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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021uniformnoise-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.781
2	resnext101_32x32d_wsl	.754
3	resnext101_32x48d_wsl	.743
4	vit_base_patch16_clip_224:openai_ft_in1k	.734
5	resnext101_32x16d_wsl	.731
6	deit3_base_patch16_224_fb_in1k	.730
7	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.728
8	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.723
9	fixres_resnext101_32x48d_wsl	.720
10	vit_large_patch14_clip_224:laion2b_ft_in1k	.719
11	convnext_large:fb_in22k_ft_in1k	.686
12	vision_transformer_vit_large_patch16_224	.685
13	SWSL_resnext101_32x8d	.675
14	resnet-152_v2_pytorch	.674
15	resnet_152_v2	.670
16	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.661
17	convnext_base_fb_in22k_ft_in1k	.652
18	resnet_101_v2	.647
19	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.647
20	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.645
21	vit_large_patch14_clip_224:openai_ft_in1k	.639
22	resnext101_32x8d_wsl	.637
23	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.635
24	CLIP_resnet50	.627
25	CLIP_resnet50_float32	.627
26	convnext_femto_ols:d1_in1k	.617
27	cvt_cvt-13-384-in1k_4_LucyV4	.614
28	vit_large_patch14_clip_336:laion2b_ft_in1k	.604
29	convnext_xlarge:fb_in22k_ft_in1k	.601
30	focalnet_tiny_lrf_in1k	.601
31	SWSL_resnet50	.599
32	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.591
33	convnext_tiny:in12k_ft_in1k	.590
34	cvt_cvt-13-224-in1k_4_LucyV4	.587
35	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.583
36	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.577
37	convnext_base:clip_laiona_augreg_ft_in1k_384	.576
38	vit_relpos_base_patch16_clsgap_224:sw_in1k	.576
39	CLIP_ViT-B_32	.572
40	nasnet_large	.555
41	convnext_base_fb_in1k	.537
42	CLIP-RN50	.537
43	ViT_L_32_imagenet1k	.524
44	convnext_tiny_imagenet_full_seed-0	.522
45	convnext_base_imagenet_full_seed-0	.520
46	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.520
47	cvt_cvt-21-384-in1k_4_LucyV4	.515
48	convnext_small_imagenet_full_seed-0	.509
49	pnasnet_large_halfPrecision	.499
50	cvt_cvt-21-224-in1k_4_LucyV4	.493
51	pnasnet_large	.493
52	ViT_B_16_imagenet1k	.476
53	effnetv2m_custom384	.475
54	pnasnet_large-tf	.473
55	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.462
56	resnet_50_v2	.458
57	ViT_B_32_imagenet1k	.455
58	resnet50_st_wzc_seed5_2_323	.453
59	convnext_large_imagenet_full_seed-0	.443
60	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.441
61	nasnet_large-tf	.418
62	deit3_base_patch16_224_fb_in22k_ft_in1k	.410
63	inception_v4	.404
64	AdvProp_efficientnet-b6	.401
65	resnet50_st_wzc_seed7_2_323	.388
66	resnet-152_v2-tf	.386
67	resnet50-VITO-8deg-cc	.381
68	inception_v3	.377
69	resnet50_st_wzc_seed4_2_323	.370
70	resnet50_ewconplus_wzc_2_323	.369
71	resnet50_ewconplus_wzc_seed5_2__323	.356
72	resnet50_ewconplus_wzc_seed3_2_323	.356
73	resnet50_st_wzc_noseed_2_323	.355
74	cv_18_dagger_408_pretrained	.354
75	convnext_base_fb_in1k_gram_aligned_dinov3	.354
