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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	CLIP-RN50	.692
44	resnext101_32x16d_wsl	.684
45	convnext_base_fb_in1k_gram_aligned_dinov3	.670
46	resnet50-vitoimagevidnet8	.669
47	cvt_cvt-21-224-in1k_4_LucyV4	.664
48	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.658
49	tv_efficientnet-b1	.655
50	convnext_tiny_sup	.650
51	deit_base_imagenet_full_seed-0	.650
52	resnet50-VITO-8deg-cc	.648
53	cv_18_dagger_408_pretrained	.642
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.628
55	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.627
56	cvt_cvt-13-224-in1k_4_LucyV4	.622
57	focalnet_tiny_lrf_in1k	.608
58	effnetv2m_custom384	.603
59	vit_relpos_base_patch16_clsgap_224:sw_in1k	.600
60	AlexNet_SIN	.595
61	AlexNet_SIN_fov4	.595
62	AlexNet_SIN_fov12	.595
63	AlexNet_SIN_fov16	.595
64	cvt_cvt-21-384-in1k_4_LucyV4	.567
65	cvt_cvt-13-384-in1k_4_LucyV4	.564
66	densenet-201-keras	.556
67	densenet-201	.556
68	densenet_201_pytorch	.556
69	densenet-169-keras	.544
70	densenet-169	.544
71	convnext_femto_ols:d1_in1k	.544
72	inception_v4	.541
73	vgg-19-keras	.536
74	resnet-101_v2-tf	.533
75	pnasnet_large_pytorch	.530
76	inception_v4-tf	.527
77	densenet-121-keras	.525
78	densenet-121	.525
79	nasnet_large	.522
80	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.514
81	deit_small_imagenet_full_seed-0	.505
82	AdvProp_efficientnet-b4	.503
83	AT_efficientnet-b4	.503
84	inception_v3_pytorch	.503
85	xception	.503
86	resnet50_st_wzc_seed6_2_323	.503
87	resnet50_st_wzc_seed7_2_323	.502
88	resnet50_eMMCR_Vanilla	.500
89	inception_v3-tf	.497
90	resnet-50_v2-tf	.495
91	resnet50_ewconplus_wzc_seed6_2_323	.495
92	resnet50_ewconplus_wzc_seed5_2__323	.495
93	pnasnet_large-tf	.494
94	resnet-152_v2-tf	.494
95	convnext_small_imagenet_100_seed-0	.494
96	nasnet_large-tf	.492
97	effnetb1_272x240	.492
98	resnet50_moco_v2	.491
99	resnet50_ewconplus_wzc_seed2_2__323	.491
100	pnasnet_large	.487
101	vgg-16-keras	.486
102	pnasnet_large_halfPrecision	.484
103	vgg_19	.481
104	AdvProp_efficientnet-b7	.480
105	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.480
106	AT_efficientnet-b2	.473
107	AdvProp_efficientnet-b2	.473
108	resnet_101_v2	.473
109	resnet-152_v2_pytorch	.464
110	efficientnet_b1_imagenet_full	.464
111	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.463
112	inception_v1-tf	.461
113	resnet50-moclr8deg	.461
114	mobilenet_v2_1-4_224_pytorch	.461
115	mobilenet_v2_1_4_224	.461
116	AdvProp_efficientnet-b6	.459
117	resnet50_st_wzc_seed3_2_323	.456
118	mobilenet_v2_0_75_224	.455
119	xception-keras	.450
120	ReAlnet05	.447
121	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.444
122	AdvProp_efficientnet-b0	.441
123	ViT_L_32_imagenet1k	.439
124	resnet50_st_wzc_2	.439
125	ViT_B_32_imagenet1k	.434
126	inception_v3	.434
127	resnet50_ewconplus_wzc	.430
128	resnet-34	.427
129	efficientnet_b0_imagenet_full	.427
130	mobilenet_v2_0_5_192	.423
131	inception_v2-tf	.422
132	mobilenet_v2_0_5_224	.417
133	mobilenet_v2_0.35_224	.414
134	ReAlnet09	.414
135	BiT-S-R50x1	.411
136	mobilenet_v2_0_75_192	.411
137	BiT-S-R101x1	.405
