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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	dinov2_vitl14_lc	.672
16	resnet_152_v2	.670
17	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.661
18	convnext_base_fb_in22k_ft_in1k	.652
19	resnet_101_v2	.647
20	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.647
21	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.645
22	vit_large_patch14_clip_224:openai_ft_in1k	.639
23	resnext101_32x8d_wsl	.637
24	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.635
25	dinov2_vitg14_lc	.630
26	CLIP_resnet50	.627
27	CLIP_resnet50_float32	.627
28	convnext_large_clip_laion2b_finelayers	.626
29	convnext_large_in22k_supervised	.619
30	convnext_femto_ols:d1_in1k	.617
31	dinov2_vitl14_reg_lc	.616
32	cvt_cvt-13-384-in1k_4_LucyV4	.614
33	vit_large_patch14_clip_336:laion2b_ft_in1k	.604
34	convnext_xlarge:fb_in22k_ft_in1k	.601
35	focalnet_tiny_lrf_in1k	.601
36	SWSL_resnet50	.599
37	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.591
38	convnext_tiny:in12k_ft_in1k	.590
39	cvt_cvt-13-224-in1k_4_LucyV4	.587
40	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.583
41	vit_large_clip336_openai	.580
42	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.577
43	convnext_base:clip_laiona_augreg_ft_in1k_384	.576
44	vit_relpos_base_patch16_clsgap_224:sw_in1k	.576
45	CLIP_ViT-B_32	.572
46	nasnet_large	.555
47	convnext_base_fb_in1k	.537
48	CLIP-RN50	.537
49	ViT_L_32_imagenet1k	.524
50	convnext_tiny_imagenet_full_seed-0	.522
51	vit_huge_clip336_laion	.521
52	convnext_base_imagenet_full_seed-0	.520
53	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.520
54	cvt_cvt-21-384-in1k_4_LucyV4	.515
55	dinov2_vitg14_reg_lc	.513
56	convnext_small_imagenet_full_seed-0	.509
57	pnasnet_large_halfPrecision	.499
58	cvt_cvt-21-224-in1k_4_LucyV4	.493
59	pnasnet_large	.493
60	eva02_large_in22k	.481
61	ViT_B_16_imagenet1k	.476
62	effnetv2m_custom384	.475
63	pnasnet_large-tf	.473
64	dinov2_vitl14_lc_res336	.472
65	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.462
66	resnet_50_v2	.458
67	ViT_B_32_imagenet1k	.455
68	convnext_xxlarge_clip	.447
69	convnext_large_imagenet_full_seed-0	.443
70	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.441
71	nasnet_large-tf	.418
72	deit3_base_patch16_224_fb_in22k_ft_in1k	.410
73	inception_v4	.404
74	AdvProp_efficientnet-b6	.401
75	resnet50_st_wzc_seed7_2_323	.388
76	resnet-152_v2-tf	.386
77	resnet50-VITO-8deg-cc	.381
78	inception_v3	.377
79	resnet50_ewconplus_wzc_seed5_2__323	.356
80	cv_18_dagger_408_pretrained	.354
81	convnext_base_fb_in1k_gram_aligned_dinov3	.354
82	resnet50_ewconplus_wzc_seed2_2__323	.350
83	inception_v3-tf	.348
84	xception-keras	.344
85	resnet101	.340
86	tv_efficientnet-b1	.338
87	inception_v4-tf	.330
88	deit_large_imagenet_full_seed-0	.328
89	xception	.322
90	resnet50_st_wzc_seed6_2_323	.317
91	resnet50_st_wzc_seed3_2_323	.313
92	AdvProp_efficientnet-b4	.309
93	AT_efficientnet-b4	.309
94	antialias-resnet152	.309
95	resnet50_st_wzc_2	.291
96	densenet-201	.289
97	densenet_201_pytorch	.289
98	effnetb1_272x240	.285
99	resnet-50_v2-tf	.284
100	resnet50_ewconplus_wzc	.282
101	antialiased-rnext101_32x8d	.279
102	densenet-201-keras	.272
103	resnet50-moclr8deg	.266
104	BiT-S-R50x3	.265
105	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.262
106	AdvProp_efficientnet-b7	.260
107	deit_base_imagenet_full_seed-0	.257
108	deit_small_imagenet_full_seed-0	.257
109	efficientnet-b7	.254
110	resnet_101_v1	.253
111	AT_efficientnet-b2	.250
112	AdvProp_efficientnet-b2	.250
113	efficientnet_b0	.249
114	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.248
115	resnet50_random_l2_perturb	.245
116	resnet50	.244
117	Res2Net50_26w_4s	.243
118	resnet50_byol	.231
119	resnet_152_v1	.231
120	antialiased-r50	.224
121	resnet50_ewconplus_wzc_seed6_2_323	.219
