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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	imagenet_l2_10_0	1.0
2	deepeisnn_ei_cifar10_resnet18	1.0
3	resnext101_32x8d_wsl	1.0
4	alexnet_training_seed_01_fov16	.999
5	BiT-M-R101x1	1.0
6	mobilenet_v2_0.75_160-tf	1.0
7	resnet-50-robust	1.0
8	resnet34_ecoset_full	1.0
9	barlow-twins-resnet50	1.0
10	yudixie_resnet50_distance_translation_rotation_0_240908	.999
11	resnet50_ewconplus_wzc_seed6_2_323	.998
12	AlexNet_SIN_fov16	.999
13	alexnet_robust_correct	.999
14	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.999
15	resnet50-vitoimagevidnet8	.999
16	mobilenet_v2_1.0_96	.999
17	resnet50_robust_l2_eps1	.998
18	resnet50_robust_l2_eps3	.999
19	resnet50_l2_3_robust	.996
20	grcnn_v2_text_noise	.999
21	gabor_filter_mean_abs_4x4_v2	.997
22	resnet-18-LC_w_sh_10_iter_m	.996
23	resnet50_linf_4_robust	.995
24	blt_vs	.996
25	alexnet_linf_8_robust	.996
26	deepeisnn_ei_cifar10_vgg19	.995
27	yudixie_resnet50_category_class_0_240908	.996
28	AdvProp_efficientnet-b8	.998
29	yudixie_resnet50_rotation_reg_0_240908	.997
30	resnet50-moclr8deg	.997
31	resnet50-SIN	.995
32	resnext101_32x32d_wsl	.994
33	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.993
34	resnet50_linf_8_robust	.994
35	resnet18_fair	.992
36	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.992
37	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.994
38	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.993
39	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.993
40	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.991
41	resnet-18-LC_1st_conv_m	.992
42	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.991
43	antialias-resnet152	.990
44	CORnetZ_CIFAR10	.991
45	alexnet_l2_3_robust	.990
46	resnet50_ecoset_full	.990
47	resnet101	.989
48	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.989
49	BiT-S-R152x2	.990
50	resnet50_FractalDB	.987
51	alexnet_training_seed_06	.989
52	resnet50_primary_visual_cortex	.988
53	vgg-16-keras	.987
54	resnet50-simclr-vissl	.985
55	resnet50_ewconplus_wzc_seed5_2__323	.986
56	texture_shape_resnet50_trained_on_SIN	.984
57	vgg-19-keras	.985
58	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.987
59	resnet18_imagenet_full	.984
60	ijepa_vith14_linhead	.985
61	resnet101_ecoset_full	.984
62	alexnet_training_seed_08	.983
63	imagenet_l2_3_0	.984
64	AdvProp_efficientnet-b4	.985
65	vgg_16	.981
66	resnet50_byol_linhead	.982
67	resnet50_ewconplus_wzc_seed2_2__323	.980
68	mobilenet_v2_1.0_128-tf	.980
69	resnet-50_untrained	.979
70	yudixie_resnet50_object_class_0_240908	.978
71	resnet50_vicreg_linhead	.975
72	alexnet_training_seed_04	.975
73	resnet50_st_wzc_2	.975
74	yudixie_resnet50_distance_rotation_0_240908	.976
75	yudixie_resnet18_category_class_0_240719	.972
76	cornet_s	.972
77	resnet50_imagenet_10_seed-0	.969
78	voneresnet-50-non_stochastic	.970
79	inception_v1	.969
80	yudixie_resnet18_distance_reg_0_240719	.968
81	BiT-M-R50x3	.969
82	compact_vgg19_V4	.969
83	yudixie_resnet50_distance_translation_0_240908	.967
84	resnet-152_v2_pytorch	.966
85	yudixie_resnet18_translation_rotation_0_240719	.965
