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