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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_Schiller1976-sf_bandwidth")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50_ewconplus_wzc_seed5_2__323	.992
2	yudixie_wbh_object_class_scaling_0415_run_2	.990
3	alexnet_early_checkpoint	.985
4	alexnet_fov16	.979
5	resnet50_ewconplus_wzc_seed2_2__323	.979
6	resnet50_primary_visual_cortex	.978
7	vonegrcnn_47e	.977
8	compact_cornets_V4	.975
9	yudixie_resnet50_translation_rotation_0_240908	.970
10	resnet101_ecoset_full	.969
11	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.967
12	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.965
13	ReAlnet01_cornet_fov16	.967
14	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.966
15	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.967
16	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.966
17	0.5x_resnet-18_LC_w_sh_1_iter	.965
18	compact_vgg19_IT	.964
19	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.966
20	resnet50_st_wzc_2	.959
21	AlexNet_SIN_fov12	.957
22	yudixie_resnet50_category_class_0_240908	.959
23	resnet152_imagenet_full	.957
24	AlexNet_SIN_fov4	.956
25	Res2Net50_26w_4s	.956
26	resnet50_st_wzc_seed3_2_323	.952
27	alexnet_fov12	.955
28	resnet-18-LC_w_sh_1_iter_m	.953
29	resnet-18-LC_w_sh_100_iter_m	.948
30	resnet101	.945
31	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.946
32	yudixie_resnet18_object_class_0_240719	.945
33	resnet50_ewconplus_wzc_seed6_2_323	.944
34	resnet-18-LC_w_sh_1_iter_conv_init_m	.942
35	resnet50_FractalDB	.941
36	ReAlnet01_cornet_fov12	.937
37	vonegrcnn_52e_full	.938
38	resnet34_ecoset_full	.939
39	ReAlnet03_cornet	.938
40	AlexNet_SIN	.938
41	efficientnet_b1_imagenet_full	.938
42	ReAlnet09	.936
43	resnet50_hvs_wzc	.937
44	compact_resnet50_robust_V4	.938
45	ReAlnet09_cornet	.939
46	ReAlnet03	.939
47	alexnet	.933
48	ReAlnet10_ALL	.933
49	resnet50_st_wzc_seed6_2_323	.932
50	ReAlnet08_cornet	.932
51	BiT-S-R152x2	.931
52	resnext101_32x8d_wsl	.931
53	ReAlnet08	.931
54	r3m_resnet50_nocrop	.928
55	voneresnet-50-non_stochastic	.929
56	resnet18_imagenet_full	.928
57	vggface	.931
58	ReAlnet10_cornet	.928
59	resnet18_ecoset_full	.926
60	mobilenet_v2_1-4_224_pytorch	.927
61	ReAlnet04_cornet	.928
62	ReAlnet10	.925
63	ReAlnet07_cornet	.923
64	ReAlnet04	.925
65	ReAlnet07	.926
66	ReAlnet05	.924
67	artResNet18_1	.926
68	mobilenet_v2_1.0_96	.921
69	yudixie_resnet18_random_0_240719	.924
70	r3m_resnet50	.924
71	ReAlnet05_cornet	.923
72	nasnet_large-tf	.922
73	resnet50-SIN_IN	.922
74	resnet-18-LC_conv_init_m	.922
75	BiT-M-R50x3	.920
76	resnet50_st_wzc_seed7_2_323	.921
77	alexnet_random_linf8_perturb	.920
78	ReAlnet08_ALL	.922
79	ReAlnet06_ALL	.921
80	ReAlnet01	.920
81	ReAlnet06	.919
82	ReAlnet06_cornet	.920
83	inception_v2-tf	.919
84	ReAlnet07_ALL	.921
85	ReAlnet01_cornet	.917
86	ReAlnet05_ALL	.918
87	ReAlnet02_cornet	.918
88	ReAlnet03_ALL	.916
89	ReAlnet04_ALL	.916
90	ReAlnet02_ALL	.918
91	ReAlnet01_ALL	.916
92	resnet152_ecoset_full	.916
93	0.5x_resnet-18	.915
94	yudixie_resnet18_rotation_reg_0_240719	.915
95	ReAlnet02	.915
