Leaderboard Explore Compare Profile Community Blog FAQ Competition 2024 Report Issue

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_halfPrecision	.376
39	pnasnet_large	.376
40	convnext_tiny_imagenet_full_seed-0	.372
41	CLIP_ViT-B_32	.367
42	resnet_101_v2	.364
43	ViT_B_16_imagenet1k	.363
44	resnext101_32x8d_wsl	.363
45	pnasnet_large-tf	.361
46	convnext_femto_ols:d1_in1k	.350
47	convnext_base_imagenet_full_seed-0	.347
48	focalnet_tiny_lrf_in1k	.344
49	cvt_cvt-13-224-in1k_4_LucyV4	.342
50	convnext_large_imagenet_full_seed-0	.339
51	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.338
52	CLIP-RN50	.331
53	resnet50_imagenet_full	.320
54	inception_v4	.312
55	nasnet_large	.308
56	effnetb1_272x240	.307
57	resnet152_imagenet_full	.289
58	convnext_small_imagenet_full_seed-0	.289
59	cvt_cvt-21-224-in1k_4_LucyV4	.289
60	cv_18_dagger_408_pretrained	.286
61	resnet101	.273
62	resnet50-VITO-8deg-cc	.269
63	ViT_L_32_imagenet1k	.268
64	inception_v3	.267
65	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.256
66	inception_v3-tf	.251
67	resnet101_imagenet_full	.250
68	AdvProp_efficientnet-b4	.250
69	AT_efficientnet-b4	.250
70	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.249
71	ViT_B_32_imagenet1k	.247
72	resnet-152_v2-tf	.246
73	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.223
74	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.222
75	xception-keras	.222
76	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.219
77	inception_v4-tf	.214
78	resnet_152_v1	.210
79	nasnet_large-tf	.209
80	deit_large_imagenet_full_seed-0	.207
81	antialias-resnet152	.202
82	inception_v3_pytorch	.195
83	resnet34_imagenet_full	.192
84	convnext_small_imagenet_100_seed-0	.189
85	resnet_101_v1	.183
86	resnet50-moclr8deg	.182
87	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.181
88	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.179
89	Res2Net50_26w_4s	.177
90	resnet50	.170
91	AdvProp_efficientnet-b7	.168
92	densenet-201-keras	.165
93	pnasnet_large_pytorch	.162
94	AT_efficientnet-b2	.161
95	AdvProp_efficientnet-b2	.161
96	alexnet_fov4	.161
97	resnet50_random_l2_perturb	.159
98	resnet-50_v2-tf	.158
99	deit_base_imagenet_full_seed-0	.156
100	efficientnet_b0	.154
101	densenet-201	.153
102	densenet_201_pytorch	.153
103	resnet50_random_linf8_perturb	.151
104	xception	.150
105	grcnn_109	.148
106	vonegrcnn_47e	.147
107	deit_small_imagenet_100_seed-0	.146
108	ReAlnet01	.145
109	resnet50_ewconplus_wzc	.145
110	BiT-S-R152x2	.144
111	resnet18_imagenet_full	.142
112	tv_efficientnet-b1	.141
113	alexnet_fov16	.139
114	custom_model_cv_18_dagger_408	.138
115	antialiased-rnext101_32x8d	.137
116	resnet_50_v1_spiking	.134
117	antialiased-r50	.134
118	densenet-169	.133
119	AdvProp_efficientnet-b6	.130
120	ReAlnet07	.129
121	alexnet_fov12	.129
122	AdvProp_efficientnet-b8	.125
123	inception_v2-tf	.123
124	alexnet_random_l2_3_perturb	.123
125	AlexNet_SIN_fov4	.121
126	densenet-169-keras	.120
127	grcnn	.120
128	alexnet_random_linf8_perturb	.119
129	resnet-50-pytorch	.118
130	resnet50-sup	.118
131	resnet50_tutorial	.118
132	resnet_50_v1	.118
133	ReAlnet03	.116
134	vonegrcnn_62e_nobn	.114
135	resnet-50_untrained	.114
136	resnet50-meshes-lt-100-original-pretrained	.114
137	deit_small_imagenet_1_seed-0	.114
138	resnet50_imagenet_10_seed-0	.114
139	deit_small_imagenet_10_seed-0	.114
140	alexnet_linf_8_robust	.112
141	ReAlnet05	.112
142	resnet50-vicregl0p9	.111
143	efficientnet-b7	.111
144	vonegrcnn_52e_full	.110
145	vonegrcnn_47e	.110
146	cornet_s	.109
147	ReAlnet06	.108
148	resnet50_moco_v2	.108
