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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	resnet50_random_l2_perturb	.159
96	resnet-50_v2-tf	.158
97	deit_base_imagenet_full_seed-0	.156
98	efficientnet_b0	.154
99	densenet-201	.153
100	densenet_201_pytorch	.153
101	resnet50_random_linf8_perturb	.151
102	xception	.150
103	grcnn_109	.148
104	vonegrcnn_47e	.147
105	deit_small_imagenet_100_seed-0	.146
106	ReAlnet01	.145
107	BiT-S-R152x2	.144
108	resnet18_imagenet_full	.142
109	tv_efficientnet-b1	.141
110	custom_model_cv_18_dagger_408	.138
111	antialiased-rnext101_32x8d	.137
112	resnet_50_v1_spiking	.134
113	antialiased-r50	.134
114	densenet-169	.133
115	AdvProp_efficientnet-b6	.130
116	ReAlnet07	.129
117	AdvProp_efficientnet-b8	.125
118	inception_v2-tf	.123
119	alexnet_random_l2_3_perturb	.123
120	densenet-169-keras	.120
121	grcnn	.120
122	alexnet_random_linf8_perturb	.119
123	resnet-50-pytorch	.118
124	resnet50-sup	.118
125	resnet50_tutorial	.118
126	resnet_50_v1	.118
127	ReAlnet03	.116
128	vonegrcnn_62e_nobn	.114
129	resnet-50_untrained	.114
130	resnet50-meshes-lt-100-original-pretrained	.114
131	deit_small_imagenet_1_seed-0	.114
132	resnet50_imagenet_10_seed-0	.114
133	deit_small_imagenet_10_seed-0	.114
134	alexnet_linf_8_robust	.112
135	ReAlnet05	.112
136	resnet50-vicregl0p9	.111
137	efficientnet-b7	.111
138	vonegrcnn_52e_full	.110
139	vonegrcnn_47e	.110
140	cornet_s	.109
141	ReAlnet06	.108
142	resnet50_moco_v2	.108
143	alexnet_training_seed_05	.107
144	alexnet_training_seed_01	.107
145	vonegrcnn_62e_nobn	.104
146	BiT-S-R50x3	.101
147	convnext_tiny_sup	.101
148	artResNet18_1	.100
149	resnet50_linf_4_robust	.100
150	nasnet_mobile-tf	.097
151	resnet-34	.094
152	resnet-101_v1-tf	.094
153	ReAlnet04	.094
154	ReAlnet10	.094
155	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.093
156	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.091
157	resnet50-barlow	.091
158	resnet50-SIN_IN	.091
159	resnet50-vitoimagevidnet8	.090
160	ReAlnet08	.090
161	deit_small_imagenet_full_seed-0	.089
162	imagenet_l2_10_0	.089
163	alexnet_l2_3_robust	.087
164	alexnet	.084
165	vonegrcnn_52e_full	.084
166	convnext_small_imagenet_10_seed-0	.083
167	vgg_19	.083
168	alexnet_early_checkpoint	.081
169	densenet-121-keras	.081
170	voneresnet-50-robust	.080
171	alexnet_robust_correct	.080
172	ReAlnet02	.080
173	voneresnet-50	.080
174	resnet50-SIN_IN_IN	.079
175	resnet_SIN_IN_FT_IN	.079
176	mobilenet_v2_1_3_224	.079
177	resnet-18	.077
178	resnet50_imagenet_100_seed-0	.076
179	densenet-121	.075
180	resnet50_robust_l2_eps1	.074
181	resnet50_linf_8_robust	.073
182	vgg-19-keras	.069
183	inception_v1-tf	.069
184	ReAlnet09	.069
185	resnet50-simclr-vissl	.068
186	mobilenet_v2_0.5_96	.067
187	texture_shape_resnet50_trained_on_SIN	.067
188	convnext_small_imagenet_1_seed-0	.066
189	barlow-twins-resnet50	.066
190	resnet-50_v1-tf	.066
191	mobilenet_v2_0.35_96	.063
192	resnet50-vicregl0p75	.060
193	alexnet_training_seed_02	.060
194	alexnet_training_seed_08	.060
195	alexnet_training_seed_04	.060
196	alexnet_training_seed_10	.060
197	alexnet_ks_torevert	.060
198	alexnet-baseline	.060
199	alexnet	.060
200	alexnet	.060
201	alexnet	.060
202	alexnet	.060
203	alexnet	.060
204	alexnet	.060
