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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	alexnet_early_checkpoint	.985
2	alexnet_fov16	.979
3	resnet50_primary_visual_cortex	.978
4	vonegrcnn_47e	.977
5	compact_cornets_V4	.975
6	yudixie_resnet50_translation_rotation_0_240908	.970
7	resnet101_ecoset_full	.969
8	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.967
9	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.965
10	ReAlnet01_cornet_fov16	.967
11	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.966
12	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.967
13	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.966
14	0.5x_resnet-18_LC_w_sh_1_iter	.965
15	compact_vgg19_IT	.964
16	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.966
17	AlexNet_SIN_fov12	.957
18	yudixie_resnet50_category_class_0_240908	.959
19	resnet152_imagenet_full	.957
20	AlexNet_SIN_fov4	.956
21	Res2Net50_26w_4s	.956
22	alexnet_fov12	.955
23	resnet-18-LC_w_sh_1_iter_m	.953
24	resnet-18-LC_w_sh_100_iter_m	.948
25	resnet101	.945
26	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.946
27	yudixie_resnet18_object_class_0_240719	.945
28	resnet-18-LC_w_sh_1_iter_conv_init_m	.942
29	resnet50_FractalDB	.941
30	ReAlnet01_cornet_fov12	.937
31	vonegrcnn_52e_full	.938
32	resnet34_ecoset_full	.939
33	ReAlnet03_cornet	.938
34	AlexNet_SIN	.938
35	efficientnet_b1_imagenet_full	.938
36	ReAlnet09	.936
37	compact_resnet50_robust_V4	.938
38	ReAlnet09_cornet	.939
39	ReAlnet03	.939
40	alexnet	.938
41	alexnet_workflow_trigger	.936
42	alexnet	.934
43	alexnet	.933
44	alexnet	.934
45	alexnet	.934
46	alexnet	.933
47	alexnet-baseline	.932
48	ReAlnet10_ALL	.933
49	alexnet	.934
50	ReAlnet08_cornet	.932
51	alexnet	.930
52	BiT-S-R152x2	.931
53	resnext101_32x8d_wsl	.931
54	ReAlnet08	.931
55	r3m_resnet50_nocrop	.928
56	voneresnet-50-non_stochastic	.929
57	resnet18_imagenet_full	.928
58	vggface	.931
59	ReAlnet10_cornet	.928
60	resnet18_ecoset_full	.926
61	mobilenet_v2_1-4_224_pytorch	.927
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	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	resnet101_imagenet_full	.911
102	cornet_s	.909
103	efficientnet_b2_imagenet_full	.907
104	BiT-M-R152x2	.907
105	nasnet_mobile	.906
106	mobilenet_v2_1.0_128-tf	.903
107	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
108	yudixie_resnet50_rotation_reg_0_240908	.905
109	mobilenet_v2_0.5_224-tf	.900
110	convnext_xlarge:fb_in22k_ft_in1k	.902
111	BiT-M-R101x3	.900
112	mobilenet_v2_0.5_192-tf	.899
113	r3m_resnet18	.902
114	alexnet_training_seed_01	.901
115	resnet-50_untrained	.901
116	texture_shape_resnet50_trained_on_SIN	.900
117	alexnet_training_seed_10	.900
118	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
119	resnet-50_v1-tf	.898
120	0.5x_resnet-18_LC	.899
121	resnet50-SIN	.898
122	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
123	RN50	.898
124	CLIP_resnet50	.894
125	alexnet_robust_correct	.896
126	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
127	CLIP_resnet50_float32	.895
128	resnet50_ewconplus_wzc	.895
129	nasnet_mobile-tf	.895
