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	1.2
2	vit_large_patch14_clip_336:laion2b_ft_in1k	1.2
3	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.2
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	convnext_small_imagenet_full_seed-0	.198
37	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.195
38	xception-keras	.194
39	vit_relpos_base_patch16_clsgap_224:sw_in1k	.192
40	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.187
41	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.184
42	effnetv2m_custom384	.183
43	inception_v4	.182
44	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.182
45	inception_v3_pytorch	.175
46	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.173
47	cvt_cvt-21-224-in1k_4_LucyV4	.168
48	AdvProp_efficientnet-b2	.163
49	AT_efficientnet-b2	.163
50	cvt_cvt-13-384-in1k_4_LucyV4	.160
51	convnext_femto_ols:d1_in1k	.158
52	pnasnet_large	.153
53	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.151
54	resnet_152_v2	.151
55	resnet_50_v2	.151
56	cvt_cvt-13-224-in1k_4_LucyV4	.151
57	inception_v4-tf	.148
58	focalnet_tiny_lrf_in1k	.144
59	resnet-152_v2_pytorch	.144
60	inception_v3-tf	.140
61	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.139
62	AdvProp_efficientnet-b6	.139
63	efficientnet-b7	.138
64	resnet50_byol	.133
65	pnasnet_large_pytorch	.132
66	antialias-resnet152	.132
67	cv_18_dagger_408_pretrained	.131
68	blt_vs	.128
69	inception_v3	.124
70	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.124
71	custom_model_cv_18_dagger_408	.122
72	resnet101	.116
73	resnet-18	.116
74	nasnet_large	.115
75	resnet50-SIN_IN	.115
76	nasnet_large-tf	.115
77	resnet50_random_linf8_perturb	.114
78	convnext_small_imagenet_100_seed-0	.113
79	densenet-201-keras	.113
80	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.113
81	deit_large_imagenet_full_seed-0	.109
82	resnet_101_v2	.109
83	squeezenet1_1	.107
84	resnet-152_v2-tf	.107
85	vgg_16	.107
86	AdvProp_efficientnet-b4	.107
87	AT_efficientnet-b4	.107
88	resnet_101_v1	.107
89	resnet50_imagenet_100_seed-0	.106
90	efficientnet_b0	.106
91	pnasnet_large-tf	.106
92	mobilenet_v2_1_0_160	.106
93	densenet-201	.105
94	densenet_201_pytorch	.105
95	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.105
96	densenet-121	.104
97	densenet-121-keras	.104
98	mobilenet_v2_1_0_128	.101
99	convnext_tiny_sup	.100
100	resnet50-vitoimagevidnet8	.099
101	inception_v1	.099
102	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.099
103	resnet-50_v2-tf	.098
104	mobilenet_v1_1.0_224	.098
105	tv_efficientnet-b1	.098
106	densenet-169	.098
107	resnet50_random_l2_perturb	.098
108	ReAlnet02	.093
109	texture_shape_resnet50_trained_on_SIN	.092
110	AdvProp_efficientnet-b7	.092
111	resnet-34	.092
112	cornet_s	.092
113	resnet-101_v1-tf	.092
114	alexnet_linf_8_robust	.092
115	alexnet_l2_3_robust	.092
116	mobilenet_v2_0_5_192	.092
117	ReAlnet10	.092
