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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021highpass-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.875
2	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.858
3	convnext_xlarge:fb_in22k_ft_in1k	.851
4	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.827
5	vit_large_patch14_clip_224:laion2b_ft_in1k	.812
6	vit_large_patch14_clip_224:openai_ft_in1k	.807
7	convnext_large:fb_in22k_ft_in1k	.806
8	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.793
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.722
10	fixres_resnext101_32x48d_wsl	.716
11	convnext_base:clip_laiona_augreg_ft_in1k_384	.709
12	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.652
13	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.592
14	CLIP_resnet50	.550
15	CLIP_resnet50_float32	.550
16	vit_base_patch16_clip_224:openai_ft_in1k	.506
17	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.493
18	convnext_tiny:in12k_ft_in1k	.457
19	convnext_tiny_imagenet_full_seed-0	.442
20	CLIP-RN50	.422
21	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.419
22	effnetv2m_custom384	.410
23	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.404
24	SWSL_resnext101_32x8d	.380
25	resnext101_32x48d_wsl	.367
26	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.364
27	convnext_large_imagenet_full_seed-0	.355
28	convnext_small_imagenet_full_seed-0	.330
29	cvt_cvt-13-384-in1k_4_LucyV4	.305
30	resnet_152_v2	.303
31	resnext101_32x16d_wsl	.294
32	cv_18_dagger_408_pretrained	.293
33	resnext101_32x32d_wsl	.291
34	convnext_base_imagenet_full_seed-0	.287
35	resnet50-VITO-8deg-cc	.285
36	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.262
37	vision_transformer_vit_large_patch16_224	.248
38	convnext_tiny_sup	.226
39	focalnet_tiny_lrf_in1k	.220
40	resnet-152_v2_pytorch	.202
41	cvt_cvt-21-384-in1k_4_LucyV4	.200
42	cvt_cvt-21-224-in1k_4_LucyV4	.195
43	vit_relpos_base_patch16_clsgap_224:sw_in1k	.189
44	pnasnet_large_halfPrecision	.177
45	pnasnet_large	.177
46	resnext101_32x8d_wsl	.176
47	convnext_femto_ols:d1_in1k	.175
48	AlexNet_SIN_fov4	.169
49	SWSL_resnet50	.154
50	cvt_cvt-13-224-in1k_4_LucyV4	.151
51	pnasnet_large_pytorch	.150
52	nasnet_large	.149
53	inception_v3	.143
54	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.142
55	CLIP_ViT-B_32	.139
56	inception_v4	.139
57	pnasnet_large-tf	.139
58	inception_v3_pytorch	.139
59	inception_v3-tf	.135
60	densenet-169	.132
61	convnext_small_imagenet_100_seed-0	.131
62	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.127
63	resnet_101_v2	.123
64	resnet-152_v2-tf	.120
65	xception	.118
66	tv_efficientnet-b1	.116
67	densenet-169-keras	.116
68	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.116
69	ViT_B_16_imagenet1k	.111
70	xception-keras	.110
71	resnet34_imagenet_full	.101
72	resnet_50_v2	.100
73	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.100
74	BiT-S-R50x3	.100
75	alexnet_training_seed_05	.096
76	alexnet_training_seed_01	.096
77	alexnet_training_seed_01_fov4	.096
78	alexnet_training_seed_01_fov12	.096
79	alexnet_training_seed_01_fov16	.096
80	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.095
81	deit_small_imagenet_1_seed-0	.095
82	deit_small_imagenet_10_seed-0	.095
83	resnet50_imagenet_10_seed-0	.095
84	imagenet_l2_10_0	.094
85	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.093
86	BiT-S-R152x2	.093
87	resnet50_FractalDB	.092
88	resnet50-vicregl0p9	.091
89	barlow-twins-resnet50	.091
