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_float32	.516
26	CLIP_resnet50	.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	AT_efficientnet-b2	.163
49	AdvProp_efficientnet-b2	.163
50	cvt_cvt-13-384-in1k_4_LucyV4	.160
51	convnext_femto_ols:d1_in1k	.158
52	pnasnet_large	.153
53	resnet_152_v2	.151
54	cvt_cvt-13-224-in1k_4_LucyV4	.151
55	resnet_50_v2	.151
56	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.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	nasnet_large-tf	.115
76	resnet50-SIN_IN	.115
77	resnet50_random_linf8_perturb	.114
78	convnext_small_imagenet_100_seed-0	.113
79	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.113
80	densenet-201-keras	.113
81	deit_large_imagenet_full_seed-0	.109
82	resnet_101_v2	.109
83	AT_efficientnet-b4	.107
84	vgg_16	.107
85	AdvProp_efficientnet-b4	.107
86	squeezenet1_1	.107
87	resnet-152_v2-tf	.107
88	resnet_101_v1	.107
89	resnet50_imagenet_100_seed-0	.106
90	pnasnet_large-tf	.106
91	efficientnet_b0	.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	AdvProp_efficientnet-b7	.092
110	texture_shape_resnet50_trained_on_SIN	.092
111	alexnet_linf_8_robust	.092
112	alexnet_l2_3_robust	.092
113	resnet-34	.092
114	cornet_s	.092
115	resnet-101_v1-tf	.092
116	ReAlnet10	.092
117	mobilenet_v2_0_5_192	.092
118	nasnet_mobile	.092
119	efficientnet_b0_imagenet_full	.091
120	efficientnet_b2_imagenet_full	.091
121	resnet18_imagenet_full	.091
122	ReAlnet08	.091
123	ReAlnet01	.091
124	mobilenet_v2_0_75_160	.091
125	inception_v1-tf	.091
126	CORnet-S	.091
127	densenet-169-keras	.090
128	resnet50-moclr8deg	.090
129	konkle_alexnetgn_ipcl_ref01_primary_model	.085
130	inception_v2-tf	.085
131	vonegrcnn_52e_full	.084
132	ReAlnet07	.084
133	resnet50_imagenet_full	.084
134	mobilenet_v2_1_0_224	.084
135	voneresnet-50-non_stochastic	.084
136	mobilenet_v2_0.35_160	.084
137	vgg_19	.084
138	mobilenet_v2_0.5_192-tf	.084
139	antialiased-rnext101_32x8d	.084
140	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
141	BiT-S-R152x2	.084
142	alexnet_robust_correct	.084
143	ReAlnet05	.084
144	ViT_B_16_imagenet1k	.084
145	ReAlnet06	.083
146	ReAlnet04	.083
147	ReAlnet03	.083
148	mobilenet_v1_0.25_160	.079
149	0.5x_resnet-18_LC_w_sh_1_iter	.078
150	antialiased-r50	.078
151	vonegrcnn_62e_nobn	.077
152	mobilenet_v2_0_75_224	.077
153	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.077
154	vonealexnet_gaussian_noise_std4_fixed	.077
155	mobilenet_v2_0.35_192	.077
156	mobilenet_v1_0.5_128	.077
157	mobilenet_v1_1.0_192	.077
158	resnet50	.077
159	alexnet	.076
160	vonegrcnn_47e	.076
161	deit_base_imagenet_full_seed-0	.076
162	xception	.076
163	alexnet_reduced_aliasing_early_checkpoint	.076
164	BiT-S-R152x4	.076
165	mobilenet_v2_1.4_224-tf	.076
166	mobilenet_v2_0.5_128	.076
167	alexnet_early_checkpoint	.076
168	BiT-S-R50x1	.076
169	Res2Net50_26w_4s	.076
170	mobilenet_v1_1.0_160	.075
171	ReAlnet09	.075
172	grcnn	.075
173	mobilenet_v2_1_3_224	.075
174	mobilenet_v1_0.75_128	.074
175	resnet101_imagenet_full	.074
176	effnetb1_272x240	.074
177	mobilenet_v2_0.75_96	.074
178	resnet34_imagenet_full	.070
179	mobilenet_v2_0_5_224	.070
180	vonegrcnn_52e_full	.070
181	mobilenet_v2_1.0_128-tf	.070
182	resnet_SIN_IN_FT_IN	.069