76	resnet50_ewconplus_wzc_seed2_2__323	.350
77	inception_v3-tf	.348
78	xception-keras	.344
79	resnet101	.340
80	tv_efficientnet-b1	.338
81	inception_v4-tf	.330
82	deit_large_imagenet_full_seed-0	.328
83	xception	.322
84	resnet50_st_wzc_seed6_2_323	.317
85	resnet50_st_wzc_seed3_2_323	.313
86	AdvProp_efficientnet-b4	.309
87	AT_efficientnet-b4	.309
88	antialias-resnet152	.309
89	resnet50_ewconplus_wzc_seed4_2_323	.299
90	resnet50_st_wzc_2_323	.298
91	resnet50_ewconplus_wzc_seed7_2_323	.298
92	resnet50_st_wzc_2	.291
93	densenet-201	.289
94	densenet_201_pytorch	.289
95	effnetb1_272x240	.285
96	resnet-50_v2-tf	.284
97	resnet50_ewconplus_wzc	.282
98	antialiased-rnext101_32x8d	.279
99	densenet-201-keras	.272
100	resnet50-moclr8deg	.266
101	BiT-S-R50x3	.265
102	resnet50_ewconplus_noseed_2_323	.262
103	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.262
104	AdvProp_efficientnet-b7	.260
105	deit_base_imagenet_full_seed-0	.257
106	deit_small_imagenet_full_seed-0	.257
107	efficientnet-b7	.254
108	resnet_101_v1	.253
109	AT_efficientnet-b2	.250
110	AdvProp_efficientnet-b2	.250
111	efficientnet_b0	.249
112	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.248
113	resnet50_random_l2_perturb	.245
114	resnet50	.244
115	Res2Net50_26w_4s	.243
116	resnet50_byol	.231
117	resnet_152_v1	.231
118	antialiased-r50	.224
119	resnet50_ewconplus_wzc_seed6_2_323	.219
120	resnet_50_v1_spiking	.209
121	resnet50_moco_v2	.209
122	convnext_small_imagenet_100_seed-0	.202
123	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.196
124	BiT-S-R101x3	.194
125	resnet-50-pytorch	.194
126	resnet50-sup	.194
127	resnet50_tutorial	.194
128	resnet_50_v1	.194
129	BiT-S-R152x2	.188
130	convnext_tiny_sup	.188
131	vonegrcnn_62e_nobn	.183
132	grcnn	.182
133	AdvProp_efficientnet-b8	.180
134	nasnet_mobile	.179
135	vonegrcnn_47e	.174
136	densenet-121-keras	.173
137	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.172
138	BiT-S-R152x4	.172
139	densenet-121	.166
140	resnet50-SIN_IN_IN	.162
141	resnet_SIN_IN_FT_IN	.162
142	vonegrcnn_62e_nobn	.157
143	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.157
144	grcnn_109	.156
145	densenet-169	.153
146	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.149
147	vonegrcnn_52e_full	.144
148	densenet-169-keras	.143
149	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.137
150	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.136
151	BiT-S-R101x1	.134
152	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.133
153	voneresnet-50-non_stochastic	.132
154	resnet101_imagenet_full	.131
155	alexnet_training_seed_05	.130
156	alexnet_training_seed_01	.130
157	alexnet_training_seed_01_fov4	.130
158	alexnet_training_seed_01_fov12	.130
159	alexnet_training_seed_01_fov16	.130
160	resnet152_imagenet_full	.129
161	vonegrcnn_47e	.127
162	resnet50-simclr-vissl	.127
163	vonegrcnn_52e_full	.125
164	resnet50_random_linf8_perturb	.122
165	BiT-S-R50x1	.121
166	voneresnet-50-robust	.121
167	mobilenet_v2_1_0_224	.121
168	resnet50-vitoimagevidnet8	.119
169	blt_vs	.119
170	eBarlow_lmda_02_1000ep	.118
171	resnet50-SIN_IN	.116
