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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	.934
42	alexnet	.933
43	alexnet	.934
44	alexnet	.934
45	alexnet	.933
46	alexnet-baseline	.932
47	ReAlnet10_ALL	.933
48	alexnet	.934
49	ReAlnet08_cornet	.932
50	alexnet	.930
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	alexnet_random_linf8_perturb	.920
77	ReAlnet08_ALL	.922
78	ReAlnet06_ALL	.921
79	ReAlnet01	.920
80	ReAlnet06	.919
81	ReAlnet06_cornet	.920
82	inception_v2-tf	.919
83	ReAlnet07_ALL	.921
84	ReAlnet01_cornet	.917
85	ReAlnet05_ALL	.918
86	ReAlnet02_cornet	.918
87	ReAlnet03_ALL	.916
88	ReAlnet04_ALL	.916
89	ReAlnet02_ALL	.918
90	ReAlnet01_ALL	.916
91	resnet152_ecoset_full	.916
92	0.5x_resnet-18	.915
93	yudixie_resnet18_rotation_reg_0_240719	.915
94	ReAlnet02	.915
95	antialiased-rnext101_32x8d	.914
96	convnext_tiny:in12k_ft_in1k	.914
97	ReAlnet09_ALL	.914
98	CORnet-S	.912
99	alexnet_training_seed_01_fov16	.909
100	resnet101_imagenet_full	.911
101	cornet_s	.909
102	efficientnet_b2_imagenet_full	.907
103	BiT-M-R152x2	.907
104	nasnet_mobile	.906
105	mobilenet_v2_1.0_128-tf	.903
106	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
107	yudixie_resnet50_rotation_reg_0_240908	.905
108	mobilenet_v2_0.5_224-tf	.900
109	convnext_xlarge:fb_in22k_ft_in1k	.902
110	BiT-M-R101x3	.900
111	mobilenet_v2_0.5_192-tf	.899
112	r3m_resnet18	.902
113	alexnet_training_seed_01	.901
114	resnet-50_untrained	.901
115	texture_shape_resnet50_trained_on_SIN	.900
116	alexnet_training_seed_10	.900
117	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
118	resnet-50_v1-tf	.898
119	0.5x_resnet-18_LC	.899
120	resnet50-SIN	.898
121	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
122	RN50	.898
123	CLIP_resnet50	.894
124	alexnet_robust_correct	.896
125	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
126	CLIP_resnet50_float32	.895
127	resnet50_ewconplus_wzc	.895
128	nasnet_mobile-tf	.895
129	resnet-18-LC_m	.894
130	r3m_resnet34	.894
131	AdvProp_efficientnet-b0	.890
132	yudixie_resnet18_distance_reg_0_240719	.890
133	grcnn_robust_v1	.888
134	voneresnet-50	.887
135	0.5x_resnet-18_LC_w_sh_100_iter	.890
136	antialiased-r50	.887
137	antialias-resnet152	.887
138	resnet50	.885
139	yudixie_resnet50_translation_reg_0_240908	.886
140	grcnn_v2_text_noise	.885
141	deit_small_imagenet_100_seed-0	.882
142	alexnet_training_seed_03	.883
143	BiT-S-R101x3	.882
144	alexnet_training_seed_07	.882
145	resnet50_ecoset_full	.880
146	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
147	alexnet_training_seed_09	.881
148	compact_cornets_IT	.877
149	resnet50_random_linf8_perturb	.877
150	BiT-S-R152x4	.876
151	voneresnet-50-robust	.876
152	mobilenet_v2_1.0_160-tf	.874
153	resnet-152_v2-tf	.874
154	densenet-169-keras	.874
155	resnet34_imagenet_full	.875
156	SWSL_resnext101_32x8d	.873
157	VOneCORnet-S	.870
158	resnet50-vicregl0p9	.871
159	resnet50_byol	.870
160	mobilenet_v2_1.0_192-tf	.868
161	yudixie_resnet18_category_class_0_240719	.868
162	vgg_16	.868
163	yudixie_resnet18_distance_rotation_0_240719	.867
164	focalnet_tiny_lrf_in1k	.864
165	grcnn_109	.864
166	convnext_large:fb_in22k_ft_in1k	.863
167	xception	.861
168	vgg-16-keras	.859
169	yudixie_resnet18_translation_rotation_0_240719	.859
170	resnet50	.858
171	resnet_50_v1	.856
172	resnet50_tutorial	.857
173	compact_vgg19_V4	.855
174	yudixie_resnet50_imagenet1kpret_0_240908	.854
175	compact_resnet50_IT	.855
176	inception_v3-tf	.855
177	resnet_50_v1_spiking_l4	.855
178	resnet_50_v1_spiking	.853
179	resnet-18_untrained	.854
180	resnet50-sup	.852
181	yudixie_resnet50_distance_translation_0_240908	.855
182	resnet_50_v1_spiking_l3	.851
183	resnet_50_v1_spiking_l4a	.851
