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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_Schiller1976-sf_selective")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	imagenet_l2_10_0	1.0
2	resnext101_32x8d_wsl	1.000
3	alexnet_training_seed_01_fov16	.999
4	BiT-M-R101x1	1.0
5	mobilenet_v2_0.75_160-tf	1.000
6	resnet-50-robust	1.0
7	resnet34_ecoset_full	1.0
8	barlow-twins-resnet50	1.000
9	yudixie_resnet50_distance_translation_rotation_0_240908	.999
10	AlexNet_SIN_fov16	.999
11	alexnet_robust_correct	.999
12	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.999
13	resnet50-vitoimagevidnet8	.999
14	mobilenet_v2_1.0_96	.999
15	resnet50_robust_l2_eps1	.998
16	resnet50_robust_l2_eps3	.999
17	resnet50_l2_3_robust	.996
18	grcnn_v2_text_noise	.999
19	resnet-18-LC_w_sh_10_iter_m	.996
20	resnet50_linf_4_robust	.995
21	blt_vs	.996
22	alexnet_linf_8_robust	.996
23	yudixie_resnet50_category_class_0_240908	.996
24	AdvProp_efficientnet-b8	.998
25	yudixie_resnet50_rotation_reg_0_240908	.997
26	resnet50-moclr8deg	.997
27	resnet50-SIN	.995
28	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.993
29	resnet50_linf_8_robust	.994
30	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.992
31	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.994
32	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.993
33	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.993
34	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.991
35	resnet-18-LC_1st_conv_m	.992
36	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.991
37	antialias-resnet152	.990
38	CORnetZ_CIFAR10	.991
39	alexnet_l2_3_robust	.990
40	resnet50_ecoset_full	.990
41	resnet101	.989
42	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.989
43	BiT-S-R152x2	.990
44	resnet50_FractalDB	.987
45	alexnet_training_seed_06	.989
46	resnet50_primary_visual_cortex	.988
47	vgg-16-keras	.987
48	resnet50-simclr-vissl	.985
49	texture_shape_resnet50_trained_on_SIN	.984
50	vgg-19-keras	.985
51	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.987
52	resnet18_imagenet_full	.984
53	resnet101_ecoset_full	.984
54	alexnet_training_seed_08	.983
55	imagenet_l2_3_0	.984
56	AdvProp_efficientnet-b4	.985
57	vgg_16	.981
58	mobilenet_v2_1.0_128-tf	.980
59	resnet-50_untrained	.979
60	yudixie_resnet50_object_class_0_240908	.978
61	alexnet_training_seed_04	.975
62	yudixie_resnet50_distance_rotation_0_240908	.976
63	yudixie_resnet18_category_class_0_240719	.972
64	cornet_s	.972
65	resnet50_imagenet_10_seed-0	.969
66	voneresnet-50-non_stochastic	.970
67	inception_v1	.969
68	yudixie_resnet18_distance_reg_0_240719	.968
69	BiT-M-R50x3	.969
70	compact_vgg19_V4	.969
71	yudixie_resnet50_distance_translation_0_240908	.967
72	resnet-152_v2_pytorch	.966
73	yudixie_resnet18_translation_rotation_0_240719	.965
74	resnet34_imagenet_full	.967
75	yudixie_resnet18_distance_translation_0_240719	.966
76	resnet_101_v1	.964
77	BiT-S-R50x1	.964
78	voneresnet-50-robust	.963
79	Res2Net50_26w_4s	.960
80	alexnet_training_seed_09	.958
81	voneresnet-50	.959
82	r3m_resnet18	.955
83	grcnn_robust_v1	.954
84	resnet-18-LC_w_sh_1_iter_conv_init_m	.951
85	VOneCORnet-S	.954
86	resnet50-cifar	.951
87	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.950
88	mobilevit_small	.947
89	yudixie_resnet50_translation_rotation_0_240908	.947
90	yudixie_resnet50_translation_reg_0_240908	.945
