Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021lowpass-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	SWSL_resnext101_32x8d	.730
2	resnext101_32x32d_wsl	.695
3	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.690
4	convnext_large:fb_in22k_ft_in1k	.667
5	vit_large_patch14_clip_224:openai_ft_in1k	.663
6	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.648
7	fixres_resnext101_32x48d_wsl	.630
8	resnet50-VITO-8deg-cc	.625
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.623
10	SWSL_resnet50	.613
11	vit_large_patch14_clip_224:laion2b_ft_in1k	.603
12	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.602
13	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.600
14	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.589
15	resnext101_32x48d_wsl	.579
16	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.578
17	resnext101_32x8d_wsl	.563
18	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.554
19	CLIP_resnet50	.544
20	CLIP_resnet50_float32	.544
21	deit_large_imagenet_full_seed-0	.543
22	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
23	resnext101_32x16d_wsl	.535
24	vit_base_patch16_clip_224:openai_ft_in1k	.535
25	convnext_xlarge:fb_in22k_ft_in1k	.527
26	effnetv2m_custom384	.527
27	convnext_base:clip_laiona_augreg_ft_in1k_384	.516
28	vision_transformer_vit_large_patch16_224	.499
29	resnet_152_v2	.498
30	effnetb1_272x240	.497
31	CLIP_ViT-B_32	.496
32	cvt_cvt-13-384-in1k_4_LucyV4	.492
33	convnext_large_imagenet_full_seed-0	.471
34	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.463
35	vit_relpos_base_patch16_clsgap_224:sw_in1k	.463
36	deit_small_imagenet_full_seed-0	.460
37	deit_base_imagenet_full_seed-0	.460
38	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.454
39	focalnet_tiny_lrf_in1k	.436
40	convnext_tiny:in12k_ft_in1k	.435
41	CLIP-RN50	.419
42	convnext_tiny_imagenet_full_seed-0	.419
43	resnet_50_v2	.415
44	pnasnet_large	.410
45	ViT_B_16_imagenet1k	.409
46	ViT_L_32_imagenet1k	.406
47	inception_v4	.400
48	resnet_101_v2	.397
49	nasnet_large	.396
50	cvt_cvt-21-384-in1k_4_LucyV4	.391
51	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.385
52	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.382
53	convnext_small_imagenet_full_seed-0	.379
54	convnext_femto_ols:d1_in1k	.379
55	resnet-152_v2_pytorch	.371
56	cvt_cvt-21-224-in1k_4_LucyV4	.368
57	cvt_cvt-13-224-in1k_4_LucyV4	.361
58	convnext_base_imagenet_full_seed-0	.354
59	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.352
60	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.338
61	pnasnet_large-tf	.338
62	antialias-resnet152	.326
63	xception-keras	.317
64	cv_18_dagger_408_pretrained	.314
65	resnet101	.313
66	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.311
67	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.306
68	nasnet_large-tf	.305
69	inception_v3-tf	.299
70	inception_v4-tf	.295
71	resnet152_imagenet_full	.290
72	resnet-152_v2-tf	.285
73	resnet50_byol	.274
74	resnet_152_v1	.270
75	resnet_101_v1	.268
76	antialiased-rnext101_32x8d	.268
77	inception_v3	.260
78	convnext_tiny_sup	.260
79	AT_efficientnet-b4	.258
80	AdvProp_efficientnet-b4	.258
81	grcnn_109	.252
82	inception_v3_pytorch	.252
83	resnet50	.251
84	ViT_B_32_imagenet1k	.244