138	resnet_50_v2	.405
139	mobilenet_v1_1.0_224	.403
140	vgg_16	.403
141	resnet50_hvs_wzc	.402
142	squeezenet1_1	.400
143	resnet-101_v1-tf	.400
144	mobilenet_v2_0.75_224-tf	.400
145	resnet34_imagenet_full	.400
146	ReAlnet10	.400
147	grcnn_109	.400
148	nasnet_mobile	.394
149	BiT-S-R152x4	.392
150	ReAlnet07	.392
151	inception_v1	.389
152	efficientnet-b7	.388
153	mobilenet_v2_0.5_224-tf	.386
154	BiT-S-R152x2	.383
155	mobilenet_v2_0.5_192-tf	.383
156	ReAlnet06	.383
157	ReAlnet04	.383
158	mobilenet_v2_0.35_192	.381
159	antialiased-rnext101_32x8d	.381
160	ReAlnet08	.380
161	ReAlnet03	.380
162	squeezenet1_0	.378
163	resnet_101_v1	.375
164	ReAlnet02	.375
165	efficientnet_b0	.373
166	CORnet-S	.372
167	mobilenet_v2_1_0_192	.372
168	mobilenet_v2_0_75_160	.372
169	resnet18_imagenet_full	.370
170	resnet101_imagenet_full	.369
171	grcnn	.369
172	ReAlnet01	.369
173	BiT-S-R101x3	.367
174	mobilenet_v2_1_0_224	.367
175	resnet50-SIN_IN	.366
176	nasnet_mobile-tf	.364
177	efficientnet_b2_imagenet_full	.362
178	mobilenet_v2_0.75_192-tf	.358
179	resnet_152_v2	.358
180	alexnet_early_checkpoint	.356
181	BiT-S-R50x3	.355
182	mobilenet_v2_1.4_224-tf	.353
183	resnet50	.352
184	alexnet_random_linf8_perturb	.350
185	mobilenet_v2_1_3_224	.348
186	resnet-18	.341
187	AdvProp_efficientnet-b8	.339
188	mobilenet_v1_0.5_224	.339
189	resnet_50_v1_spiking	.339
190	mobilenet_v2_1_0_160	.336
191	resnet50_imagenet_full	.336
192	resnet-50-pytorch	.334
193	resnet_SIN_IN_FT_IN	.334
194	resnet50-SIN_IN_IN	.334
195	resnet50-sup	.334
196	mobilenet_v2_0.75_160-tf	.334
197	resnet50_tutorial	.334
198	resnet_50_v1	.334
199	mobilenet_v2_1.0_224-tf	.331
200	blt_vs	.331
201	mobilenet_v2_1.0_192-tf	.325
202	antialiased-r50	.325
203	shufflenet_v2_x1_0	.325
204	resnet-152_v1-tf	.322
205	resnet152_imagenet_full	.319
206	mobilenet_v2_1.3_224-tf	.316
207	mobilenet_v1_1.0_192	.312
208	Res2Net50_26w_4s	.309
209	regnet_y_400mf	.305
210	mobilenet_v2_1.0_160-tf	.303
211	resnet-50_v1-tf	.300
212	antialias-resnet152	.300
213	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.298
214	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.298
215	voneresnet-50-non_stochastic	.295
216	mobilenet_v1_0.75_224	.295
217	mobilenet_v2_0.75_128	.295
218	resnet_152_v1	.294
219	mobilenet_v1_0.75_192	.291
220	custom_model_cv_18_dagger_408	.289
221	mobilenet_v2_0.5_160	.287
222	mobilenet_v2_0.35_160	.280
223	mobilenet_v2_0.5_128	.278
224	mobilenet_v2_1.0_128-tf	.275
225	mobilenet_v1_0.5_160	.272
226	resnet50_robust_l2_eps1	.269
227	resnet_50_v1_spiking_l4a	.269
228	mobilenet_v1_0.75_160	.267
229	mobilenet_v2_1_0_128	.267
230	alexnet	.264
231	alexnet_ks_torevert	.264
232	alexnet_fov4	.264
233	alexnet_fov12	.264
234	alexnet_fov16	.264
235	mobilenet_v1_0.75_128	.263
236	vonegrcnn_52e_full	.261
237	resnet-50-robust	.261
238	imagenet_l2_3_0	.261
239	resnet50_robust_l2_eps3	.261
240	vonegrcnn_62e_nobn	.253
241	mobilenet_v1_0.5_192	.253
242	voneresnet-50-robust	.253
243	vonegrcnn_47e	.250
244	mobilenet_v2_0.35_128	.248
245	resnet50_l2_3_robust	.248
246	vonegrcnn_52e_full	.248
247	vonegrcnn_47e	.245
248	mobilenet_v1_1.0_128	.244
249	resnet50_linf_8_robust	.241
