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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	resnet50_primary_visual_cortex	.978
3	vonegrcnn_47e	.977
4	yudixie_resnet50_translation_rotation_0_240908	.970
5	resnet101_ecoset_full	.969
6	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.967
7	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.965
8	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.966
9	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.967
10	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.966
11	0.5x_resnet-18_LC_w_sh_1_iter	.965
12	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.966
13	yudixie_resnet50_category_class_0_240908	.959
14	resnet152_imagenet_full	.957
15	Res2Net50_26w_4s	.956
16	resnet-18-LC_w_sh_1_iter_m	.953
17	resnet-18-LC_w_sh_100_iter_m	.948
18	resnet101	.945
19	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.946
20	yudixie_resnet18_object_class_0_240719	.945
21	resnet-18-LC_w_sh_1_iter_conv_init_m	.942
22	resnet50_FractalDB	.941
23	vonegrcnn_52e_full	.938
24	resnet34_ecoset_full	.939
25	ReAlnet03_cornet	.938
26	AlexNet_SIN	.938
27	efficientnet_b1_imagenet_full	.938
28	ReAlnet09	.936
29	ReAlnet09_cornet	.939
30	ReAlnet03	.939
31	alexnet	.938
32	alexnet	.934
33	alexnet	.933
34	alexnet	.934
35	alexnet	.934
36	alexnet-baseline	.932
37	ReAlnet10_ALL	.933
38	alexnet	.934
39	ReAlnet08_cornet	.932
40	alexnet	.930
41	BiT-S-R152x2	.931
42	resnext101_32x8d_wsl	.931
43	ReAlnet08	.931
44	r3m_resnet50_nocrop	.928
45	voneresnet-50-non_stochastic	.929
46	resnet18_imagenet_full	.928
47	vggface	.931
48	ReAlnet10_cornet	.928
49	resnet18_ecoset_full	.926
50	mobilenet_v2_1-4_224_pytorch	.927
51	ReAlnet04_cornet	.928
52	ReAlnet10	.925
53	ReAlnet07_cornet	.923
54	ReAlnet04	.925
55	ReAlnet07	.926
56	ReAlnet05	.924
57	artResNet18_1	.926
58	mobilenet_v2_1.0_96	.921
59	yudixie_resnet18_random_0_240719	.924
60	r3m_resnet50	.924
61	ReAlnet05_cornet	.923
62	nasnet_large-tf	.922
63	resnet50-SIN_IN	.922
64	resnet-18-LC_conv_init_m	.922
65	BiT-M-R50x3	.920
66	alexnet_random_linf8_perturb	.920
67	ReAlnet08_ALL	.922
68	ReAlnet06_ALL	.921
69	ReAlnet01	.920
70	ReAlnet06	.919
71	ReAlnet06_cornet	.920
72	inception_v2-tf	.919
73	ReAlnet07_ALL	.921
74	ReAlnet01_cornet	.917
75	ReAlnet05_ALL	.918
76	ReAlnet02_cornet	.918
77	ReAlnet03_ALL	.916
78	ReAlnet04_ALL	.916
79	ReAlnet02_ALL	.918
80	ReAlnet01_ALL	.916
81	resnet152_ecoset_full	.916
82	0.5x_resnet-18	.915
83	yudixie_resnet18_rotation_reg_0_240719	.915
84	ReAlnet02	.915
85	antialiased-rnext101_32x8d	.914
86	convnext_tiny:in12k_ft_in1k	.914
87	ReAlnet09_ALL	.914
88	CORnet-S	.912
89	resnet101_imagenet_full	.911
90	cornet_s	.909
91	efficientnet_b2_imagenet_full	.907
92	BiT-M-R152x2	.907
93	nasnet_mobile	.906
94	mobilenet_v2_1.0_128-tf	.903
95	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.905
96	yudixie_resnet50_rotation_reg_0_240908	.905
97	mobilenet_v2_0.5_224-tf	.900
98	convnext_xlarge:fb_in22k_ft_in1k	.902
99	BiT-M-R101x3	.900
100	mobilenet_v2_0.5_192-tf	.899
101	r3m_resnet18	.902
102	alexnet_training_seed_01	.901
103	resnet-50_untrained	.901