184	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
185	resnet50_random_l2_perturb	.849
186	BiT-S-R101x1	.847
187	resnet_50_v2	.846
188	BiT-S-R50x3	.846
189	alexnet_training_seed_02	.847
190	effnetb1_272x240	.847
191	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
192	inception_v1	.845
193	yudixie_resnet50_distance_translation_rotation_0_240908	.845
194	AdvProp_efficientnet-b8	.842
195	alexnet_reduced_aliasing_early_checkpoint	.842
196	resnet50-vicregl0p75	.840
197	resnet50_imagenet_full	.841
198	BiT-S-R50x1	.839
199	yudixie_resnet50_object_class_0_240908	.840
200	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
201	resnet50-SIN_IN_IN	.838
202	yudixie_resnet18_imagenet1kpret_0_240719	.836
203	AdvProp_efficientnet-b6	.836
204	0.5x_resnet-18_LC_w_sh_10_iter	.836
205	resnet-152_v1-tf	.834
206	resnet50_simclr	.835
207	deit_large_imagenet_full_seed-0	.834
208	BiT-M-R50x1	.833
209	resnet50_moco_v2	.834
210	SWSL_resnet50	.830
211	yudixie_resnet50_distance_rotation_0_240908	.831
212	resnet-152_v2_pytorch	.830
213	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
214	cvt_cvt-13-224-in1k_4_LucyV4	.828
215	CLIP-RN50	.828
216	barlow-twins-resnet50	.828
217	densenet-169	.827
218	inception_v3	.825
219	mobilenet_v1_0.25_128	.824
220	compact_resnet50_V4	.822
221	alexnet_l2_3_robust	.820
222	resnet-18-LC_1st_conv_m	.818
223	alexnet_fov4	.817
224	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
225	konkle_alexnetgn_ipcl_ref01_primary_model	.813
226	yudixie_resnet18_distance_translation_rotation_0_240719	.814
227	alexnet_training_seed_04	.814
228	deit_base_imagenet_full_seed-0	.813
229	ViT_L_32_imagenet1k	.815
230	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
231	BiT-M-R101x1	.809
232	yolos_tiny	.810
233	AdvProp_efficientnet-b7	.809
234	resnet50-simclr-vissl	.805
235	grcnn	.804
236	yudixie_resnet50_random_0_240908	.803
237	yudixie_resnet18_distance_translation_0_240719	.803
238	resnet-101_v1-tf	.795
239	alexnet_training_seed_05	.794
240	mobilenet_v2_0.75_96	.793
241	CORnet-Z	.795
242	resnet-50x2_untrained	.794
243	alexnet_training_seed_01_fov4	.794
244	resnet-18-LC_w_sh_10_iter_m	.793
245	yudixie_resnet50_distance_reg_0_240908	.792
246	vit_base_patch16_clip_224:openai_ft_in1k	.790
247	alexnet_training_seed_01_fov12	.789
248	alexnet_training_seed_08	.787
249	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
250	efficientnet_b0	.787
251	resnet50-moclr8deg	.785
252	inception_v1-tf	.785
253	resnet-18_test_m	.784
254	densenet-121	.783
255	resnet50-vicreg	.783
256	cvt_cvt-21-224-in1k_4_LucyV4	.782
257	AT_efficientnet-b2	.782
258	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
259	blt_vs	.780
260	AdvProp_efficientnet-b2	.779
261	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
262	mobilenet_v2_0.75_192-tf	.776
263	inception_v4-tf	.777
264	mobilenet_v2_1.4_224-tf	.773
265	vit_large_patch14_clip_224:openai_ft_in1k	.773
266	resnet50_imagenet_100_seed-0	.773
267	pnasnet_large-tf	.767
268	ViT-B/32	.764
269	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
270	mobilenet_v2_1_4_224	.765
271	resnet-50_v2-tf	.763
272	vision_transformer_vit_large_patch16_224	.762
273	resnet50-barlow	.755
274	ReAlnet01_cornet_fov4	.752
275	resnet50_imagenet_10_seed-0	.749
276	vonegrcnn_62e_nobn	.749
277	omnivore_swinT	.747
278	mobilenet_v2_0.75_128	.742
279	my-model	.743
280	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
281	AlexNet_SIN_fov16	.741
282	briaai_rmbg_1_4	.738
283	yudixie_resnet18_translation_reg_0_240719	.739
284	v1-pyr-nodown	.735
285	deit_small_imagenet_full_seed-0	.736
286	AdvProp_efficientnet-b4	.728
287	shufflenet_v2_x1_0	.729
288	resnet50_linf_4_robust	.727
289	alexnet_linf_8_robust	.724