96	antialiased-rnext101_32x8d	.914
97	convnext_tiny:in12k_ft_in1k	.914
98	ReAlnet09_ALL	.914
99	CORnet-S	.912
100	alexnet_training_seed_01_fov16	.909
101	yudixie_wbh_object_class_scaling_0415_run_1	.911
102	resnet101_imagenet_full	.911
103	cornet_s	.909
104	efficientnet_b2_imagenet_full	.907
105	BiT-M-R152x2	.907
106	nasnet_mobile	.906
107	mobilenet_v2_1.0_128-tf	.903
108	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
109	dinov2_vitl14_lc	.904
110	yudixie_resnet50_rotation_reg_0_240908	.905
111	mobilenet_v2_0.5_224-tf	.900
112	convnext_xlarge:fb_in22k_ft_in1k	.902
113	BiT-M-R101x3	.900
114	mobilenet_v2_0.5_192-tf	.899
115	r3m_resnet18	.902
116	alexnet_training_seed_01	.901
117	resnet-50_untrained	.901
118	texture_shape_resnet50_trained_on_SIN	.900
119	alexnet_training_seed_10	.900
120	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
121	resnet-50_v1-tf	.898
122	0.5x_resnet-18_LC	.899
123	resnet50-SIN	.898
124	dinov2_vitg14_lc	.897
125	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
126	RN50	.898
127	CLIP_resnet50	.894
128	alexnet_robust_correct	.896
129	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
130	CLIP_resnet50_float32	.895
131	resnet50_ewconplus_wzc	.895
132	nasnet_mobile-tf	.895
133	resnet-18-LC_m	.894
134	r3m_resnet34	.894
135	AdvProp_efficientnet-b0	.890
136	yudixie_resnet18_distance_reg_0_240719	.890
137	grcnn_robust_v1	.888
138	voneresnet-50	.887
139	0.5x_resnet-18_LC_w_sh_100_iter	.890
140	antialiased-r50	.887
141	antialias-resnet152	.887
142	yudixie_resnet50_translation_reg_0_240908	.886
143	grcnn_v2_text_noise	.885
144	deit_small_imagenet_100_seed-0	.882
145	alexnet_training_seed_03	.883
146	BiT-S-R101x3	.882
147	alexnet_training_seed_07	.882
148	resnet50_ecoset_full	.880
149	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
150	alexnet_training_seed_09	.881
151	compact_cornets_IT	.877
152	yudixie_wbh_object_class_scaling_0415_run_3	.876
153	resnet50_random_linf8_perturb	.877
154	BiT-S-R152x4	.876
155	voneresnet-50-robust	.876
156	mobilenet_v2_1.0_160-tf	.874
157	resnet-152_v2-tf	.874
158	deepeisnn_ei_imagenet_resnet18	.876
159	vit_base_patch14_dinov2-lvd142m	.875
160	densenet-169-keras	.874
161	resnet34_imagenet_full	.875
162	SWSL_resnext101_32x8d	.873
163	VOneCORnet-S	.870
164	resnet50-vicregl0p9	.871
165	resnet50_byol	.870
166	mobilenet_v2_1.0_192-tf	.868
167	yudixie_resnet18_category_class_0_240719	.868
168	vgg_16	.868
169	yudixie_resnet18_distance_rotation_0_240719	.867
170	focalnet_tiny_lrf_in1k	.864
171	grcnn_109	.864
172	convnext_large:fb_in22k_ft_in1k	.863
173	xception	.861
174	vgg-16-keras	.859
175	seresnext50_32x4d	.860
176	convnext_base_fb_in1k	.860
177	yudixie_resnet18_translation_rotation_0_240719	.859
178	yudixie_wbh_object_class_scaling_0415_run_0	.858
179	resnet50	.858
180	resnet_50_v1	.856
181	resnet50_tutorial	.857
182	compact_vgg19_V4	.855
183	yudixie_resnet50_imagenet1kpret_0_240908	.854
184	compact_resnet50_IT	.855
185	inception_v3-tf	.855
186	resnet_50_v1_spiking_l4	.855
187	resnet_50_v1_spiking	.853
188	resnet-18_untrained	.854
189	resnet50-sup	.852