104	texture_shape_resnet50_trained_on_SIN	.900
105	alexnet_training_seed_10	.900
106	resnet-18-LC_w_sh_100_iter_conv_init_m	.899
107	resnet-50_v1-tf	.898
108	0.5x_resnet-18_LC	.899
109	resnet50-SIN	.898
110	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.897
111	RN50	.898
112	CLIP_resnet50	.894
113	alexnet_robust_correct	.896
114	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.896
115	CLIP_resnet50_float32	.895
116	nasnet_mobile-tf	.895
117	resnet-18-LC_m	.894
118	r3m_resnet34	.894
119	AdvProp_efficientnet-b0	.890
120	yudixie_resnet18_distance_reg_0_240719	.890
121	grcnn_robust_v1	.888
122	voneresnet-50	.887
123	0.5x_resnet-18_LC_w_sh_100_iter	.890
124	antialiased-r50	.887
125	antialias-resnet152	.887
126	resnet50	.885
127	yudixie_resnet50_translation_reg_0_240908	.886
128	grcnn_v2_text_noise	.885
129	deit_small_imagenet_100_seed-0	.882
130	alexnet_training_seed_03	.883
131	BiT-S-R101x3	.882
132	alexnet_training_seed_07	.882
133	resnet50_ecoset_full	.880
134	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.881
135	alexnet_training_seed_09	.881
136	resnet50_random_linf8_perturb	.877
137	BiT-S-R152x4	.876
138	voneresnet-50-robust	.876
139	mobilenet_v2_1.0_160-tf	.874
140	resnet-152_v2-tf	.874
141	densenet-169-keras	.874
142	resnet34_imagenet_full	.875
143	SWSL_resnext101_32x8d	.873
144	VOneCORnet-S	.870
145	resnet50-vicregl0p9	.871
146	resnet50_byol	.870
147	mobilenet_v2_1.0_192-tf	.868
148	yudixie_resnet18_category_class_0_240719	.868
149	vgg_16	.868
150	yudixie_resnet18_distance_rotation_0_240719	.867
151	focalnet_tiny_lrf_in1k	.864
152	grcnn_109	.864
153	convnext_large:fb_in22k_ft_in1k	.863
154	xception	.861
155	vgg-16-keras	.859
156	yudixie_resnet18_translation_rotation_0_240719	.859
157	resnet50	.858
158	resnet_50_v1	.856
159	resnet50_tutorial	.857
160	yudixie_resnet50_imagenet1kpret_0_240908	.854
161	inception_v3-tf	.855
162	resnet_50_v1_spiking_l4	.855
163	resnet_50_v1_spiking	.853
164	resnet-18_untrained	.854
165	resnet50-sup	.852
166	yudixie_resnet50_distance_translation_0_240908	.855
167	resnet_50_v1_spiking_l3	.851
168	resnet_50_v1_spiking_l4a	.851
169	resnet-18-LC_d_w_sh_1x1_conv_init_m	.852
170	resnet50_random_l2_perturb	.849
171	BiT-S-R101x1	.847
172	resnet_50_v2	.846
173	BiT-S-R50x3	.846
174	alexnet_training_seed_02	.847
175	effnetb1_272x240	.847
176	resnet-18-LC_w_sh_10_iter_conv_init_m	.845
177	inception_v1	.845
178	yudixie_resnet50_distance_translation_rotation_0_240908	.845
179	AdvProp_efficientnet-b8	.842
180	alexnet_reduced_aliasing_early_checkpoint	.842
181	resnet50-vicregl0p75	.840
182	resnet50_imagenet_full	.841
183	BiT-S-R50x1	.839
184	yudixie_resnet50_object_class_0_240908	.840
185	vit_large_patch14_clip_224:laion2b_ft_in1k	.840
186	resnet50-SIN_IN_IN	.838
187	yudixie_resnet18_imagenet1kpret_0_240719	.836
188	AdvProp_efficientnet-b6	.836
189	0.5x_resnet-18_LC_w_sh_10_iter	.836
190	resnet-152_v1-tf	.834
191	resnet50_simclr	.835
192	deit_large_imagenet_full_seed-0	.834
193	BiT-M-R50x1	.833
194	resnet50_moco_v2	.834
195	SWSL_resnet50	.830
196	yudixie_resnet50_distance_rotation_0_240908	.831