86	resnet34_imagenet_full	.967
87	yudixie_resnet18_distance_translation_0_240719	.966
88	resnet_101_v1	.964
89	BiT-S-R50x1	.964
90	voneresnet-50-robust	.963
91	Res2Net50_26w_4s	.960
92	alexnet_training_seed_09	.958
93	voneresnet-50	.959
94	r3m_resnet18	.955
95	grcnn_robust_v1	.954
96	resnet50_st_wzc_seed3_2_323	.953
97	resnet-18-LC_w_sh_1_iter_conv_init_m	.951
98	VOneCORnet-S	.954
99	resnet50-cifar	.951
100	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.950
101	mobilevit_small	.947
102	yudixie_resnet50_translation_rotation_0_240908	.947
103	yudixie_resnet50_translation_reg_0_240908	.945
104	resnet50-vicregl0p9	.945
105	alexnet_training_seed_03	.944
106	resnet18_ecoset_full	.944
107	inception_v2-tf	.944
108	yudixie_resnet18_random_0_240719	.938
109	vonegrcnn_52e_full	.939
110	resnet-18-LC_w_sh_100_iter_conv_init_m	.936
111	yudixie_resnet18_object_class_0_240719	.937
112	resnet101_imagenet_full	.936
113	omnivore_swinB	.934
114	yudixie_wbh_object_class_scaling_0415_run_1	.933
115	compact_cornets_IT	.934
116	compact_resnet50_robust_IT	.930
117	yudixie_resnet50_distance_reg_0_240908	.929
118	mobilenet_v2_1.0_160-tf	.928
119	antialiased-r50	.927
120	resnet50_imagenet_100_seed-0	.928
121	mobilenet_v2_0.5_192-tf	.926
122	resnet-18-LC_w_sh_100_iter_m	.924
123	briaai_rmbg_1_4	.925
124	grcnn_109	.924
125	CLIP-RN50	.925
126	resnet50_ewconplus_wzc	.922
127	resnet-18_untrained	.922
128	vggface	.921
129	resnet-18-LC_m	.921
130	yudixie_resnet18_distance_rotation_0_240719	.919
131	eicircuit_resnet18_bn_20260919	.920
132	yudixie_resnet18_translation_reg_0_240719	.919
133	resnet50_barlowtwins_linhead	.919
134	0.5x_resnet-18	.918
135	focalnet_tiny_lrf_in1k	.917
136	vonegrcnn_47e	.916
137	inception_v1-tf	.916
138	alexnet_training_seed_10	.914
139	alexnet_training_seed_01_fov4	.914
140	vision_transformer_vit_large_patch16_224	.915
141	resnet50_st_wzc_seed6_2_323	.913
142	alexnet_random_linf8_perturb	.912
143	yudixie_resnet18_distance_translation_rotation_0_240719	.912
144	resnet50_simsiam_linhead	.909
145	alexnet_training_seed_02	.907
146	resnet50-SIN_IN	.907
147	vonealexnet_gaussian_noise_std4_fixed	.905
148	alexnet_fov4	.904
149	0.5x_resnet-18_LC_w_sh_100_iter	.904
150	resnet-50x2_untrained	.902
151	yudixie_resnet50_random_0_240908	.901
152	mobilenet_v2_0.75_96	.900
153	grcnn	.898
154	resnet50-vicregl0p75	.898
155	xception	.896
156	deepeisnn_ei_imagenet_resnet18	.896
157	alexnet_training_seed_01	.894
158	alexnet_early_checkpoint	.893
159	mehrer2020_alexnet_training_seed_03	.893
160	artResNet18_1	.893
161	resnet50_hvs_wzc	.893
162	alexnet_training_seed_01_fov12	.894
163	resnet_101_v2	.891
164	resnet152_ecoset_full	.890
165	resnet-50_v1-tf	.883
166	convnext_small_imagenet_1_seed-0	.883
167	densenet-169	.882
168	resnet50_st_wzc_seed7_2_323	.883
169	resnet-18-LC_conv_init_m	.879
170	ViT_L_32_imagenet1k	.881
171	vonegrcnn_47e	.879
172	resnet-18-LC_w_sh_10_iter_conv_init_m	.876
173	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.878
174	r3m_resnet34	.877
175	resnet50_imagenet_full	.876
176	mehrer2020_alexnet_training_seed_01_fov16	.874
177	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
