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	vgg-16-keras	.987
45	resnet50-simclr-vissl	.985
46	texture_shape_resnet50_trained_on_SIN	.984
47	vgg-19-keras	.985
48	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.987
49	resnet18_imagenet_full	.984
50	resnet101_ecoset_full	.984
51	alexnet_training_seed_08	.983
52	imagenet_l2_3_0	.984
53	AdvProp_efficientnet-b4	.985
54	vgg_16	.981
55	mobilenet_v2_1.0_128-tf	.980
56	resnet-50_untrained	.979
57	yudixie_resnet50_object_class_0_240908	.978
58	alexnet_training_seed_04	.975
59	yudixie_resnet50_distance_rotation_0_240908	.976
60	yudixie_resnet18_category_class_0_240719	.972
61	cornet_s	.972
62	resnet50_imagenet_10_seed-0	.969
63	voneresnet-50-non_stochastic	.970
64	inception_v1	.969
65	yudixie_resnet18_distance_reg_0_240719	.968
66	BiT-M-R50x3	.969
67	yudixie_resnet50_distance_translation_0_240908	.967
68	resnet-152_v2_pytorch	.966
69	yudixie_resnet18_translation_rotation_0_240719	.965
70	resnet34_imagenet_full	.967
71	yudixie_resnet18_distance_translation_0_240719	.966
72	resnet_101_v1	.964
73	BiT-S-R50x1	.964
74	voneresnet-50-robust	.963
75	Res2Net50_26w_4s	.960
76	alexnet_training_seed_09	.958
77	voneresnet-50	.959
78	r3m_resnet18	.955
79	grcnn_robust_v1	.954
80	resnet-18-LC_w_sh_1_iter_conv_init_m	.951
81	VOneCORnet-S	.954
82	resnet50-cifar	.951
83	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.950
84	mobilevit_small	.947
85	yudixie_resnet50_translation_rotation_0_240908	.947
86	yudixie_resnet50_translation_reg_0_240908	.945
87	resnet50-vicregl0p9	.945
88	alexnet_training_seed_03	.944
89	resnet18_ecoset_full	.944
90	inception_v2-tf	.944
91	yudixie_resnet18_random_0_240719	.938
92	vonegrcnn_52e_full	.939
93	resnet-18-LC_w_sh_100_iter_conv_init_m	.936
94	yudixie_resnet18_object_class_0_240719	.937
95	resnet101_imagenet_full	.936
96	omnivore_swinB	.934
97	yudixie_resnet50_distance_reg_0_240908	.929
98	mobilenet_v2_1.0_160-tf	.928
99	antialiased-r50	.927
100	resnet50_imagenet_100_seed-0	.928
101	mobilenet_v2_0.5_192-tf	.926
102	resnet-18-LC_w_sh_100_iter_m	.924
103	grcnn_109	.924
104	CLIP-RN50	.925
105	resnet-18_untrained	.922
106	vggface	.921
107	resnet-18-LC_m	.921
108	yudixie_resnet18_distance_rotation_0_240719	.919
109	yudixie_resnet18_translation_reg_0_240719	.919
110	0.5x_resnet-18	.918
111	focalnet_tiny_lrf_in1k	.917
112	vonegrcnn_47e	.916
113	inception_v1-tf	.916
114	alexnet_training_seed_10	.914
115	vision_transformer_vit_large_patch16_224	.915
116	alexnet_random_linf8_perturb	.912
117	yudixie_resnet18_distance_translation_rotation_0_240719	.912
118	alexnet_training_seed_02	.907
119	resnet50-SIN_IN	.907
120	vonealexnet_gaussian_noise_std4_fixed	.905
121	0.5x_resnet-18_LC_w_sh_100_iter	.904
122	resnet-50x2_untrained	.902
123	yudixie_resnet50_random_0_240908	.901
124	mobilenet_v2_0.75_96	.900
125	grcnn	.898
126	resnet50-vicregl0p75	.898
127	xception	.896
128	alexnet_training_seed_01	.894
129	alexnet_early_checkpoint	.893
130	artResNet18_1	.893
131	resnet_101_v2	.891
132	resnet152_ecoset_full	.890
133	alexnet	.888
134	resnet-50_v1-tf	.883
135	convnext_small_imagenet_1_seed-0	.883
136	densenet-169	.882
137	resnet-18-LC_conv_init_m	.879
138	ViT_L_32_imagenet1k	.881
139	vonegrcnn_47e	.879
140	resnet-18-LC_w_sh_10_iter_conv_init_m	.876
141	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.878
142	r3m_resnet34	.877
143	resnet50_imagenet_full	.876
144	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
145	texture_shape_alexnet_trained_on_SIN	.870
