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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	aughost_fair_resnet18	.926
62	ReAlnet04_cornet	.928
63	ReAlnet10	.925
64	ReAlnet07_cornet	.923
65	ReAlnet04	.925
66	ReAlnet07	.926
67	ReAlnet05	.924
68	artResNet18_1	.926
69	mobilenet_v2_1.0_96	.921
70	yudixie_resnet18_random_0_240719	.924
71	r3m_resnet50	.924
72	ReAlnet05_cornet	.923
73	nasnet_large-tf	.922
74	resnet50-SIN_IN	.922
75	resnet-18-LC_conv_init_m	.922
76	BiT-M-R50x3	.920
77	resnet50_st_wzc_seed7_2_323	.921
78	alexnet_random_linf8_perturb	.920
79	ReAlnet08_ALL	.922
80	ReAlnet06_ALL	.921
81	ReAlnet01	.920
82	ReAlnet06	.919
83	ReAlnet06_cornet	.920
84	inception_v2-tf	.919
85	ReAlnet07_ALL	.921
86	ReAlnet01_cornet	.917
87	ReAlnet05_ALL	.918
88	ReAlnet02_cornet	.918
89	ReAlnet03_ALL	.916
90	ReAlnet04_ALL	.916
91	ReAlnet02_ALL	.918
92	ReAlnet01_ALL	.916
93	resnet152_ecoset_full	.916
94	0.5x_resnet-18	.915
95	yudixie_resnet18_rotation_reg_0_240719	.915
96	ReAlnet02	.915
97	antialiased-rnext101_32x8d	.914
98	convnext_tiny:in12k_ft_in1k	.914
99	ReAlnet09_ALL	.914
100	CORnet-S	.912
101	alexnet_training_seed_01_fov16	.909
102	yudixie_wbh_object_class_scaling_0415_run_1	.911
103	resnet101_imagenet_full	.911
104	cornet_s	.909
105	efficientnet_b2_imagenet_full	.907
106	BiT-M-R152x2	.907
107	nasnet_mobile	.906
108	mobilenet_v2_1.0_128-tf	.903
109	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
110	dinov2_vitl14_lc	.904
111	yudixie_resnet50_rotation_reg_0_240908	.905
112	mobilenet_v2_0.5_224-tf	.900
113	convnext_xlarge:fb_in22k_ft_in1k	.902
114	BiT-M-R101x3	.900
115	mobilenet_v2_0.5_192-tf	.899
116	r3m_resnet18	.902
117	alexnet_training_seed_01	.901
118	resnet-50_untrained	.901
119	texture_shape_resnet50_trained_on_SIN	.900
120	alexnet_training_seed_10	.900
121	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
122	resnet-50_v1-tf	.898
123	0.5x_resnet-18_LC	.899
124	resnet50-SIN	.898
125	dinov2_vitg14_lc	.897
126	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
127	RN50	.898
128	CLIP_resnet50	.894
129	alexnet_robust_correct	.896
130	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
131	CLIP_resnet50_float32	.895
132	resnet50_ewconplus_wzc	.895
133	nasnet_mobile-tf	.895
134	resnet-18-LC_m	.894
135	r3m_resnet34	.894
136	AdvProp_efficientnet-b0	.890
137	yudixie_resnet18_distance_reg_0_240719	.890
138	grcnn_robust_v1	.888
139	voneresnet-50	.887
140	0.5x_resnet-18_LC_w_sh_100_iter	.890
141	antialiased-r50	.887
142	antialias-resnet152	.887
143	yudixie_resnet50_translation_reg_0_240908	.886
144	grcnn_v2_text_noise	.885
145	deit_small_imagenet_100_seed-0	.882
146	alexnet_training_seed_03	.883
147	BiT-S-R101x3	.882
148	alexnet_training_seed_07	.882
149	resnet50_ecoset_full	.880
150	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
151	alexnet_training_seed_09	.881
152	compact_cornets_IT	.877
153	yudixie_wbh_object_class_scaling_0415_run_3	.876
154	resnet50_random_linf8_perturb	.877
155	BiT-S-R152x4	.876
156	voneresnet-50-robust	.876
157	mobilenet_v2_1.0_160-tf	.874
158	resnet-152_v2-tf	.874
159	deepeisnn_ei_imagenet_resnet18	.876
