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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_patch14_clip_336:laion2b_ft_in1k	1.0
3	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
4	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
5	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.995
6	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.979
7	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.899
8	CLIP_ViT-B_32	.854
9	vit_large_patch14_clip_224:openai_ft_in1k	.807
10	vit_base_patch16_clip_224:openai_ft_in1k	.753
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.744
12	SWSL_resnext101_32x8d	.728
13	resnext101_32x16d_wsl	.719
14	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.699
15	convnext_xlarge:fb_in22k_ft_in1k	.670
16	resnext101_32x32d_wsl	.645
17	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.635
18	resnext101_32x48d_wsl	.619
19	resnext101_32x8d_wsl	.618
20	CLIP-RN50	.608
21	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.543
22	vision_transformer_vit_large_patch16_224	.543
23	convnext_large:fb_in22k_ft_in1k	.531
24	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.519
25	CLIP_resnet50	.516
26	CLIP_resnet50_float32	.516
27	fixres_resnext101_32x48d_wsl	.502
28	convnext_base_fb_in22k_ft_in1k	.447
29	SWSL_resnet50	.395
30	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.363
31	convnext_large_imagenet_full_seed-0	.285
32	resnet50-VITO-8deg-cc	.264
33	convnext_tiny_imagenet_full_seed-0	.264
34	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.252
35	cvt_cvt-21-384-in1k_4_LucyV4	.243
36	convnext_tiny:in12k_ft_in1k	.232
37	convnext_base_imagenet_full_seed-0	.219
38	AlexNet_SIN_fov4	.200
39	convnext_small_imagenet_full_seed-0	.198
40	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.195
41	xception-keras	.194
42	vit_relpos_base_patch16_clsgap_224:sw_in1k	.192
43	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.187
44	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.184
45	effnetv2m_custom384	.183
46	inception_v4	.182
47	resnet50_ewconplus_wzc_2_323	.182
48	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.182
49	inception_v3_pytorch	.175
50	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.173
51	cvt_cvt-21-224-in1k_4_LucyV4	.168
52	AT_efficientnet-b2	.163
53	AdvProp_efficientnet-b2	.163
54	cvt_cvt-13-384-in1k_4_LucyV4	.160
55	convnext_femto_ols:d1_in1k	.158
56	resnet50_ewconplus_noseed_2_323	.158
57	pnasnet_large_halfPrecision	.153
58	pnasnet_large	.153
59	cvt_cvt-13-224-in1k_4_LucyV4	.151
60	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.151
61	resnet_152_v2	.151
62	resnet_50_v2	.151
63	resnet50_st_wzc_seed6_2_323	.150
64	resnet50_st_wzc_2_323	.148
65	resnet50_ewconplus_wzc_seed7_2_323	.148
66	inception_v4-tf	.148
67	resnet50_ewconplus_wzc_seed2_2__323	.147
68	focalnet_tiny_lrf_in1k	.144
69	resnet-152_v2_pytorch	.144
70	inception_v3-tf	.140
71	resnet50_ewconplus_wzc	.139
72	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.139
73	AdvProp_efficientnet-b6	.139
