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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021edge-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
2	vit_large_clip336_openai	1.0
3	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
4	eva02_large_in22k	1.0
5	convnext_large_clip_laion2b_finelayers	1.0
6	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
7	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.995
9	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.979
10	vit_huge_clip336_laion	.945
11	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.899
12	dinov2_vitl14_reg_lc	.898
13	CLIP_ViT-B_32	.854
14	dinov2_vitg14_reg_lc	.815
15	vit_large_patch14_clip_224:openai_ft_in1k	.807
16	vit_base_patch16_clip_224:openai_ft_in1k	.753
17	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.744
18	SWSL_resnext101_32x8d	.728
19	resnext101_32x16d_wsl	.719
20	dinov2_vitl14_lc_res336	.705
21	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.699
22	dinov2_vitl14_lc	.670
23	convnext_xlarge:fb_in22k_ft_in1k	.670
24	resnext101_32x32d_wsl	.645
25	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.635
26	dinov2_vitg14_lc	.625
27	resnext101_32x48d_wsl	.619
28	resnext101_32x8d_wsl	.618
29	CLIP-RN50	.608
30	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.543
31	vision_transformer_vit_large_patch16_224	.543
32	convnext_large:fb_in22k_ft_in1k	.531
33	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.519
34	CLIP_resnet50	.516
35	CLIP_resnet50_float32	.516
36	fixres_resnext101_32x48d_wsl	.502
37	convnext_xxlarge_clip	.468
38	convnext_large_in22k_supervised	.466
39	convnext_base_fb_in22k_ft_in1k	.447
40	SWSL_resnet50	.395
41	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.363
42	convnext_large_imagenet_full_seed-0	.285
43	resnet50-VITO-8deg-cc	.264
44	convnext_tiny_imagenet_full_seed-0	.264
45	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.252
46	cvt_cvt-21-384-in1k_4_LucyV4	.243
47	convnext_tiny:in12k_ft_in1k	.232
48	convnext_base_imagenet_full_seed-0	.219
49	AlexNet_SIN_fov4	.200
50	convnext_small_imagenet_full_seed-0	.198
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.195
52	xception-keras	.194
53	vit_relpos_base_patch16_clsgap_224:sw_in1k	.192
54	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.187
55	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.184
56	effnetv2m_custom384	.183
57	inception_v4	.182
58	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.182
59	inception_v3_pytorch	.175
60	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.173
61	cvt_cvt-21-224-in1k_4_LucyV4	.168
62	AT_efficientnet-b2	.163
63	AdvProp_efficientnet-b2	.163
64	cvt_cvt-13-384-in1k_4_LucyV4	.160
65	convnext_femto_ols:d1_in1k	.158
66	pnasnet_large_halfPrecision	.153
67	pnasnet_large	.153
68	cvt_cvt-13-224-in1k_4_LucyV4	.151
69	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.151
70	resnet_152_v2	.151
71	resnet_50_v2	.151
