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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	convnext_base_fb_in1k	.860
176	yudixie_resnet18_translation_rotation_0_240719	.859
177	yudixie_wbh_object_class_scaling_0415_run_0	.858
178	resnet50	.858
179	resnet_50_v1	.856
180	resnet50_tutorial	.857
181	compact_vgg19_V4	.855
182	yudixie_resnet50_imagenet1kpret_0_240908	.854
183	compact_resnet50_IT	.855
184	inception_v3-tf	.855
185	resnet_50_v1_spiking_l4	.855
186	resnet_50_v1_spiking	.853
187	resnet-18_untrained	.854
188	resnet50-sup	.852
189	yudixie_resnet50_distance_translation_0_240908	.855
190	resnet_50_v1_spiking_l3	.851
191	resnet_50_v1_spiking_l4a	.851
192	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
193	dinov2_vitl14_reg_lc	.850
194	dinov2_vitl14_lc_res336	.851
195	dinov2_vitl14_reg_lc_featread	.849
196	resnet50_random_l2_perturb	.849
197	BiT-S-R101x1	.847
198	resnet_50_v2	.846
199	BiT-S-R50x3	.846
200	alexnet_training_seed_02	.847
201	effnetb1_272x240	.847
202	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
203	inception_v1	.845
204	yudixie_resnet50_distance_translation_rotation_0_240908	.845
205	vit_base_patch14_reg4_dinov2-lvd142m	.842
206	AdvProp_efficientnet-b8	.842
207	alexnet_reduced_aliasing_early_checkpoint	.842
208	resnet50-vicregl0p75	.840
209	resnet50_imagenet_full	.841
210	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
211	BiT-S-R50x1	.839
212	yudixie_resnet50_object_class_0_240908	.840
213	deit3_base_patch16_224_fb_in22k_ft_in1k	.839
214	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
215	resnet50-SIN_IN_IN	.838
216	yudixie_resnet18_imagenet1kpret_0_240719	.836
217	yudixie_wbh_object_class_scaling_0415_imagenet	.839
218	AdvProp_efficientnet-b6	.836
219	0.5x_resnet-18_LC_w_sh_10_iter	.836
220	resnet-152_v1-tf	.834
221	resnet50_simclr	.835
222	deit_large_imagenet_full_seed-0	.834
223	BiT-M-R50x1	.833
224	resnet50_moco_v2	.834
225	SWSL_resnet50	.830
226	yudixie_resnet50_distance_rotation_0_240908	.831
227	resnet-152_v2_pytorch	.830
228	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
229	cvt_cvt-13-224-in1k_4_LucyV4	.828
230	CLIP-RN50	.828
231	barlow-twins-resnet50	.828
232	densenet-169	.827
233	inception_v3	.825
234	mobilenet_v1_0.25_128	.824
235	compact_resnet50_V4	.822
236	alexnet_l2_3_robust	.820
237	resnet-18-LC_1st_conv_m	.818
238	alexnet_fov4	.817
239	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
240	konkle_alexnetgn_ipcl_ref01_primary_model	.813
241	yudixie_resnet18_distance_translation_rotation_0_240719	.814
242	alexnet_training_seed_04	.814
243	deit_base_imagenet_full_seed-0	.813
244	ViT_L_32_imagenet1k	.815
245	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
246	BiT-M-R101x1	.809
247	yolos_tiny	.810
248	AdvProp_efficientnet-b7	.809
249	resnet50-simclr-vissl	.805
250	grcnn	.804
251	yudixie_resnet50_random_0_240908	.803
252	yudixie_resnet18_distance_translation_0_240719	.803
253	resnet18_fair	.802
254	resnet-101_v1-tf	.795
255	alexnet_training_seed_05	.794
256	mobilenet_v2_0.75_96	.793
257	CORnet-Z	.795
258	resnet-50x2_untrained	.794
259	alexnet_training_seed_01_fov4	.794
260	resnet-18-LC_w_sh_10_iter_m	.793
261	yudixie_resnet50_distance_reg_0_240908	.792
262	vit_base_patch16_clip_224:openai_ft_in1k	.790
263	alexnet_training_seed_01_fov12	.789
264	alexnet_training_seed_08	.787
265	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
266	efficientnet_b0	.787
267	resnet50-moclr8deg	.785
268	inception_v1-tf	.785
269	resnet-18_test_m	.784
270	densenet-121	.783
271	resnet50-vicreg	.783
272	cvt_cvt-21-224-in1k_4_LucyV4	.782
273	AT_efficientnet-b2	.782
274	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
275	blt_vs	.780
276	AdvProp_efficientnet-b2	.779
277	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
278	mobilenet_v2_0.75_192-tf	.776
279	inception_v4-tf	.777
280	mobilenet_v2_1.4_224-tf	.773
281	vit_large_patch14_clip_224:openai_ft_in1k	.773
282	resnet50_imagenet_100_seed-0	.773
283	pnasnet_large-tf	.767
284	ViT-B/32	.764
285	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
286	mobilenet_v2_1_4_224	.765
287	resnet-50_v2-tf	.763
288	vision_transformer_vit_large_patch16_224	.762
289	resnet50-barlow	.755
290	ReAlnet01_cornet_fov4	.752
291	resnet50_imagenet_10_seed-0	.749
292	vonegrcnn_62e_nobn	.749
293	omnivore_swinT	.747
294	mobilenet_v2_0.75_128	.742
295	my-model	.743
296	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
297	AlexNet_SIN_fov16	.741
298	briaai_rmbg_1_4	.738
