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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	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_halfPrecision	.410
45	pnasnet_large	.410
46	ViT_B_16_imagenet1k	.409
47	ViT_L_32_imagenet1k	.406
48	inception_v4	.400
49	resnet_101_v2	.397
50	nasnet_large	.396
51	cvt_cvt-21-384-in1k_4_LucyV4	.391
52	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.385
53	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.382
54	convnext_small_imagenet_full_seed-0	.379
55	convnext_femto_ols:d1_in1k	.379
56	resnet-152_v2_pytorch	.371
57	cvt_cvt-21-224-in1k_4_LucyV4	.368
58	cvt_cvt-13-224-in1k_4_LucyV4	.361
59	convnext_base_imagenet_full_seed-0	.354
60	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.352
61	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.338
62	pnasnet_large-tf	.338
63	antialias-resnet152	.326
64	xception-keras	.317
65	cv_18_dagger_408_pretrained	.314
66	resnet101	.313
67	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.311
68	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.306
69	nasnet_large-tf	.305
70	inception_v3-tf	.299
71	inception_v4-tf	.295
72	resnet152_imagenet_full	.290
73	resnet-152_v2-tf	.285
74	resnet50_byol	.274
75	resnet_152_v1	.270
76	resnet_101_v1	.268
77	antialiased-rnext101_32x8d	.268
78	inception_v3	.260
79	convnext_tiny_sup	.260
80	AdvProp_efficientnet-b4	.258
81	AT_efficientnet-b4	.258
82	grcnn_109	.252
83	inception_v3_pytorch	.252
84	resnet50	.251
85	ViT_B_32_imagenet1k	.244
86	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.243
87	resnet50_moco_v2	.232
88	resnet50_ewconplus_wzc	.230
89	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.225
90	AdvProp_efficientnet-b6	.221
91	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.218
92	tv_efficientnet-b1	.215
93	densenet-121-keras	.214
94	antialiased-r50	.214
95	resnet50_imagenet_full	.213
96	voneresnet-50-non_stochastic	.209
97	convnext_small_imagenet_100_seed-0	.207
98	densenet-169-keras	.203
99	AdvProp_efficientnet-b7	.203
100	resnet34_imagenet_full	.203
101	resnet-50_v2-tf	.202
102	densenet-121	.201
103	densenet-201-keras	.197
104	grcnn	.194
105	resnet_50_v1_spiking	.190
106	resnet50-moclr8deg	.187
107	densenet-169	.186
108	mobilenet_v2_1_0_224	.186
109	resnet50_tutorial	.185
110	resnet_50_v1	.185
111	resnet-50-pytorch	.185
112	resnet50-sup	.185
113	efficientnet-b7	.185
114	vgg_19	.184
115	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.180
116	BiT-S-R152x4	.175
117	pnasnet_large_pytorch	.175
118	Res2Net50_26w_4s	.174
119	xception	.168
120	resnet-50_v1-tf	.167
121	resnet101_imagenet_full	.165
122	AT_efficientnet-b2	.165
123	AdvProp_efficientnet-b2	.165
124	resnet18_imagenet_full	.163
125	densenet-201	.161
126	densenet_201_pytorch	.161
127	ReAlnet04	.156
128	custom_model_cv_18_dagger_408	.156
129	resnet-101_v1-tf	.155
130	vonegrcnn_62e_nobn	.153
131	efficientnet_b1_imagenet_full	.153
132	efficientnet_b0	.152
133	eBarlow_lmda_02_1000ep	.147
134	inception_v1-tf	.144
135	deit_small_imagenet_100_seed-0	.143
136	vonegrcnn_47e	.141
137	efficientnet_b2_imagenet_full	.140
138	resnet50_random_linf8_perturb	.140
139	vonegrcnn_52e_full	.139
140	resnet-152_v1-tf	.138
141	mobilenet_v2_1.0_224-tf	.135
142	ReAlnet03	.135
143	nasnet_mobile	.133
144	mobilenet_v2_1.4_224-tf	.133
145	voneresnet-50	.132
146	ReAlnet09	.132
147	vonegrcnn_62e_nobn	.130
148	resnet50-SIN_IN	.129
149	BiT-S-R50x3	.129
150	blt_vs	.129
151	mobilenet_v2_1_0_192	.127
152	BiT-S-R101x3	.127
153	ReAlnet07	.127
154	vonegrcnn_52e_full	.126
155	vonegrcnn_47e	.126
156	ReAlnet08	.126
157	resnet50_random_l2_perturb	.126
158	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
159	inception_v2-tf	.124
160	cornet_s	.124
161	AdvProp_efficientnet-b8	.123
