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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	resnet50_ewconplus_wzc	.145
108	BiT-S-R152x2	.144
109	resnet18_imagenet_full	.142
110	tv_efficientnet-b1	.141
111	custom_model_cv_18_dagger_408	.138
112	antialiased-rnext101_32x8d	.137
113	resnet_50_v1_spiking	.134
114	antialiased-r50	.134
115	densenet-169	.133
116	AdvProp_efficientnet-b6	.130
117	ReAlnet07	.129
118	AdvProp_efficientnet-b8	.125
119	inception_v2-tf	.123
120	alexnet_random_l2_3_perturb	.123
121	densenet-169-keras	.120
122	grcnn	.120
123	alexnet_random_linf8_perturb	.119
124	resnet-50-pytorch	.118
125	resnet50-sup	.118
126	resnet50_tutorial	.118
127	resnet_50_v1	.118
128	ReAlnet03	.116
129	vonegrcnn_62e_nobn	.114
130	resnet-50_untrained	.114
131	resnet50-meshes-lt-100-original-pretrained	.114
132	deit_small_imagenet_1_seed-0	.114
133	resnet50_imagenet_10_seed-0	.114
134	deit_small_imagenet_10_seed-0	.114
135	alexnet_linf_8_robust	.112
136	ReAlnet05	.112
137	resnet50-vicregl0p9	.111
138	efficientnet-b7	.111
139	vonegrcnn_52e_full	.110
140	vonegrcnn_47e	.110
141	cornet_s	.109
142	ReAlnet06	.108
143	resnet50_moco_v2	.108
144	alexnet_training_seed_05	.107
145	alexnet_training_seed_01	.107
146	vonegrcnn_62e_nobn	.104
147	BiT-S-R50x3	.101
148	convnext_tiny_sup	.101
149	artResNet18_1	.100
150	resnet50_linf_4_robust	.100
151	nasnet_mobile-tf	.097
152	resnet-34	.094
153	resnet-101_v1-tf	.094
154	ReAlnet04	.094
155	ReAlnet10	.094
156	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.093
157	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.091
158	resnet50-barlow	.091
159	resnet50-SIN_IN	.091
160	resnet50-vitoimagevidnet8	.090
161	ReAlnet08	.090
162	deit_small_imagenet_full_seed-0	.089
163	imagenet_l2_10_0	.089
164	alexnet_l2_3_robust	.087
165	alexnet	.084
166	vonegrcnn_52e_full	.084
167	convnext_small_imagenet_10_seed-0	.083
168	vgg_19	.083
169	alexnet_early_checkpoint	.081
170	densenet-121-keras	.081
171	voneresnet-50-robust	.080
172	alexnet_robust_correct	.080
173	ReAlnet02	.080
174	voneresnet-50	.080
175	resnet50-SIN_IN_IN	.079
176	resnet_SIN_IN_FT_IN	.079
177	mobilenet_v2_1_3_224	.079
178	resnet-18	.077
179	resnet50_imagenet_100_seed-0	.076
180	densenet-121	.075
181	resnet50_robust_l2_eps1	.074
182	resnet50_linf_8_robust	.073
183	vgg-19-keras	.069
184	inception_v1-tf	.069
185	ReAlnet09	.069
186	resnet50-simclr-vissl	.068
187	mobilenet_v2_0.5_96	.067
188	texture_shape_resnet50_trained_on_SIN	.067
189	convnext_small_imagenet_1_seed-0	.066
190	barlow-twins-resnet50	.066
191	resnet-50_v1-tf	.066
192	mobilenet_v2_0.35_96	.063
193	resnet50-vicregl0p75	.060
194	alexnet_training_seed_02	.060
195	alexnet_training_seed_08	.060
196	alexnet_training_seed_04	.060
197	alexnet_training_seed_10	.060
198	alexnet_ks_torevert	.060
199	alexnet-baseline	.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	alexnet	.060
209	resnet50_l2_3_robust	.060
210	voneresnet-50-non_stochastic	.059
211	resnet50_simclr	.059
212	mobilenet_v1_0.25_128	.057
213	BiT-S-R101x1	.057
214	inception_v1	.055
215	BiT-S-R152x4	.055
216	efficientnet_b2_imagenet_full	.054
217	vgg-16-keras	.052
