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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	SWSL_resnet50	.395
29	convnext_large_imagenet_full_seed-0	.285
30	resnet50-VITO-8deg-cc	.264
31	convnext_tiny_imagenet_full_seed-0	.264
32	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.252
33	cvt_cvt-21-384-in1k_4_LucyV4	.243
34	convnext_tiny:in12k_ft_in1k	.232
35	convnext_base_imagenet_full_seed-0	.219
36	AlexNet_SIN_fov4	.200
37	convnext_small_imagenet_full_seed-0	.198
38	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.195
39	xception-keras	.194
40	vit_relpos_base_patch16_clsgap_224:sw_in1k	.192
41	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.187
42	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.184
43	effnetv2m_custom384	.183
44	inception_v4	.182
45	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.182
46	inception_v3_pytorch	.175
47	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.173
48	cvt_cvt-21-224-in1k_4_LucyV4	.168
49	AT_efficientnet-b2	.163
50	AdvProp_efficientnet-b2	.163
51	cvt_cvt-13-384-in1k_4_LucyV4	.160
52	convnext_femto_ols:d1_in1k	.158
53	pnasnet_large_halfPrecision	.153
54	pnasnet_large	.153
55	cvt_cvt-13-224-in1k_4_LucyV4	.151
56	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.151
57	resnet_152_v2	.151
58	resnet_50_v2	.151
59	inception_v4-tf	.148
60	focalnet_tiny_lrf_in1k	.144
61	resnet-152_v2_pytorch	.144
62	inception_v3-tf	.140
63	resnet50_ewconplus_wzc	.139
64	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.139
65	AdvProp_efficientnet-b6	.139
66	efficientnet-b7	.138
67	resnet50_byol	.133
68	pnasnet_large_pytorch	.132
69	antialias-resnet152	.132
70	cv_18_dagger_408_pretrained	.131
71	blt_vs	.128
72	alexnet_fov4	.124
73	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.124
74	inception_v3	.124
75	custom_model_cv_18_dagger_408	.122
76	resnet101	.116
77	resnet-18	.116
78	nasnet_large	.115
79	nasnet_large-tf	.115
80	resnet50-SIN_IN	.115
81	resnet50_random_linf8_perturb	.114
82	convnext_small_imagenet_100_seed-0	.113
83	densenet-201-keras	.113
84	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.113
85	deit_large_imagenet_full_seed-0	.109
86	resnet_101_v2	.109
87	AdvProp_efficientnet-b4	.107
88	squeezenet1_1	.107
89	resnet-152_v2-tf	.107
90	AT_efficientnet-b4	.107
91	vgg_16	.107
92	resnet_101_v1	.107
93	resnet50_imagenet_100_seed-0	.106
94	efficientnet_b0	.106
95	pnasnet_large-tf	.106
96	mobilenet_v2_1_0_160	.106
97	densenet-201	.105
98	densenet_201_pytorch	.105
99	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.105
100	densenet-121	.104
101	densenet-121-keras	.104
102	mobilenet_v2_1_0_128	.101
103	convnext_tiny_sup	.100
104	resnet50-vitoimagevidnet8	.099
105	inception_v1	.099
106	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.099
107	resnet-50_v2-tf	.098
108	mobilenet_v1_1.0_224	.098
109	tv_efficientnet-b1	.098
110	densenet-169	.098
111	resnet50_random_l2_perturb	.098
112	ReAlnet02	.093
