.129
140	alexnet_fov12	.129
141	AdvProp_efficientnet-b8	.125
142	inception_v2-tf	.123
143	alexnet_random_l2_3_perturb	.123
144	AlexNet_SIN_fov4	.121
145	densenet-169-keras	.120
146	grcnn	.120
147	alexnet_random_linf8_perturb	.119
148	resnet-50-pytorch	.118
149	resnet50-sup	.118
150	resnet50_tutorial	.118
151	resnet_50_v1	.118
152	ReAlnet03	.116
153	vonegrcnn_62e_nobn	.114
154	resnet-50_untrained	.114
155	resnet50-meshes-lt-100-original-pretrained	.114
156	deit_small_imagenet_1_seed-0	.114
157	resnet50_imagenet_10_seed-0	.114
158	deit_small_imagenet_10_seed-0	.114
159	alexnet_linf_8_robust	.112
160	ReAlnet05	.112
161	resnet50-vicregl0p9	.111
162	efficientnet-b7	.111
163	vonegrcnn_52e_full	.110
164	vonegrcnn_47e	.110
165	cornet_s	.109
166	ReAlnet06	.108
167	resnet50_moco_v2	.108
168	alexnet_training_seed_05	.107
169	alexnet_training_seed_01	.107
170	alexnet_training_seed_01_fov4	.107
171	alexnet_training_seed_01_fov12	.107
172	alexnet_training_seed_01_fov16	.107
173	vonegrcnn_62e_nobn	.104
174	BiT-S-R50x3	.101
175	convnext_tiny_sup	.101
176	artResNet18_1	.100
177	resnet50_linf_4_robust	.100
178	nasnet_mobile-tf	.097
179	resnet-34	.094
180	resnet-101_v1-tf	.094
181	ReAlnet04	.094
182	ReAlnet10	.094
183	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.093
184	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.091
185	resnet50-barlow	.091
186	resnet50-SIN_IN	.091
187	resnet50-vitoimagevidnet8	.090
188	ReAlnet08	.090
189	deit_small_imagenet_full_seed-0	.089
190	imagenet_l2_10_0	.089
191	alexnet_l2_3_robust	.087
192	vonegrcnn_52e_full	.084
193	convnext_small_imagenet_10_seed-0	.083
194	vgg_19	.083
195	alexnet_early_checkpoint	.081
196	densenet-121-keras	.081
197	voneresnet-50-robust	.080
198	alexnet_robust_correct	.080
199	ReAlnet02	.080
200	voneresnet-50	.080
201	resnet50-SIN_IN_IN	.079
202	resnet_SIN_IN_FT_IN	.079
203	mobilenet_v2_1_3_224	.079
204	resnet-18	.077
205	resnet50_imagenet_100_seed-0	.076
206	densenet-121	.075
207	resnet50_robust_l2_eps1	.074
208	resnet50_linf_8_robust	.073
209	vgg-19-keras	.069
210	inception_v1-tf	.069
211	ReAlnet09	.069
212	resnet50-simclr-vissl	.068
213	mobilenet_v2_0.5_96	.067
214	texture_shape_resnet50_trained_on_SIN	.067
215	convnext_small_imagenet_1_seed-0	.066
216	barlow-twins-resnet50	.066
217	resnet-50_v1-tf	.066
218	mobilenet_v2_0.35_96	.063
219	resnet50-vicregl0p75	.060
220	alexnet_training_seed_02	.060
221	alexnet_training_seed_08	.060
222	alexnet_training_seed_04	.060
223	alexnet_training_seed_10	.060
224	alexnet_ks_torevert	.060
225	alexnet	.060
226	resnet50_l2_3_robust	.060
227	voneresnet-50-non_stochastic	.059
228	eBarlow_lmda_02_1000ep	.059
229	resnet50_simclr	.059
230	mobilenet_v1_0.25_128	.057
231	BiT-S-R101x1	.057
232	inception_v1	.055
233	BiT-S-R152x4	.055
234	efficientnet_b2_imagenet_full	.054
235	vgg-16-keras	.052
236	mobilenet_v2_0_5_224	.052
237	CORnet-S	.051
238	resnet50_eMMCR_Vanilla	.051
239	mobilevit_small	.051
240	nasnet_mobile	.051
241	resnet50_byol	.050
242	mobilenet_v2_1_0_224	.049
243	mobilenet_v2_1-4_224_pytorch	.048
244	mobilenet_v2_1_4_224	.048
245	resnet50-SIN	.048
246	mobilenet_v2_0.35_192	.047
247	mobilenet_v2_1.0_224-tf	.047
