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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	deit3_base_patch16_224_fb_in22k_ft_in1k	.655
7	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.648
8	fixres_resnext101_32x48d_wsl	.630
9	resnet50-VITO-8deg-cc	.625
10	vit_large_patch14_clip_336:laion2b_ft_in1k	.623
11	SWSL_resnet50	.613
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.603
13	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.602
14	deit3_base_patch16_224_fb_in1k	.601
15	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.600
16	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.589
17	resnext101_32x48d_wsl	.579
18	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.578
19	resnext101_32x8d_wsl	.563
20	convnext_base_fb_in22k_ft_in1k	.559
21	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.554
22	CLIP_resnet50	.544
23	CLIP_resnet50_float32	.544
24	deit_large_imagenet_full_seed-0	.543
25	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
26	resnext101_32x16d_wsl	.535
27	vit_base_patch16_clip_224:openai_ft_in1k	.535
28	convnext_xlarge:fb_in22k_ft_in1k	.527
29	effnetv2m_custom384	.527
30	convnext_base:clip_laiona_augreg_ft_in1k_384	.516
31	vision_transformer_vit_large_patch16_224	.499
32	resnet_152_v2	.498
33	effnetb1_272x240	.497
34	CLIP_ViT-B_32	.496
35	cvt_cvt-13-384-in1k_4_LucyV4	.492
36	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.478
37	convnext_large_imagenet_full_seed-0	.471
38	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.463
39	vit_relpos_base_patch16_clsgap_224:sw_in1k	.463
40	deit_small_imagenet_full_seed-0	.460
41	deit_base_imagenet_full_seed-0	.460
42	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.454
43	focalnet_tiny_lrf_in1k	.436
44	convnext_tiny:in12k_ft_in1k	.435
45	CLIP-RN50	.419
46	convnext_tiny_imagenet_full_seed-0	.419
47	resnet_50_v2	.415
48	pnasnet_large_halfPrecision	.410
49	pnasnet_large	.410
50	ViT_B_16_imagenet1k	.409
51	ViT_L_32_imagenet1k	.406
52	inception_v4	.400
53	resnet_101_v2	.397
54	nasnet_large	.396
55	cvt_cvt-21-384-in1k_4_LucyV4	.391
56	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.385
57	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.382
58	convnext_small_imagenet_full_seed-0	.379
59	convnext_femto_ols:d1_in1k	.379
60	resnet-152_v2_pytorch	.371
61	cvt_cvt-21-224-in1k_4_LucyV4	.368
62	convnext_base_fb_in1k	.366
63	resnet50_st_wzc_seed6_2_323	.365
64	cvt_cvt-13-224-in1k_4_LucyV4	.361
65	convnext_base_imagenet_full_seed-0	.354
66	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.352
67	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.338
68	pnasnet_large-tf	.338
69	antialias-resnet152	.326
70	resnet50_st_wzc_seed5_2_323	.317
71	xception-keras	.317
72	cv_18_dagger_408_pretrained	.314
73	resnet101	.313
74	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.311
75	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.306
76	nasnet_large-tf	.305
