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

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