162	mobilenet_v2_1-4_224_pytorch	.122
163	mobilenet_v2_1_4_224	.122
164	ReAlnet02	.121
165	voneresnet-50-robust	.119
166	mobilenet_v2_0_5_192	.116
167	ReAlnet06	.115
168	nasnet_mobile-tf	.113
169	resnet50-vitoimagevidnet8	.113
170	mobilenet_v1_0.75_224	.112
171	ReAlnet10	.112
172	mobilenet_v2_0_5_224	.111
173	vgg-19-keras	.111
174	resnet50_l2_3_robust	.110
175	resnet50_linf_4_robust	.110
176	inception_v1	.109
177	resnet50-SIN_IN_IN	.109
178	resnet_SIN_IN_FT_IN	.109
179	mobilenet_v2_1.0_160-tf	.109
180	resnet_50_v1_spiking_l4a	.108
181	mobilenet_v2_1_0_160	.108
182	resnet50_linf_8_robust	.107
183	resnet-50-robust	.103
184	imagenet_l2_3_0	.103
185	resnet50_robust_l2_eps3	.103
186	vgg_16	.100
187	ReAlnet01	.100
188	BiT-S-R50x1	.100
189	CORnet-S	.097
190	vgg-16-keras	.097
191	efficientnet_b0_imagenet_full	.096
192	mobilenet_v2_1_3_224	.096
193	mobilenet_v2_0.5_192-tf	.096
194	mobilenet_v2_0.75_192-tf	.095
195	mobilenet_v1_1.0_160	.095
196	ReAlnet05	.094
197	alexnet_fov4	.092
198	mobilenet_v2_1.3_224-tf	.091
199	resnet50_eMMCR_Vanilla	.090
200	regnet_y_400mf	.087
201	imagenet_l2_10_0	.086
202	mobilenet_v2_1.0_192-tf	.085
203	resnet50_imagenet_10_seed-0	.085
204	deit_small_imagenet_10_seed-0	.085
205	deit_small_imagenet_1_seed-0	.085
206	mobilenet_v2_0.75_160-tf	.085
207	mobilenet_v2_0.5_224-tf	.084
208	mobilenet_v2_0_75_160	.084
209	resnet50_robust_l2_eps1	.082
210	mobilenet_v1_0.75_192	.081
211	alexnet_linf_8_robust	.081
212	resnet-18	.081
213	mobilenet_v2_0.75_96	.081
214	resnet-34	.080
215	alexnet_fov16	.080
216	convnext_small_imagenet_10_seed-0	.078
217	mobilenet_v2_1.0_96	.078
218	mobilenet_v2_0_75_192	.077
219	mobilenet_v2_0.35_224	.077
220	mobilenet_v2_0.5_96	.077
221	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
222	alexnet_training_seed_05	.074
223	alexnet_training_seed_01	.074
224	alexnet_training_seed_01_fov4	.074
225	alexnet_training_seed_01_fov12	.074
226	alexnet_training_seed_01_fov16	.074
227	resnet50-meshes-lt-100-original-scratch	.074
228	mobilenet_v2_0.75_128	.072
229	texture_shape_resnet50_trained_on_SIN	.071
230	alexnet_random_l2_3_perturb	.070
231	alexnet_l2_3_robust	.070
232	mobilenet_v2_0.75_224-tf	.069
233	resnet-50_untrained	.067
234	vonealexnet_gaussian_noise_std4_fixed	.066
235	mobilenet_v2_1_0_128	.065
236	resnet50-SIN	.064
237	alexnet_reduced_aliasing_early_checkpoint	.063
238	mobilenet_v2_0.35_96	.061
239	mobilenet_v2_0_75_224	.061
240	mobilenet_v2_0.35_192	.061
241	alexnet_robust_correct	.060
242	shufflenet_v2_x1_0	.060
243	BiT-S-R152x2	.059
244	texture_shape_alexnet_trained_on_SIN	.059
245	alexnet_training_seed_03	.058
246	alexnet_training_seed_09	.057
247	squeezenet1_1	.057
248	mobilenet_v1_0.75_128	.056
249	alexnet_ks_torevert	.056
250	alexnet	.056
251	AdvProp_efficientnet-b0	.055
252	mobilenet_v1_0.5_224	.055
253	resnet50-simclr-vissl	.054
254	AlexNet_SIN	.054
255	barlow-twins-resnet50	.052
256	alexnet_random_linf8_perturb	.049
257	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.049
258	resnet50_simclr	.047
259	mobilenet_v1_1.0_224	.045
260	mobilenet_v1_1.0_192	.045
261	mobilenet_v2_0.35_128	.044
262	BiT-S-R101x1	.044
263	alexnet_early_checkpoint	.043
264	AlexNet_SIN_fov4	.040
265	resnet50-vicreg	.038
266	resnet50-vicregl0p9	.038
267	mobilenet_v2_1.0_128-tf	.037
268	alexnet_fov12	.034
269	artResNet18_1	.032
270	resnet_50_v1_spiking_l4	.032
271	alexnet_training_seed_02	.031
272	alexnet_training_seed_08	.031
273	alexnet_training_seed_04	.031
274	alexnet_training_seed_10	.031
275	mobilenet_v2_0.5_160	.030
276	AlexNet_SIN_fov16	.029
277	mobilenet_v2_0.35_160	.029
278	0.5x_resnet-18_LC_w_sh_1_iter	.028
279	mobilenet_v1_0.25_160	.026
280	resnet50_FractalDB	.026
281	resnet50_imagenet_1_seed-0	.025
282	resnet_50_v1_spiking_l3	.024
283	alexnet_training_seed_06	.024