74	efficientnet-b7	.138
75	resnet50_st_wzc_noseed_2_323	.133
76	resnet50_byol	.133
77	pnasnet_large_pytorch	.132
78	resnet50_st_wzc_seed5_2_323	.132
79	resnet50_st_wzc_seed3_2_323	.132
80	convnext_base_fb_in1k	.132
81	antialias-resnet152	.132
82	cv_18_dagger_408_pretrained	.131
83	blt_vs	.128
84	alexnet_fov4	.124
85	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.124
86	inception_v3	.124
87	resnet50_st_wzc_2	.124
88	resnet50_ewconplus_wzc_seed5_2__323	.123
89	custom_model_cv_18_dagger_408	.122
90	resnet101	.116
91	resnet-18	.116
92	resnet50_st_wzc_seed7_2_323	.116
93	nasnet_large	.115
94	nasnet_large-tf	.115
95	resnet50-SIN_IN	.115
96	resnet50_random_linf8_perturb	.114
97	convnext_small_imagenet_100_seed-0	.113
98	densenet-201-keras	.113
99	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.113
100	deit_large_imagenet_full_seed-0	.109
101	resnet_101_v2	.109
102	AdvProp_efficientnet-b4	.107
103	squeezenet1_1	.107
104	resnet-152_v2-tf	.107
105	AT_efficientnet-b4	.107
106	vgg_16	.107
107	resnet_101_v1	.107
108	resnet50_imagenet_100_seed-0	.106
109	efficientnet_b0	.106
110	pnasnet_large-tf	.106
111	mobilenet_v2_1_0_160	.106
112	densenet-201	.105
113	densenet_201_pytorch	.105
114	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.105
115	densenet-121	.104
116	densenet-121-keras	.104
117	mobilenet_v2_1_0_128	.101
118	convnext_tiny_sup	.100
119	resnet50-vitoimagevidnet8	.099
120	inception_v1	.099
121	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.099
122	resnet-50_v2-tf	.098
123	mobilenet_v1_1.0_224	.098
124	tv_efficientnet-b1	.098
125	densenet-169	.098
126	resnet50_random_l2_perturb	.098
127	ReAlnet02	.093
128	AdvProp_efficientnet-b7	.092
129	texture_shape_resnet50_trained_on_SIN	.092
130	resnet-101_v1-tf	.092
131	resnet-34	.092
132	cornet_s	.092
133	alexnet_l2_3_robust	.092
134	alexnet_linf_8_robust	.092
135	ReAlnet10	.092
136	mobilenet_v2_0_5_192	.092
137	nasnet_mobile	.092
138	efficientnet_b0_imagenet_full	.091
139	efficientnet_b2_imagenet_full	.091
140	ReAlnet08	.091
141	resnet18_imagenet_full	.091
142	mobilenet_v2_0_75_160	.091
143	ReAlnet01	.091
144	CORnet-S	.091
145	inception_v1-tf	.091
146	densenet-169-keras	.090
147	resnet50-moclr8deg	.090
148	inception_v2-tf	.085
149	konkle_alexnetgn_ipcl_ref01_primary_model	.085
150	ReAlnet07	.084
151	resnet50_imagenet_full	.084
152	mobilenet_v2_1_0_224	.084
153	vonegrcnn_52e_full	.084
154	BiT-S-R152x2	.084
155	voneresnet-50-non_stochastic	.084
156	mobilenet_v2_0.5_192-tf	.084
157	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
158	mobilenet_v2_0.35_160	.084
159	alexnet_robust_correct	.084
160	vgg_19	.084
161	antialiased-rnext101_32x8d	.084
162	ReAlnet05	.084
163	AlexNet_SIN_fov12	.084
164	resnet50_eMMCR_Vanilla	.084
165	ViT_B_16_imagenet1k	.084
166	ReAlnet06	.083
167	ReAlnet04	.083
168	ReAlnet03	.083
169	mobilenet_v1_0.25_160	.079
170	0.5x_resnet-18_LC_w_sh_1_iter	.078
171	antialiased-r50	.078
172	vonegrcnn_62e_nobn	.077
173	mobilenet_v2_0_75_224	.077
