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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	mehrer2020_alexnet_training_seed_01_fov16	.963
21	resnet50_st_wzc_2	.959
22	AlexNet_SIN_fov12	.957
23	yudixie_resnet50_category_class_0_240908	.959
24	resnet152_imagenet_full	.957
25	AlexNet_SIN_fov4	.956
26	Res2Net50_26w_4s	.956
27	resnet50_st_wzc_seed3_2_323	.952
28	alexnet_fov12	.955
29	resnet-18-LC_w_sh_1_iter_m	.953
30	resnet-18-LC_w_sh_100_iter_m	.948
31	resnet101	.945
32	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.946
33	yudixie_resnet18_object_class_0_240719	.945
34	resnet50_ewconplus_wzc_seed6_2_323	.944
35	resnet-18-LC_w_sh_1_iter_conv_init_m	.942
36	resnet50_FractalDB	.941
37	ReAlnet01_cornet_fov12	.937
38	vonegrcnn_52e_full	.938
39	resnet34_ecoset_full	.939
40	ReAlnet03_cornet	.938
41	AlexNet_SIN	.938
42	efficientnet_b1_imagenet_full	.938
43	ReAlnet09	.936
44	resnet50_hvs_wzc	.937
45	compact_resnet50_robust_V4	.938
46	ReAlnet09_cornet	.939
47	ReAlnet03	.939
48	alexnet	.933
49	ReAlnet10_ALL	.933
50	resnet50_st_wzc_seed6_2_323	.932
51	ReAlnet08_cornet	.932
52	BiT-S-R152x2	.931
53	resnext101_32x8d_wsl	.931
54	ReAlnet08	.931
55	r3m_resnet50_nocrop	.928
56	voneresnet-50-non_stochastic	.929
57	resnet18_imagenet_full	.928
58	vggface	.931
59	ReAlnet10_cornet	.928
60	resnet18_ecoset_full	.926
61	mobilenet_v2_1-4_224_pytorch	.927
62	ReAlnet04_cornet	.928
63	ReAlnet10	.925
64	ReAlnet07_cornet	.923
65	ReAlnet04	.925
66	ReAlnet07	.926
67	ReAlnet05	.924
68	artResNet18_1	.926
69	mobilenet_v2_1.0_96	.921
70	yudixie_resnet18_random_0_240719	.924
71	r3m_resnet50	.924
72	ReAlnet05_cornet	.923
73	nasnet_large-tf	.922
74	resnet50-SIN_IN	.922
75	resnet-18-LC_conv_init_m	.922
76	BiT-M-R50x3	.920
77	resnet50_st_wzc_seed7_2_323	.921
78	alexnet_random_linf8_perturb	.920
79	resnext101_32x32d_wsl	.921
80	ReAlnet08_ALL	.922
81	ReAlnet06_ALL	.921
82	ReAlnet01	.920
83	ReAlnet06	.919
84	ReAlnet06_cornet	.920
85	inception_v2-tf	.919
86	ReAlnet07_ALL	.921
87	ReAlnet01_cornet	.917
88	ReAlnet05_ALL	.918
89	ReAlnet02_cornet	.918
90	ReAlnet03_ALL	.916
91	ReAlnet04_ALL	.916
92	ReAlnet02_ALL	.918
93	ReAlnet01_ALL	.916
94	resnet152_ecoset_full	.916
95	0.5x_resnet-18	.915
96	yudixie_resnet18_rotation_reg_0_240719	.915
97	ReAlnet02	.915
98	antialiased-rnext101_32x8d	.914
99	convnext_tiny:in12k_ft_in1k	.914
100	ReAlnet09_ALL	.914
101	CORnet-S	.912
102	alexnet_training_seed_01_fov16	.909
103	yudixie_wbh_object_class_scaling_0415_run_1	.911
104	resnet101_imagenet_full	.911
105	cornet_s	.909
106	efficientnet_b2_imagenet_full	.907
107	BiT-M-R152x2	.907
108	nasnet_mobile	.906
109	mobilenet_v2_1.0_128-tf	.903
110	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
111	dinov2_vitl14_lc	.904
112	yudixie_resnet50_rotation_reg_0_240908	.905
113	mobilenet_v2_0.5_224-tf	.900
114	convnext_xlarge:fb_in22k_ft_in1k	.902
115	BiT-M-R101x3	.900
116	mobilenet_v2_0.5_192-tf	.899
117	r3m_resnet18	.902