77	inception_v3-tf	.299
78	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.297
79	resnet50_ewconplus_wzc_2_323	.296
80	inception_v4-tf	.295
81	resnet152_imagenet_full	.290
82	resnet-152_v2-tf	.285
83	resnet50_st_wzc_2_323	.280
84	resnet50_st_wzc_noseed_2_323	.275
85	resnet50_byol	.274
86	resnet_152_v1	.270
87	resnet_101_v1	.268
88	antialiased-rnext101_32x8d	.268
89	resnet50_ewconplus_wzc_seed4_2_323	.264
90	inception_v3	.260
91	convnext_tiny_sup	.260
92	resnet50_ewconplus_wzc_seed5_2__323	.259
93	AdvProp_efficientnet-b4	.258
94	AT_efficientnet-b4	.258
95	resnet50_st_wzc_seed4_2_323	.255
96	grcnn_109	.252
97	inception_v3_pytorch	.252
98	resnet50	.251
99	resnet50_st_wzc_seed3_2_323	.247
100	ViT_B_32_imagenet1k	.244
101	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.243
102	resnet50_ewconplus_wzc_seed2_2__323	.238
103	resnet50_ewconplus_wzc_seed3_2_323	.236
104	resnet50_moco_v2	.232
105	convnext_base_fb_in1k_gram_aligned_dinov3	.230
106	resnet50_ewconplus_wzc	.230
107	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.225
108	resnet50_ewconplus_wzc_seed7_2_323	.224
109	AdvProp_efficientnet-b6	.221
110	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.218
111	tv_efficientnet-b1	.215
112	densenet-121-keras	.214
113	antialiased-r50	.214
114	resnet50_imagenet_full	.213
115	voneresnet-50-non_stochastic	.209
116	convnext_small_imagenet_100_seed-0	.207
117	densenet-169-keras	.203
118	AdvProp_efficientnet-b7	.203
119	resnet34_imagenet_full	.203
120	resnet-50_v2-tf	.202
121	densenet-121	.201
122	resnet50_st_wzc_2	.197
123	densenet-201-keras	.197
124	grcnn	.194
125	resnet50_ewconplus_noseed_2_323	.191
126	resnet_50_v1_spiking	.190
127	resnet50-moclr8deg	.187
128	densenet-169	.186
129	mobilenet_v2_1_0_224	.186
130	resnet50_tutorial	.185
131	resnet_50_v1	.185
132	resnet-50-pytorch	.185
133	resnet50-sup	.185
134	efficientnet-b7	.185
135	vgg_19	.184
136	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.180
137	BiT-S-R152x4	.175
138	pnasnet_large_pytorch	.175
139	Res2Net50_26w_4s	.174
140	resnet50_ewconplus_wzc_seed6_2_323	.169
141	xception	.168
142	resnet-50_v1-tf	.167
143	resnet101_imagenet_full	.165
144	AT_efficientnet-b2	.165
145	AdvProp_efficientnet-b2	.165
146	resnet18_imagenet_full	.163
147	densenet-201	.161
148	densenet_201_pytorch	.161
149	ReAlnet04	.156
150	custom_model_cv_18_dagger_408	.156
151	resnet-101_v1-tf	.155
152	vonegrcnn_62e_nobn	.153
153	efficientnet_b1_imagenet_full	.153
154	efficientnet_b0	.152
155	eBarlow_lmda_02_1000ep	.147
156	inception_v1-tf	.144
157	deit_small_imagenet_100_seed-0	.143
158	vonegrcnn_47e	.141
159	efficientnet_b2_imagenet_full	.140
160	resnet50_random_linf8_perturb	.140
161	vonegrcnn_52e_full	.139
162	resnet-152_v1-tf	.138
163	mobilenet_v2_1.0_224-tf	.135
164	ReAlnet03	.135
165	nasnet_mobile	.133
166	mobilenet_v2_1.4_224-tf	.133
167	voneresnet-50	.132
168	ReAlnet09	.132
169	vonegrcnn_62e_nobn	.130
170	resnet50-SIN_IN	.129
171	BiT-S-R50x3	.129