174	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.077
175	resnet50_ewconplus_wzc_seed6_2_323	.077
176	mobilenet_v1_1.0_192	.077
177	mobilenet_v2_0.35_192	.077
178	mobilenet_v1_0.5_128	.077
179	resnet50	.077
180	vonealexnet_gaussian_noise_std4_fixed	.077
181	vonegrcnn_47e	.076
182	deit_base_imagenet_full_seed-0	.076
183	xception	.076
184	mobilenet_v2_1.4_224-tf	.076
185	BiT-S-R50x1	.076
186	BiT-S-R152x4	.076
187	mobilenet_v2_0.5_128	.076
188	alexnet_early_checkpoint	.076
189	alexnet_reduced_aliasing_early_checkpoint	.076
190	Res2Net50_26w_4s	.076
191	mobilenet_v1_1.0_160	.075
192	ReAlnet09	.075
193	mobilenet_v2_1_3_224	.075
194	grcnn	.075
195	mobilenet_v1_0.75_128	.074
196	resnet101_imagenet_full	.074
197	mobilenet_v2_0.75_96	.074
198	effnetb1_272x240	.074
199	convnext_base_fb_in1k_gram_aligned_dinov3	.072
200	resnet34_imagenet_full	.070
201	mobilenet_v2_0_5_224	.070
202	vonegrcnn_52e_full	.070
203	mobilenet_v2_1.0_128-tf	.070
204	resnet50-SIN_IN_IN	.069
205	resnet_SIN_IN_FT_IN	.069
206	mobilenet_v2_1.0_224-tf	.069
207	AdvProp_efficientnet-b8	.069
208	alexnet	.069
209	shufflenet_v2_x1_0	.069
210	alexnet_ks_torevert	.069
211	resnet-50-pytorch	.068
212	BiT-S-R50x3	.068
213	resnet-152_v1-tf	.068
214	resnet50-sup	.068
215	mobilenet_v1_0.5_192	.068
216	ViT_L_32_imagenet1k	.068
217	resnet50_tutorial	.068
218	grcnn_109	.068
219	resnet_50_v1	.068
220	BiT-S-R101x3	.066
221	efficientnet_b1_imagenet_full	.064
222	squeezenet1_0	.063
223	mobilenet_v2_0.75_224-tf	.063
224	mobilenet_v1_0.25_224	.063
225	resnet50_moco_v2	.063
226	voneresnet-50	.062
227	vonegrcnn_62e_nobn	.062
228	resnet50-SIN	.062
229	ViT_B_32_imagenet1k	.062
230	vgg-16-keras	.062
231	mobilenet_v2_0.35_128	.062
232	regnet_y_400mf	.062
233	mobilenet_v2_1_0_192	.062
234	vonegrcnn_47e	.062
235	mobilenet_v1_0.5_224	.062
236	mobilenet_v1_0.75_160	.062
237	resnet152_imagenet_full	.062
238	mobilenet_v2_1-4_224_pytorch	.061
239	deit_small_imagenet_full_seed-0	.061
240	mobilenet_v2_1_4_224	.061
241	resnet_152_v1	.060
242	resnet50_imagenet_1_seed-0	.059
243	resnet-50_untrained	.059
244	alexnet_random_l2_3_perturb	.056
245	mobilenet_v2_0_75_192	.056
246	voneresnet-50-robust	.055
247	mobilenet_v2_0.75_192-tf	.055
248	texture_shape_alexnet_trained_on_SIN	.055
249	mobilenet_v2_0.5_224-tf	.055
250	mobilenet_v2_1.0_160-tf	.055
251	nasnet_mobile-tf	.055
252	BiT-S-R101x1	.055
253	mobilenet_v2_1.3_224-tf	.055
254	resnet-50-robust	.055
255	vgg-19-keras	.055
256	mobilenet_v2_1.0_96	.055
257	mobilenet_v1_0.75_192	.055
258	mobilenet_v1_1.0_128	.055
259	mobilenet_v2_0.75_128	.055
260	mobilenet_v2_0.35_96	.055
261	AdvProp_efficientnet-b0	.055
262	mobilenet_v2_0.5_96	.055
263	resnet50-meshes-lt-100-original-scratch	.055
264	resnet50_l2_3_robust	.055
265	resnet50_linf_4_robust	.055
266	resnet50_linf_8_robust	.055
267	resnet50_FractalDB	.055
268	imagenet_l2_3_0	.055
269	imagenet_l2_10_0	.055
270	resnet50_robust_l2_eps3	.055
271	resnet50_robust_l2_eps1	.055
272	AlexNet_SIN	.055
273	alexnet_fov12	.055
274	alexnet_fov16	.055
275	AlexNet_SIN_fov16	.055