90	alexnet_linf_8_robust	.090
91	alexnet_l2_3_robust	.090
92	resnet50_linf_8_robust	.089
93	inception_v4-tf	.089
94	alexnet_training_seed_07	.088
95	effnetb1_272x240	.088
96	alexnet_robust_correct	.086
97	custom_model_cv_18_dagger_408	.085
98	AdvProp_efficientnet-b4	.083
99	AT_efficientnet-b4	.083
100	mobilenet_v2_0_5_224	.082
101	blt_vs	.081
102	vonealexnet_gaussian_noise_std4_fixed	.081
103	mobilenet_v1_0.5_192	.080
104	resnet50_linf_4_robust	.079
105	densenet-201-keras	.078
106	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.078
107	resnet50_random_l2_perturb	.077
108	mobilenet_v1_0.25_128	.075
109	resnet50-meshes-lt-100-original-scratch	.075
110	alexnet_training_seed_02	.075
111	alexnet_training_seed_04	.075
112	alexnet_training_seed_08	.075
113	alexnet_training_seed_10	.075
114	artResNet18_1	.075
115	resnet101	.075
116	resnet_50_v1_spiking_l4a	.074
117	alexnet_fov12	.074
118	BiT-S-R101x3	.074
119	convnext_small_imagenet_10_seed-0	.073
120	resnet50_byol	.073
121	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
122	texture_shape_alexnet_trained_on_SIN	.073
123	alexnet_training_seed_03	.072
124	mobilenet_v1_0.75_160	.072
125	vgg-19-keras	.072
126	nasnet_large-tf	.071
127	resnet50-vicreg	.071
128	alexnet_fov16	.067
129	alexnet_fov4	.067
130	BiT-S-R152x4	.066
131	voneresnet-50-robust	.066
132	vgg-16-keras	.066
133	mobilenet_v1_0.25_192	.065
134	antialias-resnet152	.064
135	mobilenet_v1_1.0_224	.064
136	AdvProp_efficientnet-b8	.064
137	resnet50-moclr8deg	.063
138	mobilenet_v2_0_5_192	.062
139	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.061
140	resnet50-SIN_IN_IN	.060
141	resnet_SIN_IN_FT_IN	.060
142	resnet50_robust_l2_eps1	.060
143	resnet_101_v1	.059
144	mobilenet_v1_0.75_128	.059
145	alexnet_reduced_aliasing_early_checkpoint	.059
146	konkle_alexnetgn_ipcl_ref01_primary_model	.059
147	AdvProp_efficientnet-b0	.056
148	resnet50_l2_3_robust	.056
149	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.056
150	AlexNet_SIN	.055
151	resnet-50-robust	.055
152	imagenet_l2_3_0	.055
153	resnet50_robust_l2_eps3	.055
154	mobilenet_v2_1.0_160-tf	.055
155	mobilenet_v1_0.25_160	.055
156	densenet-201	.054
157	densenet_201_pytorch	.054
158	efficientnet_b0	.054
159	mobilenet_v2_1_0_128	.054
160	vonegrcnn_62e_nobn	.053
161	alexnet_training_seed_06	.053
162	AlexNet_SIN_fov16	.053
163	AdvProp_efficientnet-b7	.053
164	mobilenet_v1_0.5_224	.053
165	AlexNet_SIN_fov12	.053
166	vonegrcnn_52e_full	.052
167	vonegrcnn_47e	.052
168	squeezenet1_0	.051
169	convnext_small_imagenet_1_seed-0	.051
170	CORnet-S	.051
171	mobilenet_v1_1.0_128	.051
172	deit_small_imagenet_100_seed-0	.050
173	mobilenet_v2_1_0_224	.050
174	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.050
175	ReAlnet07	.050
176	mobilenet_v2_1.0_224-tf	.050
177	mobilevit_small	.049
178	resnet50-SIN	.049
179	resnet50_eMMCR_Vanilla	.048
180	ReAlnet06	.048
181	resnet152_imagenet_full	.048
182	ReAlnet08	.047
183	cornet_s	.046
184	ReAlnet10	.046
185	resnet-18	.046
186	resnet50_imagenet_full	.045
187	alexnet_ks_torevert	.045
188	alexnet	.045
189	mobilenet_v2_0_75_192	.045
190	resnet50-simclr-vissl	.044
191	resnet50-meshes-lt-100-original-pretrained	.044
192	vonegrcnn_52e_full	.043
193	nasnet_mobile	.043
194	mobilenet_v2_1_3_224	.043
195	resnet101_imagenet_full	.043
196	efficientnet-b7	.043
197	vonegrcnn_47e	.043
198	mobilenet_v1_0.5_160	.042
199	resnet50-SIN_IN	.042
200	regnet_y_400mf	.042
201	resnet50_moco_v2	.041
202	nasnet_mobile-tf	.041
203	Res2Net50_26w_4s	.040