113	AdvProp_efficientnet-b7	.092
114	texture_shape_resnet50_trained_on_SIN	.092
115	resnet-101_v1-tf	.092
116	resnet-34	.092
117	cornet_s	.092
118	alexnet_l2_3_robust	.092
119	alexnet_linf_8_robust	.092
120	ReAlnet10	.092
121	mobilenet_v2_0_5_192	.092
122	nasnet_mobile	.092
123	efficientnet_b0_imagenet_full	.091
124	efficientnet_b2_imagenet_full	.091
125	ReAlnet08	.091
126	resnet18_imagenet_full	.091
127	mobilenet_v2_0_75_160	.091
128	ReAlnet01	.091
129	CORnet-S	.091
130	inception_v1-tf	.091
131	densenet-169-keras	.090
132	resnet50-moclr8deg	.090
133	inception_v2-tf	.085
134	konkle_alexnetgn_ipcl_ref01_primary_model	.085
135	ReAlnet07	.084
136	resnet50_imagenet_full	.084
137	mobilenet_v2_1_0_224	.084
138	vonegrcnn_52e_full	.084
139	BiT-S-R152x2	.084
140	voneresnet-50-non_stochastic	.084
141	mobilenet_v2_0.5_192-tf	.084
142	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
143	mobilenet_v2_0.35_160	.084
144	alexnet_robust_correct	.084
145	vgg_19	.084
146	antialiased-rnext101_32x8d	.084
147	ReAlnet05	.084
148	AlexNet_SIN_fov12	.084
149	resnet50_eMMCR_Vanilla	.084
150	ViT_B_16_imagenet1k	.084
151	ReAlnet06	.083
152	ReAlnet04	.083
153	ReAlnet03	.083
154	mobilenet_v1_0.25_160	.079
155	0.5x_resnet-18_LC_w_sh_1_iter	.078
156	antialiased-r50	.078
157	vonegrcnn_62e_nobn	.077
158	mobilenet_v2_0_75_224	.077
159	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.077
160	mobilenet_v1_1.0_192	.077
161	mobilenet_v2_0.35_192	.077
162	mobilenet_v1_0.5_128	.077
163	resnet50	.077
164	vonealexnet_gaussian_noise_std4_fixed	.077
165	vonegrcnn_47e	.076
166	deit_base_imagenet_full_seed-0	.076
167	xception	.076
168	mobilenet_v2_1.4_224-tf	.076
169	BiT-S-R50x1	.076
170	BiT-S-R152x4	.076
171	mobilenet_v2_0.5_128	.076
172	alexnet_early_checkpoint	.076
173	alexnet_reduced_aliasing_early_checkpoint	.076
174	Res2Net50_26w_4s	.076
175	mobilenet_v1_1.0_160	.075
176	ReAlnet09	.075
177	mobilenet_v2_1_3_224	.075
178	grcnn	.075
179	mobilenet_v1_0.75_128	.074
180	resnet101_imagenet_full	.074
181	mobilenet_v2_0.75_96	.074
182	effnetb1_272x240	.074
183	resnet34_imagenet_full	.070
184	mobilenet_v2_0_5_224	.070
185	vonegrcnn_52e_full	.070
186	mobilenet_v2_1.0_128-tf	.070
187	resnet50-SIN_IN_IN	.069
188	resnet_SIN_IN_FT_IN	.069
189	mobilenet_v2_1.0_224-tf	.069
190	AdvProp_efficientnet-b8	.069
191	alexnet	.069
192	shufflenet_v2_x1_0	.069
193	alexnet_ks_torevert	.069
194	resnet-50-pytorch	.068
195	BiT-S-R50x3	.068
196	resnet-152_v1-tf	.068
197	resnet50-sup	.068
198	mobilenet_v1_0.5_192	.068
199	ViT_L_32_imagenet1k	.068
200	resnet50_tutorial	.068
201	grcnn_109	.068
202	resnet_50_v1	.068
203	BiT-S-R101x3	.066
204	efficientnet_b1_imagenet_full	.064
205	squeezenet1_0	.063
206	mobilenet_v2_0.75_224-tf	.063
207	mobilenet_v1_0.25_224	.063
208	resnet50_moco_v2	.063
209	voneresnet-50	.062
210	vonegrcnn_62e_nobn	.062
211	resnet50-SIN	.062
212	ViT_B_32_imagenet1k	.062
213	vgg-16-keras	.062
214	mobilenet_v2_0.35_128	.062
215	regnet_y_400mf	.062
