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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	.933
41	ReAlnet10_ALL	.933
42	ReAlnet08_cornet	.932
43	BiT-S-R152x2	.931
44	resnext101_32x8d_wsl	.931
45	ReAlnet08	.931
46	r3m_resnet50_nocrop	.928
47	voneresnet-50-non_stochastic	.929
48	resnet18_imagenet_full	.928
49	vggface	.931
50	ReAlnet10_cornet	.928
51	resnet18_ecoset_full	.926
52	mobilenet_v2_1-4_224_pytorch	.927
53	aughost_fair_resnet18	.926
54	ReAlnet04_cornet	.928
55	ReAlnet10	.925
56	ReAlnet07_cornet	.923
57	ReAlnet04	.925
58	ReAlnet07	.926
59	ReAlnet05	.924
60	artResNet18_1	.926
61	mobilenet_v2_1.0_96	.921
62	yudixie_resnet18_random_0_240719	.924
63	r3m_resnet50	.924
64	ReAlnet05_cornet	.923
65	nasnet_large-tf	.922
66	resnet50-SIN_IN	.922
67	resnet-18-LC_conv_init_m	.922
68	BiT-M-R50x3	.920
69	alexnet_random_linf8_perturb	.920
70	ReAlnet08_ALL	.922
71	ReAlnet06_ALL	.921
72	ReAlnet01	.920
73	ReAlnet06	.919
74	ReAlnet06_cornet	.920
75	inception_v2-tf	.919
76	ReAlnet07_ALL	.921
77	ReAlnet01_cornet	.917
78	ReAlnet05_ALL	.918
79	ReAlnet02_cornet	.918
80	ReAlnet03_ALL	.916
81	ReAlnet04_ALL	.916
82	ReAlnet02_ALL	.918
83	ReAlnet01_ALL	.916
84	resnet152_ecoset_full	.916
85	0.5x_resnet-18	.915
86	yudixie_resnet18_rotation_reg_0_240719	.915
87	ReAlnet02	.915
88	antialiased-rnext101_32x8d	.914
89	convnext_tiny:in12k_ft_in1k	.914
90	ReAlnet09_ALL	.914
91	CORnet-S	.912
92	alexnet_training_seed_01_fov16	.909
93	resnet101_imagenet_full	.911
94	cornet_s	.909
95	efficientnet_b2_imagenet_full	.907
96	BiT-M-R152x2	.907
97	nasnet_mobile	.906
98	mobilenet_v2_1.0_128-tf	.903
99	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
100	yudixie_resnet50_rotation_reg_0_240908	.905
101	mobilenet_v2_0.5_224-tf	.900
102	convnext_xlarge:fb_in22k_ft_in1k	.902
103	BiT-M-R101x3	.900
104	mobilenet_v2_0.5_192-tf	.899
105	r3m_resnet18	.902
106	alexnet_training_seed_01	.901
107	resnet-50_untrained	.901
108	texture_shape_resnet50_trained_on_SIN	.900
109	alexnet_training_seed_10	.900
110	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
111	resnet-50_v1-tf	.898
112	0.5x_resnet-18_LC	.899
113	resnet50-SIN	.898
114	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
115	RN50	.898
116	CLIP_resnet50	.894
117	alexnet_robust_correct	.896
118	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
119	CLIP_resnet50_float32	.895
120	resnet50_ewconplus_wzc	.895
121	nasnet_mobile-tf	.895
122	resnet-18-LC_m	.894
123	r3m_resnet34	.894
124	AdvProp_efficientnet-b0	.890
125	yudixie_resnet18_distance_reg_0_240719	.890
126	grcnn_robust_v1	.888
127	voneresnet-50	.887
128	0.5x_resnet-18_LC_w_sh_100_iter	.890
129	antialiased-r50	.887
130	antialias-resnet152	.887
131	resnet50	.885
132	yudixie_resnet50_translation_reg_0_240908	.886
133	grcnn_v2_text_noise	.885
134	deit_small_imagenet_100_seed-0	.882
135	alexnet_training_seed_03	.883
136	BiT-S-R101x3	.882
137	alexnet_training_seed_07	.882
138	resnet50_ecoset_full	.880
139	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
140	alexnet_training_seed_09	.881
141	compact_cornets_IT	.877
142	resnet50_random_linf8_perturb	.877
143	BiT-S-R152x4	.876
144	voneresnet-50-robust	.876
145	mobilenet_v2_1.0_160-tf	.874
146	resnet-152_v2-tf	.874
147	vit_base_patch14_dinov2-lvd142m	.875
148	densenet-169-keras	.874
149	resnet34_imagenet_full	.875
150	SWSL_resnext101_32x8d	.873
151	VOneCORnet-S	.870
152	resnet50-vicregl0p9	.871
153	resnet50_byol	.870
154	mobilenet_v2_1.0_192-tf	.868
155	yudixie_resnet18_category_class_0_240719	.868
156	vgg_16	.868
157	yudixie_resnet18_distance_rotation_0_240719	.867
158	focalnet_tiny_lrf_in1k	.864
159	grcnn_109	.864
160	convnext_large:fb_in22k_ft_in1k	.863
161	xception	.861
162	vgg-16-keras	.859
163	yudixie_resnet18_translation_rotation_0_240719	.859
164	resnet50	.858
165	resnet_50_v1	.856
166	resnet50_tutorial	.857
167	compact_vgg19_V4	.855
168	yudixie_resnet50_imagenet1kpret_0_240908	.854