204	resnet50	.040
205	mobilenet_v1_1.0_160	.040
206	mobilenet_v2_0.75_96	.039
207	mobilenet_v1_0.5_128	.038
208	mobilenet_v1_0.25_224	.038
209	squeezenet1_1	.038
210	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.038
211	alexnet_random_linf8_perturb	.037
212	voneresnet-50	.037
213	vonegrcnn_62e_nobn	.037
214	mobilenet_v2_0.75_224-tf	.036
215	inception_v1	.036
216	deit_large_imagenet_full_seed-0	.036
217	mobilenet_v2_1_0_160	.036
218	mobilenet_v2_1.3_224-tf	.035
219	mobilenet_v2_1_0_192	.035
220	alexnet_early_checkpoint	.035
221	mobilenet_v2_1.0_192-tf	.034
222	texture_shape_resnet50_trained_on_SIN	.033
223	alexnet_training_seed_09	.033
224	mobilenet_v2_0_75_160	.033
225	mobilenet_v2_0.5_96	.033
226	resnet-50_untrained	.033
227	ReAlnet05	.032
228	resnet_152_v1	.031
229	resnet50_imagenet_1_seed-0	.031
230	ReAlnet01	.031
231	resnet-50-pytorch	.030
232	resnet50-sup	.030
233	resnet50_tutorial	.030
234	resnet_50_v1	.030
235	resnet-34	.030
236	inception_v1-tf	.030
237	BiT-S-R50x1	.028
238	resnet50_ewconplus_wzc	.027
239	ReAlnet03	.027
240	resnet_50_v1_spiking_l3	.027
241	resnet_50_v1_spiking	.026
242	ReAlnet02	.026
243	mobilenet_v2_0.35_96	.026
244	ViT_B_32_imagenet1k	.024
245	resnet50_random_linf8_perturb	.024
246	BiT-S-R101x1	.023
247	resnet50-barlow	.023
248	mobilenet_v2_0.75_160-tf	.023
249	mobilenet_v1_0.75_192	.023
250	mobilenet_v2_0.35_128	.022
251	ReAlnet09	.022
252	resnet-50_v1-tf	.022
253	mobilenet_v2_0_75_224	.022
254	antialiased-rnext101_32x8d	.022
255	inception_v2-tf	.022
256	mobilenet_v2_0.5_160	.021
257	mobilenet_v2_1.0_96	.021
258	mobilenet_v1_1.0_192	.020
259	antialiased-r50	.020
260	mobilenet_v2_0.75_192-tf	.019
261	deit_small_imagenet_full_seed-0	.019
262	mobilenet_v2_1.4_224-tf	.019
263	resnet_50_v1_spiking_l4	.019
264	mobilenet_v2_0.5_224-tf	.018
265	resnet50-vicregl0p75	.016
266	resnet50_imagenet_100_seed-0	.015
267	efficientnet_b0_imagenet_full	.015
268	resnet18_imagenet_full	.015
269	resnet-152_v1-tf	.015
270	densenet-121-keras	.015
271	grcnn_109	.015
272	mobilenet_v2_1-4_224_pytorch	.015
273	mobilenet_v2_1_4_224	.015
274	0.5x_resnet-18_LC_w_sh_1_iter	.014
275	AdvProp_efficientnet-b6	.014
276	mobilenet_v1_0.75_224	.014
277	deit_base_imagenet_full_seed-0	.013
278	resnet50_simclr	.013
279	mobilenet_v2_0.5_192-tf	.013
280	mobilenet_v2_0.35_160	.012
281	mobilenet_v2_1.0_128-tf	.012
282	AT_efficientnet-b2	.010
283	AdvProp_efficientnet-b2	.010
284	mobilenet_v2_0.35_192	.010
285	vgg_16	.010
286	mobilenet_v2_0.35_224	.010
287	mobilenet_v2_0.5_128	.010
288	grcnn	.009
289	resnet-50_v2-tf	.007
290	mobilenet_v2_0.75_128	.005
291	resnet-101_v1-tf	.004
292	ReAlnet04	.004
293	alexnet_random_l2_3_perturb	.004
294	shufflenet_v2_x1_0	.004
295	vgg_19	.003
296	densenet-121	.003
297	resnet50-vitoimagevidnet8	.003
298	efficientnet_b2_imagenet_full	.002
299	ViT_L_32_imagenet1k	.002
300	efficientnet_b1_imagenet_full	.001
301	voneresnet-50-non_stochastic	.000
302	resnet-18-LC_w_sh_100_iter_conv_init_m
303	ViT-B/32
304	BiT-M-R50x3
305	BiT-M-R50x1
306	BiT-M-R152x2
307	resnet-18-LC_w_sh_1_iter_conv_init_m
308	resnet-18-LC_w_sh_10_iter_m
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_10_iter_conv_init_m
311	resnet-18-LC_w_sh_1_iter_m
312	FrankRobWobv0
313	0.5x_resnet-18_LC
314	CORnetZ_CIFAR10
315	resnet18-local_aggregation
316	resnet18-deepcluster
317	r3m_resnet18
318	CORnet-Z
319	resnet18-instance_recognition
320	vggface
321	my-model
322	0.5x_resnet-18_LC_w_sh_10_iter
323	r3m_resnet50
