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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021highpass-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	dinov2_vitl14_lc_res336	.995
2	dinov2_vitg14_lc	.989
3	dinov2_vitl14_reg_lc	.988
4	dinov2_vitg14_reg_lc	.986
5	convnext_xxlarge_clip	.984
6	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.983
7	dinov2_vitl14_lc	.981
8	eva02_large_in22k	.980
9	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.975
10	convnext_large_clip_laion2b_finelayers	.967
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.959
12	vit_large_clip336_openai	.959
13	vit_large_patch14_clip_224:openai_ft_in1k	.955
14	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.953
15	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.950
16	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.947
17	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.947
18	vit_huge_clip336_laion	.947
19	vit_large_patch14_clip_336:laion2b_ft_in1k	.941
20	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.939
21	convnext_base:clip_laiona_augreg_ft_in1k_384	.927
22	vit_large_patch14_clip_224:laion2b_ft_in1k	.922
23	convnext_large:fb_in22k_ft_in1k	.889
24	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.886
25	convnext_xlarge:fb_in22k_ft_in1k	.881
26	convnext_base_fb_in22k_ft_in1k	.864
27	vit_base_patch16_clip_224:openai_ft_in1k	.861
28	convnext_large_in22k_supervised	.856
29	resnext101_32x48d_wsl	.838
30	convnext_large_imagenet_full_seed-0	.820
31	convnext_small_imagenet_full_seed-0	.816
32	fixres_resnext101_32x48d_wsl	.811
33	convnext_tiny:in12k_ft_in1k	.811
34	convnext_base_imagenet_full_seed-0	.800
35	convnext_base_fb_in1k	.798
36	convnext_tiny_imagenet_full_seed-0	.773
37	resnext101_32x32d_wsl	.772
38	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.741
39	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.731
40	resnext101_32x8d_wsl	.720
41	resnet50-SIN	.706
42	deit_large_imagenet_full_seed-0	.705
43	regnety_032	.698
44	CLIP-RN50	.692
45	resnext101_32x16d_wsl	.684
46	convnext_base_fb_in1k_gram_aligned_dinov3	.670
47	resnet50-vitoimagevidnet8	.669
48	cvt_cvt-21-224-in1k_4_LucyV4	.664
49	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.658
50	tv_efficientnet-b1	.655
51	convnext_tiny_sup	.650
52	deit_base_imagenet_full_seed-0	.650
53	resnet50-VITO-8deg-cc	.648
54	cv_18_dagger_408_pretrained	.642
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.628
56	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.627
57	seresnext50_32x4d	.627
58	cvt_cvt-13-224-in1k_4_LucyV4	.622
59	focalnet_tiny_lrf_in1k	.608
60	effnetv2m_custom384	.603
61	vit_relpos_base_patch16_clsgap_224:sw_in1k	.600
62	AlexNet_SIN	.595
63	AlexNet_SIN_fov4	.595
64	AlexNet_SIN_fov12	.595
65	AlexNet_SIN_fov16	.595
66	cvt_cvt-21-384-in1k_4_LucyV4	.567
67	cvt_cvt-13-384-in1k_4_LucyV4	.564
68	densenet-201-keras	.556
69	densenet-201	.556
70	densenet_201_pytorch	.556
71	densenet-169-keras	.544
72	densenet-169	.544
73	convnext_femto_ols:d1_in1k	.544
74	inception_v4	.541
75	vgg-19-keras	.536