197	resnet-152_v2_pytorch	.830
198	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.828
199	cvt_cvt-13-224-in1k_4_LucyV4	.828
200	CLIP-RN50	.828
201	barlow-twins-resnet50	.828
202	densenet-169	.827
203	inception_v3	.825
204	mobilenet_v1_0.25_128	.824
205	alexnet_l2_3_robust	.820
206	resnet-18-LC_1st_conv_m	.818
207	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.814
208	konkle_alexnetgn_ipcl_ref01_primary_model	.813
209	yudixie_resnet18_distance_translation_rotation_0_240719	.814
210	alexnet_training_seed_04	.814
211	deit_base_imagenet_full_seed-0	.813
212	ViT_L_32_imagenet1k	.815
213	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.810
214	BiT-M-R101x1	.809
215	yolos_tiny	.810
216	AdvProp_efficientnet-b7	.809
217	resnet50-simclr-vissl	.805
218	grcnn	.804
219	yudixie_resnet50_random_0_240908	.803
220	yudixie_resnet18_distance_translation_0_240719	.803
221	resnet-101_v1-tf	.795
222	alexnet_training_seed_05	.794
223	mobilenet_v2_0.75_96	.793
224	CORnet-Z	.795
225	resnet-50x2_untrained	.794
226	resnet-18-LC_w_sh_10_iter_m	.793
227	yudixie_resnet50_distance_reg_0_240908	.792
228	vit_base_patch16_clip_224:openai_ft_in1k	.790
229	alexnet_training_seed_08	.787
230	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.789
231	efficientnet_b0	.787
232	resnet50-moclr8deg	.785
233	inception_v1-tf	.785
234	resnet-18_test_m	.784
235	densenet-121	.783
236	resnet50-vicreg	.783
237	cvt_cvt-21-224-in1k_4_LucyV4	.782
238	AT_efficientnet-b2	.782
239	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.780
240	blt_vs	.780
241	AdvProp_efficientnet-b2	.779
242	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.778
243	mobilenet_v2_0.75_192-tf	.776
244	inception_v4-tf	.777
245	mobilenet_v2_1.4_224-tf	.773
246	vit_large_patch14_clip_224:openai_ft_in1k	.773
247	resnet50_imagenet_100_seed-0	.773
248	pnasnet_large-tf	.767
249	ViT-B/32	.764
250	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.766
251	mobilenet_v2_1_4_224	.765
252	resnet-50_v2-tf	.763
253	vision_transformer_vit_large_patch16_224	.762
254	resnet50-barlow	.755
255	resnet50_imagenet_10_seed-0	.749
256	vonegrcnn_62e_nobn	.749
257	omnivore_swinT	.747
258	mobilenet_v2_0.75_128	.742
259	my-model	.743
260	vit_large_patch14_clip_336:laion2b_ft_in1k	.742
261	briaai_rmbg_1_4	.738
262	yudixie_resnet18_translation_reg_0_240719	.739
263	v1-pyr-nodown	.735
264	deit_small_imagenet_full_seed-0	.736
265	AdvProp_efficientnet-b4	.728
266	shufflenet_v2_x1_0	.729
267	resnet50_linf_4_robust	.727
268	alexnet_linf_8_robust	.724
269	densenet-121-keras	.724
270	efficientnet-b7	.723
271	vit_relpos_base_patch16_clsgap_224:sw_in1k	.724
272	texture_shape_alexnet_trained_on_SIN	.723
273	resnet_SIN_IN_FT_IN	.722
274	vgg_19	.718
275	vonealexnet_gaussian_noise_std4_fixed	.717
276	mobilenet_v2_1_0_128	.718
277	cv_18_dagger_408_pretrained	.716
278	mobilenet_v2_1.0_224-tf	.714
279	resnet50-VITO-8deg-cc	.714
280	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.712
281	convnext_large_imagenet_full_seed-0	.712
282	inception_resnet_v2	.708
283	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.704
