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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	.177
45	resnext101_32x8d_wsl	.176
46	convnext_femto_ols:d1_in1k	.175
47	AlexNet_SIN_fov4	.169
48	SWSL_resnet50	.154
49	cvt_cvt-13-224-in1k_4_LucyV4	.151
50	pnasnet_large_pytorch	.150
51	nasnet_large	.149
52	inception_v3	.143
53	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.142
54	CLIP_ViT-B_32	.139
55	inception_v4	.139
56	pnasnet_large-tf	.139
57	inception_v3_pytorch	.139
58	inception_v3-tf	.135
59	densenet-169	.132
60	convnext_small_imagenet_100_seed-0	.131
61	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.127
62	resnet_101_v2	.123
63	resnet-152_v2-tf	.120
64	xception	.118
65	tv_efficientnet-b1	.116
66	densenet-169-keras	.116
67	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.116
68	ViT_B_16_imagenet1k	.111
69	xception-keras	.110
70	resnet34_imagenet_full	.101
71	resnet_50_v2	.100
72	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.100
73	BiT-S-R50x3	.100
74	alexnet_training_seed_05	.096
75	alexnet_training_seed_01	.096
76	alexnet_training_seed_01_fov4	.096
77	alexnet_training_seed_01_fov12	.096
78	alexnet_training_seed_01_fov16	.096
79	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.095
80	deit_small_imagenet_1_seed-0	.095
81	deit_small_imagenet_10_seed-0	.095
82	resnet50_imagenet_10_seed-0	.095
83	imagenet_l2_10_0	.094
84	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.093
85	BiT-S-R152x2	.093
86	resnet50_FractalDB	.092
87	resnet50-vicregl0p9	.091
88	barlow-twins-resnet50	.091
89	alexnet_linf_8_robust	.090
90	alexnet_l2_3_robust	.090
91	resnet50_linf_8_robust	.089
92	inception_v4-tf	.089
93	alexnet_training_seed_07	.088
94	effnetb1_272x240	.088
95	alexnet_robust_correct	.086
96	custom_model_cv_18_dagger_408	.085
97	AdvProp_efficientnet-b4	.083
98	AT_efficientnet-b4	.083
99	mobilenet_v2_0_5_224	.082
100	blt_vs	.081
101	vonealexnet_gaussian_noise_std4_fixed	.081
102	mobilenet_v1_0.5_192	.080
103	resnet50_linf_4_robust	.079
104	densenet-201-keras	.078
105	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.078
106	resnet50_random_l2_perturb	.077
107	mobilenet_v1_0.25_128	.075
108	resnet50-meshes-lt-100-original-scratch	.075
109	alexnet_training_seed_02	.075
110	alexnet_training_seed_04	.075
111	alexnet_training_seed_08	.075
112	alexnet_training_seed_10	.075
113	artResNet18_1	.075
114	resnet101	.075
115	resnet_50_v1_spiking_l4a	.074
116	alexnet_fov12	.074
117	BiT-S-R101x3	.074
118	convnext_small_imagenet_10_seed-0	.073
119	resnet50_byol	.073
120	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
121	texture_shape_alexnet_trained_on_SIN	.073
122	alexnet_training_seed_03	.072
123	mobilenet_v1_0.75_160	.072
124	vgg-19-keras	.072
125	nasnet_large-tf	.071
126	resnet50-vicreg	.071
127	alexnet_fov16	.067
128	alexnet_fov4	.067
129	BiT-S-R152x4	.066
130	voneresnet-50-robust	.066
131	vgg-16-keras	.066
132	mobilenet_v1_0.25_192	.065
133	antialias-resnet152	.064
134	mobilenet_v1_1.0_224	.064