205	alexnet	.060
206	alexnet	.060
207	alexnet	.060
208	resnet50_l2_3_robust	.060
209	voneresnet-50-non_stochastic	.059
210	resnet50_simclr	.059
211	mobilenet_v1_0.25_128	.057
212	BiT-S-R101x1	.057
213	inception_v1	.055
214	BiT-S-R152x4	.055
215	efficientnet_b2_imagenet_full	.054
216	vgg-16-keras	.052
217	mobilenet_v2_0_5_224	.052
218	CORnet-S	.051
219	resnet50_eMMCR_Vanilla	.051
220	mobilevit_small	.051
221	nasnet_mobile	.051
222	resnet50_byol	.050
223	mobilenet_v2_1_0_224	.049
224	mobilenet_v2_1-4_224_pytorch	.048
225	mobilenet_v2_1_4_224	.048
226	resnet50-SIN	.048
227	mobilenet_v2_0.35_192	.047
228	mobilenet_v2_1.0_224-tf	.047
229	konkle_alexnetgn_ipcl_ref01_primary_model	.045
230	texture_shape_alexnet_trained_on_SIN	.045
231	regnet_y_400mf	.044
232	vgg_16	.044
233	alexnet_training_seed_03	.042
234	alexnet_training_seed_07	.041
235	resnet_50_v1_spiking_l3	.041
236	mobilenet_v2_1.0_192-tf	.040
237	mobilenet_v1_1.0_160	.040
238	alexnet_training_seed_06	.037
239	squeezenet1_1	.034
240	resnet50-meshes-lt-100-original-scratch	.033
241	mobilenet_v2_0_75_192	.033
242	mobilenet_v1_0.75_128	.033
243	mobilenet_v2_0.5_128	.032
244	mobilenet_v2_1.0_96	.032
245	AdvProp_efficientnet-b0	.031
246	alexnet_reduced_aliasing_early_checkpoint	.031
247	mobilenet_v2_0.75_160-tf	.030
248	blt_vs	.030
249	mobilenet_v2_0.75_96	.029
250	mobilenet_v2_1_0_192	.029
251	resnet-50-robust	.029
252	imagenet_l2_3_0	.029
253	resnet50_robust_l2_eps3	.029
254	alexnet_training_seed_09	.028
255	mobilenet_v2_1.3_224-tf	.028
256	mobilenet_v2_0.35_224	.027
257	AlexNet_SIN	.027
258	mobilenet_v1_0.75_160	.025
259	mobilenet_v1_0.25_224	.024
260	resnet-152_v1-tf	.023
261	mobilenet_v1_1.0_128	.023
262	resnet_50_v1_spiking_l4	.023
263	resnet50_imagenet_1_seed-0	.022
264	vonealexnet_gaussian_noise_std4_fixed	.022
265	mobilenet_v2_0_75_224	.020
266	mobilenet_v1_0.75_192	.019
267	mobilenet_v2_1_0_128	.019
268	efficientnet_b0_imagenet_full	.019
269	mobilenet_v2_0_75_160	.018
270	resnet_50_v1_spiking_l4a	.018
271	resnet50-vicreg	.017
272	mobilenet_v2_1.0_160-tf	.017
273	BiT-S-R50x1	.016
274	shufflenet_v2_x1_0	.016
275	mobilenet_v1_0.5_128	.015
276	0.5x_resnet-18_LC_w_sh_1_iter	.014
277	mobilenet_v2_0_5_192	.013
278	squeezenet1_0	.013
279	mobilenet_v2_0.75_128	.012
280	resnet50_FractalDB	.012
281	mobilenet_v1_0.25_160	.011
282	mobilenet_v1_1.0_224	.010
283	mobilenet_v2_0.5_192-tf	.009
284	mobilenet_v1_0.5_192	.008
285	BiT-S-R101x3	.007
286	mobilenet_v2_1_0_160	.007
287	mobilenet_v2_1.4_224-tf	.006
288	efficientnet_b1_imagenet_full	.006
289	mobilenet_v2_0.75_224-tf	.006
290	mobilenet_v1_0.5_160	.005
291	mobilenet_v1_1.0_192	.005
292	mobilenet_v2_0.35_128	.005
293	mobilenet_v1_0.75_224	.003
294	mobilenet_v2_0.5_224-tf	.003
295	mobilenet_v2_0.35_160	.002
296	mobilenet_v2_0.75_192-tf	.001
297	mobilenet_v2_0.5_160	.001
298	mobilenet_v1_0.5_224	.001
299	mobilenet_v1_0.25_192	.001
300	mobilenet_v2_1.0_128-tf	.000
301	resnet-18-LC_w_sh_100_iter_conv_init_m
302	ViT-B/32
303	BiT-M-R50x3
304	BiT-M-R50x1
305	BiT-M-R152x2
306	resnet-18-LC_w_sh_1_iter_conv_init_m
307	resnet-18-LC_w_sh_10_iter_m
308	resnet-18-LC_w_sh_100_iter_m
309	resnet-18-LC_w_sh_10_iter_conv_init_m
310	resnet-18-LC_w_sh_1_iter_m