160	vit_base_patch14_dinov2-lvd142m	.875
161	densenet-169-keras	.874
162	resnet34_imagenet_full	.875
163	SWSL_resnext101_32x8d	.873
164	VOneCORnet-S	.870
165	resnet50-vicregl0p9	.871
166	resnet50_byol	.870
167	mobilenet_v2_1.0_192-tf	.868
168	yudixie_resnet18_category_class_0_240719	.868
169	vgg_16	.868
170	yudixie_resnet18_distance_rotation_0_240719	.867
171	focalnet_tiny_lrf_in1k	.864
172	grcnn_109	.864
173	convnext_large:fb_in22k_ft_in1k	.863
174	xception	.861
175	vgg-16-keras	.859
176	convnext_base_fb_in1k	.860
177	yudixie_resnet18_translation_rotation_0_240719	.859
178	yudixie_wbh_object_class_scaling_0415_run_0	.858
179	resnet50	.858
180	resnet_50_v1	.856
181	resnet50_tutorial	.857
182	compact_vgg19_V4	.855
183	yudixie_resnet50_imagenet1kpret_0_240908	.854
184	compact_resnet50_IT	.855
185	inception_v3-tf	.855
186	resnet_50_v1_spiking_l4	.855
187	resnet_50_v1_spiking	.853
188	resnet-18_untrained	.854
189	resnet50-sup	.852
190	yudixie_resnet50_distance_translation_0_240908	.855
191	resnet_50_v1_spiking_l3	.851
192	resnet_50_v1_spiking_l4a	.851
193	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
194	dinov2_vitl14_reg_lc	.850
195	dinov2_vitl14_lc_res336	.851
196	dinov2_vitl14_reg_lc_featread	.849
197	resnet50_random_l2_perturb	.849
198	BiT-S-R101x1	.847
199	resnet_50_v2	.846
200	BiT-S-R50x3	.846
201	alexnet_training_seed_02	.847
202	effnetb1_272x240	.847
203	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
204	inception_v1	.845
205	yudixie_resnet50_distance_translation_rotation_0_240908	.845
206	vit_base_patch14_reg4_dinov2-lvd142m	.842
207	AdvProp_efficientnet-b8	.842
208	alexnet_reduced_aliasing_early_checkpoint	.842
209	resnet50-vicregl0p75	.840
210	resnet50_imagenet_full	.841
211	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
212	BiT-S-R50x1	.839
213	yudixie_resnet50_object_class_0_240908	.840
214	deit3_base_patch16_224_fb_in22k_ft_in1k	.839
215	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
216	resnet50-SIN_IN_IN	.838
217	yudixie_resnet18_imagenet1kpret_0_240719	.836
218	yudixie_wbh_object_class_scaling_0415_imagenet	.839
219	AdvProp_efficientnet-b6	.836
220	0.5x_resnet-18_LC_w_sh_10_iter	.836
221	resnet-152_v1-tf	.834
222	resnet50_simclr	.835
223	deit_large_imagenet_full_seed-0	.834
224	BiT-M-R50x1	.833
225	resnet50_moco_v2	.834
226	SWSL_resnet50	.830
227	yudixie_resnet50_distance_rotation_0_240908	.831
228	resnet-152_v2_pytorch	.830
229	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
230	cvt_cvt-13-224-in1k_4_LucyV4	.828
231	CLIP-RN50	.828
232	barlow-twins-resnet50	.828
233	densenet-169	.827
234	inception_v3	.825
235	mobilenet_v1_0.25_128	.824
236	compact_resnet50_V4	.822
237	alexnet_l2_3_robust	.820
238	resnet-18-LC_1st_conv_m	.818
239	alexnet_fov4	.817
240	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
241	konkle_alexnetgn_ipcl_ref01_primary_model	.813
242	yudixie_resnet18_distance_translation_rotation_0_240719	.814
243	alexnet_training_seed_04	.814
244	deit_base_imagenet_full_seed-0	.813
245	ViT_L_32_imagenet1k	.815
246	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
247	BiT-M-R101x1	.809
248	yolos_tiny	.810