169	compact_resnet50_IT	.855
170	inception_v3-tf	.855
171	resnet_50_v1_spiking_l4	.855
172	resnet_50_v1_spiking	.853
173	resnet-18_untrained	.854
174	resnet50-sup	.852
175	yudixie_resnet50_distance_translation_0_240908	.855
176	resnet_50_v1_spiking_l3	.851
177	resnet_50_v1_spiking_l4a	.851
178	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
179	resnet50_random_l2_perturb	.849
180	BiT-S-R101x1	.847
181	resnet_50_v2	.846
182	BiT-S-R50x3	.846
183	alexnet_training_seed_02	.847
184	effnetb1_272x240	.847
185	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
186	inception_v1	.845
187	yudixie_resnet50_distance_translation_rotation_0_240908	.845
188	vit_base_patch14_reg4_dinov2-lvd142m	.842
189	AdvProp_efficientnet-b8	.842
190	alexnet_reduced_aliasing_early_checkpoint	.842
191	resnet50-vicregl0p75	.840
192	resnet50_imagenet_full	.841
193	BiT-S-R50x1	.839
194	yudixie_resnet50_object_class_0_240908	.840
195	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
196	resnet50-SIN_IN_IN	.838
197	yudixie_resnet18_imagenet1kpret_0_240719	.836
198	AdvProp_efficientnet-b6	.836
199	0.5x_resnet-18_LC_w_sh_10_iter	.836
200	resnet-152_v1-tf	.834
201	resnet50_simclr	.835
202	deit_large_imagenet_full_seed-0	.834
203	BiT-M-R50x1	.833
204	resnet50_moco_v2	.834
205	SWSL_resnet50	.830
206	yudixie_resnet50_distance_rotation_0_240908	.831
207	resnet-152_v2_pytorch	.830
208	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
209	cvt_cvt-13-224-in1k_4_LucyV4	.828
210	CLIP-RN50	.828
211	barlow-twins-resnet50	.828
212	densenet-169	.827
213	inception_v3	.825
214	mobilenet_v1_0.25_128	.824
215	compact_resnet50_V4	.822
216	alexnet_l2_3_robust	.820
217	resnet-18-LC_1st_conv_m	.818
218	alexnet_fov4	.817
219	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
220	konkle_alexnetgn_ipcl_ref01_primary_model	.813
221	yudixie_resnet18_distance_translation_rotation_0_240719	.814
222	alexnet_training_seed_04	.814
223	deit_base_imagenet_full_seed-0	.813
224	ViT_L_32_imagenet1k	.815
225	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
226	BiT-M-R101x1	.809
227	yolos_tiny	.810
228	AdvProp_efficientnet-b7	.809
229	resnet50-simclr-vissl	.805
230	grcnn	.804
231	yudixie_resnet50_random_0_240908	.803
232	yudixie_resnet18_distance_translation_0_240719	.803
233	resnet-101_v1-tf	.795
234	alexnet_training_seed_05	.794
235	mobilenet_v2_0.75_96	.793
236	CORnet-Z	.795
237	resnet-50x2_untrained	.794
238	alexnet_training_seed_01_fov4	.794
239	resnet-18-LC_w_sh_10_iter_m	.793
240	yudixie_resnet50_distance_reg_0_240908	.792
241	vit_base_patch16_clip_224:openai_ft_in1k	.790
242	alexnet_training_seed_01_fov12	.789
243	alexnet_training_seed_08	.787
244	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
245	efficientnet_b0	.787
246	resnet50-moclr8deg	.785
247	inception_v1-tf	.785
248	resnet-18_test_m	.784
249	densenet-121	.783
250	resnet50-vicreg	.783
251	cvt_cvt-21-224-in1k_4_LucyV4	.782
252	AT_efficientnet-b2	.782
253	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
254	blt_vs	.780
255	AdvProp_efficientnet-b2	.779
256	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
257	mobilenet_v2_0.75_192-tf	.776
258	inception_v4-tf	.777
259	mobilenet_v2_1.4_224-tf	.773
260	vit_large_patch14_clip_224:openai_ft_in1k	.773
261	resnet50_imagenet_100_seed-0	.773
262	pnasnet_large-tf	.767
263	ViT-B/32	.764
264	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
265	mobilenet_v2_1_4_224	.765
266	resnet-50_v2-tf	.763
267	vision_transformer_vit_large_patch16_224	.762
268	resnet50-barlow	.755
269	ReAlnet01_cornet_fov4	.752
270	resnet50_imagenet_10_seed-0	.749
271	vonegrcnn_62e_nobn	.749
272	omnivore_swinT	.747
273	mobilenet_v2_0.75_128	.742
274	my-model	.743
275	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
276	AlexNet_SIN_fov16	.741
277	briaai_rmbg_1_4	.738
278	yudixie_resnet18_translation_reg_0_240719	.739
279	v1-pyr-nodown	.735
280	deit_small_imagenet_full_seed-0	.736