135	AdvProp_efficientnet-b8	.064
136	resnet50-moclr8deg	.063
137	mobilenet_v2_0_5_192	.062
138	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.061
139	resnet50-SIN_IN_IN	.060
140	resnet_SIN_IN_FT_IN	.060
141	resnet50_robust_l2_eps1	.060
142	resnet_101_v1	.059
143	mobilenet_v1_0.75_128	.059
144	alexnet_reduced_aliasing_early_checkpoint	.059
145	konkle_alexnetgn_ipcl_ref01_primary_model	.059
146	AdvProp_efficientnet-b0	.056
147	resnet50_l2_3_robust	.056
148	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.056
149	AlexNet_SIN	.055
150	resnet-50-robust	.055
151	imagenet_l2_3_0	.055
152	resnet50_robust_l2_eps3	.055
153	mobilenet_v2_1.0_160-tf	.055
154	mobilenet_v1_0.25_160	.055
155	densenet-201	.054
156	densenet_201_pytorch	.054
157	efficientnet_b0	.054
158	mobilenet_v2_1_0_128	.054
159	vonegrcnn_62e_nobn	.053
160	alexnet_training_seed_06	.053
161	AlexNet_SIN_fov16	.053
162	AdvProp_efficientnet-b7	.053
163	mobilenet_v1_0.5_224	.053
164	AlexNet_SIN_fov12	.053
165	vonegrcnn_52e_full	.052
166	vonegrcnn_47e	.052
167	squeezenet1_0	.051
168	convnext_small_imagenet_1_seed-0	.051
169	CORnet-S	.051
170	mobilenet_v1_1.0_128	.051
171	deit_small_imagenet_100_seed-0	.050
172	mobilenet_v2_1_0_224	.050
173	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.050
174	ReAlnet07	.050
175	mobilenet_v2_1.0_224-tf	.050
176	mobilevit_small	.049
177	resnet50-SIN	.049
178	resnet50_eMMCR_Vanilla	.048
179	ReAlnet06	.048
180	resnet152_imagenet_full	.048
181	ReAlnet08	.047
182	cornet_s	.046
183	ReAlnet10	.046
184	resnet-18	.046
185	resnet50_imagenet_full	.045
186	alexnet_ks_torevert	.045
187	alexnet-baseline	.045
188	alexnet	.045
189	alexnet	.045
190	alexnet	.045
191	alexnet	.045
192	alexnet	.045
193	alexnet	.045
194	alexnet	.045
195	alexnet	.045
196	alexnet	.045
197	alexnet	.045
198	mobilenet_v2_0_75_192	.045
199	resnet50-simclr-vissl	.044
200	resnet50-meshes-lt-100-original-pretrained	.044
201	vonegrcnn_52e_full	.043
202	nasnet_mobile	.043
203	mobilenet_v2_1_3_224	.043
204	resnet101_imagenet_full	.043
205	efficientnet-b7	.043
206	vonegrcnn_47e	.043
207	mobilenet_v1_0.5_160	.042
208	resnet50-SIN_IN	.042
209	regnet_y_400mf	.042
210	resnet50_moco_v2	.041
211	nasnet_mobile-tf	.041
212	Res2Net50_26w_4s	.040
213	resnet50	.040
214	mobilenet_v1_1.0_160	.040
215	mobilenet_v2_0.75_96	.039
216	mobilenet_v1_0.5_128	.038
217	mobilenet_v1_0.25_224	.038
218	squeezenet1_1	.038
219	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.038
220	alexnet_random_linf8_perturb	.037
221	voneresnet-50	.037
222	vonegrcnn_62e_nobn	.037
223	mobilenet_v2_0.75_224-tf	.036
224	inception_v1	.036
225	deit_large_imagenet_full_seed-0	.036
226	mobilenet_v2_1_0_160	.036
227	mobilenet_v2_1.3_224-tf	.035
228	mobilenet_v2_1_0_192	.035
229	alexnet_early_checkpoint	.035
230	mobilenet_v2_1.0_192-tf	.034
231	texture_shape_resnet50_trained_on_SIN	.033
232	alexnet_training_seed_09	.033
233	mobilenet_v2_0_75_160	.033
234	mobilenet_v2_0.5_96	.033
235	resnet-50_untrained	.033
236	ReAlnet05	.032