122	resnet_50_v1_spiking	.209
123	resnet50_moco_v2	.209
124	convnext_small_imagenet_100_seed-0	.202
125	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.196
126	BiT-S-R101x3	.194
127	resnet-50-pytorch	.194
128	resnet50-sup	.194
129	resnet50_tutorial	.194
130	resnet_50_v1	.194
131	resnet50_hvs_wzc	.193
132	BiT-S-R152x2	.188
133	convnext_tiny_sup	.188
134	vonegrcnn_62e_nobn	.183
135	grcnn	.182
136	AdvProp_efficientnet-b8	.180
137	nasnet_mobile	.179
138	vonegrcnn_47e	.174
139	densenet-121-keras	.173
140	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.172
141	BiT-S-R152x4	.172
142	densenet-121	.166
143	resnet50-SIN_IN_IN	.162
144	resnet_SIN_IN_FT_IN	.162
145	vonegrcnn_62e_nobn	.157
146	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.157
147	grcnn_109	.156
148	densenet-169	.153
149	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.149
150	vonegrcnn_52e_full	.144
151	densenet-169-keras	.143
152	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.137
153	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.136
154	BiT-S-R101x1	.134
155	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.133
156	voneresnet-50-non_stochastic	.132
157	resnet101_imagenet_full	.131
158	alexnet_training_seed_05	.130
159	alexnet_training_seed_01	.130
160	alexnet_training_seed_01_fov4	.130
161	alexnet_training_seed_01_fov12	.130
162	alexnet_training_seed_01_fov16	.130
163	resnet152_imagenet_full	.129
164	vonegrcnn_47e	.127
165	resnet50-simclr-vissl	.127
166	vonegrcnn_52e_full	.125
167	resnet50_random_linf8_perturb	.122
168	BiT-S-R50x1	.121
169	voneresnet-50-robust	.121
170	mobilenet_v2_1_0_224	.121
171	resnet50-vitoimagevidnet8	.119
172	blt_vs	.119
173	eBarlow_lmda_02_1000ep	.118
174	resnet50-SIN_IN	.116
175	inception_v2-tf	.112
176	imagenet_l2_10_0	.105
177	resnet50_imagenet_full	.105
178	resnet50_simclr	.103
179	alexnet_fov16	.102
180	alexnet_fov12	.101
181	alexnet_robust_correct	.099
182	alexnet_l2_3_robust	.098
183	custom_model_cv_18_dagger_408	.097
184	vgg-19-keras	.091
185	mobilenet_v1_0.25_128	.090
186	resnet50_imagenet_10_seed-0	.089
187	deit_small_imagenet_10_seed-0	.089
188	deit_small_imagenet_1_seed-0	.089
189	resnet34_imagenet_full	.088
190	alexnet_training_seed_02	.088
191	alexnet_training_seed_04	.088
192	alexnet_training_seed_08	.088
193	alexnet_training_seed_10	.088
194	vgg_19	.088
195	inception_v1	.080
196	voneresnet-50	.079
197	konkle_alexnetgn_ipcl_ref01_primary_model	.079
198	nasnet_mobile-tf	.078
199	mobilenet_v2_0.5_160	.078
200	mobilenet_v1_0.5_128	.078
201	alexnet_training_seed_07	.077
202	mobilenet_v2_1_3_224	.076
203	mobilenet_v2_0.5_128	.075
204	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
205	mobilenet_v2_1_0_192	.075
206	deit_small_imagenet_100_seed-0	.073
207	AdvProp_efficientnet-b0	.073
208	ReAlnet04	.071
209	mobilenet_v2_0.35_160	.069
210	mobilenet_v2_0.35_192	.068
211	ReAlnet08	.068
212	inception_v1-tf	.067
213	mobilenet_v1_0.5_160	.067
214	ReAlnet03	.067
215	ReAlnet09	.064
216	mobilenet_v2_0.35_128	.062
217	alexnet_fov4	.061
218	resnet50_eMMCR_Vanilla	.061
219	mobilenet_v1_0.75_128	.061
220	squeezenet1_0	.059
221	mobilenet_v1_0.5_224	.059
222	mobilenet_v2_0.75_96	.058
223	convnext_small_imagenet_1_seed-0	.058
224	AlexNet_SIN_fov4	.058
225	resnet50-vicregl0p9	.057
226	resnet50-barlow	.057
227	mobilenet_v2_0.5_192-tf	.057
228	mobilenet_v2_1-4_224_pytorch	.057
229	mobilenet_v2_1_4_224	.057
230	ReAlnet05	.056
231	mobilenet_v2_0.5_224-tf	.056
232	resnet50_imagenet_1_seed-0	.054
233	mobilenet_v1_0.25_192	.054
234	resnet50_linf_4_robust	.050
235	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.049
236	mobilenet_v1_0.25_160	.049
237	ReAlnet01	.049
238	ReAlnet06	.049
239	mobilenet_v2_0.35_224	.048
240	mobilenet_v2_0.75_192-tf	.048
241	ReAlnet07	.048
