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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_seed3_2_323	.978
6	resnet50_ewconplus_wzc_seed2_2__323	.979
7	resnet50_primary_visual_cortex	.978
8	vonegrcnn_47e	.977
9	resnet50_ewconplus_wzc_seed4_2_323	.976
10	compact_cornets_V4	.975
11	yudixie_resnet50_translation_rotation_0_240908	.970
12	resnet101_ecoset_full	.969
13	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.967
14	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.965
15	ReAlnet01_cornet_fov16	.967
16	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.966
17	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.967
18	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.966
19	0.5x_resnet-18_LC_w_sh_1_iter	.965
20	resnet50_st_wzc_2_323	.965
21	compact_vgg19_IT	.964
22	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.966
23	resnet50_st_wzc_seed4_2_323	.961
24	resnet50_st_wzc_2	.959
25	AlexNet_SIN_fov12	.957
26	yudixie_resnet50_category_class_0_240908	.959
27	resnet152_imagenet_full	.957
28	AlexNet_SIN_fov4	.956
29	Res2Net50_26w_4s	.956
30	resnet50_st_wzc_seed3_2_323	.952
31	alexnet_fov12	.955
32	resnet-18-LC_w_sh_1_iter_m	.953
33	resnet50_ewconplus_wzc_seed7_2_323	.948
34	resnet-18-LC_w_sh_100_iter_m	.948
35	resnet101	.945
36	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.946
37	yudixie_resnet18_object_class_0_240719	.945
38	resnet50_ewconplus_wzc_seed6_2_323	.944
39	resnet-18-LC_w_sh_1_iter_conv_init_m	.942
40	resnet50_ewconplus_noseed_2_323	.941
41	resnet50_FractalDB	.941
42	ReAlnet01_cornet_fov12	.937
43	vonegrcnn_52e_full	.938
44	resnet34_ecoset_full	.939
45	ReAlnet03_cornet	.938
46	AlexNet_SIN	.938
47	efficientnet_b1_imagenet_full	.938
48	ReAlnet09	.936
49	compact_resnet50_robust_V4	.938
50	ReAlnet09_cornet	.939
51	ReAlnet03	.939
52	alexnet	.933
53	ReAlnet10_ALL	.933
54	resnet50_st_wzc_seed6_2_323	.932
55	ReAlnet08_cornet	.932
56	BiT-S-R152x2	.931
57	resnext101_32x8d_wsl	.931
58	ReAlnet08	.931
59	r3m_resnet50_nocrop	.928
60	voneresnet-50-non_stochastic	.929
61	resnet18_imagenet_full	.928
62	vggface	.931
63	ReAlnet10_cornet	.928
64	resnet18_ecoset_full	.926
65	mobilenet_v2_1-4_224_pytorch	.927
66	aughost_fair_resnet18	.926
67	ReAlnet04_cornet	.928
68	ReAlnet10	.925
69	ReAlnet07_cornet	.923
70	ReAlnet04	.925
71	ReAlnet07	.926
72	ReAlnet05	.924
73	artResNet18_1	.926
74	mobilenet_v2_1.0_96	.921
75	yudixie_resnet18_random_0_240719	.924
76	r3m_resnet50	.924
77	ReAlnet05_cornet	.923
78	nasnet_large-tf	.922
79	resnet50-SIN_IN	.922
80	resnet-18-LC_conv_init_m	.922
81	BiT-M-R50x3	.920
82	resnet50_st_wzc_seed7_2_323	.921
83	alexnet_random_linf8_perturb	.920
84	ReAlnet08_ALL	.922
85	ReAlnet06_ALL	.921
86	ReAlnet01	.920
87	ReAlnet06	.919
88	ReAlnet06_cornet	.920
89	inception_v2-tf	.919
90	ReAlnet07_ALL	.921
91	ReAlnet01_cornet	.917
92	ReAlnet05_ALL	.918
93	ReAlnet02_cornet	.918
94	ReAlnet03_ALL	.916
95	ReAlnet04_ALL	.916
96	ReAlnet02_ALL	.918
97	ReAlnet01_ALL	.916
98	resnet152_ecoset_full	.916
99	0.5x_resnet-18	.915
100	yudixie_resnet18_rotation_reg_0_240719	.915
101	ReAlnet02	.915
102	antialiased-rnext101_32x8d	.914
103	convnext_tiny:in12k_ft_in1k	.914
104	ReAlnet09_ALL	.914
105	CORnet-S	.912
106	alexnet_training_seed_01_fov16	.909
107	yudixie_wbh_object_class_scaling_0415_run_1	.911
108	resnet101_imagenet_full	.911
109	cornet_s	.909
110	efficientnet_b2_imagenet_full	.907
111	BiT-M-R152x2	.907
112	nasnet_mobile	.906
113	mobilenet_v2_1.0_128-tf	.903
114	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