237	resnet_152_v1	.031
238	resnet50_imagenet_1_seed-0	.031
239	ReAlnet01	.031
240	resnet-50-pytorch	.030
241	resnet50-sup	.030
242	resnet50_tutorial	.030
243	resnet_50_v1	.030
244	resnet-34	.030
245	inception_v1-tf	.030
246	BiT-S-R50x1	.028
247	resnet50_ewconplus_wzc	.027
248	ReAlnet03	.027
249	resnet_50_v1_spiking_l3	.027
250	resnet_50_v1_spiking	.026
251	ReAlnet02	.026
252	mobilenet_v2_0.35_96	.026
253	ViT_B_32_imagenet1k	.024
254	resnet50_random_linf8_perturb	.024
255	BiT-S-R101x1	.023
256	resnet50-barlow	.023
257	alexnet	.023
258	mobilenet_v2_0.75_160-tf	.023
259	mobilenet_v1_0.75_192	.023
260	mobilenet_v2_0.35_128	.022
261	ReAlnet09	.022
262	resnet-50_v1-tf	.022
263	mobilenet_v2_0_75_224	.022
264	antialiased-rnext101_32x8d	.022
265	inception_v2-tf	.022
266	mobilenet_v2_0.5_160	.021
267	mobilenet_v2_1.0_96	.021
268	mobilenet_v1_1.0_192	.020
269	antialiased-r50	.020
270	mobilenet_v2_0.75_192-tf	.019
271	deit_small_imagenet_full_seed-0	.019
272	mobilenet_v2_1.4_224-tf	.019
273	resnet_50_v1_spiking_l4	.019
274	mobilenet_v2_0.5_224-tf	.018
275	resnet50-vicregl0p75	.016
276	resnet50_imagenet_100_seed-0	.015
277	efficientnet_b0_imagenet_full	.015
278	resnet18_imagenet_full	.015
279	resnet-152_v1-tf	.015
280	densenet-121-keras	.015
281	grcnn_109	.015
282	mobilenet_v2_1-4_224_pytorch	.015
283	mobilenet_v2_1_4_224	.015
284	0.5x_resnet-18_LC_w_sh_1_iter	.014
285	AdvProp_efficientnet-b6	.014
286	mobilenet_v1_0.75_224	.014
287	deit_base_imagenet_full_seed-0	.013
288	resnet50_simclr	.013
289	mobilenet_v2_0.5_192-tf	.013
290	mobilenet_v2_0.35_160	.012
291	mobilenet_v2_1.0_128-tf	.012
292	AT_efficientnet-b2	.010
293	AdvProp_efficientnet-b2	.010
294	mobilenet_v2_0.35_192	.010
295	vgg_16	.010
296	mobilenet_v2_0.35_224	.010
297	mobilenet_v2_0.5_128	.010
298	grcnn	.009
299	resnet-50_v2-tf	.007
300	mobilenet_v2_0.75_128	.005
301	resnet-101_v1-tf	.004
302	ReAlnet04	.004
303	alexnet_random_l2_3_perturb	.004
304	shufflenet_v2_x1_0	.004
305	vgg_19	.003
306	densenet-121	.003
307	resnet50-vitoimagevidnet8	.003
308	efficientnet_b2_imagenet_full	.002
309	ViT_L_32_imagenet1k	.002
310	efficientnet_b1_imagenet_full	.001
311	voneresnet-50-non_stochastic	-0.030
312	resnet-18-LC_w_sh_100_iter_conv_init_m
313	ViT-B/32
314	BiT-M-R50x3
315	BiT-M-R50x1
316	BiT-M-R152x2
317	resnet-18-LC_w_sh_1_iter_conv_init_m
318	resnet-18-LC_w_sh_10_iter_m
319	resnet-18-LC_w_sh_100_iter_m
320	resnet-18-LC_w_sh_10_iter_conv_init_m
321	resnet-18-LC_w_sh_1_iter_m
322	FrankRobWobv0
323	0.5x_resnet-18_LC
324	CORnetZ_CIFAR10
325	resnet18-local_aggregation
326	resnet18-deepcluster
327	r3m_resnet18
328	CORnet-Z
329	resnet18-instance_recognition
330	vggface
331	my-model
332	0.5x_resnet-18_LC_w_sh_10_iter
333	r3m_resnet50
334	resnet18-colorization
335	r3m_resnet34
336	resnet-18-LC_m
337	r3m_resnet50_nocrop
338	resnet18-supervised
339	resnet-18-LC_1st_conv_m
340	resnet-18-LC_conv_init_m
341	resnet18-contrastive_multiview
342	0.5x_resnet-18
343	RN50
344	0.5x_resnet-18_LC_w_sh_100_iter
345	RN50