72	resnet50_st_wzc_seed6_2_323	.150
73	inception_v4-tf	.148
74	resnet50_ewconplus_wzc_seed2_2__323	.147
75	focalnet_tiny_lrf_in1k	.144
76	resnet-152_v2_pytorch	.144
77	inception_v3-tf	.140
78	resnet50_ewconplus_wzc	.139
79	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.139
80	AdvProp_efficientnet-b6	.139
81	efficientnet-b7	.138
82	resnet50_byol	.133
83	pnasnet_large_pytorch	.132
84	resnet50_st_wzc_seed3_2_323	.132
85	convnext_base_fb_in1k	.132
86	antialias-resnet152	.132
87	cv_18_dagger_408_pretrained	.131
88	blt_vs	.128
89	alexnet_fov4	.124
90	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.124
91	inception_v3	.124
92	resnet50_st_wzc_2	.124
93	resnet50_ewconplus_wzc_seed5_2__323	.123
94	custom_model_cv_18_dagger_408	.122
95	resnet101	.116
96	resnet-18	.116
97	resnet50_st_wzc_seed7_2_323	.116
98	nasnet_large	.115
99	nasnet_large-tf	.115
100	resnet50-SIN_IN	.115
101	resnet50_random_linf8_perturb	.114
102	convnext_small_imagenet_100_seed-0	.113
103	densenet-201-keras	.113
104	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.113
105	deit_large_imagenet_full_seed-0	.109
106	resnet_101_v2	.109
107	AdvProp_efficientnet-b4	.107
108	squeezenet1_1	.107
109	resnet-152_v2-tf	.107
110	AT_efficientnet-b4	.107
111	vgg_16	.107
112	resnet_101_v1	.107
113	resnet50_imagenet_100_seed-0	.106
114	efficientnet_b0	.106
115	pnasnet_large-tf	.106
116	mobilenet_v2_1_0_160	.106
117	densenet-201	.105
118	densenet_201_pytorch	.105
119	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.105
120	densenet-121	.104
121	densenet-121-keras	.104
122	mobilenet_v2_1_0_128	.101
123	convnext_tiny_sup	.100
124	resnet50-vitoimagevidnet8	.099
125	inception_v1	.099
126	resnet50_hvs_wzc	.099
127	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.099
128	resnet-50_v2-tf	.098
129	mobilenet_v1_1.0_224	.098
130	tv_efficientnet-b1	.098
131	densenet-169	.098
132	resnet50_random_l2_perturb	.098
133	ReAlnet02	.093
134	AdvProp_efficientnet-b7	.092
135	texture_shape_resnet50_trained_on_SIN	.092
136	resnet-101_v1-tf	.092
137	resnet-34	.092
138	cornet_s	.092
139	alexnet_l2_3_robust	.092
140	alexnet_linf_8_robust	.092
141	ReAlnet10	.092
142	mobilenet_v2_0_5_192	.092
143	nasnet_mobile	.092
144	efficientnet_b0_imagenet_full	.091
145	efficientnet_b2_imagenet_full	.091
146	ReAlnet08	.091
147	resnet18_imagenet_full	.091
148	mobilenet_v2_0_75_160	.091
149	ReAlnet01	.091
150	CORnet-S	.091
151	inception_v1-tf	.091
152	densenet-169-keras	.090
153	resnet50-moclr8deg	.090
154	inception_v2-tf	.085
155	konkle_alexnetgn_ipcl_ref01_primary_model	.085
156	ReAlnet07	.084
157	resnet50_imagenet_full	.084
158	mobilenet_v2_1_0_224	.084
159	vonegrcnn_52e_full	.084
160	BiT-S-R152x2	.084
161	voneresnet-50-non_stochastic	.084
162	mobilenet_v2_0.5_192-tf	.084
163	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
164	mobilenet_v2_0.35_160	.084
165	alexnet_robust_correct	.084
166	vgg_19	.084
167	antialiased-rnext101_32x8d	.084
168	ReAlnet05	.084