249	AdvProp_efficientnet-b7	.809
250	resnet50-simclr-vissl	.805
251	grcnn	.804
252	yudixie_resnet50_random_0_240908	.803
253	yudixie_resnet18_distance_translation_0_240719	.803
254	resnet18_fair	.802
255	resnet-101_v1-tf	.795
256	alexnet_training_seed_05	.794
257	mobilenet_v2_0.75_96	.793
258	CORnet-Z	.795
259	resnet-50x2_untrained	.794
260	alexnet_training_seed_01_fov4	.794
261	resnet-18-LC_w_sh_10_iter_m	.793
262	yudixie_resnet50_distance_reg_0_240908	.792
263	vit_base_patch16_clip_224:openai_ft_in1k	.790
264	alexnet_training_seed_01_fov12	.789
265	alexnet_training_seed_08	.787
266	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
267	efficientnet_b0	.787
268	resnet50-moclr8deg	.785
269	inception_v1-tf	.785
270	resnet-18_test_m	.784
271	densenet-121	.783
272	resnet50-vicreg	.783
273	cvt_cvt-21-224-in1k_4_LucyV4	.782
274	AT_efficientnet-b2	.782
275	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
276	blt_vs	.780
277	AdvProp_efficientnet-b2	.779
278	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
279	mobilenet_v2_0.75_192-tf	.776
280	inception_v4-tf	.777
281	mobilenet_v2_1.4_224-tf	.773
282	vit_large_patch14_clip_224:openai_ft_in1k	.773
283	resnet50_imagenet_100_seed-0	.773
284	pnasnet_large-tf	.767
285	ViT-B/32	.764
286	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
287	mobilenet_v2_1_4_224	.765
288	resnet-50_v2-tf	.763
289	vision_transformer_vit_large_patch16_224	.762
290	resnet50-barlow	.755
291	ReAlnet01_cornet_fov4	.752
292	resnet50_imagenet_10_seed-0	.749
293	vonegrcnn_62e_nobn	.749
294	omnivore_swinT	.747
295	mobilenet_v2_0.75_128	.742
296	my-model	.743
297	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
298	AlexNet_SIN_fov16	.741
299	briaai_rmbg_1_4	.738
300	yudixie_resnet18_translation_reg_0_240719	.739
301	v1-pyr-nodown	.735
302	deit_small_imagenet_full_seed-0	.736
303	AdvProp_efficientnet-b4	.728
304	convnext_base_fb_in1k_gram_aligned_dinov3	.729
305	shufflenet_v2_x1_0	.729
306	resnet50_linf_4_robust	.727
307	alexnet_linf_8_robust	.724
308	densenet-121-keras	.724
309	efficientnet-b7	.723
310	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
311	texture_shape_alexnet_trained_on_SIN	.723
312	resnet_SIN_IN_FT_IN	.722
313	vgg_19	.718
314	vonealexnet_gaussian_noise_std4_fixed	.717
315	mobilenet_v2_1_0_128	.718
316	cv_18_dagger_408_pretrained	.716
317	mobilenet_v2_1.0_224-tf	.714
318	resnet50-VITO-8deg-cc	.714
319	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
320	convnext_large_imagenet_full_seed-0	.712
321	inception_resnet_v2	.708
322	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
323	pnasnet_large_halfPrecision	.696
324	BiT-M-R152x4	.694
325	deepeisnn_ei_cifar10_vgg19	.695
326	effnetv2m_custom384	.690
327	custom_model_cv_18_dagger_408	.685
328	mobilenet_v2_0_5_224	.680
329	deepeisnn_ei_cifar10_resnet18	.675
330	resnet-101_v2-tf	.674
331	compact_resnet50_robust_IT	.674
332	convnext_base_fb_in22k_ft_in1k	.672
333	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.674
334	resnet50-vitoimagevidnet8	.674
335	CORnetZ_CIFAR10_bs32_20_04	.670
336	deit3_base_patch16_224_fb_in1k	.670