76	resnet-101_v2-tf	.533
77	pnasnet_large_pytorch	.530
78	inception_v4-tf	.527
79	densenet-121-keras	.525
80	densenet-121	.525
81	nasnet_large	.522
82	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.514
83	deit_small_imagenet_full_seed-0	.505
84	AdvProp_efficientnet-b4	.503
85	AT_efficientnet-b4	.503
86	inception_v3_pytorch	.503
87	xception	.503
88	resnet50_st_wzc_seed6_2_323	.503
89	resnet50_st_wzc_seed7_2_323	.502
90	resnet50_eMMCR_Vanilla	.500
91	inception_v3-tf	.497
92	resnet-50_v2-tf	.495
93	resnet50_ewconplus_wzc_seed6_2_323	.495
94	resnet50_ewconplus_wzc_seed5_2__323	.495
95	pnasnet_large-tf	.494
96	resnet-152_v2-tf	.494
97	convnext_small_imagenet_100_seed-0	.494
98	nasnet_large-tf	.492
99	effnetb1_272x240	.492
100	resnet50_moco_v2	.491
101	resnet50_ewconplus_wzc_seed2_2__323	.491
102	pnasnet_large	.487
103	vgg-16-keras	.486
104	pnasnet_large_halfPrecision	.484
105	vgg_19	.481
106	AdvProp_efficientnet-b7	.480
107	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.480
108	AT_efficientnet-b2	.473
109	AdvProp_efficientnet-b2	.473
110	resnet_101_v2	.473
111	resnet-152_v2_pytorch	.464
112	efficientnet_b1_imagenet_full	.464
113	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.463
114	inception_v1-tf	.461
115	resnet50-moclr8deg	.461
116	mobilenet_v2_1-4_224_pytorch	.461
117	mobilenet_v2_1_4_224	.461
118	AdvProp_efficientnet-b6	.459
119	resnet50_st_wzc_seed3_2_323	.456
120	mobilenet_v2_0_75_224	.455
121	xception-keras	.450
122	ReAlnet05	.447
123	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.444
124	AdvProp_efficientnet-b0	.441
125	ViT_L_32_imagenet1k	.439
126	resnet50_st_wzc_2	.439
127	ViT_B_32_imagenet1k	.434
128	inception_v3	.434
129	resnet50_barlowtwins_linhead	.431
130	resnet50_ewconplus_wzc	.430
131	resnet50_vicreg_linhead	.428
132	resnet-34	.427
133	efficientnet_b0_imagenet_full	.427
134	mobilenet_v2_0_5_192	.423
135	inception_v2-tf	.422
136	resnet50_byol_linhead	.422
137	mobilenet_v2_0_5_224	.417
138	resnet50_simclr_linhead_01norm	.416
139	mobilenet_v2_0.35_224	.414
140	ReAlnet09	.414
141	BiT-S-R50x1	.411
142	mobilenet_v2_0_75_192	.411
143	BiT-S-R101x1	.405
144	resnet_50_v2	.405
145	mobilenet_v1_1.0_224	.403
146	vgg_16	.403
147	resnet50_hvs_wzc	.402
148	squeezenet1_1	.400
149	resnet-101_v1-tf	.400
150	mobilenet_v2_0.75_224-tf	.400
151	resnet34_imagenet_full	.400
152	ReAlnet10	.400
153	grcnn_109	.400
154	nasnet_mobile	.394
155	BiT-S-R152x4	.392
156	ReAlnet07	.392
157	inception_v1	.389
158	efficientnet-b7	.388
159	mobilenet_v2_0.5_224-tf	.386
160	resnet50_simsiam_linhead	.384
161	BiT-S-R152x2	.383
162	mobilenet_v2_0.5_192-tf	.383
163	ReAlnet06	.383
164	ReAlnet04	.383
165	mobilenet_v2_0.35_192	.381
166	antialiased-rnext101_32x8d	.381
167	ReAlnet08	.380
168	ReAlnet03	.380
169	squeezenet1_0	.378
170	resnet_101_v1	.375
171	ReAlnet02	.375
172	efficientnet_b0	.373
173	CORnet-S	.372
174	mobilenet_v2_1_0_192	.372
175	mobilenet_v2_0_75_160	.372
176	resnet18_imagenet_full	.370
177	resnet101_imagenet_full	.369
178	grcnn	.369
179	ReAlnet01	.369
180	BiT-S-R101x3	.367
181	mobilenet_v2_1_0_224	.367