250	vonegrcnn_62e_nobn	.241
251	mobilenet_v1_1.0_160	.234
252	mobilenet_v2_1.0_96	.231
253	voneresnet-50	.228
254	mobilenet_v2_0.75_96	.217
255	mobilenet_v1_0.5_128	.217
256	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.209
257	mobilenet_v1_0.25_224	.206
258	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.205
259	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.202
260	mobilenet_v2_0.5_96	.200
261	mobilenet_v1_0.25_160	.200
262	mobilenet_v1_0.25_192	.183
263	resnet50_imagenet_100_seed-0	.181
264	imagenet_l2_10_0	.178
265	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.175
266	mobilenet_v2_0.35_96	.172
267	deit_small_imagenet_100_seed-0	.167
268	mobilenet_v1_0.25_128	.164
269	0.5x_resnet-18_LC_w_sh_1_iter	.158
270	alexnet_robust_correct	.147
271	resnet_50_v1_spiking_l4	.144
272	alexnet_l2_3_robust	.142
273	alexnet_linf_8_robust	.117
274	resnet50_imagenet_10_seed-0	.081
275	barlow-twins-resnet50	.078
276	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.072
277	convnext_small_imagenet_10_seed-0	.072
278	artResNet18_1	.070
279	resnet50-vicregl0p9	.069
280	resnet-50_untrained	.064
281	alexnet_training_seed_09	.062
282	alexnet_training_seed_05	.062
283	alexnet_training_seed_06	.062
284	alexnet_training_seed_02	.062
285	alexnet_training_seed_04	.062
286	alexnet_training_seed_10	.062
287	alexnet_training_seed_08	.062
288	resnet50_imagenet_1_seed-0	.062
289	deit_small_imagenet_10_seed-0	.062
290	convnext_small_imagenet_1_seed-0	.062
291	alexnet_training_seed_01	.062
292	deit_small_imagenet_1_seed-0	.062
293	alexnet_training_seed_01_fov4	.062
294	alexnet_training_seed_01_fov12	.062
295	alexnet_training_seed_01_fov16	.062
296	alexnet_training_seed_03	.062
297	resnet50-barlow	.059
298	resnet50-vicregl0p75	.059
299	alexnet_training_seed_07	.053
300	resnet_50_v1_spiking_l3	.053
301	mobilevit_small	.052
302	resnet50-simclr-vissl	.050
303	ViT-B/32
304	resnet-18-LC_w_sh_100_iter_conv_init_m
305	BiT-M-R50x1
306	resnet-18-LC_w_sh_1_iter_conv_init_m
307	BiT-M-R152x2
308	resnet-18-LC_w_sh_1_iter_m
309	resnet-18-LC_w_sh_10_iter_m
310	BiT-M-R50x3
311	resnet-18-LC_w_sh_100_iter_m
312	resnet-18-LC_w_sh_10_iter_conv_init_m
313	FrankRobWobv0
314	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-local_aggregation
317	r3m_resnet18
318	resnet18-deepcluster
319	CORnet-Z
320	vggface
321	resnet18-instance_recognition
322	my-model
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	r3m_resnet34
326	resnet-18-LC_m
327	resnet18-supervised
328	r3m_resnet50_nocrop
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	0.5x_resnet-18_LC_w_sh_100_iter
334	RN50
335	RN50
336	resnet18-colorization
337	resnet18-relative_position
338	resnet18-autoencoder
339	resnet18-simclr
340	ViT_L_32
341	ViT_B_32
342	resnet18-depth_prediction
343	ViT_B_16_imagenet1k
344	ViT_B_16
345	r3m_resnet34_nocrop
346	resnet18-contrastive_predictive
347	resnet-18-LC_untrained
348	resnet-18_untrained
349	resnet-50x2_untrained
350	dcgan
351	unet_entire
352	CORnetZ_CIFAR10_bs32_20_04
353	v1-pyr-nodown
354	pixels
355	dorinet_cornet_z
356	omnivore_swinT
357	openclip
358	vonealexnet_gaussian_noise_std4_fixed