172	blt_vs	.129
173	mobilenet_v2_1_0_192	.127
174	BiT-S-R101x3	.127
175	ReAlnet07	.127
176	vonegrcnn_52e_full	.126
177	vonegrcnn_47e	.126
178	ReAlnet08	.126
179	resnet50_random_l2_perturb	.126
180	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
181	inception_v2-tf	.124
182	cornet_s	.124
183	AdvProp_efficientnet-b8	.123
184	mobilenet_v2_1-4_224_pytorch	.122
185	mobilenet_v2_1_4_224	.122
186	ReAlnet02	.121
187	voneresnet-50-robust	.119
188	mobilenet_v2_0_5_192	.116
189	ReAlnet06	.115
190	nasnet_mobile-tf	.113
191	resnet50-vitoimagevidnet8	.113
192	mobilenet_v1_0.75_224	.112
193	ReAlnet10	.112
194	mobilenet_v2_0_5_224	.111
195	vgg-19-keras	.111
196	resnet50_l2_3_robust	.110
197	resnet50_linf_4_robust	.110
198	inception_v1	.109
199	resnet50-SIN_IN_IN	.109
200	resnet_SIN_IN_FT_IN	.109
201	mobilenet_v2_1.0_160-tf	.109
202	resnet_50_v1_spiking_l4a	.108
203	mobilenet_v2_1_0_160	.108
204	resnet50_linf_8_robust	.107
205	resnet-50-robust	.103
206	imagenet_l2_3_0	.103
207	resnet50_robust_l2_eps3	.103
208	vgg_16	.100
209	ReAlnet01	.100
210	BiT-S-R50x1	.100
211	CORnet-S	.097
212	vgg-16-keras	.097
213	efficientnet_b0_imagenet_full	.096
214	mobilenet_v2_1_3_224	.096
215	mobilenet_v2_0.5_192-tf	.096
216	mobilenet_v2_0.75_192-tf	.095
217	mobilenet_v1_1.0_160	.095
218	ReAlnet05	.094
219	alexnet_fov4	.092
220	mobilenet_v2_1.3_224-tf	.091
221	resnet50_eMMCR_Vanilla	.090
222	regnet_y_400mf	.087
223	imagenet_l2_10_0	.086
224	mobilenet_v2_1.0_192-tf	.085
225	resnet50_imagenet_10_seed-0	.085
226	deit_small_imagenet_10_seed-0	.085
227	deit_small_imagenet_1_seed-0	.085
228	mobilenet_v2_0.75_160-tf	.085
229	mobilenet_v2_0.5_224-tf	.084
230	mobilenet_v2_0_75_160	.084
231	resnet50_robust_l2_eps1	.082
232	mobilenet_v1_0.75_192	.081
233	alexnet_linf_8_robust	.081
234	resnet-18	.081
235	mobilenet_v2_0.75_96	.081
236	resnet-34	.080
237	alexnet_fov16	.080
238	convnext_small_imagenet_10_seed-0	.078
239	mobilenet_v2_1.0_96	.078
240	mobilenet_v2_0_75_192	.077
241	mobilenet_v2_0.35_224	.077
242	mobilenet_v2_0.5_96	.077
243	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
244	alexnet_training_seed_05	.074
245	alexnet_training_seed_01	.074
246	alexnet_training_seed_01_fov4	.074
247	alexnet_training_seed_01_fov12	.074
248	alexnet_training_seed_01_fov16	.074
249	resnet50-meshes-lt-100-original-scratch	.074
250	mobilenet_v2_0.75_128	.072
251	texture_shape_resnet50_trained_on_SIN	.071
252	alexnet_random_l2_3_perturb	.070
253	alexnet_l2_3_robust	.070
254	mobilenet_v2_0.75_224-tf	.069
255	resnet-50_untrained	.067
256	vonealexnet_gaussian_noise_std4_fixed	.066
257	mobilenet_v2_1_0_128	.065
258	resnet50-SIN	.064
259	alexnet_reduced_aliasing_early_checkpoint	.063
260	mobilenet_v2_0.35_96	.061
261	mobilenet_v2_0_75_224	.061
262	mobilenet_v2_0.35_192	.061
263	alexnet_robust_correct	.060
264	shufflenet_v2_x1_0	.060
265	BiT-S-R152x2	.059
266	texture_shape_alexnet_trained_on_SIN	.059
267	alexnet_training_seed_03	.058