182	resnet50-SIN_IN	.366
183	eicircuit_resnet18_bn_20260919	.366
184	nasnet_mobile-tf	.364
185	efficientnet_b2_imagenet_full	.362
186	mehrer2020_alexnet_training_seed_03	.362
187	mobilenet_v2_0.75_192-tf	.358
188	resnet_152_v2	.358
189	alexnet_early_checkpoint	.356
190	BiT-S-R50x3	.355
191	mobilenet_v2_1.4_224-tf	.353
192	resnet50	.352
193	alexnet_random_linf8_perturb	.350
194	mobilenet_v2_1_3_224	.348
195	resnet-18	.341
196	AdvProp_efficientnet-b8	.339
197	mobilenet_v1_0.5_224	.339
198	resnet_50_v1_spiking	.339
199	mobilenet_v2_1_0_160	.336
200	resnet50_imagenet_full	.336
201	resnet-50-pytorch	.334
202	resnet_SIN_IN_FT_IN	.334
203	resnet50-SIN_IN_IN	.334
204	resnet50-sup	.334
205	mobilenet_v2_0.75_160-tf	.334
206	resnet50_tutorial	.334
207	resnet_50_v1	.334
208	mehrer2020_alexnet_training_seed_01	.334
209	mehrer2020_alexnet_training_seed_01_fov12	.334
210	mehrer2020_alexnet_training_seed_01_fov4	.334
211	mehrer2020_alexnet_training_seed_01_fov16	.334
212	mobilenet_v2_1.0_224-tf	.331
213	blt_vs	.331
214	mehrer2020_alexnet_training_seed_04	.330
215	mobilenet_v2_1.0_192-tf	.325
216	antialiased-r50	.325
217	shufflenet_v2_x1_0	.325
218	resnet-152_v1-tf	.322
219	mehrer2020_alexnet_training_seed_02	.320
220	resnet152_imagenet_full	.319
221	mobilenet_v2_1.3_224-tf	.316
222	mobilenet_v1_1.0_192	.312
223	Res2Net50_26w_4s	.309
224	mehrer2020_alexnet_training_seed_05	.306
225	regnet_y_400mf	.305
226	mobilenet_v2_1.0_160-tf	.303
227	resnet-50_v1-tf	.300
228	antialias-resnet152	.300
229	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.298
230	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.298
231	voneresnet-50-non_stochastic	.295
232	mobilenet_v1_0.75_224	.295
233	mobilenet_v2_0.75_128	.295
234	resnet_152_v1	.294
235	mobilenet_v1_0.75_192	.291
236	custom_model_cv_18_dagger_408	.289
237	mobilenet_v2_0.5_160	.287
238	ijepa_vith14_linhead	.283
239	mobilenet_v2_0.35_160	.280
240	mobilenet_v2_0.5_128	.278
241	mobilenet_v2_1.0_128-tf	.275
242	mobilenet_v1_0.5_160	.272
243	resnet50_robust_l2_eps1	.269
244	resnet_50_v1_spiking_l4a	.269
245	mobilenet_v1_0.75_160	.267
246	mobilenet_v2_1_0_128	.267
247	alexnet	.264
248	alexnet_ks_torevert	.264
249	alexnet_fov4	.264
250	alexnet_fov12	.264
251	alexnet_fov16	.264
252	mobilenet_v1_0.75_128	.263
253	vonegrcnn_52e_full	.261
254	resnet-50-robust	.261
255	imagenet_l2_3_0	.261
256	resnet50_robust_l2_eps3	.261
257	vonegrcnn_62e_nobn	.253
258	mobilenet_v1_0.5_192	.253
259	voneresnet-50-robust	.253
260	vonegrcnn_47e	.250
261	mobilenet_v2_0.35_128	.248
262	resnet50_l2_3_robust	.248
263	vonegrcnn_52e_full	.248
264	vonegrcnn_47e	.245
265	mobilenet_v1_1.0_128	.244
266	resnet50_linf_8_robust	.241
267	vonegrcnn_62e_nobn	.241
268	mobilenet_v1_1.0_160	.234
269	mobilenet_v2_1.0_96	.231
270	voneresnet-50	.228
271	mobilenet_v2_0.75_96	.217
272	mobilenet_v1_0.5_128	.217
273	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.209
274	mobilenet_v1_0.25_224	.206
275	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.205
276	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.202