178	yudixie_wbh_object_class_scaling_0415_run_3	.872
179	mehrer2020_alexnet_training_seed_01_fov12	.872
180	mehrer2020_alexnet_training_seed_08	.871
181	mehrer2020_alexnet_training_seed_09	.871
182	texture_shape_alexnet_trained_on_SIN	.870
183	compact_resnet50_IT	.870
184	yudixie_wbh_object_class_scaling_0415_imagenet	.869
185	yudixie_resnet18_imagenet1kpret_0_240719	.870
186	compact_resnet50_V4	.867
187	resnet50_tutorial	.867
188	yudixie_resnet50_imagenet1kpret_0_240908	.866
189	ReAlnet01_cornet_fov16	.864
190	resnet_50_v1_spiking	.863
191	0.5x_resnet-18_LC	.865
192	resnet_50_v1_spiking_l4a	.865
193	resnet50_byol	.862
194	resnet_50_v1	.863
195	resnet_50_v1_spiking_l3	.861
196	resnet_50_v1_spiking_l4	.861
197	resnet50-sup	.861
198	alexnet_random_l2_3_perturb	.858
199	resnet_50_v2	.858
200	alexnet_training_seed_07	.858
201	resnet-18_test_m	.857
202	mobilenet_v2_1_0_192	.857
203	yudixie_resnet18_rotation_reg_0_240719	.857
204	vonegrcnn_62e_nobn	.856
205	mehrer2020_alexnet_training_seed_01	.854
206	mehrer2020_alexnet_training_seed_07	.855
207	ReAlnet01_cornet_fov12	.853
208	r3m_resnet50_nocrop	.852
209	convnext_femto_ols:d1_in1k	.852
210	resnet-18-LC_w_sh_1_iter_m	.845
211	yudixie_wbh_object_class_scaling_0415_run_2	.846
212	ReAlnet10	.845
213	ReAlnet10_cornet	.844
214	CLIP_resnet50	.841
215	BiT-M-R50x1	.841
216	CORnet-S	.839
217	CORnet-Z	.840
218	hmax	.839
219	mobilenet_v2_0.75_128	.840
220	mobilenet_v2_0.5_128	.840
221	0.5x_resnet-18_LC_w_sh_10_iter	.841
222	0.5x_resnet-18_LC_w_sh_1_iter	.840
223	alexnet	.839
224	resnet-18-LC_d_w_sh_1x1_conv_init_m	.838
225	CORnetZ_CIFAR10_bs32_20_04	.840
226	alexnet_reduced_aliasing_early_checkpoint	.839
227	ReAlnet02_ALL	.839
228	ReAlnet04_ALL	.840
229	ReAlnet03_ALL	.838
230	ReAlnet05_ALL	.838
231	ReAlnet06_ALL	.839
232	ReAlnet07_ALL	.839
233	ReAlnet09_ALL	.840
234	ReAlnet10_ALL	.839
235	ReAlnet08_ALL	.840
236	AlexNet_SIN	.839
237	ReAlnet04	.841
238	ReAlnet06	.840
239	ReAlnet07	.840
240	ReAlnet08	.840
241	alexnet_fov16	.839
242	ReAlnet09	.838
243	ReAlnet05	.839
244	alexnet_fov12	.838
245	ReAlnet01_cornet	.839
246	ReAlnet02_cornet	.839
247	ReAlnet03_cornet	.840
248	ReAlnet04_cornet	.841
249	ReAlnet01_ALL	.839
250	compact_resnet50_robust_V4	.839
251	compact_vgg19_IT	.838
252	ReAlnet05_cornet	.840
253	ReAlnet06_cornet	.839
254	compact_cornets_V4	.839
255	ReAlnet07_cornet	.840
256	ReAlnet08_cornet	.838
257	ReAlnet01	.840
258	ReAlnet09_cornet	.839
259	ReAlnet02	.840
260	ReAlnet03	.839
261	AlexNet_SIN_fov12	.839
262	yudixie_wbh_object_class_scaling_0415_run_0	.839
263	resnet50_pretrained_with_retinal_waves	.838
264	CLIP_resnet50_float32	.837
265	mobilenet_v2_1_4_224	.835
266	mobilenet_v2_1_0_160	.836
267	gabor_filter_entropy_4x4_v2_forcedCosine	.831
268	densenet-169-keras	.831
269	resnet50-SIN_IN_IN	.827
270	resnet50-vicreg	.824
271	BiT-M-R152x2	.825
272	AdvProp_efficientnet-b0	.825
273	mobilenet_v1_0.25_128	.821
274	resnet50-barlow	.820
275	cvt_cvt-13-224-in1k_4_LucyV4	.816
276	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.814
277	efficientnet_b2_imagenet_full	.814
278	RN50	.813
279	vgg_19	.805
280	efficientnet-b7	.804