169	AlexNet_SIN_fov12	.084
170	resnet50_eMMCR_Vanilla	.084
171	ViT_B_16_imagenet1k	.084
172	ReAlnet06	.083
173	ReAlnet04	.083
174	ReAlnet03	.083
175	mobilenet_v1_0.25_160	.079
176	0.5x_resnet-18_LC_w_sh_1_iter	.078
177	antialiased-r50	.078
178	vonegrcnn_62e_nobn	.077
179	mobilenet_v2_0_75_224	.077
180	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.077
181	resnet50_ewconplus_wzc_seed6_2_323	.077
182	mobilenet_v1_1.0_192	.077
183	mobilenet_v2_0.35_192	.077
184	mobilenet_v1_0.5_128	.077
185	resnet50	.077
186	vonealexnet_gaussian_noise_std4_fixed	.077
187	vonegrcnn_47e	.076
188	deit_base_imagenet_full_seed-0	.076
189	xception	.076
190	mobilenet_v2_1.4_224-tf	.076
191	BiT-S-R50x1	.076
192	BiT-S-R152x4	.076
193	mobilenet_v2_0.5_128	.076
194	alexnet_early_checkpoint	.076
195	alexnet_reduced_aliasing_early_checkpoint	.076
196	Res2Net50_26w_4s	.076
197	mobilenet_v1_1.0_160	.075
198	ReAlnet09	.075
199	mobilenet_v2_1_3_224	.075
200	grcnn	.075
201	mobilenet_v1_0.75_128	.074
202	resnet101_imagenet_full	.074
203	mobilenet_v2_0.75_96	.074
204	effnetb1_272x240	.074
205	convnext_base_fb_in1k_gram_aligned_dinov3	.072
206	resnet34_imagenet_full	.070
207	mobilenet_v2_0_5_224	.070
208	vonegrcnn_52e_full	.070
209	mobilenet_v2_1.0_128-tf	.070
210	resnet50-SIN_IN_IN	.069
211	resnet_SIN_IN_FT_IN	.069
212	mobilenet_v2_1.0_224-tf	.069
213	AdvProp_efficientnet-b8	.069
214	alexnet	.069
215	shufflenet_v2_x1_0	.069
216	alexnet_ks_torevert	.069
217	resnet-50-pytorch	.068
218	BiT-S-R50x3	.068
219	resnet-152_v1-tf	.068
220	resnet50-sup	.068
221	mobilenet_v1_0.5_192	.068
222	ViT_L_32_imagenet1k	.068
223	resnet50_tutorial	.068
224	grcnn_109	.068
225	resnet_50_v1	.068
226	BiT-S-R101x3	.066
227	efficientnet_b1_imagenet_full	.064
228	squeezenet1_0	.063
229	mobilenet_v2_0.75_224-tf	.063
230	mobilenet_v1_0.25_224	.063
231	resnet50_moco_v2	.063
232	voneresnet-50	.062
233	vonegrcnn_62e_nobn	.062
234	resnet50-SIN	.062
235	ViT_B_32_imagenet1k	.062
236	vgg-16-keras	.062
237	mobilenet_v2_0.35_128	.062
238	regnet_y_400mf	.062
239	mobilenet_v2_1_0_192	.062
240	vonegrcnn_47e	.062
241	mobilenet_v1_0.5_224	.062
242	mobilenet_v1_0.75_160	.062
243	resnet152_imagenet_full	.062
244	mobilenet_v2_1-4_224_pytorch	.061
245	deit_small_imagenet_full_seed-0	.061
246	mobilenet_v2_1_4_224	.061
247	resnet_152_v1	.060
248	resnet50_imagenet_1_seed-0	.059
249	resnet-50_untrained	.059
250	alexnet_random_l2_3_perturb	.056
251	mobilenet_v2_0_75_192	.056
252	voneresnet-50-robust	.055
253	mobilenet_v2_0.75_192-tf	.055
254	texture_shape_alexnet_trained_on_SIN	.055
255	mobilenet_v2_0.5_224-tf	.055
256	mobilenet_v2_1.0_160-tf	.055
257	nasnet_mobile-tf	.055
258	BiT-S-R101x1	.055
259	mobilenet_v2_1.3_224-tf	.055
260	resnet-50-robust	.055
261	vgg-19-keras	.055
262	mobilenet_v2_1.0_96	.055
263	mobilenet_v1_0.75_192	.055
264	mobilenet_v1_1.0_128	.055
265	mobilenet_v2_0.75_128	.055
266	mobilenet_v2_0.35_96	.055
267	AdvProp_efficientnet-b0	.055