284	mobilenet_v1_0.25_224	.022
285	mobilenet_v2_0.5_128	.020
286	mobilenet_v1_0.25_192	.019
287	mobilenet_v1_0.25_128	.017
288	resnet50-meshes-lt-100-original-pretrained	.016
289	mobilenet_v1_1.0_128	.016
290	squeezenet1_0	.014
291	mobilenet_v1_0.5_192	.013
292	alexnet_training_seed_07	.013
293	mobilevit_small	.013
294	mobilenet_v1_0.75_160	.013
295	konkle_alexnetgn_ipcl_ref01_primary_model	.013
296	resnet50_imagenet_100_seed-0	.007
297	mobilenet_v1_0.5_160	.006
298	resnet50-barlow	.006
299	convnext_small_imagenet_1_seed-0	.005
300	AlexNet_SIN_fov12	.004
301	mobilenet_v1_0.5_128	.003
302	resnet50-vicregl0p75	.003
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_10_iter_m
310	resnet-18-LC_w_sh_100_iter_m
311	resnet-18-LC_w_sh_10_iter_conv_init_m
312	resnet-18-LC_w_sh_1_iter_m
313	FrankRobWobv0
314	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-local_aggregation
317	resnet18-deepcluster
318	r3m_resnet18
319	CORnet-Z
320	resnet18-instance_recognition
321	vggface
322	my-model
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	resnet18-colorization
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	unet_entire
352	v1-pyr-nodown
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_cat_obj_class_all_latents_0_240908
366	yudixie_resnet50_imagenet1kpret_0_240312
367	yudixie_resnet50_category_class_0_240908
368	resnet18_imagenet21kP
369	yudixie_resnet50_distance_reg_0_240908
370	ConvLSTM
371	PredRNN
372	SimVP
373	TAU
374	MIM
375	hmax
376	ReAlnet01_cornet
377	yolos_tiny
378	resnet152_ecoset_full
379	compact_resnet50_V4
380	yudixie_resnet18_translation_reg_0_240719
381	yudixie_resnet18_distance_reg_0_240719
382	ReAlnet03_cornet
383	yudixie_resnet18_rotation_reg_0_240719
384	ReAlnet02_cornet
385	yudixie_resnet18_imagenet1kpret_0_240719
386	vit_base_patch16_dinov3-lvd1689m
387	resnet34_ecoset_full
388	compact_resnet50_IT
389	resnet50_ecoset_full
390	vit_base_patch14_dinov2-lvd142m
391	vit_base_patch14_reg4_dinov2-lvd142m
392	resnet101_ecoset_full
393	compact_resnet50_robust_V4
394	ReAlnet01_cornet_fov4
395	yudixie_resnet50_distance_translation_0_240908
396	ReAlnet01_cornet_fov12
397	omnivore_swinB
398	aughost_fair_resnet18
399	yudixie_resnet18_distance_rotation_0_240719
400	yudixie_resnet50_distance_rotation_0_240908
401	yudixie_resnet18_category_class_0_240719
402	yudixie_resnet18_random_0_240719
403	ReAlnet05_cornet
404	yudixie_resnet18_cat_obj_class_all_latents_0_240719
405	yudixie_resnet50_rotation_reg_0_240908
406	resnet-18_test_m
407	compact_vgg19_V4
408	yudixie_resnet18_distance_translation_rotation_0_240719
409	yudixie_resnet18_object_class_0_240719
410	SeLaVi-AVE
411	r3d_18
412	ReAlnet06_cornet
413	yudixie_resnet50_translation_reg_0_240908
414	yudixie_resnet50_translation_rotation_0_240908
415	ReAlnet01_ALL
416	mc3_18
417	ReAlnet07_cornet
418	s3d
419	mvit_v1_b
420	ReAlnet08_cornet
421	ReAlnet01_cornet_fov16
422	ReAlnet02_ALL
423	ReAlnet03_ALL
424	VOneCORnet-S
425	compact_cornets_V4
426	ReAlnet04_ALL
427	ReAlnet09_cornet
428	SeLaVi-Kinetics400
429	compact_cornets_IT
430	SeLaVi-VGG-Sound
431	ReAlnet05_ALL
432	SeLaVi-Kinetics-Sound
433	compact_vgg19_IT
434	omnivore_swinS
435	mvit_v2_s
436	compact_resnet50_robust_IT
437	ReAlnet06_ALL
438	r2plus1d_18
439	ReAlnet10_cornet
440	ReAlnet07_ALL
441	resnet18_ecoset_full
442	yudixie_resnet50_distance_translation_rotation_0_240908
443	ReAlnet08_ALL
444	yudixie_resnet18_translation_rotation_0_240719
445	yudixie_resnet18_distance_translation_0_240719
446	resnet50_primary_visual_cortex
447	ReAlnet04_cornet
448	ReAlnet09_ALL
449	yudixie_resnet50_object_class_0_240908
450	briaai_rmbg_1_4
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

0.47.

Note that scores are relative to this ceiling.

Data: Geirhos2021lowpass

Metric: error_consistency