149	alexnet_training_seed_05	.107
150	alexnet_training_seed_01	.107
151	alexnet_training_seed_01_fov4	.107
152	alexnet_training_seed_01_fov12	.107
153	alexnet_training_seed_01_fov16	.107
154	vonegrcnn_62e_nobn	.104
155	BiT-S-R50x3	.101
156	convnext_tiny_sup	.101
157	artResNet18_1	.100
158	resnet50_linf_4_robust	.100
159	nasnet_mobile-tf	.097
160	resnet-34	.094
161	resnet-101_v1-tf	.094
162	ReAlnet04	.094
163	ReAlnet10	.094
164	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.093
165	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.091
166	resnet50-barlow	.091
167	resnet50-SIN_IN	.091
168	resnet50-vitoimagevidnet8	.090
169	ReAlnet08	.090
170	deit_small_imagenet_full_seed-0	.089
171	imagenet_l2_10_0	.089
172	alexnet_l2_3_robust	.087
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	.060
207	resnet50_l2_3_robust	.060
208	voneresnet-50-non_stochastic	.059
209	eBarlow_lmda_02_1000ep	.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_SIN_fov16	.043
234	AlexNet_SIN_fov12	.043
235	alexnet_training_seed_03	.042
236	alexnet_training_seed_07	.041
237	resnet_50_v1_spiking_l3	.041
238	mobilenet_v2_1.0_192-tf	.040
239	mobilenet_v1_1.0_160	.040
240	alexnet_training_seed_06	.037
241	squeezenet1_1	.034
242	resnet50-meshes-lt-100-original-scratch	.033
243	mobilenet_v2_0_75_192	.033
244	mobilenet_v1_0.75_128	.033
245	mobilenet_v2_0.5_128	.032
246	mobilenet_v2_1.0_96	.032
247	AdvProp_efficientnet-b0	.031
248	alexnet_reduced_aliasing_early_checkpoint	.031
249	mobilenet_v2_0.75_160-tf	.030
250	blt_vs	.030
251	mobilenet_v2_0.75_96	.029
252	mobilenet_v2_1_0_192	.029
253	resnet-50-robust	.029
254	imagenet_l2_3_0	.029
255	resnet50_robust_l2_eps3	.029
256	alexnet_training_seed_09	.028
257	mobilenet_v2_1.3_224-tf	.028
258	mobilenet_v2_0.35_224	.027
259	AlexNet_SIN	.027
260	mobilenet_v1_0.75_160	.025
261	mobilenet_v1_0.25_224	.024
262	resnet-152_v1-tf	.023
263	mobilenet_v1_1.0_128	.023
264	resnet_50_v1_spiking_l4	.023
265	resnet50_imagenet_1_seed-0	.022
266	vonealexnet_gaussian_noise_std4_fixed	.022
267	mobilenet_v2_0_75_224	.020
268	mobilenet_v1_0.75_192	.019
269	mobilenet_v2_1_0_128	.019
270	efficientnet_b0_imagenet_full	.019
271	mobilenet_v2_0_75_160	.018
272	resnet_50_v1_spiking_l4a	.018
273	resnet50-vicreg	.017
274	mobilenet_v2_1.0_160-tf	.017
275	BiT-S-R50x1	.016
276	shufflenet_v2_x1_0	.016
277	mobilenet_v1_0.5_128	.015
278	0.5x_resnet-18_LC_w_sh_1_iter	.014
279	mobilenet_v2_0_5_192	.013
280	squeezenet1_0	.013
281	mobilenet_v2_0.75_128	.012
282	resnet50_FractalDB	.012
283	mobilenet_v1_0.25_160	.011
284	mobilenet_v1_1.0_224	.010
285	mobilenet_v2_0.5_192-tf	.009
286	mobilenet_v1_0.5_192	.008
287	BiT-S-R101x3	.007
288	mobilenet_v2_1_0_160	.007
289	mobilenet_v2_1.4_224-tf	.006
290	efficientnet_b1_imagenet_full	.006
291	mobilenet_v2_0.75_224-tf	.006
292	mobilenet_v1_0.5_160	.005
293	mobilenet_v1_1.0_192	.005
294	mobilenet_v2_0.35_128	.005
295	mobilenet_v1_0.75_224	.003
296	mobilenet_v2_0.5_224-tf	.003
297	mobilenet_v2_0.35_160	.002
298	mobilenet_v2_0.75_192-tf	.001
299	mobilenet_v2_0.5_160	.001
300	mobilenet_v1_0.5_224	.001
301	mobilenet_v1_0.25_192	.001
302	mobilenet_v2_1.0_128-tf	.000
303	resnet-18-LC_w_sh_100_iter_conv_init_m
304	ViT-B/32
305	BiT-M-R50x3
306	BiT-M-R50x1
307	BiT-M-R152x2
308	resnet-18-LC_w_sh_1_iter_conv_init_m
309	resnet-18-LC_w_sh_10_iter_m
310	resnet-18-LC_w_sh_100_iter_m
311	resnet-18-LC_w_sh_10_iter_conv_init_m
312	resnet-18-LC_w_sh_1_iter_m
313	FrankRobWobv0
314	CORnetZ_CIFAR10
315	0.5x_resnet-18_LC
316	resnet18-local_aggregation
