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