118	alexnet_training_seed_01	.901
119	resnet-50_untrained	.901
120	texture_shape_resnet50_trained_on_SIN	.900
121	alexnet_training_seed_10	.900
122	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
123	resnet-50_v1-tf	.898
124	0.5x_resnet-18_LC	.899
125	resnet50-SIN	.898
126	dinov2_vitg14_lc	.897
127	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
128	RN50	.898
129	CLIP_resnet50	.894
130	alexnet_robust_correct	.896
131	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
132	CLIP_resnet50_float32	.895
133	resnet50_ewconplus_wzc	.895
134	nasnet_mobile-tf	.895
135	resnet-18-LC_m	.894
136	r3m_resnet34	.894
137	AdvProp_efficientnet-b0	.890
138	yudixie_resnet18_distance_reg_0_240719	.890
139	grcnn_robust_v1	.888
140	voneresnet-50	.887
141	0.5x_resnet-18_LC_w_sh_100_iter	.890
142	antialiased-r50	.887
143	antialias-resnet152	.887
144	yudixie_resnet50_translation_reg_0_240908	.886
145	grcnn_v2_text_noise	.885
146	deit_small_imagenet_100_seed-0	.882
147	alexnet_training_seed_03	.883
148	BiT-S-R101x3	.882
149	alexnet_training_seed_07	.882
150	resnet50_ecoset_full	.880
151	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
152	alexnet_training_seed_09	.881
153	compact_cornets_IT	.877
154	yudixie_wbh_object_class_scaling_0415_run_3	.876
155	resnet50_random_linf8_perturb	.877
156	BiT-S-R152x4	.876
157	voneresnet-50-robust	.876
158	mobilenet_v2_1.0_160-tf	.874
159	resnet-152_v2-tf	.874
160	deepeisnn_ei_imagenet_resnet18	.876
161	vit_base_patch14_dinov2-lvd142m	.875
162	densenet-169-keras	.874
163	resnet34_imagenet_full	.875
164	resnext101_32x16d_wsl	.871
165	SWSL_resnext101_32x8d	.873
166	VOneCORnet-S	.870
167	resnet50-vicregl0p9	.871
168	resnet50_byol	.870
169	mobilenet_v2_1.0_192-tf	.868
170	yudixie_resnet18_category_class_0_240719	.868
171	vgg_16	.868
172	yudixie_resnet18_distance_rotation_0_240719	.867
173	focalnet_tiny_lrf_in1k	.864
174	grcnn_109	.864
175	convnext_large:fb_in22k_ft_in1k	.863
176	xception	.861
177	vgg-16-keras	.859
178	seresnext50_32x4d	.860
179	convnext_base_fb_in1k	.860
180	yudixie_resnet18_translation_rotation_0_240719	.859
181	yudixie_wbh_object_class_scaling_0415_run_0	.858
182	resnet50	.858
183	resnet_50_v1	.856
184	resnet50_tutorial	.857
185	compact_vgg19_V4	.855
186	yudixie_resnet50_imagenet1kpret_0_240908	.854
187	compact_resnet50_IT	.855
188	inception_v3-tf	.855
189	resnet_50_v1_spiking_l4	.855
190	resnet_50_v1_spiking	.853
191	resnet-18_untrained	.854
192	resnet50-sup	.852
193	yudixie_resnet50_distance_translation_0_240908	.855
194	resnet_50_v1_spiking_l3	.851
195	resnet_50_v1_spiking_l4a	.851
196	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
197	dinov2_vitl14_reg_lc	.850
198	dinov2_vitl14_lc_res336	.851
199	mehrer2020_alexnet_training_seed_08	.850
200	dinov2_vitl14_reg_lc_featread	.849
201	resnet50_random_l2_perturb	.849
202	BiT-S-R101x1	.847
203	resnet_50_v2	.846
204	BiT-S-R50x3	.846
205	alexnet_training_seed_02	.847
206	effnetb1_272x240	.847