118	nasnet_mobile	.092
119	efficientnet_b0_imagenet_full	.091
120	ReAlnet08	.091
121	ReAlnet_incorrect	.091
122	ReAlnet01	.091
123	mobilenet_v2_0_75_160	.091
124	resnet18_imagenet_full	.091
125	efficientnet_b2_imagenet_full	.091
126	inception_v1-tf	.091
127	CORnet-S	.091
128	resnet50-moclr8deg	.090
129	densenet-169-keras	.090
130	konkle_alexnetgn_ipcl_ref01_primary_model	.085
131	inception_v2-tf	.085
132	ReAlnet07	.084
133	resnet50_imagenet_full	.084
134	vonegrcnn_52e_full	.084
135	mobilenet_v2_1_0_224	.084
136	BiT-S-R152x2	.084
137	antialiased-rnext101_32x8d	.084
138	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
139	vgg_19	.084
140	voneresnet-50-non_stochastic	.084
141	mobilenet_v2_0.5_192-tf	.084
142	mobilenet_v2_0.35_160	.084
143	alexnet_robust_correct	.084
144	ReAlnet05	.084
145	ViT_B_16_imagenet1k	.084
146	ReAlnet03	.083
147	ReAlnet06	.083
148	ReAlnet04	.083
149	mobilenet_v1_0.25_160	.079
150	0.5x_resnet-18_LC_w_sh_1_iter	.078
151	antialiased-r50	.078
152	vonegrcnn_62e_nobn	.077
153	mobilenet_v2_0_75_224	.077
154	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.077
155	mobilenet_v2_0.35_192	.077
156	mobilenet_v1_0.5_128	.077
157	resnet50	.077
158	mobilenet_v1_1.0_192	.077
159	vonealexnet_gaussian_noise_std4_fixed	.077
160	alexnet	.076
161	vonegrcnn_47e	.076
162	xception	.076
163	deit_base_imagenet_full_seed-0	.076
164	alexnet_reduced_aliasing_early_checkpoint	.076
165	mobilenet_v2_1.4_224-tf	.076
166	Res2Net50_26w_4s	.076
167	mobilenet_v2_0.5_128	.076
168	BiT-S-R152x4	.076
169	BiT-S-R50x1	.076
170	alexnet_early_checkpoint	.076
171	mobilenet_v1_1.0_160	.075
172	ReAlnet09	.075
173	mobilenet_v2_1_3_224	.075
174	grcnn	.075
175	mobilenet_v1_0.75_128	.074
176	resnet101_imagenet_full	.074
177	effnetb1_272x240	.074
178	mobilenet_v2_0.75_96	.074
179	resnet34_imagenet_full	.070
180	mobilenet_v2_0_5_224	.070
181	vonegrcnn_52e_full	.070
182	mobilenet_v2_1.0_128-tf	.070
183	resnet50-SIN_IN_IN	.069
184	resnet_SIN_IN_FT_IN	.069
185	alexnet-baseline	.069
186	AdvProp_efficientnet-b8	.069
187	alexnet	.069
188	alexnet	.069
189	alexnet	.069
190	alexnet	.069
191	alexnet	.069
192	alexnet	.069
193	alexnet	.069
194	mobilenet_v2_1.0_224-tf	.069
195	alexnet	.069
196	shufflenet_v2_x1_0	.069
197	alexnet	.069
198	alexnet_ks_torevert	.069
199	resnet-152_v1-tf	.068
200	BiT-S-R50x3	.068
201	ViT_L_32_imagenet1k	.068
202	resnet50-sup	.068
203	mobilenet_v1_0.5_192	.068
204	resnet-50-pytorch	.068
205	resnet_50_v1	.068
206	grcnn_109	.068
207	resnet50_tutorial	.068
208	BiT-S-R101x3	.066
209	efficientnet_b1_imagenet_full	.064
210	resnet50_moco_v2	.063
211	mobilenet_v1_0.25_224	.063
212	squeezenet1_0	.063
213	mobilenet_v2_0.75_224-tf	.063
214	voneresnet-50	.062
215	vonegrcnn_62e_nobn	.062
216	resnet50-SIN	.062
217	ViT_B_32_imagenet1k	.062
218	mobilenet_v2_0.35_128	.062
219	vgg-16-keras	.062
220	mobilenet_v2_1_0_192	.062
221	vonegrcnn_47e	.062
