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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_ewconplus_wzc	.430
130	resnet-34	.427
131	efficientnet_b0_imagenet_full	.427
132	mobilenet_v2_0_5_192	.423
133	inception_v2-tf	.422
134	mobilenet_v2_0_5_224	.417
135	mobilenet_v2_0.35_224	.414
136	ReAlnet09	.414
137	BiT-S-R50x1	.411
138	mobilenet_v2_0_75_192	.411
139	BiT-S-R101x1	.405
140	resnet_50_v2	.405
141	mobilenet_v1_1.0_224	.403
142	vgg_16	.403
143	resnet50_hvs_wzc	.402
144	squeezenet1_1	.400
145	resnet-101_v1-tf	.400
146	mobilenet_v2_0.75_224-tf	.400
147	resnet34_imagenet_full	.400
148	ReAlnet10	.400
149	grcnn_109	.400
150	nasnet_mobile	.394
151	BiT-S-R152x4	.392
152	ReAlnet07	.392
153	inception_v1	.389
154	efficientnet-b7	.388
155	mobilenet_v2_0.5_224-tf	.386
156	BiT-S-R152x2	.383
157	mobilenet_v2_0.5_192-tf	.383
158	ReAlnet06	.383
159	ReAlnet04	.383
160	mobilenet_v2_0.35_192	.381
161	antialiased-rnext101_32x8d	.381
162	ReAlnet08	.380
163	ReAlnet03	.380
164	squeezenet1_0	.378
165	resnet_101_v1	.375
166	ReAlnet02	.375
167	efficientnet_b0	.373
168	CORnet-S	.372
169	mobilenet_v2_1_0_192	.372
170	mobilenet_v2_0_75_160	.372
171	resnet18_imagenet_full	.370
172	resnet101_imagenet_full	.369
173	grcnn	.369
174	ReAlnet01	.369
175	BiT-S-R101x3	.367
176	mobilenet_v2_1_0_224	.367
177	resnet50-SIN_IN	.366
178	eicircuit_resnet18_bn_20260919	.366
179	nasnet_mobile-tf	.364
180	efficientnet_b2_imagenet_full	.362
181	mobilenet_v2_0.75_192-tf	.358
182	resnet_152_v2	.358
183	alexnet_early_checkpoint	.356
184	BiT-S-R50x3	.355
185	mobilenet_v2_1.4_224-tf	.353
186	resnet50	.352
187	alexnet_random_linf8_perturb	.350
188	mobilenet_v2_1_3_224	.348
189	resnet-18	.341
190	AdvProp_efficientnet-b8	.339
191	mobilenet_v1_0.5_224	.339
192	resnet_50_v1_spiking	.339
193	mobilenet_v2_1_0_160	.336
194	resnet50_imagenet_full	.336
195	resnet-50-pytorch	.334
196	resnet_SIN_IN_FT_IN	.334
197	resnet50-SIN_IN_IN	.334
198	resnet50-sup	.334
199	mobilenet_v2_0.75_160-tf	.334
200	resnet50_tutorial	.334
201	resnet_50_v1	.334
202	mobilenet_v2_1.0_224-tf	.331
203	blt_vs	.331
204	mobilenet_v2_1.0_192-tf	.325
205	antialiased-r50	.325
206	shufflenet_v2_x1_0	.325
207	resnet-152_v1-tf	.322
208	resnet152_imagenet_full	.319
209	mobilenet_v2_1.3_224-tf	.316
210	mobilenet_v1_1.0_192	.312
211	Res2Net50_26w_4s	.309
212	regnet_y_400mf	.305
213	mobilenet_v2_1.0_160-tf	.303
214	resnet-50_v1-tf	.300
215	antialias-resnet152	.300
216	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.298
217	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.298
218	voneresnet-50-non_stochastic	.295
219	mobilenet_v1_0.75_224	.295
220	mobilenet_v2_0.75_128	.295
221	resnet_152_v1	.294
222	mobilenet_v1_0.75_192	.291
223	custom_model_cv_18_dagger_408	.289
224	mobilenet_v2_0.5_160	.287
225	mobilenet_v2_0.35_160	.280
226	mobilenet_v2_0.5_128	.278
227	mobilenet_v2_1.0_128-tf	.275
228	mobilenet_v1_0.5_160	.272
229	resnet50_robust_l2_eps1	.269
230	resnet_50_v1_spiking_l4a	.269
231	mobilenet_v1_0.75_160	.267
232	mobilenet_v2_1_0_128	.267
233	alexnet	.264
234	alexnet_ks_torevert	.264
235	alexnet_fov4	.264
236	alexnet_fov12	.264
237	alexnet_fov16	.264
238	mobilenet_v1_0.75_128	.263
239	vonegrcnn_52e_full	.261
240	resnet-50-robust	.261
241	imagenet_l2_3_0	.261
242	resnet50_robust_l2_eps3	.261
243	vonegrcnn_62e_nobn	.253