276	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.055
277	mobilenet_v2_0.75_160-tf	.053
278	mobilenet_v1_0.25_192	.053
279	mobilenet_v2_0.35_224	.053
280	mobilenet_v2_0.5_160	.053
281	eBarlow_lmda_02_1000ep	.053
282	mobilevit_small	.050
283	resnet50_simclr	.046
284	mobilenet_v1_0.75_224	.046
285	resnet50-barlow	.042
286	resnet50-meshes-lt-100-original-pretrained	.042
287	convnext_small_imagenet_1_seed-0	.042
288	resnet-50_v1-tf	.039
289	resnet_50_v1_spiking_l4a	.039
290	alexnet_random_linf8_perturb	.038
291	resnet50-vicregl0p9	.037
292	alexnet_training_seed_03	.036
293	resnet_50_v1_spiking	.035
294	mobilenet_v2_1.0_192-tf	.034
295	alexnet_training_seed_09	.034
296	mobilenet_v1_0.25_128	.032
297	alexnet_training_seed_02	.032
298	alexnet_training_seed_08	.032
299	alexnet_training_seed_04	.032
300	alexnet_training_seed_10	.032
301	alexnet_training_seed_07	.032
302	mobilenet_v1_0.5_160	.032
303	alexnet_training_seed_05	.024
304	alexnet_training_seed_01	.024
305	alexnet_training_seed_01_fov4	.024
306	alexnet_training_seed_01_fov12	.024
307	alexnet_training_seed_01_fov16	.024
308	barlow-twins-resnet50	.024
309	resnet50-vicreg	.024
310	resnet_50_v1_spiking_l4	.023
311	deit_small_imagenet_10_seed-0	.021
312	resnet50_imagenet_10_seed-0	.021
313	convnext_small_imagenet_10_seed-0	.021
314	deit_small_imagenet_1_seed-0	.021
315	artResNet18_1	.021
316	resnet_50_v1_spiking_l3	.015
317	deit_small_imagenet_100_seed-0	.014
318	resnet50-simclr-vissl	.011
319	resnet50-vicregl0p75	.010
320	alexnet_training_seed_06	.007
321	resnet-18-LC_w_sh_100_iter_conv_init_m
322	ViT-B/32
323	BiT-M-R50x3
324	BiT-M-R50x1
325	BiT-M-R152x2
326	resnet-18-LC_w_sh_1_iter_conv_init_m
327	resnet-18-LC_w_sh_1_iter_m
328	resnet-18-LC_w_sh_10_iter_m
329	resnet-18-LC_w_sh_100_iter_m
330	resnet-18-LC_w_sh_10_iter_conv_init_m
331	FrankRobWobv0
332	resnet18-local_aggregation
333	0.5x_resnet-18_LC
334	CORnetZ_CIFAR10
335	resnet18-deepcluster
336	r3m_resnet18
337	CORnet-Z
338	resnet18-instance_recognition
339	my-model
340	vggface
341	resnet18-colorization
342	0.5x_resnet-18_LC_w_sh_10_iter
343	r3m_resnet50
344	r3m_resnet34
345	resnet-18-LC_m
346	r3m_resnet50_nocrop
347	resnet18-supervised
348	resnet-18-LC_1st_conv_m
349	resnet-18-LC_conv_init_m
350	resnet18-contrastive_multiview
351	0.5x_resnet-18
352	RN50
353	0.5x_resnet-18_LC_w_sh_100_iter
354	RN50
355	resnet18-relative_position
356	resnet18-autoencoder
357	resnet18-simclr
358	ViT_L_32
359	ViT_B_32
360	resnet18-depth_prediction
361	ViT_B_16
362	r3m_resnet34_nocrop
363	resnet18-contrastive_predictive
364	omnivore_swinT
365	resnet-18-LC_untrained
366	resnet-18_untrained
367	resnet-50x2_untrained
368	dcgan
369	v1-pyr-nodown
370	unet_entire
371	CORnetZ_CIFAR10_bs32_20_04
372	pixels
373	dorinet_cornet_z
374	my-model
375	resnet50
376	openclip
377	resnet50-cifar
378	resnet50_pretrained_with_retinal_waves
379	resnet50_ImageNet
380	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
381	yudixie_resnet50_random_0_240908