222	regnet_y_400mf	.062
223	mobilenet_v1_0.75_160	.062
224	mobilenet_v1_0.5_224	.062
225	resnet152_imagenet_full	.062
226	mobilenet_v2_1-4_224_pytorch	.061
227	mobilenet_v2_1_4_224	.061
228	deit_small_imagenet_full_seed-0	.061
229	resnet_152_v1	.060
230	resnet50_imagenet_1_seed-0	.059
231	resnet-50_untrained	.059
232	alexnet_random_l2_3_perturb	.056
233	mobilenet_v2_0_75_192	.056
234	voneresnet-50-robust	.055
235	mobilenet_v2_0.75_192-tf	.055
236	texture_shape_alexnet_trained_on_SIN	.055
237	imagenet_l2_10_0	.055
238	resnet50_FractalDB	.055
239	vgg-19-keras	.055
240	mobilenet_v2_1.0_96	.055
241	mobilenet_v2_1.3_224-tf	.055
242	mobilenet_v1_0.75_192	.055
243	resnet50_robust_l2_eps3	.055
244	resnet50_robust_l2_eps1	.055
245	imagenet_l2_3_0	.055
246	resnet-50-robust	.055
247	mobilenet_v2_0.5_224-tf	.055
248	mobilenet_v1_1.0_128	.055
249	BiT-S-R101x1	.055
250	resnet50_l2_3_robust	.055
251	mobilenet_v2_1.0_160-tf	.055
252	resnet50_linf_4_robust	.055
253	resnet50_linf_8_robust	.055
254	mobilenet_v2_0.5_96	.055
255	resnet50-meshes-lt-100-original-scratch	.055
256	mobilenet_v2_0.75_128	.055
257	mobilenet_v2_0.35_96	.055
258	nasnet_mobile-tf	.055
259	AdvProp_efficientnet-b0	.055
260	AlexNet_SIN	.055
261	mobilenet_v1_0.25_192	.053
262	mobilenet_v2_0.75_160-tf	.053
263	mobilenet_v2_0.35_224	.053
264	mobilenet_v2_0.5_160	.053
265	mobilevit_small	.050
266	resnet50_simclr	.046
267	mobilenet_v1_0.75_224	.046
268	resnet50-barlow	.042
269	resnet50-meshes-lt-100-original-pretrained	.042
270	convnext_small_imagenet_1_seed-0	.042
271	resnet-50_v1-tf	.039
272	alexnet_random_linf8_perturb	.038
273	resnet50-vicregl0p9	.037
274	alexnet_training_seed_03	.036
275	mobilenet_v2_1.0_192-tf	.034
276	alexnet_training_seed_09	.034
277	mobilenet_v1_0.25_128	.032
278	alexnet_training_seed_08	.032
279	alexnet_training_seed_04	.032
280	alexnet_training_seed_10	.032
281	alexnet_training_seed_07	.032
282	alexnet_training_seed_02	.032
283	mobilenet_v1_0.5_160	.032
284	alexnet_training_seed_01	.024
285	alexnet_training_seed_05	.024
286	resnet50-vicreg	.024
287	barlow-twins-resnet50	.024
288	deit_small_imagenet_10_seed-0	.021
289	resnet50_imagenet_10_seed-0	.021
290	convnext_small_imagenet_10_seed-0	.021
291	deit_small_imagenet_1_seed-0	.021
292	artResNet18_1	.021
293	deit_small_imagenet_100_seed-0	.014
294	resnet50-simclr-vissl	.011
295	resnet50-vicregl0p75	.010
296	alexnet_training_seed_06	.007
297	hmax	X
298	r3m_resnet50_nocrop	X
299	yudixie_resnet50_distance_reg_0_240908	X
300	ViT-B/32	X
301	resnet-18-LC_m	X
302	r3m_resnet34	X
303	r3m_resnet50	X
304	resnet-18-LC_conv_init_m	X
305	resnet18-contrastive_multiview	X
306	resnet18_imagenet21kP	X
307	0.5x_resnet-18	X
308	RN50	X
309	yudixie_resnet50_category_class_0_240908	X
310	ReAlnet01_cornet	X
311	my-model	X
312	0.5x_resnet-18_LC_w_sh_100_iter	X
313	RN50	X
314	0.5x_resnet-18_LC_w_sh_10_iter	X
315	resnet18-colorization	X