190	yudixie_resnet50_distance_translation_0_240908	.855
191	resnet_50_v1_spiking_l3	.851
192	resnet_50_v1_spiking_l4a	.851
193	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
194	dinov2_vitl14_reg_lc	.850
195	dinov2_vitl14_lc_res336	.851
196	dinov2_vitl14_reg_lc_featread	.849
197	resnet50_random_l2_perturb	.849
198	BiT-S-R101x1	.847
199	resnet_50_v2	.846
200	BiT-S-R50x3	.846
201	alexnet_training_seed_02	.847
202	effnetb1_272x240	.847
203	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
204	inception_v1	.845
205	yudixie_resnet50_distance_translation_rotation_0_240908	.845
206	vit_base_patch14_reg4_dinov2-lvd142m	.842
207	AdvProp_efficientnet-b8	.842
208	alexnet_reduced_aliasing_early_checkpoint	.842
209	resnet50-vicregl0p75	.840
210	resnet50_imagenet_full	.841
211	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
212	BiT-S-R50x1	.839
213	yudixie_resnet50_object_class_0_240908	.840
214	deit3_base_patch16_224_fb_in22k_ft_in1k	.839
215	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
216	resnet50-SIN_IN_IN	.838
217	yudixie_resnet18_imagenet1kpret_0_240719	.836
218	yudixie_wbh_object_class_scaling_0415_imagenet	.839
219	AdvProp_efficientnet-b6	.836
220	0.5x_resnet-18_LC_w_sh_10_iter	.836
221	resnet-152_v1-tf	.834
222	resnet50_simclr	.835
223	deit_large_imagenet_full_seed-0	.834
224	BiT-M-R50x1	.833
225	resnet50_moco_v2	.834
226	SWSL_resnet50	.830
227	yudixie_resnet50_distance_rotation_0_240908	.831
228	resnet-152_v2_pytorch	.830
229	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
230	cvt_cvt-13-224-in1k_4_LucyV4	.828
231	CLIP-RN50	.828
232	barlow-twins-resnet50	.828
233	densenet-169	.827
234	inception_v3	.825
235	mobilenet_v1_0.25_128	.824
236	compact_resnet50_V4	.822
237	alexnet_l2_3_robust	.820
238	resnet-18-LC_1st_conv_m	.818
239	alexnet_fov4	.817
240	regnety_032	.816
241	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
242	konkle_alexnetgn_ipcl_ref01_primary_model	.813
243	yudixie_resnet18_distance_translation_rotation_0_240719	.814
244	alexnet_training_seed_04	.814
245	deit_base_imagenet_full_seed-0	.813
246	ViT_L_32_imagenet1k	.815
247	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
248	BiT-M-R101x1	.809
249	yolos_tiny	.810
250	AdvProp_efficientnet-b7	.809
251	resnet50-simclr-vissl	.805
252	grcnn	.804
253	yudixie_resnet50_random_0_240908	.803
254	yudixie_resnet18_distance_translation_0_240719	.803
255	resnet18_fair	.802
256	resnet-101_v1-tf	.795
257	alexnet_training_seed_05	.794
258	mobilenet_v2_0.75_96	.793
259	CORnet-Z	.795
260	resnet-50x2_untrained	.794
261	alexnet_training_seed_01_fov4	.794
262	resnet-18-LC_w_sh_10_iter_m	.793
263	yudixie_resnet50_distance_reg_0_240908	.792
264	vit_base_patch16_clip_224:openai_ft_in1k	.790
265	alexnet_training_seed_01_fov12	.789
266	alexnet_training_seed_08	.787
267	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
268	efficientnet_b0	.787
269	resnet50-moclr8deg	.785
270	inception_v1-tf	.785
271	resnet-18_test_m	.784
272	densenet-121	.783
273	resnet50-vicreg	.783
274	cvt_cvt-21-224-in1k_4_LucyV4	.782
275	AT_efficientnet-b2	.782
276	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