317	resnet18-deepcluster
318	r3m_resnet18
319	CORnet-Z
320	resnet18-instance_recognition
321	vggface
322	my-model
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	r3m_resnet34
326	resnet18-colorization
327	resnet-18-LC_m
328	r3m_resnet50_nocrop
329	resnet18-supervised
330	resnet-18-LC_1st_conv_m
331	resnet-18-LC_conv_init_m
332	resnet18-contrastive_multiview
333	0.5x_resnet-18
334	RN50
335	0.5x_resnet-18_LC_w_sh_100_iter
336	RN50
337	resnet18-relative_position
338	resnet18-autoencoder
339	resnet18-simclr
340	ViT_L_32
341	ViT_B_32
342	resnet18-depth_prediction
343	ViT_B_16
344	r3m_resnet34_nocrop
345	resnet18-contrastive_predictive
346	omnivore_swinT
347	resnet-18-LC_untrained
348	resnet-18_untrained
349	resnet-50x2_untrained
350	dcgan
351	unet_entire
352	v1-pyr-nodown
353	CORnetZ_CIFAR10_bs32_20_04
354	pixels
355	dorinet_cornet_z
356	my-model
357	resnet50
358	openclip
359	resnet50-cifar
360	resnet50_pretrained_with_retinal_waves
361	resnet50_ImageNet
362	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
363	yudixie_resnet50_random_0_240908
364	ReAlnet10_ALL
365	yudixie_resnet50_imagenet1kpret_0_240908
366	yudixie_resnet50_cat_obj_class_all_latents_0_240908
367	yudixie_resnet50_object_class_0_240908
368	resnet18_imagenet21kP
369	yudixie_resnet50_distance_reg_0_240908
370	ConvLSTM
371	SimVP
372	TAU
373	MIM
374	ReAlnet01_cornet
375	briaai_rmbg_1_4
376	yolos_tiny
377	yudixie_resnet50_imagenet1kpret_0_240312
378	compact_resnet50_V4
379	yudixie_resnet18_rotation_reg_0_240719
380	ReAlnet03_cornet
381	ReAlnet02_cornet
382	vit_base_patch16_dinov3-lvd1689m
383	resnet18_ecoset_full
384	resnet34_ecoset_full
385	resnet50_ecoset_full
386	VOneCORnet-S
387	vit_base_patch14_dinov2-lvd142m
388	vit_base_patch14_reg4_dinov2-lvd142m
389	ReAlnet01_cornet_fov12
390	yudixie_resnet50_distance_rotation_0_240908
391	yudixie_resnet50_rotation_reg_0_240908
392	compact_resnet50_robust_V4
393	resnet152_ecoset_full
394	yudixie_resnet18_distance_translation_0_240719
395	ReAlnet04_cornet
396	yudixie_resnet50_distance_translation_0_240908
397	yudixie_resnet18_translation_rotation_0_240719
398	yudixie_resnet18_distance_rotation_0_240719
399	compact_vgg19_V4
400	yudixie_resnet18_category_class_0_240719
401	compact_resnet50_IT
402	ReAlnet05_cornet
403	yudixie_resnet18_random_0_240719
404	resnet-18_test_m
405	yudixie_resnet18_cat_obj_class_all_latents_0_240719
406	aughost_fair_resnet18
407	ReAlnet01_cornet_fov4
408	SeLaVi-AVE
409	yudixie_resnet18_distance_translation_rotation_0_240719
410	yudixie_resnet18_object_class_0_240719
411	yudixie_resnet18_translation_reg_0_240719
412	hmax
413	ReAlnet01_cornet_fov16
414	r3d_18
415	ReAlnet06_cornet
416	yudixie_resnet50_translation_reg_0_240908
417	compact_vgg19_IT
418	yudixie_resnet18_distance_reg_0_240719
419	mc3_18
420	ReAlnet01_ALL
421	yudixie_resnet50_translation_rotation_0_240908
422	s3d
423	mvit_v1_b
424	ReAlnet08_cornet
425	ReAlnet03_ALL
426	ReAlnet02_ALL
427	omnivore_swinB
428	ReAlnet04_ALL
429	SeLaVi-Kinetics400
430	ReAlnet09_cornet
431	compact_cornets_V4
432	SeLaVi-VGG-Sound
433	compact_cornets_IT
434	ReAlnet05_ALL
435	SeLaVi-Kinetics-Sound
436	omnivore_swinS
437	ReAlnet06_ALL
438	mvit_v2_s
439	ReAlnet10_cornet
440	r2plus1d_18
441	compact_resnet50_robust_IT
442	resnet101_ecoset_full
443	ReAlnet07_ALL
444	yudixie_resnet50_distance_translation_rotation_0_240908
445	ReAlnet08_ALL
446	yudixie_resnet18_imagenet1kpret_0_240719
447	ReAlnet07_cornet
448	ReAlnet09_ALL
449	resnet50_primary_visual_cortex
450	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