346	resnet18-relative_position
347	resnet18-autoencoder
348	resnet18-simclr
349	ViT_L_32
350	ViT_B_32
351	resnet18-depth_prediction
352	ViT_B_16
353	r3m_resnet34_nocrop
354	resnet18-contrastive_predictive
355	omnivore_swinT
356	resnet-18-LC_untrained
357	resnet-18_untrained
358	resnet-50x2_untrained
359	dcgan
360	v1-pyr-nodown
361	unet_entire
362	CORnetZ_CIFAR10_bs32_20_04
363	pixels
364	dorinet_cornet_z
365	my-model
366	resnet50
367	openclip
368	resnet50-cifar
369	resnet50_pretrained_with_retinal_waves
370	resnet50_ImageNet
371	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
372	ReAlnet07_ALL
373	yudixie_resnet50_distance_translation_rotation_0_240908
374	yudixie_resnet50_imagenet1kpret_0_240312
375	yudixie_resnet50_random_0_240908
376	yudixie_resnet50_imagenet1kpret_0_240908
377	yudixie_resnet50_cat_obj_class_all_latents_0_240908
378	yudixie_resnet50_object_class_0_240908
379	yudixie_resnet50_category_class_0_240908
380	resnet18_imagenet21kP
381	ConvLSTM
382	PredRNN
383	yudixie_resnet50_distance_reg_0_240908
384	SimVP
385	TAU
386	MIM
387	hmax
388	ReAlnet01_cornet
389	briaai_rmbg_1_4
390	compact_resnet50_V4
391	yudixie_resnet18_translation_reg_0_240719
392	yudixie_resnet18_distance_reg_0_240719
393	yolos_tiny
394	yudixie_resnet18_rotation_reg_0_240719
395	ReAlnet02_cornet
396	yudixie_resnet18_imagenet1kpret_0_240719
397	ReAlnet03_cornet
398	compact_resnet50_IT
399	resnet18_ecoset_full
400	resnet34_ecoset_full
401	ReAlnet01_cornet_fov4
402	VOneCORnet-S
403	resnet101_ecoset_full
404	resnet152_ecoset_full
405	yudixie_resnet50_rotation_reg_0_240908
406	yudixie_resnet50_distance_translation_0_240908
407	yudixie_resnet50_distance_rotation_0_240908
408	compact_vgg19_V4
409	yudixie_resnet18_distance_translation_0_240719
410	yudixie_resnet18_translation_rotation_0_240719
411	yudixie_resnet18_category_class_0_240719
412	yudixie_resnet18_random_0_240719
413	yudixie_resnet18_cat_obj_class_all_latents_0_240719
414	resnet-18_test_m
415	SeLaVi-AVE
416	yudixie_resnet18_distance_translation_rotation_0_240719
417	ReAlnet01_cornet_fov12
418	yudixie_resnet18_object_class_0_240719
419	compact_resnet50_robust_V4
420	compact_vgg19_IT
421	r3d_18
422	ReAlnet06_cornet
423	yudixie_resnet50_translation_reg_0_240908
424	omnivore_swinS
425	mc3_18
426	yudixie_resnet50_translation_rotation_0_240908
427	ReAlnet07_cornet
428	s3d
429	ReAlnet02_ALL
430	mvit_v1_b
431	ReAlnet04_ALL
432	ReAlnet01_cornet_fov16
433	compact_cornets_V4
434	ReAlnet08_cornet
435	ReAlnet03_ALL
436	omnivore_swinB
437	compact_cornets_IT
438	SeLaVi-Kinetics400
439	ReAlnet05_ALL
440	ReAlnet09_cornet
441	SeLaVi-VGG-Sound
442	ReAlnet06_ALL
443	SeLaVi-Kinetics-Sound
444	compact_resnet50_robust_IT
445	r2plus1d_18
446	mvit_v2_s
447	ReAlnet10_cornet
448	resnet50_ecoset_full
449	ReAlnet08_ALL
450	yudixie_resnet18_distance_rotation_0_240719
451	ReAlnet09_ALL
452	ReAlnet04_cornet
453	ReAlnet05_cornet
454	resnet50_primary_visual_cortex
455	ReAlnet10_ALL
456	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