244	mobilenet_v1_0.5_192	.253
245	voneresnet-50-robust	.253
246	vonegrcnn_47e	.250
247	mobilenet_v2_0.35_128	.248
248	resnet50_l2_3_robust	.248
249	vonegrcnn_52e_full	.248
250	vonegrcnn_47e	.245
251	mobilenet_v1_1.0_128	.244
252	resnet50_linf_8_robust	.241
253	vonegrcnn_62e_nobn	.241
254	mobilenet_v1_1.0_160	.234
255	mobilenet_v2_1.0_96	.231
256	voneresnet-50	.228
257	mobilenet_v2_0.75_96	.217
258	mobilenet_v1_0.5_128	.217
259	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.209
260	mobilenet_v1_0.25_224	.206
261	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.205
262	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.202
263	mobilenet_v2_0.5_96	.200
264	mobilenet_v1_0.25_160	.200
265	mobilenet_v1_0.25_192	.183
266	resnet50_imagenet_100_seed-0	.181
267	imagenet_l2_10_0	.178
268	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.175
269	mobilenet_v2_0.35_96	.172
270	deit_small_imagenet_100_seed-0	.167
271	mobilenet_v1_0.25_128	.164
272	0.5x_resnet-18_LC_w_sh_1_iter	.158
273	alexnet_robust_correct	.147
274	resnet_50_v1_spiking_l4	.144
275	alexnet_l2_3_robust	.142
276	alexnet_linf_8_robust	.117
277	resnet50_imagenet_10_seed-0	.081
278	barlow-twins-resnet50	.078
279	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.072
280	convnext_small_imagenet_10_seed-0	.072
281	artResNet18_1	.070
282	resnet50-vicregl0p9	.069
283	resnet-50_untrained	.064
284	alexnet_training_seed_09	.062
285	alexnet_training_seed_05	.062
286	alexnet_training_seed_06	.062
287	alexnet_training_seed_02	.062
288	alexnet_training_seed_04	.062
289	alexnet_training_seed_10	.062
290	alexnet_training_seed_08	.062
291	resnet50_imagenet_1_seed-0	.062
292	deit_small_imagenet_10_seed-0	.062
293	convnext_small_imagenet_1_seed-0	.062
294	alexnet_training_seed_01	.062
295	deit_small_imagenet_1_seed-0	.062
296	alexnet_training_seed_01_fov4	.062
297	alexnet_training_seed_01_fov12	.062
298	alexnet_training_seed_01_fov16	.062
299	alexnet_training_seed_03	.062
300	resnet50-barlow	.059
301	resnet50-vicregl0p75	.059
302	alexnet_training_seed_07	.053
303	resnet_50_v1_spiking_l3	.053
304	mobilevit_small	.052
305	resnet50-simclr-vissl	.050
306	ViT-B/32
307	resnet-18-LC_w_sh_100_iter_conv_init_m
308	BiT-M-R50x1
309	resnet-18-LC_w_sh_1_iter_conv_init_m
310	BiT-M-R152x2
311	resnet-18-LC_w_sh_1_iter_m
312	resnet-18-LC_w_sh_10_iter_m
313	BiT-M-R50x3
314	resnet-18-LC_w_sh_100_iter_m
315	resnet-18-LC_w_sh_10_iter_conv_init_m
316	FrankRobWobv0
317	0.5x_resnet-18_LC
318	CORnetZ_CIFAR10
319	resnet18-local_aggregation
320	r3m_resnet18
321	resnet18-deepcluster
322	CORnet-Z
323	vggface
324	resnet18-instance_recognition
325	my-model
326	0.5x_resnet-18_LC_w_sh_10_iter
327	r3m_resnet50
328	r3m_resnet34
329	resnet-18-LC_m
330	resnet18-supervised
331	r3m_resnet50_nocrop
332	resnet-18-LC_1st_conv_m
333	resnet-18-LC_conv_init_m
334	resnet18-contrastive_multiview
335	0.5x_resnet-18
336	0.5x_resnet-18_LC_w_sh_100_iter
337	RN50
338	RN50
339	resnet18-colorization
340	resnet18-relative_position
341	resnet18-autoencoder
342	resnet18-simclr
343	ViT_L_32
344	ViT_B_32
345	resnet18-depth_prediction
346	ViT_B_16_imagenet1k
347	ViT_B_16
348	r3m_resnet34_nocrop
349	resnet18-contrastive_predictive
350	resnet-18-LC_untrained
351	resnet-18_untrained
352	resnet-50x2_untrained
353	dcgan
354	unet_entire
355	CORnetZ_CIFAR10_bs32_20_04