311	FrankRobWobv0
312	CORnetZ_CIFAR10
313	0.5x_resnet-18_LC
314	resnet18-local_aggregation
315	resnet18-deepcluster
316	r3m_resnet18
317	CORnet-Z
318	resnet18-instance_recognition
319	vggface
320	my-model
321	0.5x_resnet-18_LC_w_sh_10_iter
322	r3m_resnet50
323	r3m_resnet34
324	resnet18-colorization
325	resnet-18-LC_m
326	r3m_resnet50_nocrop
327	resnet18-supervised
328	resnet-18-LC_1st_conv_m
329	resnet-18-LC_conv_init_m
330	resnet18-contrastive_multiview
331	0.5x_resnet-18
332	RN50
333	0.5x_resnet-18_LC_w_sh_100_iter
334	RN50
335	resnet18-relative_position
336	resnet18-autoencoder
337	resnet18-simclr
338	ViT_L_32
339	ViT_B_32
340	resnet18-depth_prediction
341	ViT_B_16
342	r3m_resnet34_nocrop
343	resnet18-contrastive_predictive
344	omnivore_swinT
345	resnet-18-LC_untrained
346	resnet-18_untrained
347	resnet-50x2_untrained
348	dcgan
349	unet_entire
350	v1-pyr-nodown
351	CORnetZ_CIFAR10_bs32_20_04
352	pixels
353	dorinet_cornet_z
354	my-model
355	resnet50
356	openclip
357	resnet50-cifar
358	resnet50_pretrained_with_retinal_waves
359	resnet50_ImageNet
360	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
361	yudixie_resnet50_random_0_240908
362	ReAlnet10_ALL
363	yudixie_resnet50_imagenet1kpret_0_240908
364	yudixie_resnet50_cat_obj_class_all_latents_0_240908
365	yudixie_resnet50_object_class_0_240908
366	resnet18_imagenet21kP
367	yudixie_resnet50_distance_reg_0_240908
368	ConvLSTM
369	SimVP
370	TAU
371	MIM
372	ReAlnet01_cornet
373	briaai_rmbg_1_4
374	yolos_tiny
375	yudixie_resnet50_imagenet1kpret_0_240312
376	yudixie_resnet18_rotation_reg_0_240719
377	ReAlnet03_cornet
378	ReAlnet02_cornet
379	resnet18_ecoset_full
380	resnet34_ecoset_full
381	resnet50_ecoset_full
382	VOneCORnet-S
383	yudixie_resnet50_distance_rotation_0_240908
384	yudixie_resnet50_rotation_reg_0_240908
385	resnet152_ecoset_full
386	yudixie_resnet18_distance_translation_0_240719
387	ReAlnet04_cornet
388	yudixie_resnet50_distance_translation_0_240908
389	yudixie_resnet18_translation_rotation_0_240719
390	yudixie_resnet18_distance_rotation_0_240719
391	yudixie_resnet18_category_class_0_240719
392	ReAlnet05_cornet
393	yudixie_resnet18_random_0_240719
394	resnet-18_test_m
395	yudixie_resnet18_cat_obj_class_all_latents_0_240719
396	SeLaVi-AVE
397	yudixie_resnet18_distance_translation_rotation_0_240719
398	yudixie_resnet18_object_class_0_240719
399	yudixie_resnet18_translation_reg_0_240719
400	hmax
401	r3d_18
402	ReAlnet06_cornet
403	yudixie_resnet50_translation_reg_0_240908
404	yudixie_resnet18_distance_reg_0_240719
405	mc3_18
406	ReAlnet01_ALL
407	yudixie_resnet50_translation_rotation_0_240908
408	s3d
409	mvit_v1_b
410	ReAlnet08_cornet
411	ReAlnet03_ALL
412	ReAlnet02_ALL
413	omnivore_swinB
414	ReAlnet04_ALL
415	SeLaVi-Kinetics400
416	ReAlnet09_cornet
417	SeLaVi-VGG-Sound
418	ReAlnet05_ALL
419	SeLaVi-Kinetics-Sound
420	omnivore_swinS
421	ReAlnet06_ALL
422	mvit_v2_s
423	ReAlnet10_cornet
424	r2plus1d_18
425	resnet101_ecoset_full
426	ReAlnet07_ALL
427	yudixie_resnet50_distance_translation_rotation_0_240908
428	ReAlnet08_ALL
429	yudixie_resnet18_imagenet1kpret_0_240719
430	ReAlnet07_cornet
431	ReAlnet09_ALL
432	resnet50_primary_visual_cortex
433	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