299	yudixie_resnet18_translation_reg_0_240719	.739
300	v1-pyr-nodown	.735
301	deit_small_imagenet_full_seed-0	.736
302	AdvProp_efficientnet-b4	.728
303	convnext_base_fb_in1k_gram_aligned_dinov3	.729
304	shufflenet_v2_x1_0	.729
305	resnet50_linf_4_robust	.727
306	alexnet_linf_8_robust	.724
307	densenet-121-keras	.724
308	efficientnet-b7	.723
309	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
310	texture_shape_alexnet_trained_on_SIN	.723
311	resnet_SIN_IN_FT_IN	.722
312	vgg_19	.718
313	vonealexnet_gaussian_noise_std4_fixed	.717
314	mobilenet_v2_1_0_128	.718
315	cv_18_dagger_408_pretrained	.716
316	mobilenet_v2_1.0_224-tf	.714
317	resnet50-VITO-8deg-cc	.714
318	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
319	convnext_large_imagenet_full_seed-0	.712
320	inception_resnet_v2	.708
321	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
322	pnasnet_large_halfPrecision	.696
323	BiT-M-R152x4	.694
324	deepeisnn_ei_cifar10_vgg19	.695
325	effnetv2m_custom384	.690
326	custom_model_cv_18_dagger_408	.685
327	mobilenet_v2_0_5_224	.680
328	deepeisnn_ei_cifar10_resnet18	.675
329	resnet-101_v2-tf	.674
330	compact_resnet50_robust_IT	.674
331	convnext_base_fb_in22k_ft_in1k	.672
332	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.674
333	resnet50-vitoimagevidnet8	.674
334	CORnetZ_CIFAR10_bs32_20_04	.670
335	deit3_base_patch16_224_fb_in1k	.670
336	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.669
337	cvt_cvt-21-384-in1k_4_LucyV4	.666
338	resnet50_l2_3_robust	.667
339	resnet50_robust_l2_eps1	.665
340	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
341	resnet18_imagenet21kP	.664
342	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
343	hmax	.662
344	mobilenet_v2_0.5_128	.660
345	resnet50-cifar	.661
346	convnext_tiny_imagenet_full_seed-0	.661
347	vit_huge_clip336_laion	.657
348	mobilenet_v2_0_75_224	.655
349	resnet50_robust_l2_eps3	.653
350	mobilenet_v2_0_5_192	.650
351	convnext_small_imagenet_100_seed-0	.649
352	CLIP_ViT-B_32	.646
353	cvt_cvt-13-384-in1k_4_LucyV4	.644
354	imagenet_l2_3_0	.643
355	imagenet_l2_10_0	.640
356	vit_large_clip336_openai	.639
357	mobilenet_v2_0.75_224-tf	.626
358	vgg-19-keras	.617
359	mobilenet_v2_0_75_192	.615
360	omnivore_swinB	.609
361	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
362	densenet-201	.604
363	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
364	resnet50_linf_8_robust	.596
365	resnet-50-robust	.596
366	mobilenet_v2_1_0_160	.595
367	densenet-201-keras	.590
368	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
369	omnivore_swinS	.585
370	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
371	mobilenet_v2_0.35_224	.572
372	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
373	FrankRobWobv0	.567
374	convnext_femto_ols:d1_in1k	.557
375	alexnet_random_l2_3_perturb	.552
376	mobilenet_v2_1_0_224	.550
377	convnext_base_imagenet_full_seed-0	.546
378	vit_base_patch16_dinov3-lvd1689m	.545
379	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
380	convnext_large_clip_laion2b_finelayers	.530
381	mobilenet_v2_1_0_192	.523
382	mobilenet_v2_0_75_160	.520
383	convnext_small_imagenet_full_seed-0	.501
384	CORnetZ_CIFAR10	.495
385	efficientnet_b0_imagenet_full	.491
386	convnext_small_imagenet_10_seed-0	.488
387	resnet50_imagenet_1_seed-0	.481
388	mobilevit_small	.477
389	mobilenet_v2_0.75_160-tf	.471
390	resnet-18-LC_untrained	.454
391	mobilenet_v2_1_3_224	.442
392	convnext_large_in22k_supervised	.441
393	alexnet_training_seed_06	.428
394	mobilenet_v2_1.3_224-tf	.399
395	resnet50_pretrained_with_retinal_waves	.397
396	convnext_tiny_sup	.331
397	convnext_small_imagenet_1_seed-0	.293
398	my-model	.226
399	mobilenet_v2_0.5_160	.103
400	deit_small_imagenet_10_seed-0	.000
401	mobilenet_v2_0.35_160
402	resnet18-supervised
403	fitvid_trained_on_physion
404	openclip
405	resnext101_32x32d_wsl
406	resnext101_32x16d_wsl
407	resnext101_32x48d_wsl
408	resnet50_ImageNet
409	regnet_y_400mf
410	tv_efficientnet-b1
411	alexnet_ks_torevert
412	yudixie_resnet50_imagenet1kpret_0_240312
413	resnet_101_v1
414	resnet_152_v1
415	densenet_201_pytorch
416	pnasnet_large_pytorch
417	inception_v3_pytorch
418	eBarlow_lmda_02_1000ep
419	inception_v4
420	nasnet_large
421	deit_small_imagenet_1_seed-0
422	pnasnet_large
423	vonegrcnn_62e_nobn
424	resnet_101_v2
425	vonegrcnn_47e
426	vonegrcnn_52e_full
427	resnet_152_v2
428	pr1_stl10_disentangled_alpha04
429	predictive_coding_pc
430	dinov2_vitg14_reg_lc
431	eva02_large_in22k
432	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