277	mobilenet_v2_0.5_96	.200
278	mobilenet_v1_0.25_160	.200
279	mobilenet_v1_0.25_192	.183
280	resnet50_imagenet_100_seed-0	.181
281	imagenet_l2_10_0	.178
282	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.175
283	mobilenet_v2_0.35_96	.172
284	deit_small_imagenet_100_seed-0	.167
285	mobilenet_v1_0.25_128	.164
286	0.5x_resnet-18_LC_w_sh_1_iter	.158
287	alexnet_robust_correct	.147
288	resnet_50_v1_spiking_l4	.144
289	alexnet_l2_3_robust	.142
290	alexnet_linf_8_robust	.117
291	resnet50_imagenet_10_seed-0	.081
292	barlow-twins-resnet50	.078
293	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.072
294	convnext_small_imagenet_10_seed-0	.072
295	artResNet18_1	.070
296	resnet50-vicregl0p9	.069
297	resnet-50_untrained	.064
298	alexnet_training_seed_09	.062
299	alexnet_training_seed_05	.062
300	alexnet_training_seed_06	.062
301	alexnet_training_seed_02	.062
302	alexnet_training_seed_04	.062
303	alexnet_training_seed_10	.062
304	alexnet_training_seed_08	.062
305	resnet50_imagenet_1_seed-0	.062
306	deit_small_imagenet_10_seed-0	.062
307	convnext_small_imagenet_1_seed-0	.062
308	alexnet_training_seed_01	.062
309	deit_small_imagenet_1_seed-0	.062
310	alexnet_training_seed_01_fov4	.062
311	alexnet_training_seed_01_fov12	.062
312	alexnet_training_seed_01_fov16	.062
313	alexnet_training_seed_03	.062
314	resnet50-barlow	.059
315	resnet50-vicregl0p75	.059
316	alexnet_training_seed_07	.053
317	resnet_50_v1_spiking_l3	.053
318	mobilevit_small	.052
319	resnet50-simclr-vissl	.050
320	ViT-B/32
321	resnet-18-LC_w_sh_100_iter_conv_init_m
322	BiT-M-R50x1
323	resnet-18-LC_w_sh_1_iter_conv_init_m
324	BiT-M-R152x2
325	resnet-18-LC_w_sh_1_iter_m
326	resnet-18-LC_w_sh_10_iter_m
327	BiT-M-R50x3
328	resnet-18-LC_w_sh_100_iter_m
329	resnet-18-LC_w_sh_10_iter_conv_init_m
330	FrankRobWobv0
331	0.5x_resnet-18_LC
332	CORnetZ_CIFAR10
333	resnet18-local_aggregation
334	r3m_resnet18
335	resnet18-deepcluster
336	CORnet-Z
337	vggface
338	resnet18-instance_recognition
339	my-model
340	0.5x_resnet-18_LC_w_sh_10_iter
341	r3m_resnet50
342	r3m_resnet34
343	resnet-18-LC_m
344	resnet18-supervised
345	r3m_resnet50_nocrop
346	resnet-18-LC_1st_conv_m
347	resnet-18-LC_conv_init_m
348	resnet18-contrastive_multiview
349	0.5x_resnet-18
350	0.5x_resnet-18_LC_w_sh_100_iter
351	RN50
352	RN50
353	resnet18-colorization
354	resnet18-relative_position
355	resnet18-autoencoder
356	resnet18-simclr
357	ViT_L_32
358	ViT_B_32
359	resnet18-depth_prediction
360	ViT_B_16_imagenet1k
361	ViT_B_16
362	r3m_resnet34_nocrop
363	resnet18-contrastive_predictive
364	resnet-18-LC_untrained
365	resnet-18_untrained
366	resnet-50x2_untrained
367	dcgan
368	unet_entire
369	CORnetZ_CIFAR10_bs32_20_04
370	v1-pyr-nodown
371	pixels
372	dorinet_cornet_z
373	omnivore_swinT
374	openclip
375	vonealexnet_gaussian_noise_std4_fixed
376	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
377	yudixie_resnet50_category_class_0_240908
378	resnet18_imagenet21kP
379	ConvLSTM
380	PredRNN
381	SimVP
382	TAU
383	MIM
384	hmax
385	vit_base_patch16_dinov3-lvd1689m
386	yolos_tiny