216	mobilenet_v2_1_0_192	.062
217	vonegrcnn_47e	.062
218	mobilenet_v1_0.5_224	.062
219	mobilenet_v1_0.75_160	.062
220	resnet152_imagenet_full	.062
221	mobilenet_v2_1-4_224_pytorch	.061
222	deit_small_imagenet_full_seed-0	.061
223	mobilenet_v2_1_4_224	.061
224	resnet_152_v1	.060
225	resnet50_imagenet_1_seed-0	.059
226	resnet-50_untrained	.059
227	alexnet_random_l2_3_perturb	.056
228	mobilenet_v2_0_75_192	.056
229	voneresnet-50-robust	.055
230	mobilenet_v2_0.75_192-tf	.055
231	texture_shape_alexnet_trained_on_SIN	.055
232	mobilenet_v2_0.5_224-tf	.055
233	mobilenet_v2_1.0_160-tf	.055
234	nasnet_mobile-tf	.055
235	BiT-S-R101x1	.055
236	mobilenet_v2_1.3_224-tf	.055
237	resnet-50-robust	.055
238	vgg-19-keras	.055
239	mobilenet_v2_1.0_96	.055
240	mobilenet_v1_0.75_192	.055
241	mobilenet_v1_1.0_128	.055
242	mobilenet_v2_0.75_128	.055
243	mobilenet_v2_0.35_96	.055
244	AdvProp_efficientnet-b0	.055
245	mobilenet_v2_0.5_96	.055
246	resnet50-meshes-lt-100-original-scratch	.055
247	resnet50_l2_3_robust	.055
248	resnet50_linf_4_robust	.055
249	resnet50_linf_8_robust	.055
250	resnet50_FractalDB	.055
251	imagenet_l2_3_0	.055
252	imagenet_l2_10_0	.055
253	resnet50_robust_l2_eps3	.055
254	resnet50_robust_l2_eps1	.055
255	AlexNet_SIN	.055
256	alexnet_fov12	.055
257	alexnet_fov16	.055
258	AlexNet_SIN_fov16	.055
259	mobilenet_v2_0.75_160-tf	.053
260	mobilenet_v1_0.25_192	.053
261	mobilenet_v2_0.35_224	.053
262	mobilenet_v2_0.5_160	.053
263	eBarlow_lmda_02_1000ep	.053
264	mobilevit_small	.050
265	resnet50_simclr	.046
266	mobilenet_v1_0.75_224	.046
267	resnet50-barlow	.042
268	resnet50-meshes-lt-100-original-pretrained	.042
269	convnext_small_imagenet_1_seed-0	.042
270	resnet-50_v1-tf	.039
271	resnet_50_v1_spiking_l4a	.039
272	alexnet_random_linf8_perturb	.038
273	resnet50-vicregl0p9	.037
274	alexnet_training_seed_03	.036
275	resnet_50_v1_spiking	.035
276	mobilenet_v2_1.0_192-tf	.034
277	alexnet_training_seed_09	.034
278	mobilenet_v1_0.25_128	.032
279	alexnet_training_seed_02	.032
280	alexnet_training_seed_08	.032
281	alexnet_training_seed_04	.032
282	alexnet_training_seed_10	.032
283	alexnet_training_seed_07	.032
284	mobilenet_v1_0.5_160	.032
285	alexnet_training_seed_05	.024
286	alexnet_training_seed_01	.024
287	alexnet_training_seed_01_fov4	.024
288	alexnet_training_seed_01_fov12	.024
289	alexnet_training_seed_01_fov16	.024
290	barlow-twins-resnet50	.024
291	resnet50-vicreg	.024
292	resnet_50_v1_spiking_l4	.023
293	deit_small_imagenet_10_seed-0	.021
294	resnet50_imagenet_10_seed-0	.021
295	convnext_small_imagenet_10_seed-0	.021
296	deit_small_imagenet_1_seed-0	.021
297	artResNet18_1	.021
298	resnet_50_v1_spiking_l3	.015
299	deit_small_imagenet_100_seed-0	.014
300	resnet50-simclr-vissl	.011
301	resnet50-vicregl0p75	.010
302	alexnet_training_seed_06	.007
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_1_iter_m
310	resnet-18-LC_w_sh_10_iter_m
311	resnet-18-LC_w_sh_100_iter_m
312	resnet-18-LC_w_sh_10_iter_conv_init_m
313	FrankRobWobv0