207	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
208	inception_v1	.845
209	yudixie_resnet50_distance_translation_rotation_0_240908	.845
210	mehrer2020_alexnet_training_seed_02	.844
211	mehrer2020_alexnet_training_seed_01_fov12	.843
212	vit_base_patch14_reg4_dinov2-lvd142m	.842
213	AdvProp_efficientnet-b8	.842
214	alexnet_reduced_aliasing_early_checkpoint	.842
215	resnet50-vicregl0p75	.840
216	resnet50_vicreg_linhead	.842
217	resnet50_imagenet_full	.841
218	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
219	BiT-S-R50x1	.839
220	yudixie_resnet50_object_class_0_240908	.840
221	deit3_base_patch16_224_fb_in22k_ft_in1k	.839
222	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
223	resnet50-SIN_IN_IN	.838
224	yudixie_resnet18_imagenet1kpret_0_240719	.836
225	yudixie_wbh_object_class_scaling_0415_imagenet	.839
226	AdvProp_efficientnet-b6	.836
227	0.5x_resnet-18_LC_w_sh_10_iter	.836
228	resnet-152_v1-tf	.834
229	resnet50_simclr	.835
230	deit_large_imagenet_full_seed-0	.834
231	BiT-M-R50x1	.833
232	resnext101_32x48d_wsl	.835
233	resnet50_moco_v2	.834
234	SWSL_resnet50	.830
235	yudixie_resnet50_distance_rotation_0_240908	.831
236	resnet-152_v2_pytorch	.830
237	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
238	cvt_cvt-13-224-in1k_4_LucyV4	.828
239	CLIP-RN50	.828
240	barlow-twins-resnet50	.828
241	mehrer2020_alexnet_training_seed_09	.826
242	densenet-169	.827
243	inception_v3	.825
244	mobilenet_v1_0.25_128	.824
245	compact_resnet50_V4	.822
246	ijepa_vith14_linhead	.822
247	alexnet_l2_3_robust	.820
248	resnet-18-LC_1st_conv_m	.818
249	alexnet_fov4	.817
250	regnety_032	.816
251	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
252	konkle_alexnetgn_ipcl_ref01_primary_model	.813
253	yudixie_resnet18_distance_translation_rotation_0_240719	.814
254	alexnet_training_seed_04	.814
255	deit_base_imagenet_full_seed-0	.813
256	ViT_L_32_imagenet1k	.815
257	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
258	mehrer2020_alexnet_training_seed_03	.811
259	BiT-M-R101x1	.809
260	yolos_tiny	.810
261	resnet50_simsiam_linhead	.809
262	AdvProp_efficientnet-b7	.809
263	mehrer2020_alexnet_training_seed_01	.807
264	resnet50-simclr-vissl	.805
265	grcnn	.804
266	yudixie_resnet50_random_0_240908	.803
267	yudixie_resnet18_distance_translation_0_240719	.803
268	resnet18_fair	.802
269	resnet-101_v1-tf	.795
270	alexnet_training_seed_05	.794
271	mobilenet_v2_0.75_96	.793
272	CORnet-Z	.795
273	resnet-50x2_untrained	.794
274	alexnet_training_seed_01_fov4	.794
275	resnet-18-LC_w_sh_10_iter_m	.793
276	yudixie_resnet50_distance_reg_0_240908	.792
277	vit_base_patch16_clip_224:openai_ft_in1k	.790
278	alexnet_training_seed_01_fov12	.789
279	alexnet_training_seed_08	.787
280	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
281	efficientnet_b0	.787
282	resnet50-moclr8deg	.785
283	inception_v1-tf	.785
284	resnet-18_test_m	.784
285	mehrer2020_alexnet_training_seed_05	.783
286	densenet-121	.783
