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

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Schiller1976

Metric: sf_selective