85	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.243
86	resnet50_moco_v2	.232
87	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.225
88	AdvProp_efficientnet-b6	.221
89	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.218
90	densenet-121-keras	.214
91	antialiased-r50	.214
92	resnet50_imagenet_full	.213
93	voneresnet-50-non_stochastic	.209
94	convnext_small_imagenet_100_seed-0	.207
95	densenet-169-keras	.203
96	AdvProp_efficientnet-b7	.203
97	resnet34_imagenet_full	.203
98	resnet-50_v2-tf	.202
99	densenet-121	.201
100	densenet-201-keras	.197
101	grcnn	.194
102	resnet50-moclr8deg	.187
103	densenet-169	.186
104	mobilenet_v2_1_0_224	.186
105	resnet50_tutorial	.185
106	resnet_50_v1	.185
107	resnet50-sup	.185
108	resnet-50-pytorch	.185
109	efficientnet-b7	.185
110	vgg_19	.184
111	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.180
112	BiT-S-R152x4	.175
113	pnasnet_large_pytorch	.175
114	Res2Net50_26w_4s	.174
115	xception	.168
116	resnet-50_v1-tf	.167
117	resnet101_imagenet_full	.165
118	AdvProp_efficientnet-b2	.165
119	AT_efficientnet-b2	.165
120	resnet18_imagenet_full	.163
121	densenet-201	.161
122	densenet_201_pytorch	.161
123	ReAlnet04	.156
124	custom_model_cv_18_dagger_408	.156
125	resnet-101_v1-tf	.155
126	vonegrcnn_62e_nobn	.153
127	efficientnet_b1_imagenet_full	.153
128	efficientnet_b0	.152
129	inception_v1-tf	.144
130	deit_small_imagenet_100_seed-0	.143
131	vonegrcnn_47e	.141
132	efficientnet_b2_imagenet_full	.140
133	resnet50_random_linf8_perturb	.140
134	vonegrcnn_52e_full	.139
135	resnet-152_v1-tf	.138
136	mobilenet_v2_1.0_224-tf	.135
137	ReAlnet03	.135
138	nasnet_mobile	.133
139	mobilenet_v2_1.4_224-tf	.133
140	voneresnet-50	.132
141	ReAlnet09	.132
142	vonegrcnn_62e_nobn	.130
143	resnet50-SIN_IN	.129
144	BiT-S-R50x3	.129
145	blt_vs	.129
146	mobilenet_v2_1_0_192	.127
147	BiT-S-R101x3	.127
148	ReAlnet07	.127
149	vonegrcnn_52e_full	.126
150	vonegrcnn_47e	.126
151	ReAlnet08	.126
152	resnet50_random_l2_perturb	.126
153	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
154	inception_v2-tf	.124
155	cornet_s	.124
156	AdvProp_efficientnet-b8	.123
157	mobilenet_v2_1-4_224_pytorch	.122
158	mobilenet_v2_1_4_224	.122
159	ReAlnet02	.121
160	voneresnet-50-robust	.119
161	mobilenet_v2_0_5_192	.116
162	ReAlnet06	.115
163	nasnet_mobile-tf	.113
164	resnet50-vitoimagevidnet8	.113
165	mobilenet_v1_0.75_224	.112
166	ReAlnet10	.112
167	mobilenet_v2_0_5_224	.111
168	vgg-19-keras	.111
169	resnet50_l2_3_robust	.110
170	resnet50_linf_4_robust	.110
171	inception_v1	.109
172	resnet_SIN_IN_FT_IN	.109
173	resnet50-SIN_IN_IN	.109
174	mobilenet_v2_1.0_160-tf	.109
175	mobilenet_v2_1_0_160	.108
176	resnet50_linf_8_robust	.107
177	imagenet_l2_3_0	.103
178	resnet50_robust_l2_eps3	.103
179	resnet-50-robust	.103
180	vgg_16	.100
181	ReAlnet01	.100
182	ReAlnet_incorrect	.100
183	BiT-S-R50x1	.100
184	CORnet-S	.097
185	vgg-16-keras	.097
186	efficientnet_b0_imagenet_full	.096
187	mobilenet_v2_1_3_224	.096
188	mobilenet_v2_0.5_192-tf	.096
189	mobilenet_v2_0.75_192-tf	.095
190	mobilenet_v1_1.0_160	.095
191	ReAlnet05	.094
192	mobilenet_v2_1.3_224-tf	.091
193	regnet_y_400mf	.087
194	imagenet_l2_10_0	.086
195	mobilenet_v2_1.0_192-tf	.085
196	deit_small_imagenet_10_seed-0	.085
197	deit_small_imagenet_1_seed-0	.085