287	resnet50-vicreg	.783
288	cvt_cvt-21-224-in1k_4_LucyV4	.782
289	AT_efficientnet-b2	.782
290	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
291	blt_vs	.780
292	AdvProp_efficientnet-b2	.779
293	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
294	mobilenet_v2_0.75_192-tf	.776
295	inception_v4-tf	.777
296	mobilenet_v2_1.4_224-tf	.773
297	vit_large_patch14_clip_224:openai_ft_in1k	.773
298	resnet50_imagenet_100_seed-0	.773
299	pnasnet_large-tf	.767
300	ViT-B/32	.764
301	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
302	mobilenet_v2_1_4_224	.765
303	resnet-50_v2-tf	.763
304	vision_transformer_vit_large_patch16_224	.762
305	resnet50-barlow	.755
306	ReAlnet01_cornet_fov4	.752
307	resnet50_imagenet_10_seed-0	.749
308	vonegrcnn_62e_nobn	.749
309	omnivore_swinT	.747
310	mobilenet_v2_0.75_128	.742
311	my-model	.743
312	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
313	eicircuit_resnet18_bn_20260919	.742
314	AlexNet_SIN_fov16	.741
315	briaai_rmbg_1_4	.738
316	yudixie_resnet18_translation_reg_0_240719	.739
317	v1-pyr-nodown	.735
318	deit_small_imagenet_full_seed-0	.736
319	AdvProp_efficientnet-b4	.728
320	convnext_base_fb_in1k_gram_aligned_dinov3	.729
321	shufflenet_v2_x1_0	.729
322	resnet50_linf_4_robust	.727
323	alexnet_linf_8_robust	.724
324	densenet-121-keras	.724
325	efficientnet-b7	.723
326	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
327	texture_shape_alexnet_trained_on_SIN	.723
328	resnet50_barlowtwins_linhead	.721
329	resnet_SIN_IN_FT_IN	.722
330	vgg_19	.718
331	vonealexnet_gaussian_noise_std4_fixed	.717
332	mobilenet_v2_1_0_128	.718
333	cv_18_dagger_408_pretrained	.716
334	mobilenet_v2_1.0_224-tf	.714
335	resnet50-VITO-8deg-cc	.714
336	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
337	convnext_large_imagenet_full_seed-0	.712
338	inception_resnet_v2	.708
339	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
340	pnasnet_large_halfPrecision	.696
341	BiT-M-R152x4	.694
342	deepeisnn_ei_cifar10_vgg19	.695
343	effnetv2m_custom384	.690
344	custom_model_cv_18_dagger_408	.685
345	mobilenet_v2_0_5_224	.680
346	deepeisnn_ei_cifar10_resnet18	.675
347	resnet-101_v2-tf	.674
348	compact_resnet50_robust_IT	.674
349	convnext_base_fb_in22k_ft_in1k	.672
350	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.674
351	resnet50-vitoimagevidnet8	.674
352	CORnetZ_CIFAR10_bs32_20_04	.670
353	deit3_base_patch16_224_fb_in1k	.670
354	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.669
355	cvt_cvt-21-384-in1k_4_LucyV4	.666
356	resnet50_l2_3_robust	.667
357	resnet50_robust_l2_eps1	.665
358	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
359	resnet18_imagenet21kP	.664
360	gabor_filter_entropy_4x4_v2_forcedCosine	.663
361	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
362	hmax	.662
363	mobilenet_v2_0.5_128	.660
364	resnet50-cifar	.661
365	gabor_filter_mean_abs_4x4_v2	.662
366	convnext_tiny_imagenet_full_seed-0	.661
367	vit_huge_clip336_laion	.657