172	inception_v2-tf	.112
173	imagenet_l2_10_0	.105
174	resnet50_imagenet_full	.105
175	resnet50_simclr	.103
176	alexnet_fov16	.102
177	alexnet_fov12	.101
178	alexnet_robust_correct	.099
179	alexnet_l2_3_robust	.098
180	custom_model_cv_18_dagger_408	.097
181	vgg-19-keras	.091
182	mobilenet_v1_0.25_128	.090
183	resnet50_imagenet_10_seed-0	.089
184	deit_small_imagenet_10_seed-0	.089
185	deit_small_imagenet_1_seed-0	.089
186	resnet34_imagenet_full	.088
187	alexnet_training_seed_02	.088
188	alexnet_training_seed_04	.088
189	alexnet_training_seed_08	.088
190	alexnet_training_seed_10	.088
191	resnet50-meshes-lt-100-original-pretrained	.088
192	vgg_19	.088
193	inception_v1	.080
194	voneresnet-50	.079
195	konkle_alexnetgn_ipcl_ref01_primary_model	.079
196	nasnet_mobile-tf	.078
197	mobilenet_v2_0.5_160	.078
198	mobilenet_v1_0.5_128	.078
199	alexnet_training_seed_07	.077
200	mobilenet_v2_1_3_224	.076
201	mobilenet_v2_0.5_128	.075
202	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
203	mobilenet_v2_1_0_192	.075
204	deit_small_imagenet_100_seed-0	.073
205	AdvProp_efficientnet-b0	.073
206	ReAlnet04	.071
207	mobilenet_v2_0.35_160	.069
208	mobilenet_v2_0.35_192	.068
209	ReAlnet08	.068
210	inception_v1-tf	.067
211	mobilenet_v1_0.5_160	.067
212	ReAlnet03	.067
213	ReAlnet09	.064
214	mobilenet_v2_0.35_128	.062
215	alexnet_fov4	.061
216	resnet50_eMMCR_Vanilla	.061
217	mobilenet_v1_0.75_128	.061
218	squeezenet1_0	.059
219	mobilenet_v1_0.5_224	.059
220	mobilenet_v2_0.75_96	.058
221	convnext_small_imagenet_1_seed-0	.058
222	AlexNet_SIN_fov4	.058
223	resnet50-vicregl0p9	.057
224	resnet50-barlow	.057
225	mobilenet_v2_0.5_192-tf	.057
226	mobilenet_v2_1-4_224_pytorch	.057
227	mobilenet_v2_1_4_224	.057
228	ReAlnet05	.056
229	mobilenet_v2_0.5_224-tf	.056
230	resnet50_imagenet_1_seed-0	.054
231	mobilenet_v1_0.25_192	.054
232	resnet50_linf_4_robust	.050
233	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.049
234	mobilenet_v1_0.25_160	.049
235	ReAlnet01	.049
236	ReAlnet06	.049
237	mobilenet_v2_0.35_224	.048
238	mobilenet_v2_0.75_192-tf	.048
239	ReAlnet07	.048
240	mobilenet_v2_1_0_160	.048
241	mobilenet_v2_0.75_224-tf	.047
242	alexnet_random_l2_3_perturb	.047
243	resnet-34	.047
244	mobilenet_v1_0.5_192	.046
245	mobilenet_v2_1.0_224-tf	.046
246	mobilenet_v1_0.75_160	.046
247	resnet50_l2_3_robust	.046
248	resnet_50_v1_spiking_l4	.046
249	resnet-152_v1-tf	.045
250	vgg-16-keras	.045
251	resnet50_imagenet_100_seed-0	.045
252	resnet50_FractalDB	.044
253	mobilenet_v1_0.75_192	.044
254	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.043
255	alexnet_linf_8_robust	.043
256	alexnet_random_linf8_perturb	.041
257	resnet-101_v1-tf	.041
258	alexnet_early_checkpoint	.040
259	vgg_16	.039
260	resnet-18	.039
261	alexnet	.038
262	alexnet_ks_torevert	.038
263	resnet-50-robust	.038
264	imagenet_l2_3_0	.038
265	resnet50_robust_l2_eps3	.038
266	resnet18_imagenet_full	.038
267	mobilenet_v2_0.75_160-tf	.038
268	efficientnet_b1_imagenet_full	.036
269	mobilenet_v2_0.75_128	.036
270	mobilenet_v2_1.0_128-tf	.036