198	resnet50_imagenet_10_seed-0	.085
199	mobilenet_v2_0.75_160-tf	.085
200	mobilenet_v2_0.5_224-tf	.084
201	mobilenet_v2_0_75_160	.084
202	resnet50_robust_l2_eps1	.082
203	mobilenet_v1_0.75_192	.081
204	alexnet_linf_8_robust	.081
205	resnet-18	.081
206	mobilenet_v2_0.75_96	.081
207	resnet-34	.080
208	alexnet	.079
209	convnext_small_imagenet_10_seed-0	.078
210	mobilenet_v2_1.0_96	.078
211	mobilenet_v2_0_75_192	.077
212	mobilenet_v2_0.35_224	.077
213	mobilenet_v2_0.5_96	.077
214	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
215	alexnet_training_seed_05	.074
216	alexnet_training_seed_01	.074
217	resnet50-meshes-lt-100-original-scratch	.074
218	mobilenet_v2_0.75_128	.072
219	texture_shape_resnet50_trained_on_SIN	.071
220	alexnet_random_l2_3_perturb	.070
221	alexnet_l2_3_robust	.070
222	mobilenet_v2_0.75_224-tf	.069
223	resnet-50_untrained	.067
224	vonealexnet_gaussian_noise_std4_fixed	.066
225	mobilenet_v2_1_0_128	.065
226	resnet50-SIN	.064
227	alexnet_reduced_aliasing_early_checkpoint	.063
228	mobilenet_v2_0.35_96	.061
229	mobilenet_v2_0_75_224	.061
230	mobilenet_v2_0.35_192	.061
231	alexnet_robust_correct	.060
232	shufflenet_v2_x1_0	.060
233	BiT-S-R152x2	.059
234	texture_shape_alexnet_trained_on_SIN	.059
235	alexnet_training_seed_03	.058
236	alexnet_training_seed_09	.057
237	squeezenet1_1	.057
238	mobilenet_v1_0.75_128	.056
239	alexnet_ks_torevert	.056
240	alexnet	.056
241	alexnet-baseline	.056
242	alexnet	.056
243	alexnet	.056
244	alexnet	.056
245	alexnet	.056
246	alexnet	.056
247	alexnet	.056
248	alexnet	.056
249	alexnet	.056
250	AdvProp_efficientnet-b0	.055
251	mobilenet_v1_0.5_224	.055
252	resnet50-simclr-vissl	.054
253	AlexNet_SIN	.054
254	barlow-twins-resnet50	.052
255	alexnet_random_linf8_perturb	.049
256	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.049
257	resnet50_simclr	.047
258	mobilenet_v1_1.0_224	.045
259	mobilenet_v1_1.0_192	.045
260	mobilenet_v2_0.35_128	.044
261	BiT-S-R101x1	.044
262	alexnet_early_checkpoint	.043
263	resnet50-vicreg	.038
264	resnet50-vicregl0p9	.038
265	mobilenet_v2_1.0_128-tf	.037
266	artResNet18_1	.032
267	alexnet_training_seed_08	.031
268	alexnet_training_seed_02	.031
269	alexnet_training_seed_10	.031
270	alexnet_training_seed_04	.031
271	mobilenet_v2_0.5_160	.030
272	mobilenet_v2_0.35_160	.029
273	0.5x_resnet-18_LC_w_sh_1_iter	.028
274	mobilenet_v1_0.25_160	.026
275	resnet50_FractalDB	.026
276	resnet50_imagenet_1_seed-0	.025
277	alexnet_training_seed_06	.024
278	mobilenet_v1_0.25_224	.022
279	mobilenet_v2_0.5_128	.020
280	mobilenet_v1_0.25_192	.019
281	mobilenet_v1_0.25_128	.017
282	resnet50-meshes-lt-100-original-pretrained	.016
283	mobilenet_v1_1.0_128	.016
284	squeezenet1_0	.014
285	mobilenet_v1_0.5_192	.013
286	alexnet_training_seed_07	.013
287	mobilevit_small	.013
288	mobilenet_v1_0.75_160	.013
289	konkle_alexnetgn_ipcl_ref01_primary_model	.013
290	resnet50_imagenet_100_seed-0	.007
291	mobilenet_v1_0.5_160	.006
292	resnet50-barlow	.006
293	convnext_small_imagenet_1_seed-0	.005
294	mobilenet_v1_0.5_128	.003
295	resnet50-vicregl0p75	.003
296	FrankRobWobv0	X
297	CORnet-Z	X
298	yudixie_resnet50_imagenet1kpret_0_240312	X
299	resnet-18-LC_w_sh_1_iter_m	X
300	yudixie_resnet50_object_class_0_240908	X
301	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