324	resnet18-colorization
325	r3m_resnet34
326	resnet-18-LC_m
327	r3m_resnet50_nocrop
328	resnet18-supervised
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	RN50
334	0.5x_resnet-18_LC_w_sh_100_iter
335	RN50
336	resnet18-relative_position
337	resnet18-autoencoder
338	resnet18-simclr
339	ViT_L_32
340	ViT_B_32
341	resnet18-depth_prediction
342	ViT_B_16
343	r3m_resnet34_nocrop
344	resnet18-contrastive_predictive
345	omnivore_swinT
346	resnet-18-LC_untrained
347	resnet-18_untrained
348	resnet-50x2_untrained
349	dcgan
350	v1-pyr-nodown
351	unet_entire
352	CORnetZ_CIFAR10_bs32_20_04
353	pixels
354	dorinet_cornet_z
355	my-model
356	resnet50
357	openclip
358	resnet50-cifar
359	resnet50_pretrained_with_retinal_waves
360	resnet50_ImageNet
361	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
362	ReAlnet07_ALL
363	yudixie_resnet50_distance_translation_rotation_0_240908
364	yudixie_resnet50_imagenet1kpret_0_240312
365	yudixie_resnet50_random_0_240908
366	yudixie_resnet50_imagenet1kpret_0_240908
367	yudixie_resnet50_cat_obj_class_all_latents_0_240908
368	yudixie_resnet50_object_class_0_240908
369	yudixie_resnet50_category_class_0_240908
370	resnet18_imagenet21kP
371	ConvLSTM
372	PredRNN
373	yudixie_resnet50_distance_reg_0_240908
374	SimVP
375	TAU
376	MIM
377	hmax
378	vit_base_patch16_dinov3-lvd1689m
379	ReAlnet01_cornet
380	briaai_rmbg_1_4
381	compact_resnet50_V4
382	yudixie_resnet18_translation_reg_0_240719
383	yudixie_resnet18_distance_reg_0_240719
384	yolos_tiny
385	yudixie_resnet18_rotation_reg_0_240719
386	ReAlnet02_cornet
387	yudixie_resnet18_imagenet1kpret_0_240719
388	eBarlow_lmda_02_1000ep
389	ReAlnet03_cornet
390	compact_resnet50_IT
391	vit_base_patch14_dinov2-lvd142m
392	resnet18_ecoset_full
393	resnet34_ecoset_full
394	ReAlnet01_cornet_fov4
395	VOneCORnet-S
396	resnet101_ecoset_full
397	resnet152_ecoset_full
398	yudixie_resnet50_rotation_reg_0_240908
399	yudixie_resnet50_distance_translation_0_240908
400	yudixie_resnet50_distance_rotation_0_240908
401	compact_vgg19_V4
402	yudixie_resnet18_distance_translation_0_240719
403	yudixie_resnet18_translation_rotation_0_240719
404	aughost_fair_resnet18
405	vit_base_patch14_reg4_dinov2-lvd142m
406	yudixie_resnet18_category_class_0_240719
407	yudixie_resnet18_random_0_240719
408	yudixie_resnet18_cat_obj_class_all_latents_0_240719
409	resnet-18_test_m
410	SeLaVi-AVE
411	yudixie_resnet18_distance_translation_rotation_0_240719
412	ReAlnet01_cornet_fov12
413	yudixie_resnet18_object_class_0_240719
414	compact_resnet50_robust_V4
415	compact_vgg19_IT
416	r3d_18
417	ReAlnet06_cornet
418	yudixie_resnet50_translation_reg_0_240908
419	omnivore_swinS
420	mc3_18
421	yudixie_resnet50_translation_rotation_0_240908
422	ReAlnet07_cornet
423	s3d
424	ReAlnet02_ALL
425	mvit_v1_b
426	ReAlnet04_ALL
427	ReAlnet01_cornet_fov16
428	compact_cornets_V4
429	ReAlnet08_cornet
430	ReAlnet03_ALL
431	omnivore_swinB
432	compact_cornets_IT
433	SeLaVi-Kinetics400
434	ReAlnet05_ALL
435	ReAlnet09_cornet
436	SeLaVi-VGG-Sound
437	ReAlnet06_ALL
438	SeLaVi-Kinetics-Sound
439	compact_resnet50_robust_IT
440	r2plus1d_18
441	mvit_v2_s
442	ReAlnet10_cornet
443	resnet50_ecoset_full
444	ReAlnet08_ALL
445	yudixie_resnet18_distance_rotation_0_240719
446	ReAlnet09_ALL
447	ReAlnet04_cornet
448	ReAlnet05_cornet
449	resnet50_primary_visual_cortex
450	ReAlnet10_ALL
451	ReAlnet01_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

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021highpass

Metric: error_consistency