130	resnet-18-LC_m	.894
131	r3m_resnet34	.894
132	AdvProp_efficientnet-b0	.890
133	yudixie_resnet18_distance_reg_0_240719	.890
134	grcnn_robust_v1	.888
135	voneresnet-50	.887
136	0.5x_resnet-18_LC_w_sh_100_iter	.890
137	antialiased-r50	.887
138	antialias-resnet152	.887
139	resnet50	.885
140	yudixie_resnet50_translation_reg_0_240908	.886
141	grcnn_v2_text_noise	.885
142	deit_small_imagenet_100_seed-0	.882
143	alexnet_training_seed_03	.883
144	BiT-S-R101x3	.882
145	alexnet_training_seed_07	.882
146	resnet50_ecoset_full	.880
147	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
148	alexnet_training_seed_09	.881
149	compact_cornets_IT	.877
150	resnet50_random_linf8_perturb	.877
151	BiT-S-R152x4	.876
152	voneresnet-50-robust	.876
153	mobilenet_v2_1.0_160-tf	.874
154	resnet-152_v2-tf	.874
155	densenet-169-keras	.874
156	resnet34_imagenet_full	.875
157	vit_base_patch14_dinov2:lvd142m	.872
158	SWSL_resnext101_32x8d	.873
159	VOneCORnet-S	.870
160	resnet50-vicregl0p9	.871
161	resnet50_byol	.870
162	mobilenet_v2_1.0_192-tf	.868
163	yudixie_resnet18_category_class_0_240719	.868
164	vgg_16	.868
165	yudixie_resnet18_distance_rotation_0_240719	.867
166	focalnet_tiny_lrf_in1k	.864
167	grcnn_109	.864
168	convnext_large:fb_in22k_ft_in1k	.863
169	xception	.861
170	vgg-16-keras	.859
171	yudixie_resnet18_translation_rotation_0_240719	.859
172	resnet50	.858
173	resnet_50_v1	.856
174	resnet50_tutorial	.857
175	compact_vgg19_V4	.855
176	yudixie_resnet50_imagenet1kpret_0_240908	.854
177	compact_resnet50_IT	.855
178	inception_v3-tf	.855
179	resnet_50_v1_spiking_l4	.855
180	resnet_50_v1_spiking	.853
181	resnet-18_untrained	.854
182	resnet50-sup	.852
183	yudixie_resnet50_distance_translation_0_240908	.855
184	resnet_50_v1_spiking_l3	.851
185	resnet_50_v1_spiking_l4a	.851
186	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
187	resnet50_random_l2_perturb	.849
188	BiT-S-R101x1	.847
189	resnet_50_v2	.846
190	BiT-S-R50x3	.846
191	alexnet_training_seed_02	.847
192	effnetb1_272x240	.847
193	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
194	inception_v1	.845
195	yudixie_resnet50_distance_translation_rotation_0_240908	.845
196	AdvProp_efficientnet-b8	.842
197	alexnet_reduced_aliasing_early_checkpoint	.842
198	resnet50-vicregl0p75	.840
199	resnet50_imagenet_full	.841
200	vit_base_patch14_reg4_dinov2:lvd142m	.840
201	BiT-S-R50x1	.839
202	yudixie_resnet50_object_class_0_240908	.840
203	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
204	resnet50-SIN_IN_IN	.838
205	yudixie_resnet18_imagenet1kpret_0_240719	.836
206	AdvProp_efficientnet-b6	.836
207	0.5x_resnet-18_LC_w_sh_10_iter	.836
208	resnet-152_v1-tf	.834
209	resnet50_simclr	.835
210	deit_large_imagenet_full_seed-0	.834
211	BiT-M-R50x1	.833
212	resnet50_moco_v2	.834
213	SWSL_resnet50	.830
214	yudixie_resnet50_distance_rotation_0_240908	.831
215	resnet-152_v2_pytorch	.830
216	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
217	cvt_cvt-13-224-in1k_4_LucyV4	.828
218	CLIP-RN50	.828
219	barlow-twins-resnet50	.828
220	densenet-169	.827
221	inception_v3	.825
222	mobilenet_v1_0.25_128	.824