268	mobilenet_v2_0.5_96	.055
269	resnet50_l2_3_robust	.055
270	resnet50_linf_4_robust	.055
271	resnet50_linf_8_robust	.055
272	resnet50_FractalDB	.055
273	imagenet_l2_3_0	.055
274	imagenet_l2_10_0	.055
275	resnet50_robust_l2_eps3	.055
276	resnet50_robust_l2_eps1	.055
277	AlexNet_SIN	.055
278	alexnet_fov12	.055
279	alexnet_fov16	.055
280	AlexNet_SIN_fov16	.055
281	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.055
282	mobilenet_v2_0.75_160-tf	.053
283	mobilenet_v1_0.25_192	.053
284	mobilenet_v2_0.35_224	.053
285	mobilenet_v2_0.5_160	.053
286	eBarlow_lmda_02_1000ep	.053
287	mobilevit_small	.050
288	resnet50_simclr	.046
289	mobilenet_v1_0.75_224	.046
290	resnet50-barlow	.042
291	convnext_small_imagenet_1_seed-0	.042
292	resnet-50_v1-tf	.039
293	resnet_50_v1_spiking_l4a	.039
294	alexnet_random_linf8_perturb	.038
295	resnet50-vicregl0p9	.037
296	alexnet_training_seed_03	.036
297	resnet_50_v1_spiking	.035
298	mobilenet_v2_1.0_192-tf	.034
299	alexnet_training_seed_09	.034
300	mobilenet_v1_0.25_128	.032
301	alexnet_training_seed_02	.032
302	alexnet_training_seed_08	.032
303	alexnet_training_seed_04	.032
304	alexnet_training_seed_10	.032
305	alexnet_training_seed_07	.032
306	mobilenet_v1_0.5_160	.032
307	alexnet_training_seed_05	.024
308	alexnet_training_seed_01	.024
309	alexnet_training_seed_01_fov4	.024
310	alexnet_training_seed_01_fov12	.024
311	alexnet_training_seed_01_fov16	.024
312	barlow-twins-resnet50	.024
313	resnet50-vicreg	.024
314	resnet_50_v1_spiking_l4	.023
315	deit_small_imagenet_10_seed-0	.021
316	resnet50_imagenet_10_seed-0	.021
317	convnext_small_imagenet_10_seed-0	.021
318	deit_small_imagenet_1_seed-0	.021
319	artResNet18_1	.021
320	resnet_50_v1_spiking_l3	.015
321	deit_small_imagenet_100_seed-0	.014
322	resnet50-simclr-vissl	.011
323	resnet50-vicregl0p75	.010
324	alexnet_training_seed_06	.007
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	BiT-M-R152x2
330	resnet-18-LC_w_sh_1_iter_conv_init_m
331	resnet-18-LC_w_sh_1_iter_m
332	resnet-18-LC_w_sh_10_iter_m
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_10_iter_conv_init_m
335	FrankRobWobv0
336	resnet18-local_aggregation
337	0.5x_resnet-18_LC
338	CORnetZ_CIFAR10
339	resnet18-deepcluster
340	r3m_resnet18
341	CORnet-Z
342	resnet18-instance_recognition
343	my-model
344	vggface
345	resnet18-colorization
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-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	omnivore_swinT
369	resnet-18-LC_untrained
370	resnet-18_untrained
371	resnet-50x2_untrained
372	dcgan
373	v1-pyr-nodown
374	unet_entire
375	CORnetZ_CIFAR10_bs32_20_04
376	pixels
377	dorinet_cornet_z
378	my-model
379	openclip
380	resnet50-cifar
381	resnet50_pretrained_with_retinal_waves
382	resnet50_ImageNet
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	yudixie_resnet50_random_0_240908
385	yudixie_resnet50_imagenet1kpret_0_240908
386	yudixie_resnet50_object_class_0_240908
387	yudixie_resnet50_category_class_0_240908