218	mobilenet_v2_0_5_224	.052
219	CORnet-S	.051
220	resnet50_eMMCR_Vanilla	.051
221	mobilevit_small	.051
222	nasnet_mobile	.051
223	resnet50_byol	.050
224	mobilenet_v2_1_0_224	.049
225	mobilenet_v2_1-4_224_pytorch	.048
226	mobilenet_v2_1_4_224	.048
227	resnet50-SIN	.048
228	mobilenet_v2_0.35_192	.047
229	mobilenet_v2_1.0_224-tf	.047
230	konkle_alexnetgn_ipcl_ref01_primary_model	.045
231	texture_shape_alexnet_trained_on_SIN	.045
232	regnet_y_400mf	.044
233	vgg_16	.044
234	alexnet_training_seed_03	.042
235	alexnet_training_seed_07	.041
236	resnet_50_v1_spiking_l3	.041
237	mobilenet_v2_1.0_192-tf	.040
238	mobilenet_v1_1.0_160	.040
239	alexnet_training_seed_06	.037
240	squeezenet1_1	.034
241	resnet50-meshes-lt-100-original-scratch	.033
242	mobilenet_v2_0_75_192	.033
243	mobilenet_v1_0.75_128	.033
244	mobilenet_v2_0.5_128	.032
245	mobilenet_v2_1.0_96	.032
246	AdvProp_efficientnet-b0	.031
247	alexnet_reduced_aliasing_early_checkpoint	.031
248	mobilenet_v2_0.75_160-tf	.030
249	blt_vs	.030
250	mobilenet_v2_0.75_96	.029
251	mobilenet_v2_1_0_192	.029
252	resnet-50-robust	.029
253	imagenet_l2_3_0	.029
254	resnet50_robust_l2_eps3	.029
255	alexnet_training_seed_09	.028
256	mobilenet_v2_1.3_224-tf	.028
257	mobilenet_v2_0.35_224	.027
258	AlexNet_SIN	.027
259	mobilenet_v1_0.75_160	.025
260	mobilenet_v1_0.25_224	.024
261	resnet-152_v1-tf	.023
262	mobilenet_v1_1.0_128	.023
263	resnet_50_v1_spiking_l4	.023
264	resnet50_imagenet_1_seed-0	.022
265	vonealexnet_gaussian_noise_std4_fixed	.022
266	mobilenet_v2_0_75_224	.020
267	mobilenet_v1_0.75_192	.019
268	mobilenet_v2_1_0_128	.019
269	efficientnet_b0_imagenet_full	.019
270	mobilenet_v2_0_75_160	.018
271	resnet_50_v1_spiking_l4a	.018
272	resnet50-vicreg	.017
273	mobilenet_v2_1.0_160-tf	.017
274	BiT-S-R50x1	.016
275	shufflenet_v2_x1_0	.016
276	mobilenet_v1_0.5_128	.015
277	0.5x_resnet-18_LC_w_sh_1_iter	.014
278	mobilenet_v2_0_5_192	.013
279	squeezenet1_0	.013
280	mobilenet_v2_0.75_128	.012
281	resnet50_FractalDB	.012
282	mobilenet_v1_0.25_160	.011
283	mobilenet_v1_1.0_224	.010
284	mobilenet_v2_0.5_192-tf	.009
285	mobilenet_v1_0.5_192	.008
286	BiT-S-R101x3	.007
287	mobilenet_v2_1_0_160	.007
288	mobilenet_v2_1.4_224-tf	.006
289	efficientnet_b1_imagenet_full	.006
290	mobilenet_v2_0.75_224-tf	.006
291	mobilenet_v1_0.5_160	.005
292	mobilenet_v1_1.0_192	.005
293	mobilenet_v2_0.35_128	.005
294	mobilenet_v1_0.75_224	.003
295	mobilenet_v2_0.5_224-tf	.003
296	mobilenet_v2_0.35_160	.002
297	mobilenet_v2_0.75_192-tf	.001
298	mobilenet_v2_0.5_160	.001
299	mobilenet_v1_0.5_224	.001
300	mobilenet_v1_0.25_192	.001
301	mobilenet_v2_1.0_128-tf	.000
302	resnet-18-LC_w_sh_100_iter_conv_init_m
303	ViT-B/32
304	BiT-M-R50x3
305	BiT-M-R50x1
306	BiT-M-R152x2
307	resnet-18-LC_w_sh_1_iter_conv_init_m
308	resnet-18-LC_w_sh_10_iter_m
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_10_iter_conv_init_m
311	resnet-18-LC_w_sh_1_iter_m
312	FrankRobWobv0
313	CORnetZ_CIFAR10
314	0.5x_resnet-18_LC
315	resnet18-local_aggregation
316	resnet18-deepcluster
317	r3m_resnet18
318	CORnet-Z
319	resnet18-instance_recognition
320	vggface
321	my-model
322	0.5x_resnet-18_LC_w_sh_10_iter