271	texture_shape_resnet50_trained_on_SIN	.036
272	mobilenet_v1_1.0_160	.036
273	efficientnet_b0_imagenet_full	.035
274	shufflenet_v2_x1_0	.034
275	resnet_50_v1_spiking_l4a	.034
276	resnet50_robust_l2_eps1	.033
277	mobilenet_v1_0.25_224	.033
278	alexnet_reduced_aliasing_early_checkpoint	.031
279	squeezenet1_1	.031
280	mobilenet_v1_1.0_192	.030
281	mobilenet_v2_1.0_96	.028
282	resnet50-meshes-lt-100-original-scratch	.028
283	alexnet_training_seed_09	.027
284	regnet_y_400mf	.027
285	resnet50_linf_8_robust	.026
286	alexnet_training_seed_03	.024
287	mobilenet_v2_0.5_96	.024
288	resnet50-SIN	.023
289	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.023
290	mobilenet_v2_1_0_128	.022
291	mobilenet_v1_1.0_224	.022
292	convnext_small_imagenet_10_seed-0	.022
293	resnet50-vicregl0p75	.022
294	resnet-50_untrained	.021
295	0.5x_resnet-18_LC_w_sh_1_iter	.021
296	mobilenet_v2_1.0_160-tf	.020
297	mobilenet_v1_0.75_224	.017
298	mobilenet_v1_1.0_128	.016
299	mobilenet_v2_0_75_224	.016
300	AlexNet_SIN_fov12	.014
301	mobilenet_v2_0.35_96	.013
302	AlexNet_SIN_fov16	.012
303	ReAlnet02	.011
304	cornet_s	.011
305	ReAlnet10	.011
306	mobilenet_v2_1.3_224-tf	.010
307	vonealexnet_gaussian_noise_std4_fixed	.010
308	resnet_50_v1_spiking_l3	.009
309	mobilenet_v2_1.4_224-tf	.008
310	barlow-twins-resnet50	.006
311	mobilenet_v2_0_5_192	.006
312	efficientnet_b2_imagenet_full	.006
313	CORnet-S	.005
314	mobilenet_v2_0_75_160	.005
315	alexnet_training_seed_06	.005
316	texture_shape_alexnet_trained_on_SIN	.005
317	artResNet18_1	.004
318	mobilenet_v2_1.0_192-tf	.004
319	resnet50-vicreg	.003
320	mobilenet_v2_0_75_192	.003
321	AlexNet_SIN	.002
322	mobilenet_v2_0_5_224	.001
323	resnet-50_v1-tf	.000
324	resnet-18-LC_w_sh_100_iter_conv_init_m
325	ViT-B/32
326	BiT-M-R50x3
327	BiT-M-R50x1
328	resnet-18-LC_w_sh_1_iter_conv_init_m
329	resnet-18-LC_w_sh_1_iter_m
330	BiT-M-R152x2
331	resnet-18-LC_w_sh_10_iter_m
332	ReAlnet09_ALL
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_10_iter_conv_init_m
335	FrankRobWobv0
336	0.5x_resnet-18_LC
337	CORnetZ_CIFAR10
338	resnet18-local_aggregation
339	r3m_resnet18
340	CORnet-Z
341	resnet18-deepcluster
342	vggface
343	my-model
344	resnet18-instance_recognition
345	0.5x_resnet-18_LC_w_sh_10_iter
346	r3m_resnet50
347	r3m_resnet34
348	resnet-18-LC_m
349	r3m_resnet50_nocrop
350	resnet18-supervised
351	resnet-18-LC_1st_conv_m
352	resnet-18-LC_conv_init_m
353	resnet18-contrastive_multiview
354	0.5x_resnet-18
355	RN50
356	0.5x_resnet-18_LC_w_sh_100_iter
357	RN50
358	resnet18-colorization
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	resnet18-contrastive_predictive
368	resnet-18-LC_untrained
369	resnet-18_untrained
370	resnet-50x2_untrained
371	dcgan
372	unet_entire
373	v1-pyr-nodown
374	CORnetZ_CIFAR10_bs32_20_04
375	pixels
376	dorinet_cornet_z
377	omnivore_swinT
378	my-model
379	resnet50
380	openclip
381	resnet50-cifar
382	resnet50_pretrained_with_retinal_waves
383	resnet50_ImageNet
384	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
385	ReAlnet08_ALL
386	yudixie_resnet50_random_0_240908
387	yudixie_resnet50_imagenet1kpret_0_240908