248	konkle_alexnetgn_ipcl_ref01_primary_model	.045
249	texture_shape_alexnet_trained_on_SIN	.045
250	regnet_y_400mf	.044
251	vgg_16	.044
252	AlexNet_SIN_fov16	.043
253	AlexNet_SIN_fov12	.043
254	alexnet_training_seed_03	.042
255	alexnet_training_seed_07	.041
256	resnet_50_v1_spiking_l3	.041
257	mobilenet_v2_1.0_192-tf	.040
258	mobilenet_v1_1.0_160	.040
259	alexnet_training_seed_06	.037
260	squeezenet1_1	.034
261	resnet50-meshes-lt-100-original-scratch	.033
262	mobilenet_v2_0_75_192	.033
263	mobilenet_v1_0.75_128	.033
264	mobilenet_v2_0.5_128	.032
265	mobilenet_v2_1.0_96	.032
266	AdvProp_efficientnet-b0	.031
267	alexnet_reduced_aliasing_early_checkpoint	.031
268	mobilenet_v2_0.75_160-tf	.030
269	blt_vs	.030
270	mobilenet_v2_0.75_96	.029
271	mobilenet_v2_1_0_192	.029
272	resnet-50-robust	.029
273	imagenet_l2_3_0	.029
274	resnet50_robust_l2_eps3	.029
275	alexnet_training_seed_09	.028
276	mobilenet_v2_1.3_224-tf	.028
277	mobilenet_v2_0.35_224	.027
278	AlexNet_SIN	.027
279	mobilenet_v1_0.75_160	.025
280	mobilenet_v1_0.25_224	.024
281	resnet-152_v1-tf	.023
282	mobilenet_v1_1.0_128	.023
283	resnet_50_v1_spiking_l4	.023
284	resnet50_imagenet_1_seed-0	.022
285	vonealexnet_gaussian_noise_std4_fixed	.022
286	mobilenet_v2_0_75_224	.020
287	mobilenet_v1_0.75_192	.019
288	mobilenet_v2_1_0_128	.019
289	efficientnet_b0_imagenet_full	.019
290	mobilenet_v2_0_75_160	.018
291	resnet_50_v1_spiking_l4a	.018
292	resnet50-vicreg	.017
293	mobilenet_v2_1.0_160-tf	.017
294	BiT-S-R50x1	.016
295	shufflenet_v2_x1_0	.016
296	mobilenet_v1_0.5_128	.015
297	0.5x_resnet-18_LC_w_sh_1_iter	.014
298	mobilenet_v2_0_5_192	.013
299	squeezenet1_0	.013
300	mobilenet_v2_0.75_128	.012
301	resnet50_FractalDB	.012
302	mobilenet_v1_0.25_160	.011
303	mobilenet_v1_1.0_224	.010
304	mobilenet_v2_0.5_192-tf	.009
305	mobilenet_v1_0.5_192	.008
306	BiT-S-R101x3	.007
307	mobilenet_v2_1_0_160	.007
308	mobilenet_v2_1.4_224-tf	.006
309	efficientnet_b1_imagenet_full	.006
310	mobilenet_v2_0.75_224-tf	.006
311	mobilenet_v1_0.5_160	.005
312	mobilenet_v1_1.0_192	.005
313	mobilenet_v2_0.35_128	.005
314	mobilenet_v1_0.75_224	.003
315	mobilenet_v2_0.5_224-tf	.003
316	mobilenet_v2_0.35_160	.002
317	mobilenet_v2_0.75_192-tf	.001
318	mobilenet_v2_0.5_160	.001
319	mobilenet_v1_0.5_224	.001
320	mobilenet_v1_0.25_192	.001
321	mobilenet_v2_1.0_128-tf	.000
322	resnet-18-LC_w_sh_100_iter_conv_init_m
323	ViT-B/32
324	BiT-M-R50x3
325	BiT-M-R50x1
326	BiT-M-R152x2
327	resnet-18-LC_w_sh_1_iter_conv_init_m
328	resnet-18-LC_w_sh_10_iter_m
329	resnet-18-LC_w_sh_100_iter_m
330	resnet-18-LC_w_sh_10_iter_conv_init_m
331	resnet-18-LC_w_sh_1_iter_m
332	FrankRobWobv0
333	CORnetZ_CIFAR10
334	0.5x_resnet-18_LC
335	resnet18-local_aggregation
336	resnet18-deepcluster
337	r3m_resnet18
338	CORnet-Z
339	resnet18-instance_recognition
340	vggface
341	my-model
342	0.5x_resnet-18_LC_w_sh_10_iter
343	r3m_resnet50
344	r3m_resnet34
345	resnet18-colorization
346	resnet-18-LC_m
347	r3m_resnet50_nocrop
348	resnet18-supervised
349	resnet-18-LC_1st_conv_m
350	resnet-18-LC_conv_init_m
351	resnet18-contrastive_multiview
352	0.5x_resnet-18
353	RN50
354	0.5x_resnet-18_LC_w_sh_100_iter
355	RN50
356	resnet18-relative_position
357	resnet18-autoencoder