268	alexnet_training_seed_09	.057
269	squeezenet1_1	.057
270	mobilenet_v1_0.75_128	.056
271	alexnet_ks_torevert	.056
272	alexnet	.056
273	AdvProp_efficientnet-b0	.055
274	mobilenet_v1_0.5_224	.055
275	resnet50-simclr-vissl	.054
276	AlexNet_SIN	.054
277	barlow-twins-resnet50	.052
278	alexnet_random_linf8_perturb	.049
279	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.049
280	resnet50_simclr	.047
281	mobilenet_v1_1.0_224	.045
282	mobilenet_v1_1.0_192	.045
283	mobilenet_v2_0.35_128	.044
284	BiT-S-R101x1	.044
285	alexnet_early_checkpoint	.043
286	AlexNet_SIN_fov4	.040
287	resnet50-vicreg	.038
288	resnet50-vicregl0p9	.038
289	mobilenet_v2_1.0_128-tf	.037
290	alexnet_fov12	.034
291	artResNet18_1	.032
292	resnet_50_v1_spiking_l4	.032
293	alexnet_training_seed_02	.031
294	alexnet_training_seed_08	.031
295	alexnet_training_seed_04	.031
296	alexnet_training_seed_10	.031
297	mobilenet_v2_0.5_160	.030
298	AlexNet_SIN_fov16	.029
299	mobilenet_v2_0.35_160	.029
300	0.5x_resnet-18_LC_w_sh_1_iter	.028
301	mobilenet_v1_0.25_160	.026
302	resnet50_FractalDB	.026
303	resnet50_imagenet_1_seed-0	.025
304	resnet_50_v1_spiking_l3	.024
305	alexnet_training_seed_06	.024
306	mobilenet_v1_0.25_224	.022
307	mobilenet_v2_0.5_128	.020
308	mobilenet_v1_0.25_192	.019
309	mobilenet_v1_0.25_128	.017
310	resnet50-meshes-lt-100-original-pretrained	.016
311	mobilenet_v1_1.0_128	.016
312	squeezenet1_0	.014
313	mobilenet_v1_0.5_192	.013
314	alexnet_training_seed_07	.013
315	mobilevit_small	.013
316	mobilenet_v1_0.75_160	.013
317	konkle_alexnetgn_ipcl_ref01_primary_model	.013
318	resnet50_imagenet_100_seed-0	.007
319	mobilenet_v1_0.5_160	.006
320	resnet50-barlow	.006
321	convnext_small_imagenet_1_seed-0	.005
322	AlexNet_SIN_fov12	.004
323	mobilenet_v1_0.5_128	.003
324	resnet50-vicregl0p75	.003
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	BiT-M-R152x2
330	resnet-18-LC_w_sh_1_iter_conv_init_m
331	resnet-18-LC_w_sh_10_iter_m
332	resnet-18-LC_w_sh_100_iter_m
333	resnet-18-LC_w_sh_10_iter_conv_init_m
334	resnet-18-LC_w_sh_1_iter_m
335	FrankRobWobv0
336	0.5x_resnet-18_LC
337	CORnetZ_CIFAR10
338	resnet18-local_aggregation
339	resnet18-deepcluster
340	r3m_resnet18
341	CORnet-Z
342	resnet18-instance_recognition
343	vggface
344	my-model
345	0.5x_resnet-18_LC_w_sh_10_iter
346	r3m_resnet50
347	resnet18-colorization
348	r3m_resnet34
349	resnet-18-LC_m
350	r3m_resnet50_nocrop
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	resnet18-contrastive_predictive
368	omnivore_swinT
369	resnet-18-LC_untrained
370	resnet-18_untrained
371	resnet-50x2_untrained
372	dcgan
373	unet_entire
374	v1-pyr-nodown
375	CORnetZ_CIFAR10_bs32_20_04
376	pixels
377	dorinet_cornet_z
378	my-model
379	resnet50
380	openclip
381	resnet50-cifar
382	resnet50_pretrained_with_retinal_waves
383	resnet50_ImageNet
384	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
385	yudixie_resnet50_random_0_240908
386	yudixie_resnet50_imagenet1kpret_0_240908