91	resnet50-vicregl0p9	.945
92	alexnet_training_seed_03	.944
93	resnet18_ecoset_full	.944
94	inception_v2-tf	.944
95	yudixie_resnet18_random_0_240719	.938
96	vonegrcnn_52e_full	.939
97	resnet-18-LC_w_sh_100_iter_conv_init_m	.936
98	yudixie_resnet18_object_class_0_240719	.937
99	resnet101_imagenet_full	.936
100	omnivore_swinB	.934
101	compact_cornets_IT	.934
102	compact_resnet50_robust_IT	.930
103	yudixie_resnet50_distance_reg_0_240908	.929
104	mobilenet_v2_1.0_160-tf	.928
105	antialiased-r50	.927
106	resnet50_imagenet_100_seed-0	.928
107	mobilenet_v2_0.5_192-tf	.926
108	resnet-18-LC_w_sh_100_iter_m	.924
109	briaai_rmbg_1_4	.925
110	grcnn_109	.924
111	CLIP-RN50	.925
112	resnet50_ewconplus_wzc	.922
113	resnet-18_untrained	.922
114	vggface	.921
115	resnet-18-LC_m	.921
116	yudixie_resnet18_distance_rotation_0_240719	.919
117	yudixie_resnet18_translation_reg_0_240719	.919
118	0.5x_resnet-18	.918
119	focalnet_tiny_lrf_in1k	.917
120	vonegrcnn_47e	.916
121	inception_v1-tf	.916
122	alexnet_training_seed_10	.914
123	alexnet_training_seed_01_fov4	.914
124	vision_transformer_vit_large_patch16_224	.915
125	alexnet_random_linf8_perturb	.912
126	yudixie_resnet18_distance_translation_rotation_0_240719	.912
127	alexnet_training_seed_02	.907
128	resnet50-SIN_IN	.907
129	vonealexnet_gaussian_noise_std4_fixed	.905
130	alexnet_fov4	.904
131	0.5x_resnet-18_LC_w_sh_100_iter	.904
132	resnet-50x2_untrained	.902
133	yudixie_resnet50_random_0_240908	.901
134	mobilenet_v2_0.75_96	.900
135	grcnn	.898
136	resnet50-vicregl0p75	.898
137	xception	.896
138	alexnet_training_seed_01	.894
139	alexnet_early_checkpoint	.893
140	artResNet18_1	.893
141	alexnet_training_seed_01_fov12	.894
142	resnet_101_v2	.891
143	resnet152_ecoset_full	.890
144	alexnet	.888
145	resnet-50_v1-tf	.883
146	convnext_small_imagenet_1_seed-0	.883
147	densenet-169	.882
148	resnet-18-LC_conv_init_m	.879
149	ViT_L_32_imagenet1k	.881
150	vonegrcnn_47e	.879
151	resnet-18-LC_w_sh_10_iter_conv_init_m	.876
152	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.878
153	r3m_resnet34	.877
154	resnet50_imagenet_full	.876
155	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
156	texture_shape_alexnet_trained_on_SIN	.870
157	compact_resnet50_IT	.870
158	yudixie_resnet18_imagenet1kpret_0_240719	.870
159	compact_resnet50_V4	.867
160	resnet50_tutorial	.867
161	yudixie_resnet50_imagenet1kpret_0_240908	.866
162	ReAlnet01_cornet_fov16	.864
163	resnet_50_v1_spiking	.863
164	0.5x_resnet-18_LC	.865
165	resnet_50_v1_spiking_l4a	.865
166	resnet50_byol	.862
167	resnet_50_v1	.863
168	resnet_50_v1_spiking_l3	.861
169	resnet_50_v1_spiking_l4	.861
170	resnet50-sup	.861
171	alexnet_random_l2_3_perturb	.858
172	resnet_50_v2	.858
173	alexnet_training_seed_07	.858
174	resnet-18_test_m	.857
175	mobilenet_v2_1_0_192	.857
176	yudixie_resnet18_rotation_reg_0_240719	.857
177	vonegrcnn_62e_nobn	.856
178	ReAlnet01_cornet_fov12	.853
179	r3m_resnet50_nocrop	.852
180	convnext_femto_ols:d1_in1k	.852
181	resnet-18-LC_w_sh_1_iter_m	.845
182	ReAlnet10	.845
183	ReAlnet10_cornet	.844
184	CLIP_resnet50	.841
185	BiT-M-R50x1	.841
186	CORnet-S	.839
187	CORnet-Z	.840
188	hmax	.839
189	mobilenet_v2_0.75_128	.840
190	mobilenet_v2_0.5_128	.840
191	0.5x_resnet-18_LC_w_sh_10_iter	.841
192	alexnet	.840
193	0.5x_resnet-18_LC_w_sh_1_iter	.840
194	alexnet-baseline	.840