281	nasnet_large-tf	.804
282	BiT-M-R152x4	.802
283	vit_base_patch16_dinov3-lvd1689m	.799
284	mobilenet_v2_0_75_192	.800
285	BiT-S-R101x1	.800
286	mobilenet_v2_0.75_224-tf	.799
287	resnet50-VITO-8deg-cc	.799
288	AdvProp_efficientnet-b7	.800
289	AdvProp_efficientnet-b6	.796
290	mobilenet_v2_1.0_224-tf	.791
291	SWSL_resnext101_32x8d	.782
292	my-model	.782
293	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.781
294	convnext_base_fb_in22k_ft_in1k	.780
295	mobilenet_v2_1_0_224	.773
296	pnasnet_large_halfPrecision	.773
297	resnet50_simclr	.774
298	BiT-M-R101x3	.773
299	resnet50	.767
300	effnetb1_272x240	.768
301	inception_v4-tf	.769
302	resnet-152_v1-tf	.763
303	SWSL_resnet50	.759
304	densenet-201	.761
305	regnety_032	.758
306	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.756
307	densenet-121	.754
308	inception_v3	.749
309	omnivore_swinS	.748
310	resnet50_random_linf8_perturb	.743
311	resnet152_imagenet_full	.740
312	convnext_small_imagenet_10_seed-0	.735
313	r3m_resnet50	.733
314	convnext_small_imagenet_full_seed-0	.732
315	v1-pyr-nodown	.731
316	resnet18_imagenet21kP	.728
317	convnext_large_imagenet_full_seed-0	.726
318	resnet50_random_l2_perturb	.721
319	pnasnet_large-tf	.722
320	mobilenet_v2_0.75_192-tf	.720
321	nasnet_mobile-tf	.720
322	konkle_alexnetgn_ipcl_ref01_primary_model	.720
323	mobilenet_v2_0_5_224	.718
324	resnext101_32x16d_wsl	.713
325	alexnet_training_seed_05	.710
326	CLIP_ViT-B_32	.708
327	resnext101_32x48d_wsl	.696
328	resnet_SIN_IN_FT_IN	.694
329	mobilenet_v2_0.5_224-tf	.694
330	nasnet_mobile	.694
331	efficientnet_b0	.691
332	deit_base_imagenet_full_seed-0	.692
333	deit_small_imagenet_full_seed-0	.688
334	inception_v3-tf	.688
335	BiT-S-R50x3	.687
336	yolos_tiny	.685
337	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.683
338	convnext_tiny_imagenet_full_seed-0	.679
339	efficientnet_b1_imagenet_full	.679
340	my-model	.672
341	vit_base_patch16_clip_224:openai_ft_in1k	.671
342	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.664
343	BiT-S-R152x4	.661
344	mobilenet_v2_1.4_224-tf	.660
345	mobilenet_v2_1.0_192-tf	.653
346	convnext_large:fb_in22k_ft_in1k	.647
347	densenet-201-keras	.643
348	densenet-121-keras	.641
349	convnext_xlarge:fb_in22k_ft_in1k	.640
350	mobilenet_v2_1-4_224_pytorch	.639
351	dinov2_vitl14_reg_lc	.639
352	cvt_cvt-21-224-in1k_4_LucyV4	.638
353	resnet50_moco_v2	.637
354	mobilenet_v2_1.3_224-tf	.636
355	mobilenet_v2_0_5_192	.636
356	dinov2_vitl14_reg_lc_featread	.632
357	FrankRobWobv0	.623
358	mobilenet_v2_0.5_160	.623
359	resnet-101_v2-tf	.620
360	deit_small_imagenet_10_seed-0	.618
361	mobilenet_v2_1_0_128	.617
362	dinov2_vitl14_lc	.617
363	convnext_tiny_sup	.618
364	antialiased-rnext101_32x8d	.613
365	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.608
366	vit_relpos_base_patch16_clsgap_224:sw_in1k	.605
367	convnext_tiny:in12k_ft_in1k	.603
368	vit_base_patch14_dinov2-lvd142m	.601
369	efficientnet_b0_imagenet_full	.600
370	effnetv2m_custom384	.599
371	convnext_base_fb_in1k	.592
372	convnext_base_fb_in1k_gram_aligned_dinov3	.585
373	resnet-101_v1-tf	.584
374	mobilenet_v2_1_3_224	.585
375	dinov2_vitg14_lc	.585