115	yudixie_resnet50_rotation_reg_0_240908	.905
116	mobilenet_v2_0.5_224-tf	.900
117	convnext_xlarge:fb_in22k_ft_in1k	.902
118	BiT-M-R101x3	.900
119	mobilenet_v2_0.5_192-tf	.899
120	r3m_resnet18	.902
121	alexnet_training_seed_01	.901
122	resnet-50_untrained	.901
123	texture_shape_resnet50_trained_on_SIN	.900
124	alexnet_training_seed_10	.900
125	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
126	resnet-50_v1-tf	.898
127	0.5x_resnet-18_LC	.899
128	resnet50-SIN	.898
129	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
130	RN50	.898
131	CLIP_resnet50	.894
132	alexnet_robust_correct	.896
133	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
134	CLIP_resnet50_float32	.895
135	resnet50_ewconplus_wzc	.895
136	nasnet_mobile-tf	.895
137	resnet-18-LC_m	.894
138	r3m_resnet34	.894
139	AdvProp_efficientnet-b0	.890
140	yudixie_resnet18_distance_reg_0_240719	.890
141	grcnn_robust_v1	.888
142	voneresnet-50	.887
143	0.5x_resnet-18_LC_w_sh_100_iter	.890
144	antialiased-r50	.887
145	antialias-resnet152	.887
146	resnet50	.885
147	resnet50_st_wzc_seed5_2_323	.886
148	yudixie_resnet50_translation_reg_0_240908	.886
149	grcnn_v2_text_noise	.885
150	deit_small_imagenet_100_seed-0	.882
151	alexnet_training_seed_03	.883
152	BiT-S-R101x3	.882
153	alexnet_training_seed_07	.882
154	resnet50_ecoset_full	.880
155	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
156	alexnet_training_seed_09	.881
157	compact_cornets_IT	.877
158	yudixie_wbh_object_class_scaling_0415_run_3	.876
159	resnet50_random_linf8_perturb	.877
160	BiT-S-R152x4	.876
161	voneresnet-50-robust	.876
162	mobilenet_v2_1.0_160-tf	.874
163	resnet-152_v2-tf	.874
164	deepeisnn_ei_imagenet_resnet18	.876
165	vit_base_patch14_dinov2-lvd142m	.875
166	densenet-169-keras	.874
167	resnet34_imagenet_full	.875
168	SWSL_resnext101_32x8d	.873
169	VOneCORnet-S	.870
170	resnet50_ewconplus_wzc_2_323	.873
171	resnet50-vicregl0p9	.871
172	resnet50_byol	.870
173	mobilenet_v2_1.0_192-tf	.868
174	yudixie_resnet18_category_class_0_240719	.868
175	vgg_16	.868
176	yudixie_resnet18_distance_rotation_0_240719	.867
177	focalnet_tiny_lrf_in1k	.864
178	grcnn_109	.864
179	convnext_large:fb_in22k_ft_in1k	.863
180	xception	.861
181	resnet50_st_wzc_noseed_2_323	.861
182	vgg-16-keras	.859
183	convnext_base_fb_in1k	.860
184	yudixie_resnet18_translation_rotation_0_240719	.859
185	yudixie_wbh_object_class_scaling_0415_run_0	.858
186	resnet50	.858
187	resnet_50_v1	.856
188	resnet50_tutorial	.857
189	compact_vgg19_V4	.855
190	yudixie_resnet50_imagenet1kpret_0_240908	.854
191	compact_resnet50_IT	.855
192	inception_v3-tf	.855
193	resnet_50_v1_spiking_l4	.855
194	resnet_50_v1_spiking	.853
195	resnet-18_untrained	.854
196	resnet50-sup	.852
197	yudixie_resnet50_distance_translation_0_240908	.855
198	resnet_50_v1_spiking_l3	.851
199	resnet_50_v1_spiking_l4a	.851
200	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
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	vit_base_patch14_reg4_dinov2-lvd142m	.842
211	AdvProp_efficientnet-b8	.842
212	alexnet_reduced_aliasing_early_checkpoint	.842
213	resnet50-vicregl0p75	.840
214	resnet50_imagenet_full	.841
215	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
216	BiT-S-R50x1	.839
217	yudixie_resnet50_object_class_0_240908	.840
218	deit3_base_patch16_224_fb_in22k_ft_in1k	.839
219	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
220	resnet50-SIN_IN_IN	.838
221	yudixie_resnet18_imagenet1kpret_0_240719	.836
222	yudixie_wbh_object_class_scaling_0415_imagenet	.839