388	resnet18_imagenet21kP
389	yudixie_resnet50_distance_reg_0_240908
390	ConvLSTM
391	PredRNN
392	SimVP
393	TAU
394	MIM
395	vit_base_patch16_dinov3-lvd1689m
396	ReAlnet01_cornet
397	briaai_rmbg_1_4
398	yudixie_resnet18_translation_reg_0_240719
399	yudixie_resnet18_distance_reg_0_240719
400	yudixie_resnet18_rotation_reg_0_240719
401	yolos_tiny
402	yudixie_resnet18_imagenet1kpret_0_240719
403	ReAlnet02_cornet
404	compact_resnet50_V4
405	vit_base_patch14_dinov2-lvd142m
406	vit_base_patch14_reg4_dinov2-lvd142m
407	ReAlnet03_cornet
408	compact_resnet50_IT
409	ReAlnet01_cornet_fov4
410	resnet101_ecoset_full
411	ReAlnet01_cornet_fov12
412	compact_resnet50_robust_V4
413	yudixie_resnet50_rotation_reg_0_240908
414	resnet152_ecoset_full
415	yudixie_resnet50_distance_rotation_0_240908
416	yudixie_resnet50_distance_translation_0_240908
417	yudixie_resnet18_translation_rotation_0_240719
418	yudixie_resnet18_distance_rotation_0_240719
419	compact_vgg19_V4
420	yudixie_resnet18_category_class_0_240719
421	aughost_fair_resnet18
422	ReAlnet05_cornet
423	resnet-18_test_m
424	SeLaVi-AVE
425	yudixie_resnet18_distance_translation_rotation_0_240719
426	yudixie_resnet18_object_class_0_240719
427	hmax
428	ReAlnet06_cornet
429	r3d_18
430	compact_vgg19_IT
431	ReAlnet01_cornet_fov16
432	yudixie_resnet50_translation_reg_0_240908
433	mc3_18
434	ReAlnet01_ALL
435	yudixie_resnet50_translation_rotation_0_240908
436	s3d
437	ReAlnet07_cornet
438	mvit_v1_b
439	ReAlnet02_ALL
440	ReAlnet08_cornet
441	ReAlnet03_ALL
442	compact_cornets_V4
443	VOneCORnet-S
444	ReAlnet04_ALL
445	omnivore_swinB
446	ReAlnet09_cornet
447	SeLaVi-VGG-Sound
448	compact_cornets_IT
449	ReAlnet05_ALL
450	SeLaVi-Kinetics-Sound
451	SeLaVi-Kinetics400
452	r2plus1d_18
453	ReAlnet06_ALL
454	omnivore_swinS
455	mvit_v2_s
456	compact_resnet50_robust_IT
457	ReAlnet10_cornet
458	resnet18_ecoset_full
459	resnet34_ecoset_full
460	ReAlnet07_ALL
461	resnet50_ecoset_full
462	yudixie_resnet18_cat_obj_class_all_latents_0_240719
463	yudixie_resnet18_random_0_240719
464	ReAlnet08_ALL
465	yudixie_resnet18_distance_translation_0_240719
466	yudixie_resnet50_imagenet1kpret_0_240312
467	ReAlnet04_cornet
468	yudixie_resnet50_distance_translation_rotation_0_240908
469	yudixie_resnet50_cat_obj_class_all_latents_0_240908
470	resnet50_primary_visual_cortex
471	ReAlnet09_ALL
472	ReAlnet10_ALL
473	resnet18_fair
474	pr1_stl10_disentangled_alpha04
475	predictive_coding_pc
476	yudixie_wbh_object_class_scaling_0415_run_0
477	yudixie_wbh_object_class_scaling_0415_run_3
478	yudixie_wbh_object_class_scaling_0415_imagenet
479	yudixie_wbh_object_class_scaling_0415_run_2
480	yudixie_wbh_object_class_scaling_0415_run_1
481	deepeisnn_ei_cifar10_vgg19
482	deepeisnn_ei_cifar10_resnet18
483	deepeisnn_ei_imagenet_resnet18
484	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

0.32.

Note that scores are relative to this ceiling.

Data: Geirhos2021edge

Metric: error_consistency