183	resnet50-SIN_IN_IN	.069
184	mobilenet_v2_1.0_224-tf	.069
185	alexnet	.069
186	alexnet	.069
187	alexnet	.069
188	alexnet	.069
189	alexnet	.069
190	alexnet	.069
191	alexnet	.069
192	alexnet	.069
193	alexnet-baseline	.069
194	shufflenet_v2_x1_0	.069
195	alexnet	.069
196	AdvProp_efficientnet-b8	.069
197	alexnet_ks_torevert	.069
198	resnet-50-pytorch	.068
199	resnet50-sup	.068
200	ViT_L_32_imagenet1k	.068
201	resnet-152_v1-tf	.068
202	mobilenet_v1_0.5_192	.068
203	BiT-S-R50x3	.068
204	grcnn_109	.068
205	resnet_50_v1	.068
206	resnet50_tutorial	.068
207	BiT-S-R101x3	.066
208	efficientnet_b1_imagenet_full	.064
209	mobilenet_v2_0.75_224-tf	.063
210	resnet50_moco_v2	.063
211	mobilenet_v1_0.25_224	.063
212	squeezenet1_0	.063
213	vonegrcnn_62e_nobn	.062
214	voneresnet-50	.062
215	resnet50-SIN	.062
216	ViT_B_32_imagenet1k	.062
217	vgg-16-keras	.062
218	mobilenet_v2_0.35_128	.062
219	vonegrcnn_47e	.062
220	mobilenet_v2_1_0_192	.062
221	regnet_y_400mf	.062
222	mobilenet_v1_0.75_160	.062
223	mobilenet_v1_0.5_224	.062
224	resnet152_imagenet_full	.062
225	deit_small_imagenet_full_seed-0	.061
226	mobilenet_v2_1_4_224	.061
227	mobilenet_v2_1-4_224_pytorch	.061
228	resnet_152_v1	.060
229	resnet50_imagenet_1_seed-0	.059
230	resnet-50_untrained	.059
231	alexnet_random_l2_3_perturb	.056
232	mobilenet_v2_0_75_192	.056
233	voneresnet-50-robust	.055
234	mobilenet_v2_0.75_192-tf	.055
235	texture_shape_alexnet_trained_on_SIN	.055
236	resnet50_robust_l2_eps3	.055
237	resnet50_robust_l2_eps1	.055
238	mobilenet_v2_1.0_160-tf	.055
239	resnet50-meshes-lt-100-original-scratch	.055
240	mobilenet_v2_0.5_96	.055
241	nasnet_mobile-tf	.055
242	resnet50_FractalDB	.055
243	AdvProp_efficientnet-b0	.055
244	mobilenet_v1_0.75_192	.055
245	BiT-S-R101x1	.055
246	mobilenet_v2_0.35_96	.055
247	resnet-50-robust	.055
248	mobilenet_v2_0.5_224-tf	.055
249	vgg-19-keras	.055
250	mobilenet_v2_1.0_96	.055
251	resnet50_linf_8_robust	.055
252	resnet50_linf_4_robust	.055
253	resnet50_l2_3_robust	.055
254	imagenet_l2_10_0	.055
255	mobilenet_v1_1.0_128	.055
256	imagenet_l2_3_0	.055
257	mobilenet_v2_0.75_128	.055
258	mobilenet_v2_1.3_224-tf	.055
259	AlexNet_SIN	.055
260	mobilenet_v1_0.25_192	.053
261	mobilenet_v2_0.75_160-tf	.053
262	mobilenet_v2_0.35_224	.053
263	mobilenet_v2_0.5_160	.053
264	mobilevit_small	.050
265	resnet50_simclr	.046
266	mobilenet_v1_0.75_224	.046
267	resnet50-meshes-lt-100-original-pretrained	.042
268	resnet50-barlow	.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_10	.032
280	alexnet_training_seed_02	.032
281	alexnet_training_seed_04	.032
282	alexnet_training_seed_07	.032
283	alexnet_training_seed_08	.032
284	mobilenet_v1_0.5_160	.032
285	alexnet_training_seed_05	.024
286	alexnet_training_seed_01	.024
287	barlow-twins-resnet50	.024
288	resnet50-vicreg	.024
289	resnet_50_v1_spiking_l4	.023
290	convnext_small_imagenet_10_seed-0	.021
291	deit_small_imagenet_10_seed-0	.021
292	resnet50_imagenet_10_seed-0	.021
293	deit_small_imagenet_1_seed-0	.021
294	artResNet18_1	.021
295	resnet_50_v1_spiking_l3	.015
296	deit_small_imagenet_100_seed-0	.014
297	resnet50-simclr-vissl	.011
298	resnet50-vicregl0p75	.010
299	alexnet_training_seed_06	.007
300	hmax