314	resnet18-local_aggregation
315	0.5x_resnet-18_LC
316	CORnetZ_CIFAR10
317	resnet18-deepcluster
318	r3m_resnet18
319	CORnet-Z
320	resnet18-instance_recognition
321	my-model
322	vggface
323	resnet18-colorization
324	0.5x_resnet-18_LC_w_sh_10_iter
325	r3m_resnet50
326	r3m_resnet34
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	v1-pyr-nodown
352	unet_entire
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	yudixie_resnet50_imagenet1kpret_0_240908
365	yudixie_resnet50_object_class_0_240908
366	yudixie_resnet50_category_class_0_240908
367	resnet18_imagenet21kP
368	yudixie_resnet50_distance_reg_0_240908
369	ConvLSTM
370	PredRNN
371	SimVP
372	TAU
373	MIM
374	vit_base_patch16_dinov3-lvd1689m
375	ReAlnet01_cornet
376	briaai_rmbg_1_4
377	yudixie_resnet18_translation_reg_0_240719
378	yudixie_resnet18_distance_reg_0_240719
379	yudixie_resnet18_rotation_reg_0_240719
380	yolos_tiny
381	yudixie_resnet18_imagenet1kpret_0_240719
382	ReAlnet02_cornet
383	compact_resnet50_V4
384	vit_base_patch14_dinov2-lvd142m
385	vit_base_patch14_reg4_dinov2-lvd142m
386	ReAlnet03_cornet
387	compact_resnet50_IT
388	ReAlnet01_cornet_fov4
389	resnet101_ecoset_full
390	ReAlnet01_cornet_fov12
391	compact_resnet50_robust_V4
392	yudixie_resnet50_rotation_reg_0_240908
393	resnet152_ecoset_full
394	yudixie_resnet50_distance_rotation_0_240908
395	yudixie_resnet50_distance_translation_0_240908
396	yudixie_resnet18_translation_rotation_0_240719
397	yudixie_resnet18_distance_rotation_0_240719
398	compact_vgg19_V4
399	yudixie_resnet18_category_class_0_240719
400	aughost_fair_resnet18
401	ReAlnet05_cornet
402	resnet-18_test_m
403	SeLaVi-AVE
404	yudixie_resnet18_distance_translation_rotation_0_240719
405	yudixie_resnet18_object_class_0_240719
406	hmax
407	ReAlnet06_cornet
408	r3d_18
409	compact_vgg19_IT
410	ReAlnet01_cornet_fov16
411	yudixie_resnet50_translation_reg_0_240908
412	mc3_18
413	ReAlnet01_ALL
414	yudixie_resnet50_translation_rotation_0_240908
415	s3d
416	ReAlnet07_cornet
417	mvit_v1_b
418	ReAlnet02_ALL
419	ReAlnet08_cornet
420	ReAlnet03_ALL
421	compact_cornets_V4
422	VOneCORnet-S
423	ReAlnet04_ALL
424	omnivore_swinB
425	ReAlnet09_cornet
426	SeLaVi-VGG-Sound
427	compact_cornets_IT
428	ReAlnet05_ALL
429	SeLaVi-Kinetics-Sound
430	SeLaVi-Kinetics400
431	r2plus1d_18
432	ReAlnet06_ALL
433	omnivore_swinS
434	mvit_v2_s
435	compact_resnet50_robust_IT
436	ReAlnet10_cornet
437	resnet18_ecoset_full
438	resnet34_ecoset_full
439	ReAlnet07_ALL
440	resnet50_ecoset_full
441	yudixie_resnet18_cat_obj_class_all_latents_0_240719
442	yudixie_resnet18_random_0_240719
443	ReAlnet08_ALL
444	yudixie_resnet18_distance_translation_0_240719
445	yudixie_resnet50_imagenet1kpret_0_240312
446	ReAlnet04_cornet
447	yudixie_resnet50_distance_translation_rotation_0_240908
448	yudixie_resnet50_cat_obj_class_all_latents_0_240908
449	resnet50_primary_visual_cortex
450	ReAlnet09_ALL
451	ReAlnet10_ALL

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