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

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