382	yudixie_resnet50_imagenet1kpret_0_240908
383	yudixie_resnet50_object_class_0_240908
384	yudixie_resnet50_category_class_0_240908
385	resnet18_imagenet21kP
386	yudixie_resnet50_distance_reg_0_240908
387	ConvLSTM
388	PredRNN
389	SimVP
390	TAU
391	MIM
392	vit_base_patch16_dinov3-lvd1689m
393	ReAlnet01_cornet
394	briaai_rmbg_1_4
395	yudixie_resnet18_translation_reg_0_240719
396	yudixie_resnet18_distance_reg_0_240719
397	yudixie_resnet18_rotation_reg_0_240719
398	yolos_tiny
399	yudixie_resnet18_imagenet1kpret_0_240719
400	ReAlnet02_cornet
401	compact_resnet50_V4
402	vit_base_patch14_dinov2-lvd142m
403	vit_base_patch14_reg4_dinov2-lvd142m
404	ReAlnet03_cornet
405	compact_resnet50_IT
406	ReAlnet01_cornet_fov4
407	resnet101_ecoset_full
408	ReAlnet01_cornet_fov12
409	compact_resnet50_robust_V4
410	yudixie_resnet50_rotation_reg_0_240908
411	resnet152_ecoset_full
412	yudixie_resnet50_distance_rotation_0_240908
413	yudixie_resnet50_distance_translation_0_240908
414	yudixie_resnet18_translation_rotation_0_240719
415	yudixie_resnet18_distance_rotation_0_240719
416	compact_vgg19_V4
417	yudixie_resnet18_category_class_0_240719
418	aughost_fair_resnet18
419	ReAlnet05_cornet
420	resnet-18_test_m
421	SeLaVi-AVE
422	yudixie_resnet18_distance_translation_rotation_0_240719
423	yudixie_resnet18_object_class_0_240719
424	hmax
425	ReAlnet06_cornet
426	r3d_18
427	compact_vgg19_IT
428	ReAlnet01_cornet_fov16
429	yudixie_resnet50_translation_reg_0_240908
430	mc3_18
431	ReAlnet01_ALL
432	yudixie_resnet50_translation_rotation_0_240908
433	s3d
434	ReAlnet07_cornet
435	mvit_v1_b
436	ReAlnet02_ALL
437	ReAlnet08_cornet
438	ReAlnet03_ALL
439	compact_cornets_V4
440	VOneCORnet-S
441	ReAlnet04_ALL
442	omnivore_swinB
443	ReAlnet09_cornet
444	SeLaVi-VGG-Sound
445	compact_cornets_IT
446	ReAlnet05_ALL
447	SeLaVi-Kinetics-Sound
448	SeLaVi-Kinetics400
449	r2plus1d_18
450	ReAlnet06_ALL
451	omnivore_swinS
452	mvit_v2_s
453	compact_resnet50_robust_IT
454	ReAlnet10_cornet
455	resnet18_ecoset_full
456	resnet34_ecoset_full
457	ReAlnet07_ALL
458	resnet50_ecoset_full
459	yudixie_resnet18_cat_obj_class_all_latents_0_240719
460	yudixie_resnet18_random_0_240719
461	ReAlnet08_ALL
462	yudixie_resnet18_distance_translation_0_240719
463	yudixie_resnet50_imagenet1kpret_0_240312
464	ReAlnet04_cornet
465	yudixie_resnet50_distance_translation_rotation_0_240908
466	yudixie_resnet50_cat_obj_class_all_latents_0_240908
467	resnet50_primary_visual_cortex
468	ReAlnet09_ALL
469	ReAlnet10_ALL
470	resnet18_fair
471	pr1_stl10_disentangled_alpha04
472	predictive_coding_pc
473	yudixie_wbh_object_class_scaling_0415_run_0
474	yudixie_wbh_object_class_scaling_0415_run_3
475	yudixie_wbh_object_class_scaling_0415_imagenet
476	yudixie_wbh_object_class_scaling_0415_run_2
477	yudixie_wbh_object_class_scaling_0415_run_1
478	deepeisnn_ei_cifar10_vgg19
479	deepeisnn_ei_cifar10_resnet18
480	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

0.32.

Note that scores are relative to this ceiling.

Data: Geirhos2021edge

Metric: error_consistency