277	blt_vs	.780
278	AdvProp_efficientnet-b2	.779
279	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
280	mobilenet_v2_0.75_192-tf	.776
281	inception_v4-tf	.777
282	mobilenet_v2_1.4_224-tf	.773
283	vit_large_patch14_clip_224:openai_ft_in1k	.773
284	resnet50_imagenet_100_seed-0	.773
285	pnasnet_large-tf	.767
286	ViT-B/32	.764
287	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
288	mobilenet_v2_1_4_224	.765
289	resnet-50_v2-tf	.763
290	vision_transformer_vit_large_patch16_224	.762
291	resnet50-barlow	.755
292	ReAlnet01_cornet_fov4	.752
293	resnet50_imagenet_10_seed-0	.749
294	vonegrcnn_62e_nobn	.749
295	omnivore_swinT	.747
296	mobilenet_v2_0.75_128	.742
297	my-model	.743
298	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
299	eicircuit_resnet18_bn_20260919	.742
300	AlexNet_SIN_fov16	.741
301	briaai_rmbg_1_4	.738
302	yudixie_resnet18_translation_reg_0_240719	.739
303	v1-pyr-nodown	.735
304	deit_small_imagenet_full_seed-0	.736
305	AdvProp_efficientnet-b4	.728
306	convnext_base_fb_in1k_gram_aligned_dinov3	.729
307	shufflenet_v2_x1_0	.729
308	resnet50_linf_4_robust	.727
309	alexnet_linf_8_robust	.724
310	densenet-121-keras	.724
311	efficientnet-b7	.723
312	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
313	texture_shape_alexnet_trained_on_SIN	.723
314	resnet_SIN_IN_FT_IN	.722
315	vgg_19	.718
316	vonealexnet_gaussian_noise_std4_fixed	.717
317	mobilenet_v2_1_0_128	.718
318	cv_18_dagger_408_pretrained	.716
319	mobilenet_v2_1.0_224-tf	.714
320	resnet50-VITO-8deg-cc	.714
321	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
322	convnext_large_imagenet_full_seed-0	.712
323	inception_resnet_v2	.708
324	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
325	pnasnet_large_halfPrecision	.696
326	BiT-M-R152x4	.694
327	deepeisnn_ei_cifar10_vgg19	.695
328	effnetv2m_custom384	.690
329	custom_model_cv_18_dagger_408	.685
330	mobilenet_v2_0_5_224	.680
331	deepeisnn_ei_cifar10_resnet18	.675
332	resnet-101_v2-tf	.674
333	compact_resnet50_robust_IT	.674
334	convnext_base_fb_in22k_ft_in1k	.672
335	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.674
336	resnet50-vitoimagevidnet8	.674
337	CORnetZ_CIFAR10_bs32_20_04	.670
338	deit3_base_patch16_224_fb_in1k	.670
339	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.669
340	cvt_cvt-21-384-in1k_4_LucyV4	.666
341	resnet50_l2_3_robust	.667
342	resnet50_robust_l2_eps1	.665
343	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
344	resnet18_imagenet21kP	.664
345	gabor_filter_entropy_4x4_v2_forcedCosine	.663
346	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
347	hmax	.662
348	mobilenet_v2_0.5_128	.660
349	resnet50-cifar	.661
350	gabor_filter_mean_abs_4x4_v2	.662
351	convnext_tiny_imagenet_full_seed-0	.661
352	vit_huge_clip336_laion	.657
353	mobilenet_v2_0_75_224	.655
354	resnet50_robust_l2_eps3	.653
355	mobilenet_v2_0_5_192	.650
356	convnext_small_imagenet_100_seed-0	.649
357	CLIP_ViT-B_32	.646
358	cvt_cvt-13-384-in1k_4_LucyV4	.644
359	imagenet_l2_3_0	.643
360	imagenet_l2_10_0	.640
361	vit_large_clip336_openai	.639
362	mobilenet_v2_0.75_224-tf	.626