358	resnet18-simclr
359	ViT_L_32
360	ViT_B_32
361	resnet18-depth_prediction
362	ViT_B_16
363	r3m_resnet34_nocrop
364	resnet18-contrastive_predictive
365	omnivore_swinT
366	resnet-18-LC_untrained
367	resnet-18_untrained
368	resnet-50x2_untrained
369	dcgan
370	unet_entire
371	v1-pyr-nodown
372	CORnetZ_CIFAR10_bs32_20_04
373	pixels
374	dorinet_cornet_z
375	my-model
376	resnet50
377	openclip
378	resnet50-cifar
379	resnet50_pretrained_with_retinal_waves
380	resnet50_ImageNet
381	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
382	yudixie_resnet50_random_0_240908
383	ReAlnet10_ALL
384	yudixie_resnet50_imagenet1kpret_0_240908
385	yudixie_resnet50_cat_obj_class_all_latents_0_240908
386	yudixie_resnet50_object_class_0_240908
387	resnet18_imagenet21kP
388	yudixie_resnet50_distance_reg_0_240908
389	ConvLSTM
390	SimVP
391	TAU
392	MIM
393	ReAlnet01_cornet
394	briaai_rmbg_1_4
395	yolos_tiny
396	yudixie_resnet50_imagenet1kpret_0_240312
397	compact_resnet50_V4
398	yudixie_resnet18_rotation_reg_0_240719
399	ReAlnet03_cornet
400	ReAlnet02_cornet
401	vit_base_patch16_dinov3-lvd1689m
402	resnet18_ecoset_full
403	resnet34_ecoset_full
404	resnet50_ecoset_full
405	VOneCORnet-S
406	vit_base_patch14_dinov2-lvd142m
407	vit_base_patch14_reg4_dinov2-lvd142m
408	ReAlnet01_cornet_fov12
409	yudixie_resnet50_distance_rotation_0_240908
410	yudixie_resnet50_rotation_reg_0_240908
411	compact_resnet50_robust_V4
412	resnet152_ecoset_full
413	yudixie_resnet18_distance_translation_0_240719
414	ReAlnet04_cornet
415	yudixie_resnet50_distance_translation_0_240908
416	yudixie_resnet18_translation_rotation_0_240719
417	yudixie_resnet18_distance_rotation_0_240719
418	compact_vgg19_V4
419	yudixie_resnet18_category_class_0_240719
420	compact_resnet50_IT
421	ReAlnet05_cornet
422	yudixie_resnet18_random_0_240719
423	resnet-18_test_m
424	yudixie_resnet18_cat_obj_class_all_latents_0_240719
425	aughost_fair_resnet18
426	ReAlnet01_cornet_fov4
427	SeLaVi-AVE
428	yudixie_resnet18_distance_translation_rotation_0_240719
429	yudixie_resnet18_object_class_0_240719
430	yudixie_resnet18_translation_reg_0_240719
431	hmax
432	ReAlnet01_cornet_fov16
433	r3d_18
434	ReAlnet06_cornet
435	yudixie_resnet50_translation_reg_0_240908
436	compact_vgg19_IT
437	yudixie_resnet18_distance_reg_0_240719
438	mc3_18
439	ReAlnet01_ALL
440	yudixie_resnet50_translation_rotation_0_240908
441	s3d
442	mvit_v1_b
443	ReAlnet08_cornet
444	ReAlnet03_ALL
445	ReAlnet02_ALL
446	omnivore_swinB
447	ReAlnet04_ALL
448	SeLaVi-Kinetics400
449	ReAlnet09_cornet
450	compact_cornets_V4
451	SeLaVi-VGG-Sound
452	compact_cornets_IT
453	ReAlnet05_ALL
454	SeLaVi-Kinetics-Sound
455	omnivore_swinS
456	ReAlnet06_ALL
457	mvit_v2_s
458	ReAlnet10_cornet
459	r2plus1d_18
460	compact_resnet50_robust_IT
461	resnet101_ecoset_full
462	ReAlnet07_ALL
463	yudixie_resnet50_distance_translation_rotation_0_240908
464	ReAlnet08_ALL
465	yudixie_resnet18_imagenet1kpret_0_240719
466	ReAlnet07_cornet
467	ReAlnet09_ALL
468	resnet50_primary_visual_cortex
469	yudixie_resnet50_category_class_0_240908
470	resnet18_fair
471	pr1_stl10_disentangled_alpha04
472	predictive_coding_pc

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