302	yudixie_resnet50_imagenet1kpret_0_240908	X
303	ReAlnet01_cornet	X
304	yudixie_resnet50_random_0_240908	X
305	resnet18-instance_recognition	X
306	vggface	X
307	resnet152_ecoset_full	X
308	my-model	X
309	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
310	0.5x_resnet-18_LC_w_sh_10_iter	X
311	resnet-18-LC_w_sh_10_iter_conv_init_m	X
312	resnet-18-LC_w_sh_100_iter_m	X
313	resnet50_ImageNet	X
314	resnet50_pretrained_with_retinal_waves	X
315	yudixie_resnet18_translation_reg_0_240719	X
316	yudixie_resnet18_distance_reg_0_240719	X
317	ReAlnet03_cornet	X
318	yudixie_resnet18_rotation_reg_0_240719	X
319	ReAlnet02_cornet	X
320	yudixie_resnet18_imagenet1kpret_0_240719	X
321	resnet50-cifar	X
322	resnet-18-LC_w_sh_10_iter_m	X
323	resnet18_ecoset_full	X
324	r3m_resnet50	X
325	resnet34_ecoset_full	X
326	resnet18-colorization	X
327	resnet50_ecoset_full	X
328	r3m_resnet34	X
329	resnet101_ecoset_full	X
330	resnet-18-LC_m	X
331	r3m_resnet50_nocrop	X
332	yudixie_resnet18_distance_translation_0_240719	X
333	yudixie_resnet50_distance_translation_0_240908	X
334	ReAlnet04_cornet	X
335	omnivore_swinB	X
336	yudixie_resnet18_translation_rotation_0_240719	X
337	yudixie_resnet18_distance_rotation_0_240719	X
338	yudixie_resnet50_distance_rotation_0_240908	X
339	yudixie_resnet18_category_class_0_240719	X
340	yudixie_resnet18_random_0_240719	X
341	ReAlnet05_cornet	X
342	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
343	yudixie_resnet50_rotation_reg_0_240908	X
344	openclip	X
345	resnet-18_test_m	X
346	yudixie_resnet18_distance_translation_rotation_0_240719	X
347	yudixie_resnet18_object_class_0_240719	X
348	resnet50	X
349	resnet-18-LC_w_sh_1_iter_conv_init_m	X
350	my-model	X
351	ReAlnet06_cornet	X
352	yudixie_resnet50_translation_reg_0_240908	X
353	yudixie_resnet50_translation_rotation_0_240908	X
354	BiT-M-R152x2	X
355	ReAlnet07_cornet	X
356	dorinet_cornet_z	X
357	pixels	X
358	CORnetZ_CIFAR10_bs32_20_04	X
359	v1-pyr-nodown	X
360	unet_entire	X
361	ReAlnet08_cornet	X
362	dcgan	X
363	resnet-50x2_untrained	X
364	VOneCORnet-S	X
365	resnet-18_untrained	X
366	resnet-18-LC_untrained	X
367	ReAlnet09_cornet	X
368	BiT-M-R50x1	X
369	omnivore_swinT	X
370	BiT-M-R50x3	X
371	resnet18-supervised	X
372	resnet18-contrastive_predictive	X
373	r3m_resnet34_nocrop	X
374	omnivore_swinS	X
375	ViT_B_16	X
376	resnet-18-LC_1st_conv_m	X
377	ReAlnet10_cornet	X
378	yudixie_resnet50_category_class_0_240908	X
379	ViT_B_32	X
380	ViT_L_32	X
381	resnet-18-LC_conv_init_m	X
382	yudixie_resnet50_distance_translation_rotation_0_240908	X
383	resnet18-simclr	X
384	ViT-B/32	X
385	resnet18-autoencoder	X
386	resnet18-relative_position	X
387	RN50	X
388	0.5x_resnet-18_LC_w_sh_100_iter	X
389	RN50	X
390	resnet18-contrastive_multiview	X
391	resnet-18-LC_w_sh_100_iter_conv_init_m	X
392	0.5x_resnet-18	X
393	resnet18-depth_prediction	X
394	0.5x_resnet-18_LC	X
395	CORnetZ_CIFAR10	X
396	resnet18-local_aggregation	X
397	resnet18_imagenet21kP	X
398	yudixie_resnet50_distance_reg_0_240908	X
399	ConvLSTM	X
400	resnet18-deepcluster	X
401	PredRNN	X
402	SimVP	X
403	TAU	X
404	MIM	X
405	hmax	X
406	r3m_resnet18	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

0.47.

Note that scores are relative to this ceiling.

Data: Geirhos2021lowpass

Metric: error_consistency