387	yudixie_resnet50_cat_obj_class_all_latents_0_240908
388	yudixie_resnet50_imagenet1kpret_0_240312
389	yudixie_resnet50_category_class_0_240908
390	resnet18_imagenet21kP
391	yudixie_resnet50_distance_reg_0_240908
392	ConvLSTM
393	PredRNN
394	SimVP
395	TAU
396	MIM
397	hmax
398	ReAlnet01_cornet
399	yolos_tiny
400	resnet152_ecoset_full
401	compact_resnet50_V4
402	yudixie_resnet18_translation_reg_0_240719
403	yudixie_resnet18_distance_reg_0_240719
404	ReAlnet03_cornet
405	yudixie_resnet18_rotation_reg_0_240719
406	ReAlnet02_cornet
407	yudixie_resnet18_imagenet1kpret_0_240719
408	vit_base_patch16_dinov3-lvd1689m
409	resnet34_ecoset_full
410	compact_resnet50_IT
411	resnet50_ecoset_full
412	vit_base_patch14_dinov2-lvd142m
413	vit_base_patch14_reg4_dinov2-lvd142m
414	resnet101_ecoset_full
415	compact_resnet50_robust_V4
416	ReAlnet01_cornet_fov4
417	yudixie_resnet50_distance_translation_0_240908
418	ReAlnet01_cornet_fov12
419	omnivore_swinB
420	aughost_fair_resnet18
421	yudixie_resnet18_distance_rotation_0_240719
422	yudixie_resnet50_distance_rotation_0_240908
423	yudixie_resnet18_category_class_0_240719
424	yudixie_resnet18_random_0_240719
425	ReAlnet05_cornet
426	yudixie_resnet18_cat_obj_class_all_latents_0_240719
427	yudixie_resnet50_rotation_reg_0_240908
428	resnet-18_test_m
429	compact_vgg19_V4
430	yudixie_resnet18_distance_translation_rotation_0_240719
431	yudixie_resnet18_object_class_0_240719
432	SeLaVi-AVE
433	r3d_18
434	ReAlnet06_cornet
435	yudixie_resnet50_translation_reg_0_240908
436	yudixie_resnet50_translation_rotation_0_240908
437	ReAlnet01_ALL
438	mc3_18
439	ReAlnet07_cornet
440	s3d
441	mvit_v1_b
442	ReAlnet08_cornet
443	ReAlnet01_cornet_fov16
444	ReAlnet02_ALL
445	ReAlnet03_ALL
446	VOneCORnet-S
447	compact_cornets_V4
448	ReAlnet04_ALL
449	ReAlnet09_cornet
450	SeLaVi-Kinetics400
451	compact_cornets_IT
452	SeLaVi-VGG-Sound
453	ReAlnet05_ALL
454	SeLaVi-Kinetics-Sound
455	compact_vgg19_IT
456	omnivore_swinS
457	mvit_v2_s
458	compact_resnet50_robust_IT
459	ReAlnet06_ALL
460	r2plus1d_18
461	ReAlnet10_cornet
462	ReAlnet07_ALL
463	resnet18_ecoset_full
464	yudixie_resnet50_distance_translation_rotation_0_240908
465	ReAlnet08_ALL
466	yudixie_resnet18_translation_rotation_0_240719
467	yudixie_resnet18_distance_translation_0_240719
468	resnet50_primary_visual_cortex
469	ReAlnet04_cornet
470	ReAlnet09_ALL
471	yudixie_resnet50_object_class_0_240908
472	briaai_rmbg_1_4
473	ReAlnet10_ALL
474	resnet18_fair
475	predictive_coding_pc
476	yudixie_wbh_object_class_scaling_0415_run_0
477	yudixie_wbh_object_class_scaling_0415_run_3
478	yudixie_wbh_object_class_scaling_0415_imagenet
479	yudixie_wbh_object_class_scaling_0415_run_2
480	yudixie_wbh_object_class_scaling_0415_run_1
481	deepeisnn_ei_cifar10_vgg19
482	deepeisnn_ei_cifar10_resnet18
483	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

0.47.

Note that scores are relative to this ceiling.

Data: Geirhos2021lowpass

Metric: error_consistency