242	mobilenet_v2_1_0_160	.048
243	mobilenet_v2_0.75_224-tf	.047
244	alexnet_random_l2_3_perturb	.047
245	resnet-34	.047
246	mobilenet_v1_0.5_192	.046
247	mobilenet_v2_1.0_224-tf	.046
248	mobilenet_v1_0.75_160	.046
249	resnet50_l2_3_robust	.046
250	resnet_50_v1_spiking_l4	.046
251	resnet-152_v1-tf	.045
252	vgg-16-keras	.045
253	resnet50_imagenet_100_seed-0	.045
254	resnet50_FractalDB	.044
255	mobilenet_v1_0.75_192	.044
256	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.043
257	alexnet_linf_8_robust	.043
258	alexnet_random_linf8_perturb	.041
259	resnet-101_v1-tf	.041
260	alexnet_early_checkpoint	.040
261	vgg_16	.039
262	resnet-18	.039
263	alexnet	.038
264	alexnet_ks_torevert	.038
265	resnet-50-robust	.038
266	imagenet_l2_3_0	.038
267	resnet50_robust_l2_eps3	.038
268	resnet18_imagenet_full	.038
269	mobilenet_v2_0.75_160-tf	.038
270	efficientnet_b1_imagenet_full	.036
271	mobilenet_v2_0.75_128	.036
272	mobilenet_v2_1.0_128-tf	.036
273	texture_shape_resnet50_trained_on_SIN	.036
274	mobilenet_v1_1.0_160	.036
275	efficientnet_b0_imagenet_full	.035
276	shufflenet_v2_x1_0	.034
277	resnet_50_v1_spiking_l4a	.034
278	resnet50_robust_l2_eps1	.033
279	mobilenet_v1_0.25_224	.033
280	alexnet_reduced_aliasing_early_checkpoint	.031
281	squeezenet1_1	.031
282	mobilenet_v1_1.0_192	.030
283	mobilenet_v2_1.0_96	.028
284	alexnet_training_seed_09	.027
285	regnet_y_400mf	.027
286	resnet50_linf_8_robust	.026
287	alexnet_training_seed_03	.024
288	mobilenet_v2_0.5_96	.024
289	resnet50-SIN	.023
290	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.023
291	mobilenet_v2_1_0_128	.022
292	mobilenet_v1_1.0_224	.022
293	convnext_small_imagenet_10_seed-0	.022
294	resnet50-vicregl0p75	.022
295	resnet-50_untrained	.021
296	0.5x_resnet-18_LC_w_sh_1_iter	.021
297	mobilenet_v2_1.0_160-tf	.020
298	mobilenet_v1_0.75_224	.017
299	mobilenet_v1_1.0_128	.016
300	mobilenet_v2_0_75_224	.016
301	AlexNet_SIN_fov12	.014
302	mobilenet_v2_0.35_96	.013
303	AlexNet_SIN_fov16	.012
304	ReAlnet02	.011
305	cornet_s	.011
306	ReAlnet10	.011
307	mobilenet_v2_1.3_224-tf	.010
308	vonealexnet_gaussian_noise_std4_fixed	.010
309	resnet_50_v1_spiking_l3	.009
310	mobilenet_v2_1.4_224-tf	.008
311	barlow-twins-resnet50	.006
312	mobilenet_v2_0_5_192	.006
313	efficientnet_b2_imagenet_full	.006
314	CORnet-S	.005
315	mobilenet_v2_0_75_160	.005
316	alexnet_training_seed_06	.005
317	texture_shape_alexnet_trained_on_SIN	.005
318	artResNet18_1	.004
319	mobilenet_v2_1.0_192-tf	.004
320	resnet50-vicreg	.003
321	mobilenet_v2_0_75_192	.003
322	AlexNet_SIN	.002
323	mobilenet_v2_0_5_224	.001
324	resnet-50_v1-tf	.000
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	resnet-18-LC_w_sh_1_iter_conv_init_m
330	resnet-18-LC_w_sh_1_iter_m
331	BiT-M-R152x2
332	resnet-18-LC_w_sh_10_iter_m
333	ReAlnet09_ALL
334	resnet-18-LC_w_sh_100_iter_m
335	resnet-18-LC_w_sh_10_iter_conv_init_m
336	FrankRobWobv0
337	0.5x_resnet-18_LC
338	CORnetZ_CIFAR10
339	resnet18-local_aggregation
340	r3m_resnet18
341	CORnet-Z
342	resnet18-deepcluster
343	vggface
344	my-model
345	resnet18-instance_recognition
346	0.5x_resnet-18_LC_w_sh_10_iter
347	r3m_resnet50
348	r3m_resnet34
349	resnet-18-LC_m
350	r3m_resnet50_nocrop
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-colorization
360	resnet18-relative_position
361	resnet18-autoencoder
362	resnet18-simclr
363	ViT_L_32
364	ViT_B_32
365	resnet18-depth_prediction
366	ViT_B_16
367	r3m_resnet34_nocrop
368	resnet18-contrastive_predictive
369	resnet-18-LC_untrained
370	resnet-18_untrained
371	resnet-50x2_untrained
372	dcgan
373	unet_entire
374	v1-pyr-nodown
375	CORnetZ_CIFAR10_bs32_20_04
376	pixels
377	dorinet_cornet_z
378	omnivore_swinT
379	my-model
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
486	dinov2_vitl14_reg_lc_featread

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