363	vgg-19-keras	.617
364	mobilenet_v2_0_75_192	.615
365	omnivore_swinB	.609
366	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
367	densenet-201	.604
368	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
369	resnet50_linf_8_robust	.596
370	resnet-50-robust	.596
371	mobilenet_v2_1_0_160	.595
372	densenet-201-keras	.590
373	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
374	omnivore_swinS	.585
375	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
376	mobilenet_v2_0.35_224	.572
377	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
378	FrankRobWobv0	.567
379	convnext_femto_ols:d1_in1k	.557
380	alexnet_random_l2_3_perturb	.552
381	mobilenet_v2_1_0_224	.550
382	convnext_base_imagenet_full_seed-0	.546
383	vit_base_patch16_dinov3-lvd1689m	.545
384	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
385	convnext_large_clip_laion2b_finelayers	.530
386	mobilenet_v2_1_0_192	.523
387	mobilenet_v2_0_75_160	.520
388	convnext_small_imagenet_full_seed-0	.501
389	CORnetZ_CIFAR10	.495
390	efficientnet_b0_imagenet_full	.491
391	convnext_small_imagenet_10_seed-0	.488
392	resnet50_imagenet_1_seed-0	.481
393	mobilevit_small	.477
394	mobilenet_v2_0.75_160-tf	.471
395	resnet-18-LC_untrained	.454
396	mobilenet_v2_1_3_224	.442
397	convnext_large_in22k_supervised	.441
398	alexnet_training_seed_06	.428
399	mobilenet_v2_1.3_224-tf	.399
400	resnet50_pretrained_with_retinal_waves	.397
401	convnext_tiny_sup	.331
402	convnext_small_imagenet_1_seed-0	.293
403	my-model	.226
404	mobilenet_v2_0.5_160	.103
405	deit_small_imagenet_10_seed-0	.000
406	gabor_filter_entropy_4x4	.000
407	gabor_filter_entropy_4x4_v2	.000
408	mobilenet_v2_0.35_160
409	resnet18-supervised
410	fitvid_trained_on_physion
411	openclip
412	resnext101_32x32d_wsl
413	resnext101_32x16d_wsl
414	resnext101_32x48d_wsl
415	resnet50_ImageNet
416	regnet_y_400mf
417	tv_efficientnet-b1
418	alexnet_ks_torevert
419	yudixie_resnet50_imagenet1kpret_0_240312
420	resnet_101_v1
421	resnet_152_v1
422	densenet_201_pytorch
423	pnasnet_large_pytorch
424	inception_v3_pytorch
425	eBarlow_lmda_02_1000ep
426	inception_v4
427	nasnet_large
428	deit_small_imagenet_1_seed-0
429	pnasnet_large
430	vonegrcnn_62e_nobn
431	resnet_101_v2
432	vonegrcnn_47e
433	vonegrcnn_52e_full
434	resnet_152_v2
435	pr1_stl10_disentangled_alpha04
436	predictive_coding_pc
437	dinov2_vitg14_reg_lc
438	eva02_large_in22k
439	convnext_xxlarge_clip

Benchmark bibtex

@article{Schiller1976,
            author = {Schiller, P. H. and Finlay, B. L. and Volman, S. F.},
            doi = {10.1152/jn.1976.39.6.1352},
            issn = {0022-3077},
            journal = {Journal of neurophysiology},
            number = {6},
            pages = {1334--1351},
            pmid = {825624},
            title = {{Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial Frequency}},
            url = {http://www.ncbi.nlm.nih.gov/pubmed/825624},
            volume = {39},
            year = {1976}
            }

Ceiling

0.93.

Note that scores are relative to this ceiling.

Data: Marques2020_Schiller1976

Metric: sf_bandwidth