368	mobilenet_v2_0_75_224	.655
369	resnet50_byol_linhead	.653
370	resnet50_robust_l2_eps3	.653
371	mobilenet_v2_0_5_192	.650
372	convnext_small_imagenet_100_seed-0	.649
373	CLIP_ViT-B_32	.646
374	cvt_cvt-13-384-in1k_4_LucyV4	.644
375	imagenet_l2_3_0	.643
376	imagenet_l2_10_0	.640
377	vit_large_clip336_openai	.639
378	mobilenet_v2_0.75_224-tf	.626
379	vgg-19-keras	.617
380	mobilenet_v2_0_75_192	.615
381	omnivore_swinB	.609
382	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
383	densenet-201	.604
384	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
385	resnet50_linf_8_robust	.596
386	resnet-50-robust	.596
387	mobilenet_v2_1_0_160	.595
388	densenet-201-keras	.590
389	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
390	omnivore_swinS	.585
391	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
392	mobilenet_v2_0.35_224	.572
393	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
394	FrankRobWobv0	.567
395	convnext_femto_ols:d1_in1k	.557
396	alexnet_random_l2_3_perturb	.552
397	mobilenet_v2_1_0_224	.550
398	convnext_base_imagenet_full_seed-0	.546
399	vit_base_patch16_dinov3-lvd1689m	.545
400	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
401	convnext_large_clip_laion2b_finelayers	.530
402	mobilenet_v2_1_0_192	.523
403	mobilenet_v2_0_75_160	.520
404	convnext_small_imagenet_full_seed-0	.501
405	CORnetZ_CIFAR10	.495
406	efficientnet_b0_imagenet_full	.491
407	convnext_small_imagenet_10_seed-0	.488
408	resnet50_imagenet_1_seed-0	.481
409	mobilevit_small	.477
410	mobilenet_v2_0.75_160-tf	.471
411	resnet-18-LC_untrained	.454
412	mobilenet_v2_1_3_224	.442
413	convnext_large_in22k_supervised	.441
414	alexnet_training_seed_06	.428
415	mobilenet_v2_1.3_224-tf	.399
416	resnet50_pretrained_with_retinal_waves	.397
417	convnext_tiny_sup	.331
418	convnext_small_imagenet_1_seed-0	.293
419	my-model	.226
420	mobilenet_v2_0.5_160	.103
421	deit_small_imagenet_10_seed-0	.000
422	gabor_filter_entropy_4x4	.000
423	gabor_filter_entropy_4x4_v2	.000
424	mobilenet_v2_0.35_160
425	resnet18-supervised
426	fitvid_trained_on_physion
427	openclip
428	resnet50_ImageNet
429	regnet_y_400mf
430	tv_efficientnet-b1
431	alexnet_ks_torevert
432	yudixie_resnet50_imagenet1kpret_0_240312
433	resnet_101_v1
434	resnet_152_v1
435	densenet_201_pytorch
436	pnasnet_large_pytorch
437	inception_v3_pytorch
438	eBarlow_lmda_02_1000ep
439	inception_v4
440	nasnet_large
441	deit_small_imagenet_1_seed-0
442	pnasnet_large
443	vonegrcnn_62e_nobn
444	resnet_101_v2
445	vonegrcnn_47e
446	vonegrcnn_52e_full
447	resnet_152_v2
448	resnet50_simclr_linhead_01norm
449	mehrer2020_alexnet_training_seed_01_fov4
450	mehrer2020_alexnet_training_seed_04
451	mehrer2020_alexnet_training_seed_06
452	mehrer2020_alexnet_training_seed_07
453	mehrer2020_alexnet_training_seed_10
454	pr1_stl10_disentangled_alpha04
455	predictive_coding_pc
456	dinov2_vitg14_reg_lc
457	eva02_large_in22k
458	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