195	alexnet	.839
196	alexnet	.839
197	alexnet	.841
198	resnet-18-LC_d_w_sh_1x1_conv_init_m	.838
199	alexnet	.840
200	CORnetZ_CIFAR10_bs32_20_04	.840
201	alexnet	.840
202	alexnet_reduced_aliasing_early_checkpoint	.839
203	alexnet	.840
204	alexnet	.839
205	ReAlnet02_ALL	.839
206	ReAlnet04_ALL	.840
207	ReAlnet03_ALL	.838
208	ReAlnet05_ALL	.838
209	ReAlnet06_ALL	.839
210	ReAlnet07_ALL	.839
211	ReAlnet09_ALL	.840
212	ReAlnet10_ALL	.839
213	ReAlnet08_ALL	.840
214	AlexNet_SIN	.839
215	ReAlnet04	.841
216	ReAlnet06	.840
217	ReAlnet07	.840
218	ReAlnet08	.840
219	alexnet_fov16	.839
220	ReAlnet09	.838
221	ReAlnet05	.839
222	alexnet_fov12	.838
223	ReAlnet01_cornet	.839
224	ReAlnet02_cornet	.839
225	ReAlnet03_cornet	.840
226	alexnet_workflow_trigger	.841
227	ReAlnet04_cornet	.841
228	ReAlnet01_ALL	.839
229	compact_resnet50_robust_V4	.839
230	compact_vgg19_IT	.838
231	ReAlnet05_cornet	.840
232	ReAlnet06_cornet	.839
233	compact_cornets_V4	.839
234	ReAlnet07_cornet	.840
235	ReAlnet08_cornet	.838
236	ReAlnet01	.840
237	ReAlnet09_cornet	.839
238	ReAlnet02	.840
239	ReAlnet03	.839
240	AlexNet_SIN_fov12	.839
241	resnet50_pretrained_with_retinal_waves	.838
242	CLIP_resnet50_float32	.837
243	mobilenet_v2_1_4_224	.835
244	mobilenet_v2_1_0_160	.836
245	densenet-169-keras	.831
246	resnet50-SIN_IN_IN	.827
247	resnet50-vicreg	.824
248	BiT-M-R152x2	.825
249	AdvProp_efficientnet-b0	.825
250	mobilenet_v1_0.25_128	.821
251	resnet50-barlow	.820
252	cvt_cvt-13-224-in1k_4_LucyV4	.816
253	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.814
254	efficientnet_b2_imagenet_full	.814
255	RN50	.813
256	vgg_19	.805
257	efficientnet-b7	.804
258	nasnet_large-tf	.804
259	BiT-M-R152x4	.802
260	mobilenet_v2_0_75_192	.800
261	BiT-S-R101x1	.800
262	mobilenet_v2_0.75_224-tf	.799
263	resnet50-VITO-8deg-cc	.799
264	AdvProp_efficientnet-b7	.800
265	AdvProp_efficientnet-b6	.796
266	mobilenet_v2_1.0_224-tf	.791
267	SWSL_resnext101_32x8d	.782
268	my-model	.782
269	mobilenet_v2_1_0_224	.773
270	resnet50_simclr	.774
271	BiT-M-R101x3	.773
272	resnet50	.767
273	effnetb1_272x240	.768
274	inception_v4-tf	.769
275	resnet50	.767
276	resnet-152_v1-tf	.763
277	SWSL_resnet50	.759
278	densenet-201	.761
279	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.756
280	densenet-121	.754
281	inception_v3	.749
282	omnivore_swinS	.748
283	resnet50_random_linf8_perturb	.743
284	resnet152_imagenet_full	.740
285	convnext_small_imagenet_10_seed-0	.735
286	r3m_resnet50	.733
287	convnext_small_imagenet_full_seed-0	.732
288	v1-pyr-nodown	.731
289	resnet18_imagenet21kP	.728
290	convnext_large_imagenet_full_seed-0	.726
291	resnet50_random_l2_perturb	.721
292	pnasnet_large-tf	.722
293	mobilenet_v2_0.75_192-tf	.720
294	nasnet_mobile-tf	.720
295	konkle_alexnetgn_ipcl_ref01_primary_model	.720
296	mobilenet_v2_0_5_224	.718
297	alexnet_training_seed_05	.710
298	CLIP_ViT-B_32	.708
299	resnet_SIN_IN_FT_IN	.694
300	mobilenet_v2_0.5_224-tf	.694
301	nasnet_mobile	.694
302	efficientnet_b0	.691
303	deit_base_imagenet_full_seed-0	.692
304	deit_small_imagenet_full_seed-0	.688
305	inception_v3-tf	.688
306	BiT-S-R50x3	.687
307	yolos_tiny	.685
308	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.683
309	convnext_tiny_imagenet_full_seed-0	.679
310	efficientnet_b1_imagenet_full	.679
311	my-model	.672
312	vit_base_patch16_clip_224:openai_ft_in1k	.671