223	AdvProp_efficientnet-b6	.836
224	0.5x_resnet-18_LC_w_sh_10_iter	.836
225	resnet-152_v1-tf	.834
226	resnet50_simclr	.835
227	deit_large_imagenet_full_seed-0	.834
228	BiT-M-R50x1	.833
229	resnet50_moco_v2	.834
230	SWSL_resnet50	.830
231	yudixie_resnet50_distance_rotation_0_240908	.831
232	resnet-152_v2_pytorch	.830
233	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
234	cvt_cvt-13-224-in1k_4_LucyV4	.828
235	CLIP-RN50	.828
236	barlow-twins-resnet50	.828
237	densenet-169	.827
238	inception_v3	.825
239	mobilenet_v1_0.25_128	.824
240	compact_resnet50_V4	.822
241	alexnet_l2_3_robust	.820
242	resnet-18-LC_1st_conv_m	.818
243	alexnet_fov4	.817
244	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
245	konkle_alexnetgn_ipcl_ref01_primary_model	.813
246	yudixie_resnet18_distance_translation_rotation_0_240719	.814
247	alexnet_training_seed_04	.814
248	deit_base_imagenet_full_seed-0	.813
249	ViT_L_32_imagenet1k	.815
250	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
251	BiT-M-R101x1	.809
252	yolos_tiny	.810
253	AdvProp_efficientnet-b7	.809
254	resnet50-simclr-vissl	.805
255	grcnn	.804
256	yudixie_resnet50_random_0_240908	.803
257	yudixie_resnet18_distance_translation_0_240719	.803
258	resnet18_fair	.802
259	resnet-101_v1-tf	.795
260	alexnet_training_seed_05	.794
261	mobilenet_v2_0.75_96	.793
262	CORnet-Z	.795
263	resnet-50x2_untrained	.794
264	alexnet_training_seed_01_fov4	.794
265	resnet-18-LC_w_sh_10_iter_m	.793
266	yudixie_resnet50_distance_reg_0_240908	.792
267	vit_base_patch16_clip_224:openai_ft_in1k	.790
268	alexnet_training_seed_01_fov12	.789
269	alexnet_training_seed_08	.787
270	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
271	efficientnet_b0	.787
272	resnet50-moclr8deg	.785
273	inception_v1-tf	.785
274	resnet-18_test_m	.784
275	densenet-121	.783
276	resnet50-vicreg	.783
277	cvt_cvt-21-224-in1k_4_LucyV4	.782
278	AT_efficientnet-b2	.782
279	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
280	blt_vs	.780
281	AdvProp_efficientnet-b2	.779
282	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
283	mobilenet_v2_0.75_192-tf	.776
284	inception_v4-tf	.777
285	mobilenet_v2_1.4_224-tf	.773
286	vit_large_patch14_clip_224:openai_ft_in1k	.773
287	resnet50_imagenet_100_seed-0	.773
288	pnasnet_large-tf	.767
289	ViT-B/32	.764
290	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
291	mobilenet_v2_1_4_224	.765
292	resnet-50_v2-tf	.763
293	vision_transformer_vit_large_patch16_224	.762
294	resnet50-barlow	.755
295	ReAlnet01_cornet_fov4	.752
296	resnet50_imagenet_10_seed-0	.749
297	vonegrcnn_62e_nobn	.749
298	omnivore_swinT	.747
299	mobilenet_v2_0.75_128	.742
300	my-model	.743
301	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
302	AlexNet_SIN_fov16	.741
303	briaai_rmbg_1_4	.738
304	yudixie_resnet18_translation_reg_0_240719	.739
305	v1-pyr-nodown	.735
306	deit_small_imagenet_full_seed-0	.736
307	AdvProp_efficientnet-b4	.728
308	convnext_base_fb_in1k_gram_aligned_dinov3	.729
309	shufflenet_v2_x1_0	.729
310	resnet50_linf_4_robust	.727
311	alexnet_linf_8_robust	.724
312	densenet-121-keras	.724
313	efficientnet-b7	.723
314	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
315	texture_shape_alexnet_trained_on_SIN	.723
316	resnet_SIN_IN_FT_IN	.722
317	vgg_19	.718
318	vonealexnet_gaussian_noise_std4_fixed	.717
319	mobilenet_v2_1_0_128	.718
320	cv_18_dagger_408_pretrained	.716
321	mobilenet_v2_1.0_224-tf	.714
322	resnet50-VITO-8deg-cc	.714
323	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