359	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
360	yudixie_resnet50_category_class_0_240908
361	resnet18_imagenet21kP
362	ConvLSTM
363	PredRNN
364	SimVP
365	TAU
366	MIM
367	hmax
368	ReAlnet01_cornet
369	vit_base_patch16_dinov3-lvd1689m
370	yolos_tiny
371	yudixie_resnet50_imagenet1kpret_0_240312
372	compact_resnet50_V4
373	yudixie_resnet18_translation_reg_0_240719
374	yudixie_resnet18_distance_reg_0_240719
375	ReAlnet02_cornet
376	yudixie_resnet18_imagenet1kpret_0_240719
377	eBarlow_lmda_02_1000ep
378	yudixie_resnet50_rotation_reg_0_240908
379	yudixie_resnet50_distance_translation_0_240908
380	yudixie_resnet50_distance_rotation_0_240908
381	vit_base_patch14_dinov2-lvd142m
382	resnet18_ecoset_full
383	vit_base_patch14_reg4_dinov2-lvd142m
384	ReAlnet04_cornet
385	ReAlnet01_cornet_fov4
386	resnet34_ecoset_full
387	resnet50_ecoset_full
388	compact_resnet50_IT
389	resnet101_ecoset_full
390	ReAlnet03_cornet
391	ReAlnet01_cornet_fov12
392	resnet152_ecoset_full
393	compact_vgg19_V4
394	yudixie_resnet18_translation_rotation_0_240719
395	resnet-18_test_m
396	yudixie_resnet18_category_class_0_240719
397	ReAlnet05_cornet
398	aughost_fair_resnet18
399	yudixie_resnet18_random_0_240719
400	yudixie_resnet18_cat_obj_class_all_latents_0_240719
401	yudixie_resnet18_distance_translation_rotation_0_240719
402	yudixie_resnet18_object_class_0_240719
403	compact_vgg19_IT
404	ReAlnet06_cornet
405	SeLaVi-AVE
406	compact_resnet50_robust_V4
407	r3d_18
408	ReAlnet01_cornet_fov16
409	yudixie_resnet50_translation_rotation_0_240908
410	ReAlnet07_cornet
411	ReAlnet01_ALL
412	omnivore_swinB
413	mc3_18
414	ReAlnet02_ALL
415	s3d
416	VOneCORnet-S
417	ReAlnet08_cornet
418	resnet50-vicreg
419	mvit_v1_b
420	compact_cornets_V4
421	yudixie_resnet18_rotation_reg_0_240719
422	ReAlnet03_ALL
423	compact_cornets_IT
424	ReAlnet09_cornet
425	omnivore_swinS
426	SeLaVi-VGG-Sound
427	ReAlnet06_ALL
428	SeLaVi-Kinetics-Sound
429	compact_resnet50_robust_IT
430	ReAlnet10_cornet
431	SeLaVi-Kinetics400
432	r2plus1d_18
433	mvit_v2_s
434	yudixie_resnet50_random_0_240908
435	ReAlnet07_ALL
436	yudixie_resnet50_imagenet1kpret_0_240908
437	yudixie_resnet50_distance_translation_rotation_0_240908
438	ReAlnet08_ALL
439	yudixie_resnet50_cat_obj_class_all_latents_0_240908
440	yudixie_resnet18_distance_rotation_0_240719
441	ReAlnet09_ALL
442	yudixie_resnet18_distance_translation_0_240719
443	yudixie_resnet50_translation_reg_0_240908
444	resnet50_primary_visual_cortex
445	yudixie_resnet50_object_class_0_240908
446	yudixie_resnet50_distance_reg_0_240908
447	ReAlnet10_ALL
448	briaai_rmbg_1_4
449	ReAlnet04_ALL
450	ReAlnet05_ALL
451	resnet18_fair
452	pr1_stl10_disentangled_alpha04
453	yudixie_wbh_object_class_scaling_0415_run_0
454	yudixie_wbh_object_class_scaling_0415_run_3
455	yudixie_wbh_object_class_scaling_0415_imagenet
456	yudixie_wbh_object_class_scaling_0415_run_2
457	yudixie_wbh_object_class_scaling_0415_run_1
458	deepeisnn_ei_cifar10_vgg19
459	deepeisnn_ei_cifar10_resnet18
460	deepeisnn_ei_imagenet_resnet18
461	dinov2_vitl14_reg_lc_featread

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