223	compact_resnet50_V4	.822
224	alexnet_l2_3_robust	.820
225	resnet-18-LC_1st_conv_m	.818
226	alexnet_fov4	.817
227	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
228	konkle_alexnetgn_ipcl_ref01_primary_model	.813
229	yudixie_resnet18_distance_translation_rotation_0_240719	.814
230	alexnet_training_seed_04	.814
231	deit_base_imagenet_full_seed-0	.813
232	ViT_L_32_imagenet1k	.815
233	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
234	BiT-M-R101x1	.809
235	yolos_tiny	.810
236	AdvProp_efficientnet-b7	.809
237	resnet50-simclr-vissl	.805
238	grcnn	.804
239	yudixie_resnet50_random_0_240908	.803
240	yudixie_resnet18_distance_translation_0_240719	.803
241	resnet-101_v1-tf	.795
242	alexnet_training_seed_05	.794
243	mobilenet_v2_0.75_96	.793
244	CORnet-Z	.795
245	resnet-50x2_untrained	.794
246	alexnet_training_seed_01_fov4	.794
247	resnet-18-LC_w_sh_10_iter_m	.793
248	yudixie_resnet50_distance_reg_0_240908	.792
249	vit_base_patch16_clip_224:openai_ft_in1k	.790
250	alexnet_training_seed_01_fov12	.789
251	alexnet_training_seed_08	.787
252	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
253	efficientnet_b0	.787
254	resnet50-moclr8deg	.785
255	inception_v1-tf	.785
256	resnet-18_test_m	.784
257	densenet-121	.783
258	resnet50-vicreg	.783
259	cvt_cvt-21-224-in1k_4_LucyV4	.782
260	AT_efficientnet-b2	.782
261	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
262	blt_vs	.780
263	AdvProp_efficientnet-b2	.779
264	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
265	mobilenet_v2_0.75_192-tf	.776
266	inception_v4-tf	.777
267	mobilenet_v2_1.4_224-tf	.773
268	vit_large_patch14_clip_224:openai_ft_in1k	.773
269	resnet50_imagenet_100_seed-0	.773
270	pnasnet_large-tf	.767
271	ViT-B/32	.764
272	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
273	mobilenet_v2_1_4_224	.765
274	resnet-50_v2-tf	.763
275	vision_transformer_vit_large_patch16_224	.762
276	resnet50-barlow	.755
277	ReAlnet01_cornet_fov4	.752
278	resnet50_imagenet_10_seed-0	.749
279	vonegrcnn_62e_nobn	.749
280	omnivore_swinT	.747
281	mobilenet_v2_0.75_128	.742
282	my-model	.743
283	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
284	AlexNet_SIN_fov16	.741
285	briaai_rmbg_1_4	.738
286	yudixie_resnet18_translation_reg_0_240719	.739
287	v1-pyr-nodown	.735
288	deit_small_imagenet_full_seed-0	.736
289	AdvProp_efficientnet-b4	.728
290	shufflenet_v2_x1_0	.729
291	resnet50_linf_4_robust	.727
292	alexnet_linf_8_robust	.724
293	densenet-121-keras	.724
294	efficientnet-b7	.723
295	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
296	texture_shape_alexnet_trained_on_SIN	.723
297	resnet_SIN_IN_FT_IN	.722
298	vgg_19	.718
299	vonealexnet_gaussian_noise_std4_fixed	.717
300	mobilenet_v2_1_0_128	.718
301	cv_18_dagger_408_pretrained	.716
302	mobilenet_v2_1.0_224-tf	.714
303	resnet50-VITO-8deg-cc	.714
304	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
305	convnext_large_imagenet_full_seed-0	.712
306	inception_resnet_v2	.708
307	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
308	BiT-M-R152x4	.694
309	effnetv2m_custom384	.690