387	yudixie_resnet50_imagenet1kpret_0_240312
388	compact_resnet50_V4
389	yudixie_resnet18_translation_reg_0_240719
390	yudixie_resnet18_distance_reg_0_240719
391	ReAlnet02_cornet
392	yudixie_resnet18_imagenet1kpret_0_240719
393	eBarlow_lmda_02_1000ep
394	yudixie_resnet50_rotation_reg_0_240908
395	yudixie_resnet50_distance_translation_0_240908
396	yudixie_resnet50_distance_rotation_0_240908
397	vit_base_patch14_dinov2-lvd142m
398	resnet18_ecoset_full
399	vit_base_patch14_reg4_dinov2-lvd142m
400	ReAlnet04_cornet
401	ReAlnet01_cornet_fov4
402	resnet34_ecoset_full
403	resnet50_ecoset_full
404	compact_resnet50_IT
405	ReAlnet03_cornet
406	ReAlnet01_cornet_fov12
407	compact_vgg19_V4
408	yudixie_resnet18_translation_rotation_0_240719
409	resnet-18_test_m
410	yudixie_resnet18_category_class_0_240719
411	yudixie_resnet18_random_0_240719
412	yudixie_resnet18_cat_obj_class_all_latents_0_240719
413	yudixie_resnet18_distance_translation_rotation_0_240719
414	yudixie_resnet18_object_class_0_240719
415	compact_vgg19_IT
416	SeLaVi-AVE
417	compact_resnet50_robust_V4
418	r3d_18
419	yudixie_resnet50_translation_rotation_0_240908
420	ReAlnet07_cornet
421	ReAlnet01_ALL
422	omnivore_swinB
423	mc3_18
424	ReAlnet02_ALL
425	s3d
426	VOneCORnet-S
427	resnet50-vicreg
428	mvit_v1_b
429	compact_cornets_V4
430	yudixie_resnet18_rotation_reg_0_240719
431	ReAlnet03_ALL
432	compact_cornets_IT
433	ReAlnet09_cornet
434	omnivore_swinS
435	SeLaVi-VGG-Sound
436	ReAlnet06_ALL
437	SeLaVi-Kinetics-Sound
438	compact_resnet50_robust_IT
439	SeLaVi-Kinetics400
440	r2plus1d_18
441	mvit_v2_s
442	yudixie_resnet50_random_0_240908
443	ReAlnet07_ALL
444	yudixie_resnet50_imagenet1kpret_0_240908
445	yudixie_resnet50_distance_translation_rotation_0_240908
446	ReAlnet08_ALL
447	yudixie_resnet50_cat_obj_class_all_latents_0_240908
448	yudixie_resnet18_distance_rotation_0_240719
449	ReAlnet09_ALL
450	yudixie_resnet18_distance_translation_0_240719
451	yudixie_resnet50_translation_reg_0_240908
452	resnet50_primary_visual_cortex
453	yudixie_resnet50_object_class_0_240908
454	yudixie_resnet50_distance_reg_0_240908
455	ReAlnet10_ALL
456	briaai_rmbg_1_4
457	ReAlnet04_ALL
458	ReAlnet05_ALL
459	ReAlnet01_cornet
460	ReAlnet05_cornet
461	ReAlnet01_cornet_fov16
462	ReAlnet06_cornet
463	ReAlnet10_cornet
464	ReAlnet08_cornet
465	resnet101_ecoset_full
466	resnet152_ecoset_full
467	resnet18_fair
468	pr1_stl10_disentangled_alpha04
469	yudixie_wbh_object_class_scaling_0415_run_0
470	yudixie_wbh_object_class_scaling_0415_run_3
471	yudixie_wbh_object_class_scaling_0415_imagenet
472	yudixie_wbh_object_class_scaling_0415_run_2
473	yudixie_wbh_object_class_scaling_0415_run_1
474	deepeisnn_ei_cifar10_vgg19
475	deepeisnn_ei_cifar10_resnet18
476	deepeisnn_ei_imagenet_resnet18
477	dinov2_vitl14_reg_lc_featread
478	gabor_filter_entropy_4x4
479	gabor_filter_entropy_4x4_v2
480	gabor_filter_entropy_4x4_v2_forcedCosine
481	gabor_filter_mean_abs_4x4_v2

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

1.00.

Note that scores are relative to this ceiling.

Data: Geirhos2021highpass

Metric: top1