356	v1-pyr-nodown
357	pixels
358	dorinet_cornet_z
359	omnivore_swinT
360	openclip
361	vonealexnet_gaussian_noise_std4_fixed
362	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
363	yudixie_resnet50_category_class_0_240908
364	resnet18_imagenet21kP
365	ConvLSTM
366	PredRNN
367	SimVP
368	TAU
369	MIM
370	hmax
371	ReAlnet01_cornet
372	vit_base_patch16_dinov3-lvd1689m
373	yolos_tiny
374	yudixie_resnet50_imagenet1kpret_0_240312
375	compact_resnet50_V4
376	yudixie_resnet18_translation_reg_0_240719
377	yudixie_resnet18_distance_reg_0_240719
378	ReAlnet02_cornet
379	yudixie_resnet18_imagenet1kpret_0_240719
380	eBarlow_lmda_02_1000ep
381	yudixie_resnet50_rotation_reg_0_240908
382	yudixie_resnet50_distance_translation_0_240908
383	yudixie_resnet50_distance_rotation_0_240908
384	vit_base_patch14_dinov2-lvd142m
385	resnet18_ecoset_full
386	vit_base_patch14_reg4_dinov2-lvd142m
387	ReAlnet04_cornet
388	ReAlnet01_cornet_fov4
389	resnet34_ecoset_full
390	resnet50_ecoset_full
391	compact_resnet50_IT
392	resnet101_ecoset_full
393	ReAlnet03_cornet
394	ReAlnet01_cornet_fov12
395	resnet152_ecoset_full
396	compact_vgg19_V4
397	yudixie_resnet18_translation_rotation_0_240719
398	resnet-18_test_m
399	yudixie_resnet18_category_class_0_240719
400	ReAlnet05_cornet
401	yudixie_resnet18_random_0_240719
402	yudixie_resnet18_cat_obj_class_all_latents_0_240719
403	yudixie_resnet18_distance_translation_rotation_0_240719
404	yudixie_resnet18_object_class_0_240719
405	compact_vgg19_IT
406	ReAlnet06_cornet
407	SeLaVi-AVE
408	compact_resnet50_robust_V4
409	r3d_18
410	ReAlnet01_cornet_fov16
411	yudixie_resnet50_translation_rotation_0_240908
412	ReAlnet07_cornet
413	ReAlnet01_ALL
414	omnivore_swinB
415	mc3_18
416	ReAlnet02_ALL
417	s3d
418	VOneCORnet-S
419	ReAlnet08_cornet
420	resnet50-vicreg
421	mvit_v1_b
422	compact_cornets_V4
423	yudixie_resnet18_rotation_reg_0_240719
424	ReAlnet03_ALL
425	compact_cornets_IT
426	ReAlnet09_cornet
427	omnivore_swinS
428	SeLaVi-VGG-Sound
429	ReAlnet06_ALL
430	SeLaVi-Kinetics-Sound
431	compact_resnet50_robust_IT
432	ReAlnet10_cornet
433	SeLaVi-Kinetics400
434	r2plus1d_18
435	mvit_v2_s
436	yudixie_resnet50_random_0_240908
437	ReAlnet07_ALL
438	yudixie_resnet50_imagenet1kpret_0_240908
439	yudixie_resnet50_distance_translation_rotation_0_240908
440	ReAlnet08_ALL
441	yudixie_resnet50_cat_obj_class_all_latents_0_240908
442	yudixie_resnet18_distance_rotation_0_240719
443	ReAlnet09_ALL
444	yudixie_resnet18_distance_translation_0_240719
445	yudixie_resnet50_translation_reg_0_240908
446	resnet50_primary_visual_cortex
447	yudixie_resnet50_object_class_0_240908
448	yudixie_resnet50_distance_reg_0_240908
449	ReAlnet10_ALL
450	briaai_rmbg_1_4
451	ReAlnet04_ALL
452	ReAlnet05_ALL
453	resnet18_fair
454	pr1_stl10_disentangled_alpha04
455	yudixie_wbh_object_class_scaling_0415_run_0
456	yudixie_wbh_object_class_scaling_0415_run_3
457	yudixie_wbh_object_class_scaling_0415_imagenet
458	yudixie_wbh_object_class_scaling_0415_run_2
459	yudixie_wbh_object_class_scaling_0415_run_1
460	deepeisnn_ei_cifar10_vgg19
461	deepeisnn_ei_cifar10_resnet18
462	deepeisnn_ei_imagenet_resnet18
463	dinov2_vitl14_reg_lc_featread
464	gabor_filter_entropy_4x4
465	gabor_filter_entropy_4x4_v2
466	gabor_filter_entropy_4x4_v2_forcedCosine
467	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