316	vggface	X
317	my-model	X
318	resnet18-relative_position	X
319	yudixie_resnet18_translation_reg_0_240719	X
320	BiT-M-R50x3	X
321	yudixie_resnet18_distance_reg_0_240719	X
322	yudixie_resnet18_rotation_reg_0_240719	X
323	yudixie_resnet18_imagenet1kpret_0_240719	X
324	BiT-M-R50x1	X
325	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
326	resnet18-autoencoder	X
327	ReAlnet02_cornet	X
328	resnet18-simclr	X
329	ReAlnet03_cornet	X
330	resnet18-instance_recognition	X
331	yudixie_resnet50_object_class_0_240908	X
332	resnet18_ecoset_full	X
333	CORnet-Z	X
334	yudixie_resnet50_imagenet1kpret_0_240908	X
335	resnet34_ecoset_full	X
336	yudixie_resnet50_imagenet1kpret_0_240312	X
337	resnet50_ecoset_full	X
338	ViT_L_32	X
339	resnet101_ecoset_full	X
340	yudixie_resnet50_rotation_reg_0_240908	X
341	resnet152_ecoset_full	X
342	yudixie_resnet50_random_0_240908	X
343	resnet-18-LC_w_sh_1_iter_conv_init_m	X
344	yudixie_resnet50_distance_rotation_0_240908	X
345	ReAlnet04_cornet	X
346	resnet-18-LC_w_sh_100_iter_conv_init_m	X
347	yudixie_resnet18_distance_translation_0_240719	X
348	yudixie_resnet50_distance_translation_0_240908	X
349	yudixie_resnet18_translation_rotation_0_240719	X
350	ViT_B_32	X
351	yudixie_resnet18_distance_rotation_0_240719	X
352	yudixie_resnet18_category_class_0_240719	X
353	ReAlnet05_cornet	X
354	yudixie_resnet18_random_0_240719	X
355	resnet-18_test_m	X
356	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
357	resnet18-depth_prediction	X
358	yudixie_resnet18_distance_translation_rotation_0_240719	X
359	yudixie_resnet18_object_class_0_240719	X
360	resnet50	X
361	r3m_resnet18	X
362	ReAlnet06_cornet	X
363	yudixie_resnet50_translation_reg_0_240908	X
364	ViT_B_16	X
365	resnet18-deepcluster	X
366	yudixie_resnet50_translation_rotation_0_240908	X
367	ReAlnet07_cornet	X
368	r3m_resnet34_nocrop	X
369	CORnetZ_CIFAR10	X
370	resnet18-contrastive_predictive	X
371	ReAlnet08_cornet	X
372	0.5x_resnet-18_LC	X
373	resnet18-local_aggregation	X
374	VOneCORnet-S	X
375	omnivore_swinB	X
376	omnivore_swinT	X
377	resnet-18-LC_untrained	X
378	ReAlnet09_cornet	X
379	openclip	X
380	resnet-18_untrained	X
381	FrankRobWobv0	X
382	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
383	resnet-50x2_untrained	X
384	dcgan	X
385	resnet50_ImageNet	X
386	omnivore_swinS	X
387	resnet50_pretrained_with_retinal_waves	X
388	resnet50-cifar	X
389	ReAlnet10_cornet	X
390	resnet-18-LC_w_sh_10_iter_conv_init_m	X
391	v1-pyr-nodown	X
392	unet_entire	X
393	resnet-18-LC_w_sh_100_iter_m	X
394	CORnetZ_CIFAR10_bs32_20_04	X
395	yudixie_resnet50_distance_translation_rotation_0_240908	X
396	resnet-18-LC_w_sh_1_iter_m	X
397	BiT-M-R152x2	X
398	pixels	X
399	resnet-18-LC_w_sh_10_iter_m	X
400	dorinet_cornet_z	X
401	ConvLSTM	X
402	PredRNN	X
403	resnet-18-LC_1st_conv_m	X
404	resnet18-supervised	X
405	SimVP	X
406	TAU	X
407	MIM	X

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