376	mobilenet_v2_0_75_160	.583
377	deit_small_imagenet_100_seed-0	.580
378	resnet-50_v2-tf	.579
379	BiT-S-R101x3	.575
380	cvt_cvt-21-384-in1k_4_LucyV4	.575
381	cvt_cvt-13-384-in1k_4_LucyV4	.571
382	AlexNet_SIN_fov4	.558
383	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.557
384	resnet-152_v2-tf	.557
385	convnext_large_clip_laion2b_finelayers	.554
386	AT_efficientnet-b2	.550
387	AdvProp_efficientnet-b2	.549
388	convnext_base_imagenet_full_seed-0	.543
389	deit_large_imagenet_full_seed-0	.543
390	inception_resnet_v2	.541
391	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.540
392	eva02_large_in22k	.538
393	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.535
394	shufflenet_v2_x1_0	.534
395	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.532
396	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.527
397	gabor_filter_entropy_4x4_v2	.523
398	ViT-B/32	.520
399	custom_model_cv_18_dagger_408	.513
400	mobilenet_v2_0.35_224	.498
401	convnext_xxlarge_clip	.495
402	deit3_base_patch16_224_fb_in22k_ft_in1k	.481
403	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.476
404	vit_large_patch14_clip_336:laion2b_ft_in1k	.467
405	vit_large_patch14_clip_224:laion2b_ft_in1k	.465
406	omnivore_swinT	.465
407	cv_18_dagger_408_pretrained	.462
408	mobilenet_v2_0_75_224	.456
409	convnext_small_imagenet_100_seed-0	.457
410	resnet50_imagenet_1_seed-0	.443
411	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.437
412	convnext_large_in22k_supervised	.415
413	resnet-18-LC_untrained	.414
414	convnext_base:clip_laiona_augreg_ft_in1k_384	.413
415	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.409
416	vit_base_patch14_reg4_dinov2-lvd142m	.406
417	vit_large_patch14_clip_224:openai_ft_in1k	.404
418	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.395
419	vit_large_clip336_openai	.390
420	deit3_base_patch16_224_fb_in1k	.374
421	ReAlnet01_cornet_fov4	.370
422	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.369
423	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.346
424	dinov2_vitl14_lc_res336	.306
425	gabor_filter_entropy_4x4	.000
426	mobilenet_v2_0.35_160
427	resnet18-supervised
428	fitvid_trained_on_physion
429	openclip
430	resnet50_ImageNet
431	regnet_y_400mf
432	tv_efficientnet-b1
433	alexnet_ks_torevert
434	yudixie_resnet50_imagenet1kpret_0_240312
435	resnet_152_v1
436	densenet_201_pytorch
437	pnasnet_large_pytorch
438	inception_v3_pytorch
439	eBarlow_lmda_02_1000ep
440	inception_v4
441	nasnet_large
442	deit_small_imagenet_1_seed-0
443	pnasnet_large
444	vonegrcnn_62e_nobn
445	vonegrcnn_52e_full
446	resnet_152_v2
447	resnet50_simclr_linhead_01norm
448	mehrer2020_alexnet_training_seed_01_fov4
449	mehrer2020_alexnet_training_seed_02
450	mehrer2020_alexnet_training_seed_04
451	mehrer2020_alexnet_training_seed_05
452	mehrer2020_alexnet_training_seed_06
453	mehrer2020_alexnet_training_seed_10
454	pr1_stl10_disentangled_alpha04
455	predictive_coding_pc
456	dinov2_vitg14_reg_lc
457	vit_huge_clip336_laion
458	seresnext50_32x4d

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

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Schiller1976

Metric: sf_selective