146	yudixie_resnet18_imagenet1kpret_0_240719	.870
147	resnet50_tutorial	.867
148	yudixie_resnet50_imagenet1kpret_0_240908	.866
149	0.5x_resnet-18_LC	.865
150	resnet50_byol	.862
151	resnet_50_v1	.863
152	resnet50-sup	.861
153	alexnet_random_l2_3_perturb	.858
154	resnet_50_v2	.858
155	alexnet_training_seed_07	.858
156	resnet-18_test_m	.857
157	mobilenet_v2_1_0_192	.857
158	yudixie_resnet18_rotation_reg_0_240719	.857
159	vonegrcnn_62e_nobn	.856
160	r3m_resnet50_nocrop	.852
161	convnext_femto_ols:d1_in1k	.852
162	resnet-18-LC_w_sh_1_iter_m	.845
163	ReAlnet10	.845
164	ReAlnet10_cornet	.844
165	CLIP_resnet50	.841
166	BiT-M-R50x1	.841
167	alexnet	.840
168	CORnet-Z	.840
169	hmax	.839
170	mobilenet_v2_0.75_128	.840
171	mobilenet_v2_0.5_128	.840
172	0.5x_resnet-18_LC_w_sh_10_iter	.841
173	alexnet	.840
174	0.5x_resnet-18_LC_w_sh_1_iter	.840
175	alexnet-baseline	.840
176	alexnet	.839
177	alexnet	.841
178	resnet-18-LC_d_w_sh_1x1_conv_init_m	.838
179	alexnet	.840
180	CORnetZ_CIFAR10_bs32_20_04	.840
181	alexnet	.840
182	alexnet_reduced_aliasing_early_checkpoint	.839
183	CORnet-S	.839
184	alexnet	.839
185	ReAlnet07_cornet	.840
186	ReAlnet03_cornet	.840
187	ReAlnet04_cornet	.841
188	ReAlnet05_cornet	.840
189	ReAlnet09_cornet	.839
190	ReAlnet01	.840
191	ReAlnet08_cornet	.838
192	ReAlnet06_cornet	.839
193	ReAlnet03	.839
194	ReAlnet08	.840
195	ReAlnet05	.839
196	ReAlnet07	.840
197	ReAlnet02	.840
198	ReAlnet09	.838
199	ReAlnet06	.840
200	ReAlnet_incorrect	.839
201	ReAlnet04	.841
202	ReAlnet01_cornet	.839
203	AlexNet_SIN	.839
204	ReAlnet02_cornet	.839
205	resnet50_pretrained_with_retinal_waves	.838
206	CLIP_resnet50_float32	.837
207	mobilenet_v2_1_4_224	.835
208	mobilenet_v2_1_0_160	.836
209	densenet-169-keras	.831
210	resnet50-SIN_IN_IN	.827
211	resnet50-vicreg	.824
212	BiT-M-R152x2	.825
213	AdvProp_efficientnet-b0	.825
214	mobilenet_v1_0.25_128	.821
215	resnet50-barlow	.820
216	cvt_cvt-13-224-in1k_4_LucyV4	.816
217	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.814
218	efficientnet_b2_imagenet_full	.814
219	RN50	.813
220	vgg_19	.805
221	efficientnet-b7	.804
222	nasnet_large-tf	.804
223	BiT-M-R152x4	.802
224	mobilenet_v2_0_75_192	.800
225	BiT-S-R101x1	.800
226	mobilenet_v2_0.75_224-tf	.799
227	resnet50-VITO-8deg-cc	.799
228	AdvProp_efficientnet-b7	.800
229	AdvProp_efficientnet-b6	.796
230	mobilenet_v2_1.0_224-tf	.791
231	SWSL_resnext101_32x8d	.782
232	my-model	.782
233	mobilenet_v2_1_0_224	.773
234	resnet50_simclr	.774
235	BiT-M-R101x3	.773
236	resnet50	.767
237	effnetb1_272x240	.768
238	inception_v4-tf	.769
239	resnet50	.767
240	resnet-152_v1-tf	.763
241	SWSL_resnet50	.759
242	densenet-201	.761
243	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.756
244	densenet-121	.754
245	inception_v3	.749
246	omnivore_swinS	.748
247	resnet50_random_linf8_perturb	.743
248	resnet152_imagenet_full	.740
249	convnext_small_imagenet_10_seed-0	.735
250	r3m_resnet50	.733
251	convnext_small_imagenet_full_seed-0	.732
252	v1-pyr-nodown	.731
253	resnet18_imagenet21kP	.728
254	convnext_large_imagenet_full_seed-0	.726
255	resnet50_random_l2_perturb	.721
256	pnasnet_large-tf	.722
257	mobilenet_v2_0.75_192-tf	.720
258	nasnet_mobile-tf	.720
259	konkle_alexnetgn_ipcl_ref01_primary_model	.720
260	mobilenet_v2_0_5_224	.718
261	alexnet_training_seed_05	.710
262	CLIP_ViT-B_32	.708
263	resnet_SIN_IN_FT_IN	.694
264	mobilenet_v2_0.5_224-tf	.694
265	nasnet_mobile	.694
266	efficientnet_b0	.691
267	deit_base_imagenet_full_seed-0	.692