323	r3m_resnet50
324	r3m_resnet34
325	resnet18-colorization
326	resnet-18-LC_m
327	r3m_resnet50_nocrop
328	resnet18-supervised
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	RN50
334	0.5x_resnet-18_LC_w_sh_100_iter
335	RN50
336	resnet18-relative_position
337	resnet18-autoencoder
338	resnet18-simclr
339	ViT_L_32
340	ViT_B_32
341	resnet18-depth_prediction
342	ViT_B_16
343	r3m_resnet34_nocrop
344	resnet18-contrastive_predictive
345	omnivore_swinT
346	resnet-18-LC_untrained
347	resnet-18_untrained
348	resnet-50x2_untrained
349	dcgan
350	unet_entire
351	v1-pyr-nodown
352	CORnetZ_CIFAR10_bs32_20_04
353	pixels
354	dorinet_cornet_z
355	my-model
356	resnet50
357	openclip
358	resnet50-cifar
359	resnet50_pretrained_with_retinal_waves
360	resnet50_ImageNet
361	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
362	yudixie_resnet50_random_0_240908
363	ReAlnet10_ALL
364	yudixie_resnet50_imagenet1kpret_0_240908
365	yudixie_resnet50_cat_obj_class_all_latents_0_240908
366	yudixie_resnet50_object_class_0_240908
367	resnet18_imagenet21kP
368	yudixie_resnet50_distance_reg_0_240908
369	ConvLSTM
370	SimVP
371	TAU
372	MIM
373	ReAlnet01_cornet
374	briaai_rmbg_1_4
375	yolos_tiny
376	yudixie_resnet50_imagenet1kpret_0_240312
377	compact_resnet50_V4
378	yudixie_resnet18_rotation_reg_0_240719
379	ReAlnet03_cornet
380	ReAlnet02_cornet
381	resnet18_ecoset_full
382	resnet34_ecoset_full
383	resnet50_ecoset_full
384	VOneCORnet-S
385	yudixie_resnet50_distance_rotation_0_240908
386	yudixie_resnet50_rotation_reg_0_240908
387	compact_resnet50_robust_V4
388	resnet152_ecoset_full
389	yudixie_resnet18_distance_translation_0_240719
390	ReAlnet04_cornet
391	yudixie_resnet50_distance_translation_0_240908
392	yudixie_resnet18_translation_rotation_0_240719
393	yudixie_resnet18_distance_rotation_0_240719
394	compact_vgg19_V4
395	yudixie_resnet18_category_class_0_240719
396	compact_resnet50_IT
397	ReAlnet05_cornet
398	yudixie_resnet18_random_0_240719
399	resnet-18_test_m
400	yudixie_resnet18_cat_obj_class_all_latents_0_240719
401	SeLaVi-AVE
402	yudixie_resnet18_distance_translation_rotation_0_240719
403	yudixie_resnet18_object_class_0_240719
404	yudixie_resnet18_translation_reg_0_240719
405	hmax
406	r3d_18
407	ReAlnet06_cornet
408	yudixie_resnet50_translation_reg_0_240908
409	compact_vgg19_IT
410	yudixie_resnet18_distance_reg_0_240719
411	mc3_18
412	ReAlnet01_ALL
413	yudixie_resnet50_translation_rotation_0_240908
414	s3d
415	mvit_v1_b
416	ReAlnet08_cornet
417	ReAlnet03_ALL
418	ReAlnet02_ALL
419	omnivore_swinB
420	ReAlnet04_ALL
421	SeLaVi-Kinetics400
422	ReAlnet09_cornet
423	compact_cornets_V4
424	SeLaVi-VGG-Sound
425	compact_cornets_IT
426	ReAlnet05_ALL
427	SeLaVi-Kinetics-Sound
428	omnivore_swinS
429	ReAlnet06_ALL
430	mvit_v2_s
431	ReAlnet10_cornet
432	r2plus1d_18
433	compact_resnet50_robust_IT
434	resnet101_ecoset_full
435	ReAlnet07_ALL
436	yudixie_resnet50_distance_translation_rotation_0_240908
437	ReAlnet08_ALL
438	yudixie_resnet18_imagenet1kpret_0_240719
439	ReAlnet07_cornet
440	ReAlnet09_ALL
441	resnet50_primary_visual_cortex
442	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