301	resnet-18-LC_w_sh_10_iter_conv_init_m
302	resnet18_imagenet21kP
303	FrankRobWobv0
304	yudixie_resnet50_category_class_0_240908
305	resnet18-local_aggregation
306	resnet-18-LC_w_sh_100_iter_m
307	0.5x_resnet-18_LC
308	CORnetZ_CIFAR10
309	resnet18-deepcluster
310	yudixie_resnet50_cat_obj_class_all_latents_0_240908
311	ReAlnet01_cornet
312	r3m_resnet18
313	yudixie_resnet50_object_class_0_240908
314	yudixie_resnet50_imagenet1kpret_0_240908
315	resnet-18-LC_w_sh_10_iter_m
316	CORnet-Z
317	briaai_rmbg_1_4
318	resnet-18-LC_w_sh_1_iter_m
319	yudixie_resnet50_imagenet1kpret_0_240312
320	yudixie_resnet50_random_0_240908
321	resnet18-instance_recognition
322	my-model
323	yudixie_resnet18_translation_reg_0_240719
324	vggface
325	yudixie_resnet18_distance_reg_0_240719
326	yudixie_resnet18_rotation_reg_0_240719
327	yolos_tiny
328	yudixie_resnet18_imagenet1kpret_0_240719
329	resnet18-colorization
330	0.5x_resnet-18_LC_w_sh_10_iter
331	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
332	ReAlnet02_cornet
333	resnet50_ImageNet
334	ReAlnet03_cornet
335	resnet50_pretrained_with_retinal_waves
336	resnet50-cifar
337	resnet18_ecoset_full
338	resnet-18-LC_w_sh_1_iter_conv_init_m
339	resnet34_ecoset_full
340	r3m_resnet50
341	resnet50_ecoset_full
342	r3m_resnet34
343	resnet101_ecoset_full
344	yudixie_resnet50_rotation_reg_0_240908
345	resnet152_ecoset_full
346	resnet-18-LC_m
347	r3m_resnet50_nocrop
348	yudixie_resnet50_distance_rotation_0_240908
349	ReAlnet04_cornet
350	BiT-M-R152x2
351	yudixie_resnet18_distance_translation_0_240719
352	yudixie_resnet50_distance_translation_0_240908
353	yudixie_resnet18_translation_rotation_0_240719
354	resnet18-supervised
355	yudixie_resnet18_distance_rotation_0_240719
356	yudixie_resnet18_category_class_0_240719
357	ReAlnet05_cornet
358	yudixie_resnet18_random_0_240719
359	resnet-18_test_m
360	yudixie_resnet18_cat_obj_class_all_latents_0_240719
361	resnet-18-LC_1st_conv_m
362	yudixie_resnet18_distance_translation_rotation_0_240719
363	yudixie_resnet18_object_class_0_240719
364	RN50
365	resnet-18-LC_conv_init_m
366	ReAlnet06_cornet
367	yudixie_resnet50_translation_reg_0_240908
368	resnet18-contrastive_multiview
369	0.5x_resnet-18
370	yudixie_resnet50_translation_rotation_0_240908
371	ReAlnet07_cornet
372	openclip
373	RN50
374	resnet50
375	ReAlnet08_cornet
376	my-model
377	BiT-M-R50x1
378	VOneCORnet-S
379	omnivore_swinB
380	dorinet_cornet_z
381	pixels
382	ReAlnet09_cornet
383	resnet18-relative_position
384	CORnetZ_CIFAR10_bs32_20_04
385	BiT-M-R50x3
386	unet_entire
387	v1-pyr-nodown
388	dcgan
389	resnet-50x2_untrained
390	omnivore_swinS
391	resnet-18_untrained
392	resnet-18-LC_untrained
393	omnivore_swinT
394	ReAlnet10_cornet
395	resnet18-contrastive_predictive
396	ViT-B/32
397	r3m_resnet34_nocrop
398	ViT_B_16
399	resnet18-depth_prediction
400	yudixie_resnet50_distance_translation_rotation_0_240908
401	0.5x_resnet-18_LC_w_sh_100_iter
402	ViT_B_32
403	ViT_L_32
404	resnet-18-LC_w_sh_100_iter_conv_init_m
405	resnet50_primary_visual_cortex
406	resnet18-simclr
407	resnet18-autoencoder
408	ConvLSTM
409	PredRNN
410	yudixie_resnet50_distance_reg_0_240908
411	SimVP
412	TAU
413	MIM

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