284	BiT-M-R152x4	.694
285	effnetv2m_custom384	.690
286	custom_model_cv_18_dagger_408	.685
287	mobilenet_v2_0_5_224	.680
288	resnet-101_v2-tf	.674
289	resnet50-vitoimagevidnet8	.674
290	CORnetZ_CIFAR10_bs32_20_04	.670
291	cvt_cvt-21-384-in1k_4_LucyV4	.666
292	resnet50_l2_3_robust	.667
293	resnet50_robust_l2_eps1	.665
294	convnext_base:clip_laiona_augreg_ft_in1k_384	.664
295	resnet18_imagenet21kP	.664
296	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.663
297	hmax	.662
298	mobilenet_v2_0.5_128	.660
299	resnet50-cifar	.661
300	convnext_tiny_imagenet_full_seed-0	.661
301	mobilenet_v2_0_75_224	.655
302	resnet50_robust_l2_eps3	.653
303	mobilenet_v2_0_5_192	.650
304	convnext_small_imagenet_100_seed-0	.649
305	CLIP_ViT-B_32	.646
306	cvt_cvt-13-384-in1k_4_LucyV4	.644
307	imagenet_l2_3_0	.643
308	imagenet_l2_10_0	.640
309	alexnet	.632
310	mobilenet_v2_0.75_224-tf	.626
311	vgg-19-keras	.617
312	mobilenet_v2_0_75_192	.615
313	omnivore_swinB	.609
314	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.607
315	densenet-201	.604
316	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.601
317	resnet50_linf_8_robust	.596
318	resnet-50-robust	.596
319	mobilenet_v2_1_0_160	.595
320	densenet-201-keras	.590
321	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.586
322	omnivore_swinS	.585
323	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.584
324	mobilenet_v2_0.35_224	.572
325	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.570
326	FrankRobWobv0	.567
327	convnext_femto_ols:d1_in1k	.557
328	alexnet_random_l2_3_perturb	.552
329	mobilenet_v2_1_0_224	.550
330	convnext_base_imagenet_full_seed-0	.546
331	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.532
332	mobilenet_v2_1_0_192	.523
333	mobilenet_v2_0_75_160	.520
334	convnext_small_imagenet_full_seed-0	.501
335	CORnetZ_CIFAR10	.495
336	efficientnet_b0_imagenet_full	.491
337	convnext_small_imagenet_10_seed-0	.488
338	resnet50_imagenet_1_seed-0	.481
339	mobilevit_small	.477
340	mobilenet_v2_0.75_160-tf	.471
341	resnet50-meshes-lt-100-original-pretrained	.456
342	resnet-18-LC_untrained	.454
343	mobilenet_v2_1_3_224	.442
344	alexnet_training_seed_06	.428
345	mobilenet_v2_1.3_224-tf	.399
346	resnet50_pretrained_with_retinal_waves	.397
347	resnet50-meshes-lt-100-original-scratch	.338
348	convnext_tiny_sup	.331
349	convnext_small_imagenet_1_seed-0	.293
350	my-model	.226
351	mobilenet_v2_0.5_160	.103
352	deit_small_imagenet_10_seed-0	.000
353	mobilenet_v2_0.35_160
354	resnet18-supervised
355	fitvid_trained_on_physion
356	openclip
357	resnext101_32x32d_wsl
358	resnext101_32x16d_wsl
359	resnext101_32x48d_wsl
360	resnet50_ImageNet
361	regnet_y_400mf
362	tv_efficientnet-b1
363	alexnet_ks_torevert
364	yudixie_resnet50_imagenet1kpret_0_240312
365	resnet_101_v1
366	resnet_152_v1
367	densenet_201_pytorch
368	pnasnet_large_pytorch
369	inception_v3_pytorch
370	inception_v4
371	nasnet_large
372	deit_small_imagenet_1_seed-0
373	pnasnet_large
374	vonegrcnn_62e_nobn
375	resnet_101_v2
376	vonegrcnn_47e
377	vonegrcnn_52e_full
378	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