337	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.669
338	cvt_cvt-21-384-in1k_4_LucyV4	.666
339	resnet50_l2_3_robust	.667
340	resnet50_robust_l2_eps1	.665
341	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
342	resnet18_imagenet21kP	.664
343	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
344	hmax	.662
345	mobilenet_v2_0.5_128	.660
346	resnet50-cifar	.661
347	convnext_tiny_imagenet_full_seed-0	.661
348	vit_huge_clip336_laion	.657
349	mobilenet_v2_0_75_224	.655
350	resnet50_robust_l2_eps3	.653
351	mobilenet_v2_0_5_192	.650
352	convnext_small_imagenet_100_seed-0	.649
353	CLIP_ViT-B_32	.646
354	cvt_cvt-13-384-in1k_4_LucyV4	.644
355	imagenet_l2_3_0	.643
356	imagenet_l2_10_0	.640
357	vit_large_clip336_openai	.639
358	mobilenet_v2_0.75_224-tf	.626
359	vgg-19-keras	.617
360	mobilenet_v2_0_75_192	.615
361	omnivore_swinB	.609
362	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
363	densenet-201	.604
364	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
365	resnet50_linf_8_robust	.596
366	resnet-50-robust	.596
367	mobilenet_v2_1_0_160	.595
368	densenet-201-keras	.590
369	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
370	omnivore_swinS	.585
371	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
372	mobilenet_v2_0.35_224	.572
373	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
374	FrankRobWobv0	.567
375	convnext_femto_ols:d1_in1k	.557
376	alexnet_random_l2_3_perturb	.552
377	mobilenet_v2_1_0_224	.550
378	convnext_base_imagenet_full_seed-0	.546
379	vit_base_patch16_dinov3-lvd1689m	.545
380	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
381	convnext_large_clip_laion2b_finelayers	.530
382	mobilenet_v2_1_0_192	.523
383	mobilenet_v2_0_75_160	.520
384	convnext_small_imagenet_full_seed-0	.501
385	CORnetZ_CIFAR10	.495
386	efficientnet_b0_imagenet_full	.491
387	convnext_small_imagenet_10_seed-0	.488
388	resnet50_imagenet_1_seed-0	.481
389	mobilevit_small	.477
390	mobilenet_v2_0.75_160-tf	.471
391	resnet-18-LC_untrained	.454
392	mobilenet_v2_1_3_224	.442
393	convnext_large_in22k_supervised	.441
394	alexnet_training_seed_06	.428
395	mobilenet_v2_1.3_224-tf	.399
396	resnet50_pretrained_with_retinal_waves	.397
397	convnext_tiny_sup	.331
398	convnext_small_imagenet_1_seed-0	.293
399	my-model	.226
400	mobilenet_v2_0.5_160	.103
401	deit_small_imagenet_10_seed-0	.000
402	mobilenet_v2_0.35_160
403	resnet18-supervised
404	fitvid_trained_on_physion
405	openclip
406	resnext101_32x32d_wsl
407	resnext101_32x16d_wsl
408	resnext101_32x48d_wsl
409	resnet50_ImageNet
410	regnet_y_400mf
411	tv_efficientnet-b1
412	alexnet_ks_torevert
413	yudixie_resnet50_imagenet1kpret_0_240312
414	resnet_101_v1
415	resnet_152_v1
416	densenet_201_pytorch
417	pnasnet_large_pytorch
418	inception_v3_pytorch
419	eBarlow_lmda_02_1000ep
420	inception_v4
421	nasnet_large
422	deit_small_imagenet_1_seed-0
423	pnasnet_large
424	vonegrcnn_62e_nobn
425	resnet_101_v2
426	vonegrcnn_47e
427	vonegrcnn_52e_full
428	resnet_152_v2
429	pr1_stl10_disentangled_alpha04
430	predictive_coding_pc
431	dinov2_vitg14_reg_lc
432	eva02_large_in22k
433	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