310	custom_model_cv_18_dagger_408	.685
311	mobilenet_v2_0_5_224	.680
312	resnet-101_v2-tf	.674
313	compact_resnet50_robust_IT	.674
314	resnet50-vitoimagevidnet8	.674
315	CORnetZ_CIFAR10_bs32_20_04	.670
316	cvt_cvt-21-384-in1k_4_LucyV4	.666
317	resnet50_l2_3_robust	.667
318	resnet50_robust_l2_eps1	.665
319	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
320	resnet18_imagenet21kP	.664
321	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
322	hmax	.662
323	mobilenet_v2_0.5_128	.660
324	resnet50-cifar	.661
325	convnext_tiny_imagenet_full_seed-0	.661
326	mobilenet_v2_0_75_224	.655
327	resnet50_robust_l2_eps3	.653
328	mobilenet_v2_0_5_192	.650
329	convnext_small_imagenet_100_seed-0	.649
330	CLIP_ViT-B_32	.646
331	cvt_cvt-13-384-in1k_4_LucyV4	.644
332	imagenet_l2_3_0	.643
333	imagenet_l2_10_0	.640
334	alexnet	.632
335	mobilenet_v2_0.75_224-tf	.626
336	vgg-19-keras	.617
337	mobilenet_v2_0_75_192	.615
338	omnivore_swinB	.609
339	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
340	densenet-201	.604
341	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
342	resnet50_linf_8_robust	.596
343	resnet-50-robust	.596
344	mobilenet_v2_1_0_160	.595
345	densenet-201-keras	.590
346	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
347	omnivore_swinS	.585
348	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
349	mobilenet_v2_0.35_224	.572
350	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
351	FrankRobWobv0	.567
352	convnext_femto_ols:d1_in1k	.557
353	alexnet_random_l2_3_perturb	.552
354	mobilenet_v2_1_0_224	.550
355	convnext_base_imagenet_full_seed-0	.546
356	vit_base_patch16_dinov3:lvd1689m	.545
357	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
358	mobilenet_v2_1_0_192	.523
359	mobilenet_v2_0_75_160	.520
360	convnext_small_imagenet_full_seed-0	.501
361	CORnetZ_CIFAR10	.495
362	efficientnet_b0_imagenet_full	.491
363	convnext_small_imagenet_10_seed-0	.488
364	resnet50_imagenet_1_seed-0	.481
365	mobilevit_small	.477
366	mobilenet_v2_0.75_160-tf	.471
367	resnet50-meshes-lt-100-original-pretrained	.456
368	resnet-18-LC_untrained	.454
369	mobilenet_v2_1_3_224	.442
370	alexnet_training_seed_06	.428
371	mobilenet_v2_1.3_224-tf	.399
372	resnet50_pretrained_with_retinal_waves	.397
373	resnet50-meshes-lt-100-original-scratch	.338
374	convnext_tiny_sup	.331
375	convnext_small_imagenet_1_seed-0	.293
376	my-model	.226
377	mobilenet_v2_0.5_160	.103
378	deit_small_imagenet_10_seed-0	.000
379	mobilenet_v2_0.35_160
380	resnet18-supervised
381	fitvid_trained_on_physion
382	openclip
383	resnext101_32x32d_wsl
384	resnext101_32x16d_wsl
385	resnext101_32x48d_wsl
386	resnet50_ImageNet
387	regnet_y_400mf
388	tv_efficientnet-b1
389	alexnet_ks_torevert
390	yudixie_resnet50_imagenet1kpret_0_240312
391	resnet_101_v1
392	resnet_152_v1
393	densenet_201_pytorch
394	pnasnet_large_pytorch
395	inception_v3_pytorch
396	inception_v4
397	nasnet_large
398	deit_small_imagenet_1_seed-0
399	pnasnet_large
400	vonegrcnn_62e_nobn
401	resnet_101_v2
402	vonegrcnn_47e
403	vonegrcnn_52e_full
404	resnet_152_v2

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