313	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.664
314	BiT-S-R152x4	.661
315	mobilenet_v2_1.4_224-tf	.660
316	mobilenet_v2_1.0_192-tf	.653
317	convnext_large:fb_in22k_ft_in1k	.647
318	densenet-201-keras	.643
319	densenet-121-keras	.641
320	convnext_xlarge:fb_in22k_ft_in1k	.640
321	mobilenet_v2_1-4_224_pytorch	.639
322	cvt_cvt-21-224-in1k_4_LucyV4	.638
323	resnet50_moco_v2	.637
324	mobilenet_v2_1.3_224-tf	.636
325	mobilenet_v2_0_5_192	.636
326	FrankRobWobv0	.623
327	mobilenet_v2_0.5_160	.623
328	resnet-101_v2-tf	.620
329	deit_small_imagenet_10_seed-0	.618
330	mobilenet_v2_1_0_128	.617
331	convnext_tiny_sup	.618
332	antialiased-rnext101_32x8d	.613
333	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.608
334	vit_relpos_base_patch16_clsgap_224:sw_in1k	.605
335	convnext_tiny:in12k_ft_in1k	.603
336	vit_base_patch14_dinov2:lvd142m	.604
337	efficientnet_b0_imagenet_full	.600
338	effnetv2m_custom384	.599
339	resnet-101_v1-tf	.584
340	mobilenet_v2_1_3_224	.585
341	mobilenet_v2_0_75_160	.583
342	deit_small_imagenet_100_seed-0	.580
343	resnet-50_v2-tf	.579
344	BiT-S-R101x3	.575
345	cvt_cvt-21-384-in1k_4_LucyV4	.575
346	cvt_cvt-13-384-in1k_4_LucyV4	.571
347	AlexNet_SIN_fov4	.558
348	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.557
349	resnet-152_v2-tf	.557
350	AT_efficientnet-b2	.550
351	AdvProp_efficientnet-b2	.549
352	convnext_base_imagenet_full_seed-0	.543
353	deit_large_imagenet_full_seed-0	.543
354	inception_resnet_v2	.541
355	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.540
356	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.535
357	shufflenet_v2_x1_0	.534
358	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.532
359	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.527
360	ViT-B/32	.520
361	custom_model_cv_18_dagger_408	.513
362	mobilenet_v2_0.35_224	.498
363	vit_large_patch14_clip_336:laion2b_ft_in1k	.467
364	vit_large_patch14_clip_224:laion2b_ft_in1k	.465
365	omnivore_swinT	.465
366	cv_18_dagger_408_pretrained	.462
367	mobilenet_v2_0_75_224	.456
368	convnext_small_imagenet_100_seed-0	.457
369	resnet50_imagenet_1_seed-0	.443
370	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.437
371	resnet-18-LC_untrained	.414
372	convnext_base:clip_laiona_augreg_ft_in1k_384	.413
373	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.409
374	vit_base_patch14_reg4_dinov2:lvd142m	.409
375	vit_large_patch14_clip_224:openai_ft_in1k	.404
376	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.395
377	ReAlnet01_cornet_fov4	.370
378	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.346
379	resnet50-meshes-lt-100-original-pretrained	.161
380	resnet50-meshes-lt-100-original-scratch	.063
381	mobilenet_v2_0.35_160
382	resnet18-supervised
383	fitvid_trained_on_physion
384	openclip
385	resnext101_32x32d_wsl
386	resnext101_32x16d_wsl
387	resnext101_32x48d_wsl
388	resnet50_ImageNet
389	regnet_y_400mf
390	tv_efficientnet-b1
391	alexnet_ks_torevert
392	yudixie_resnet50_imagenet1kpret_0_240312
393	resnet_152_v1
394	densenet_201_pytorch
395	pnasnet_large_pytorch
396	inception_v3_pytorch
397	inception_v4
398	nasnet_large
399	deit_small_imagenet_1_seed-0
400	pnasnet_large
401	vonegrcnn_62e_nobn
402	vonegrcnn_52e_full
403	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

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Schiller1976

Metric: sf_selective