388	yudixie_resnet50_imagenet1kpret_0_240312
389	yudixie_resnet50_cat_obj_class_all_latents_0_240908
390	yudixie_resnet50_object_class_0_240908
391	yudixie_resnet50_category_class_0_240908
392	resnet18_imagenet21kP
393	yudixie_resnet50_distance_reg_0_240908
394	ConvLSTM
395	PredRNN
396	SimVP
397	TAU
398	vit_base_patch16_dinov3-lvd1689m
399	MIM
400	hmax
401	briaai_rmbg_1_4
402	pnasnet_large_pytorch
403	inception_v3_pytorch
404	vit_base_patch14_reg4_dinov2-lvd142m
405	ReAlnet01_cornet
406	yolos_tiny
407	yudixie_resnet18_translation_reg_0_240719
408	ReAlnet02_cornet
409	yudixie_resnet18_distance_reg_0_240719
410	yudixie_resnet18_rotation_reg_0_240719
411	yudixie_resnet18_imagenet1kpret_0_240719
412	compact_resnet50_V4
413	vit_base_patch14_dinov2-lvd142m
414	compact_resnet50_IT
415	resnet18_ecoset_full
416	ReAlnet03_cornet
417	ReAlnet01_cornet_fov4
418	resnet34_ecoset_full
419	VOneCORnet-S
420	resnet50_ecoset_full
421	resnet101_ecoset_full
422	aughost_fair_resnet18
423	yudixie_resnet50_distance_translation_0_240908
424	compact_vgg19_V4
425	yudixie_resnet18_distance_translation_0_240719
426	ReAlnet01_cornet_fov12
427	yudixie_resnet18_translation_rotation_0_240719
428	ReAlnet04_cornet
429	omnivore_swinB
430	yudixie_resnet18_distance_rotation_0_240719
431	yudixie_resnet18_category_class_0_240719
432	yudixie_resnet50_rotation_reg_0_240908
433	ReAlnet05_cornet
434	yudixie_resnet18_random_0_240719
435	resnet-18_test_m
436	yudixie_resnet50_distance_rotation_0_240908
437	yudixie_resnet18_cat_obj_class_all_latents_0_240719
438	SeLaVi-AVE
439	yudixie_resnet18_distance_translation_rotation_0_240719
440	yudixie_resnet18_object_class_0_240719
441	compact_resnet50_robust_V4
442	compact_vgg19_IT
443	ReAlnet06_cornet
444	yudixie_resnet50_translation_reg_0_240908
445	r3d_18
446	ReAlnet07_cornet
447	mc3_18
448	ReAlnet02_ALL
449	s3d
450	ReAlnet08_cornet
451	mvit_v1_b
452	ReAlnet04_ALL
453	compact_cornets_V4
454	ReAlnet01_cornet_fov16
455	ReAlnet03_ALL
456	compact_cornets_IT
457	ReAlnet09_cornet
458	SeLaVi-Kinetics400
459	ReAlnet05_ALL
460	SeLaVi-VGG-Sound
461	ReAlnet06_ALL
462	omnivore_swinS
463	r2plus1d_18
464	compact_resnet50_robust_IT
465	ReAlnet07_ALL
466	mvit_v2_s
467	ReAlnet10_cornet
468	SeLaVi-Kinetics-Sound
469	resnet152_ecoset_full
470	yudixie_resnet50_distance_translation_rotation_0_240908
471	yudixie_resnet50_translation_rotation_0_240908
472	resnet50_primary_visual_cortex
473	ReAlnet10_ALL
474	ReAlnet01_ALL
475	resnet18_fair
476	pr1_stl10_disentangled_alpha04
477	predictive_coding_pc
478	yudixie_wbh_object_class_scaling_0415_run_0
479	yudixie_wbh_object_class_scaling_0415_imagenet
480	yudixie_wbh_object_class_scaling_0415_run_3
481	yudixie_wbh_object_class_scaling_0415_run_2
482	yudixie_wbh_object_class_scaling_0415_run_1
483	deepeisnn_ei_cifar10_vgg19
484	deepeisnn_ei_cifar10_resnet18
485	deepeisnn_ei_imagenet_resnet18

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

0.43.

Note that scores are relative to this ceiling.

Data: Geirhos2021uniformnoise

Metric: error_consistency