290	densenet-121-keras	.724
291	efficientnet-b7	.723
292	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
293	texture_shape_alexnet_trained_on_SIN	.723
294	resnet_SIN_IN_FT_IN	.722
295	vgg_19	.718
296	vonealexnet_gaussian_noise_std4_fixed	.717
297	mobilenet_v2_1_0_128	.718
298	cv_18_dagger_408_pretrained	.716
299	mobilenet_v2_1.0_224-tf	.714
300	resnet50-VITO-8deg-cc	.714
301	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
302	convnext_large_imagenet_full_seed-0	.712
303	inception_resnet_v2	.708
304	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
305	BiT-M-R152x4	.694
306	effnetv2m_custom384	.690
307	custom_model_cv_18_dagger_408	.685
308	mobilenet_v2_0_5_224	.680
309	resnet-101_v2-tf	.674
310	compact_resnet50_robust_IT	.674
311	resnet50-vitoimagevidnet8	.674
312	CORnetZ_CIFAR10_bs32_20_04	.670
313	cvt_cvt-21-384-in1k_4_LucyV4	.666
314	resnet50_l2_3_robust	.667
315	resnet50_robust_l2_eps1	.665
316	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
317	resnet18_imagenet21kP	.664
318	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
319	hmax	.662
320	mobilenet_v2_0.5_128	.660
321	resnet50-cifar	.661
322	convnext_tiny_imagenet_full_seed-0	.661
323	mobilenet_v2_0_75_224	.655
324	resnet50_robust_l2_eps3	.653
325	mobilenet_v2_0_5_192	.650
326	convnext_small_imagenet_100_seed-0	.649
327	CLIP_ViT-B_32	.646
328	cvt_cvt-13-384-in1k_4_LucyV4	.644
329	imagenet_l2_3_0	.643
330	imagenet_l2_10_0	.640
331	alexnet	.632
332	mobilenet_v2_0.75_224-tf	.626
333	vgg-19-keras	.617
334	mobilenet_v2_0_75_192	.615
335	omnivore_swinB	.609
336	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
337	densenet-201	.604
338	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
339	resnet50_linf_8_robust	.596
340	resnet-50-robust	.596
341	mobilenet_v2_1_0_160	.595
342	densenet-201-keras	.590
343	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
344	omnivore_swinS	.585
345	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
346	mobilenet_v2_0.35_224	.572
347	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
348	FrankRobWobv0	.567
349	convnext_femto_ols:d1_in1k	.557
350	alexnet_random_l2_3_perturb	.552
351	mobilenet_v2_1_0_224	.550
352	convnext_base_imagenet_full_seed-0	.546
353	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
354	mobilenet_v2_1_0_192	.523
355	mobilenet_v2_0_75_160	.520
356	convnext_small_imagenet_full_seed-0	.501
357	CORnetZ_CIFAR10	.495
358	efficientnet_b0_imagenet_full	.491
359	convnext_small_imagenet_10_seed-0	.488
360	resnet50_imagenet_1_seed-0	.481
361	mobilevit_small	.477
362	mobilenet_v2_0.75_160-tf	.471
363	resnet50-meshes-lt-100-original-pretrained	.456
364	resnet-18-LC_untrained	.454
365	mobilenet_v2_1_3_224	.442
366	alexnet_training_seed_06	.428
367	mobilenet_v2_1.3_224-tf	.399
368	resnet50_pretrained_with_retinal_waves	.397
369	resnet50-meshes-lt-100-original-scratch	.338
370	convnext_tiny_sup	.331
371	convnext_small_imagenet_1_seed-0	.293
372	my-model	.226
373	mobilenet_v2_0.5_160	.103
374	deit_small_imagenet_10_seed-0	.000
375	mobilenet_v2_0.35_160
376	resnet18-supervised
377	fitvid_trained_on_physion
378	openclip
379	resnext101_32x32d_wsl
380	resnext101_32x16d_wsl
381	resnext101_32x48d_wsl
382	resnet50_ImageNet
383	regnet_y_400mf
384	tv_efficientnet-b1
385	alexnet_ks_torevert
386	yudixie_resnet50_imagenet1kpret_0_240312
387	resnet_101_v1
388	resnet_152_v1
389	densenet_201_pytorch
390	pnasnet_large_pytorch
391	inception_v3_pytorch
392	inception_v4
393	nasnet_large
394	deit_small_imagenet_1_seed-0
395	pnasnet_large
396	vonegrcnn_62e_nobn
397	resnet_101_v2
398	vonegrcnn_47e
399	vonegrcnn_52e_full
400	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