268	deit_small_imagenet_full_seed-0	.688
269	inception_v3-tf	.688
270	BiT-S-R50x3	.687
271	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.683
272	convnext_tiny_imagenet_full_seed-0	.679
273	efficientnet_b1_imagenet_full	.679
274	my-model	.672
275	vit_base_patch16_clip_224:openai_ft_in1k	.671
276	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.664
277	BiT-S-R152x4	.661
278	mobilenet_v2_1.4_224-tf	.660
279	mobilenet_v2_1.0_192-tf	.653
280	convnext_large:fb_in22k_ft_in1k	.647
281	densenet-201-keras	.643
282	densenet-121-keras	.641
283	convnext_xlarge:fb_in22k_ft_in1k	.640
284	mobilenet_v2_1-4_224_pytorch	.639
285	cvt_cvt-21-224-in1k_4_LucyV4	.638
286	resnet50_moco_v2	.637
287	mobilenet_v2_1.3_224-tf	.636
288	mobilenet_v2_0_5_192	.636
289	FrankRobWobv0	.623
290	mobilenet_v2_0.5_160	.623
291	resnet-101_v2-tf	.620
292	deit_small_imagenet_10_seed-0	.618
293	mobilenet_v2_1_0_128	.617
294	convnext_tiny_sup	.618
295	antialiased-rnext101_32x8d	.613
296	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.608
297	vit_relpos_base_patch16_clsgap_224:sw_in1k	.605
298	convnext_tiny:in12k_ft_in1k	.603
299	efficientnet_b0_imagenet_full	.600
300	effnetv2m_custom384	.599
301	resnet-101_v1-tf	.584
302	mobilenet_v2_1_3_224	.585
303	mobilenet_v2_0_75_160	.583
304	deit_small_imagenet_100_seed-0	.580
305	resnet-50_v2-tf	.579
306	BiT-S-R101x3	.575
307	cvt_cvt-21-384-in1k_4_LucyV4	.575
308	cvt_cvt-13-384-in1k_4_LucyV4	.571
309	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.557
310	resnet-152_v2-tf	.557
311	AT_efficientnet-b2	.550
312	AdvProp_efficientnet-b2	.549
313	convnext_base_imagenet_full_seed-0	.543
314	deit_large_imagenet_full_seed-0	.543
315	inception_resnet_v2	.541
316	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.540
317	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.535
318	shufflenet_v2_x1_0	.534
319	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.532
320	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.527
321	ViT-B/32	.520
322	custom_model_cv_18_dagger_408	.513
323	mobilenet_v2_0.35_224	.498
324	vit_large_patch14_clip_336:laion2b_ft_in1k	.467
325	vit_large_patch14_clip_224:laion2b_ft_in1k	.465
326	omnivore_swinT	.465
327	cv_18_dagger_408_pretrained	.462
328	mobilenet_v2_0_75_224	.456
329	convnext_small_imagenet_100_seed-0	.457
330	resnet50_imagenet_1_seed-0	.443
331	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.437
332	resnet-18-LC_untrained	.414
333	convnext_base:clip_laiona_augreg_ft_in1k_384	.413
334	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.409
335	vit_large_patch14_clip_224:openai_ft_in1k	.404
336	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.395
337	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.346
338	resnet50-meshes-lt-100-original-pretrained	.161
339	resnet50-meshes-lt-100-original-scratch	.063
340	fitvid_trained_on_physion	X
341	regnet_y_400mf	X
342	vonegrcnn_52e_full	X
343	nasnet_large	X
344	mobilenet_v2_0.35_160	X
345	inception_v4	X
346	densenet_201_pytorch	X
347	pnasnet_large_pytorch	X
348	inception_v3_pytorch	X
349	resnet_152_v2	X
350	resnext101_32x32d_wsl	X
351	resnext101_32x16d_wsl	X
352	resnet18-supervised	X
353	resnet_152_v1	X
354	resnext101_32x48d_wsl	X
355	vonegrcnn_62e_nobn	X
356	openclip	X
357	pnasnet_large	X
358	deit_small_imagenet_1_seed-0	X
359	yudixie_resnet50_imagenet1kpret_0_240312	X
360	alexnet_ks_torevert	X
361	tv_efficientnet-b1	X
362	resnet50_ImageNet	X

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