324	convnext_large_imagenet_full_seed-0	.712
325	inception_resnet_v2	.708
326	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
327	pnasnet_large_halfPrecision	.696
328	BiT-M-R152x4	.694
329	deepeisnn_ei_cifar10_vgg19	.695
330	effnetv2m_custom384	.690
331	custom_model_cv_18_dagger_408	.685
332	mobilenet_v2_0_5_224	.680
333	deepeisnn_ei_cifar10_resnet18	.675
334	resnet-101_v2-tf	.674
335	compact_resnet50_robust_IT	.674
336	convnext_base_fb_in22k_ft_in1k	.672
337	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.674
338	resnet50-vitoimagevidnet8	.674
339	CORnetZ_CIFAR10_bs32_20_04	.670
340	deit3_base_patch16_224_fb_in1k	.670
341	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.669
342	cvt_cvt-21-384-in1k_4_LucyV4	.666
343	resnet50_l2_3_robust	.667
344	resnet50_robust_l2_eps1	.665
345	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
346	resnet18_imagenet21kP	.664
347	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
348	hmax	.662
349	mobilenet_v2_0.5_128	.660
350	resnet50-cifar	.661
351	convnext_tiny_imagenet_full_seed-0	.661
352	mobilenet_v2_0_75_224	.655
353	resnet50_robust_l2_eps3	.653
354	mobilenet_v2_0_5_192	.650
355	convnext_small_imagenet_100_seed-0	.649
356	CLIP_ViT-B_32	.646
357	cvt_cvt-13-384-in1k_4_LucyV4	.644
358	imagenet_l2_3_0	.643
359	imagenet_l2_10_0	.640
360	mobilenet_v2_0.75_224-tf	.626
361	vgg-19-keras	.617
362	mobilenet_v2_0_75_192	.615
363	omnivore_swinB	.609
364	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
365	densenet-201	.604
366	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
367	resnet50_linf_8_robust	.596
368	resnet-50-robust	.596
369	mobilenet_v2_1_0_160	.595
370	densenet-201-keras	.590
371	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
372	omnivore_swinS	.585
373	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
374	mobilenet_v2_0.35_224	.572
375	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
376	FrankRobWobv0	.567
377	convnext_femto_ols:d1_in1k	.557
378	alexnet_random_l2_3_perturb	.552
379	mobilenet_v2_1_0_224	.550
380	convnext_base_imagenet_full_seed-0	.546
381	vit_base_patch16_dinov3-lvd1689m	.545
382	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
383	mobilenet_v2_1_0_192	.523
384	mobilenet_v2_0_75_160	.520
385	convnext_small_imagenet_full_seed-0	.501
386	CORnetZ_CIFAR10	.495
387	efficientnet_b0_imagenet_full	.491
388	convnext_small_imagenet_10_seed-0	.488
389	resnet50_imagenet_1_seed-0	.481
390	mobilevit_small	.477
391	mobilenet_v2_0.75_160-tf	.471
392	resnet50-meshes-lt-100-original-pretrained	.456
393	resnet-18-LC_untrained	.454
394	mobilenet_v2_1_3_224	.442
395	alexnet_training_seed_06	.428
396	mobilenet_v2_1.3_224-tf	.399
397	resnet50_pretrained_with_retinal_waves	.397
398	resnet50-meshes-lt-100-original-scratch	.338
399	convnext_tiny_sup	.331
400	convnext_small_imagenet_1_seed-0	.293
401	my-model	.226
402	mobilenet_v2_0.5_160	.103
403	deit_small_imagenet_10_seed-0	.000
404	mobilenet_v2_0.35_160
405	resnet18-supervised
406	fitvid_trained_on_physion
407	openclip
408	resnext101_32x32d_wsl
409	resnext101_32x16d_wsl
410	resnext101_32x48d_wsl
411	resnet50_ImageNet
412	regnet_y_400mf
413	tv_efficientnet-b1
414	alexnet_ks_torevert
415	yudixie_resnet50_imagenet1kpret_0_240312
416	resnet_101_v1
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	resnet_101_v2
428	vonegrcnn_47e
429	vonegrcnn_52e_full
430	resnet_152_v2
431	pr1_stl10_disentangled_alpha04
432	predictive_coding_pc

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