281	AdvProp_efficientnet-b4	.728
282	shufflenet_v2_x1_0	.729
283	resnet50_linf_4_robust	.727
284	alexnet_linf_8_robust	.724
285	densenet-121-keras	.724
286	efficientnet-b7	.723
287	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
288	texture_shape_alexnet_trained_on_SIN	.723
289	resnet_SIN_IN_FT_IN	.722
290	vgg_19	.718
291	vonealexnet_gaussian_noise_std4_fixed	.717
292	mobilenet_v2_1_0_128	.718
293	cv_18_dagger_408_pretrained	.716
294	mobilenet_v2_1.0_224-tf	.714
295	resnet50-VITO-8deg-cc	.714
296	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
297	convnext_large_imagenet_full_seed-0	.712
298	inception_resnet_v2	.708
299	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
300	pnasnet_large_halfPrecision	.696
301	BiT-M-R152x4	.694
302	effnetv2m_custom384	.690
303	custom_model_cv_18_dagger_408	.685
304	mobilenet_v2_0_5_224	.680
305	resnet-101_v2-tf	.674
306	compact_resnet50_robust_IT	.674
307	resnet50-vitoimagevidnet8	.674
308	CORnetZ_CIFAR10_bs32_20_04	.670
309	cvt_cvt-21-384-in1k_4_LucyV4	.666
310	resnet50_l2_3_robust	.667
311	resnet50_robust_l2_eps1	.665
312	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
313	resnet18_imagenet21kP	.664
314	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
315	hmax	.662
316	mobilenet_v2_0.5_128	.660
317	resnet50-cifar	.661
318	convnext_tiny_imagenet_full_seed-0	.661
319	mobilenet_v2_0_75_224	.655
320	resnet50_robust_l2_eps3	.653
321	mobilenet_v2_0_5_192	.650
322	convnext_small_imagenet_100_seed-0	.649
323	CLIP_ViT-B_32	.646
324	cvt_cvt-13-384-in1k_4_LucyV4	.644
325	imagenet_l2_3_0	.643
326	imagenet_l2_10_0	.640
327	mobilenet_v2_0.75_224-tf	.626
328	vgg-19-keras	.617
329	mobilenet_v2_0_75_192	.615
330	omnivore_swinB	.609
331	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
332	densenet-201	.604
333	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
334	resnet50_linf_8_robust	.596
335	resnet-50-robust	.596
336	mobilenet_v2_1_0_160	.595
337	densenet-201-keras	.590
338	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
339	omnivore_swinS	.585
340	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
341	mobilenet_v2_0.35_224	.572
342	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
343	FrankRobWobv0	.567
344	convnext_femto_ols:d1_in1k	.557
345	alexnet_random_l2_3_perturb	.552
346	mobilenet_v2_1_0_224	.550
347	convnext_base_imagenet_full_seed-0	.546
348	vit_base_patch16_dinov3-lvd1689m	.545
349	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
350	mobilenet_v2_1_0_192	.523
351	mobilenet_v2_0_75_160	.520
352	convnext_small_imagenet_full_seed-0	.501
353	CORnetZ_CIFAR10	.495
354	efficientnet_b0_imagenet_full	.491
355	convnext_small_imagenet_10_seed-0	.488
356	resnet50_imagenet_1_seed-0	.481
357	mobilevit_small	.477
358	mobilenet_v2_0.75_160-tf	.471
359	resnet50-meshes-lt-100-original-pretrained	.456
360	resnet-18-LC_untrained	.454
361	mobilenet_v2_1_3_224	.442
362	alexnet_training_seed_06	.428
363	mobilenet_v2_1.3_224-tf	.399
364	resnet50_pretrained_with_retinal_waves	.397
365	resnet50-meshes-lt-100-original-scratch	.338
366	convnext_tiny_sup	.331
367	convnext_small_imagenet_1_seed-0	.293
368	my-model	.226
369	mobilenet_v2_0.5_160	.103
370	deit_small_imagenet_10_seed-0	.000
371	mobilenet_v2_0.35_160
372	resnet18-supervised
373	fitvid_trained_on_physion
374	openclip
375	resnext101_32x32d_wsl
376	resnext101_32x16d_wsl
377	resnext101_32x48d_wsl
378	resnet50_ImageNet
379	regnet_y_400mf
380	tv_efficientnet-b1
381	alexnet_ks_torevert
382	yudixie_resnet50_imagenet1kpret_0_240312
383	resnet_101_v1
384	resnet_152_v1
385	densenet_201_pytorch
386	pnasnet_large_pytorch
387	inception_v3_pytorch
388	eBarlow_lmda_02_1000ep
389	inception_v4
390	nasnet_large
391	deit_small_imagenet_1_seed-0
392	pnasnet_large
393	vonegrcnn_62e_nobn
394	resnet_101_v2
395	vonegrcnn_47e
396	vonegrcnn_52e_full
397	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