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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	deepeisnn_ei_cifar10_resnet18	1.0
3	resnext101_32x8d_wsl	1.0
4	alexnet_training_seed_01_fov16	.999
5	BiT-M-R101x1	1.0
6	mobilenet_v2_0.75_160-tf	1.0
7	resnet-50-robust	1.0
8	resnet34_ecoset_full	1.0
9	barlow-twins-resnet50	1.0
10	yudixie_resnet50_distance_translation_rotation_0_240908	.999
11	resnet50_ewconplus_wzc_seed6_2_323	.998
12	AlexNet_SIN_fov16	.999
13	alexnet_robust_correct	.999
14	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.999
15	resnet50-vitoimagevidnet8	.999
16	mobilenet_v2_1.0_96	.999
17	resnet50_ewconplus_wzc_seed4_2_323	.996
18	resnet50_robust_l2_eps1	.998
19	resnet50_robust_l2_eps3	.999
20	resnet50_l2_3_robust	.996
21	grcnn_v2_text_noise	.999
22	resnet-18-LC_w_sh_10_iter_m	.996
23	resnet50_linf_4_robust	.995
24	blt_vs	.996
25	alexnet_linf_8_robust	.996
26	deepeisnn_ei_cifar10_vgg19	.995
27	yudixie_resnet50_category_class_0_240908	.996
28	AdvProp_efficientnet-b8	.998
29	yudixie_resnet50_rotation_reg_0_240908	.997
30	resnet50-moclr8deg	.997
31	resnet50-SIN	.995
32	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.993
33	resnet50_linf_8_robust	.994
34	resnet18_fair	.992
35	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.992
36	resnet50_st_wzc_2_323	.993
37	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.994
38	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.993
39	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.993
40	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.991
41	resnet-18-LC_1st_conv_m	.992
42	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.991
43	antialias-resnet152	.990
44	resnet50_ewconplus_wzc_seed7_2_323	.991
45	CORnetZ_CIFAR10	.991
46	alexnet_l2_3_robust	.990
47	resnet50_ecoset_full	.990
48	resnet101	.989
49	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.989
50	BiT-S-R152x2	.990
51	resnet50_FractalDB	.987
52	alexnet_training_seed_06	.989
53	resnet50_primary_visual_cortex	.988
54	resnet50_ewconplus_wzc_seed3_2_323	.985
55	vgg-16-keras	.987
56	resnet50-simclr-vissl	.985
57	resnet50_ewconplus_wzc_seed5_2__323	.986
58	texture_shape_resnet50_trained_on_SIN	.984
59	vgg-19-keras	.985
60	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.987
61	resnet18_imagenet_full	.984
62	resnet101_ecoset_full	.984
63	resnet50_ewconplus_wzc_2_323	.983
64	alexnet_training_seed_08	.983
65	imagenet_l2_3_0	.984
66	AdvProp_efficientnet-b4	.985
67	vgg_16	.981
68	resnet50_ewconplus_wzc_seed2_2__323	.980
69	mobilenet_v2_1.0_128-tf	.980
70	resnet-50_untrained	.979
71	yudixie_resnet50_object_class_0_240908	.978
72	alexnet_training_seed_04	.975
73	resnet50_st_wzc_2	.975
74	yudixie_resnet50_distance_rotation_0_240908	.976
75	yudixie_resnet18_category_class_0_240719	.972
76	cornet_s	.972
77	resnet50_st_wzc_seed5_2_323	.970
78	resnet50_imagenet_10_seed-0	.969
79	voneresnet-50-non_stochastic	.970
80	inception_v1	.969
81	yudixie_resnet18_distance_reg_0_240719	.968
82	BiT-M-R50x3	.969
83	compact_vgg19_V4	.969
84	yudixie_resnet50_distance_translation_0_240908	.967
85	resnet-152_v2_pytorch	.966
86	yudixie_resnet18_translation_rotation_0_240719	.965
87	resnet34_imagenet_full	.967
88	yudixie_resnet18_distance_translation_0_240719	.966
89	resnet_101_v1	.964
90	BiT-S-R50x1	.964
91	voneresnet-50-robust	.963
92	Res2Net50_26w_4s	.960
93	alexnet_training_seed_09	.958
94	voneresnet-50	.959
95	r3m_resnet18	.955
96	resnet50_st_wzc_noseed_2_323	.956
97	grcnn_robust_v1	.954
98	resnet50_st_wzc_seed3_2_323	.953
99	resnet-18-LC_w_sh_1_iter_conv_init_m	.951
100	VOneCORnet-S	.954
101	resnet50-cifar	.951
102	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.950
103	resnet50_st_wzc_seed4_2_323	.948
104	mobilevit_small	.947
105	yudixie_resnet50_translation_rotation_0_240908	.947
106	yudixie_resnet50_translation_reg_0_240908	.945
107	resnet50-vicregl0p9	.945
108	alexnet_training_seed_03	.944
109	resnet18_ecoset_full	.944
110	inception_v2-tf	.944
111	yudixie_resnet18_random_0_240719	.938
112	vonegrcnn_52e_full	.939
113	resnet-18-LC_w_sh_100_iter_conv_init_m	.936
114	yudixie_resnet18_object_class_0_240719	.937
115	resnet101_imagenet_full	.936
116	omnivore_swinB	.934
117	yudixie_wbh_object_class_scaling_0415_run_1	.933
118	compact_cornets_IT	.934
119	compact_resnet50_robust_IT	.930
120	yudixie_resnet50_distance_reg_0_240908	.929
121	mobilenet_v2_1.0_160-tf	.928
122	antialiased-r50	.927
123	resnet50_imagenet_100_seed-0	.928
124	mobilenet_v2_0.5_192-tf	.926
125	resnet-18-LC_w_sh_100_iter_m	.924
126	briaai_rmbg_1_4	.925
127	grcnn_109	.924
128	CLIP-RN50	.925
129	resnet50_ewconplus_wzc	.922
130	resnet-18_untrained	.922
131	vggface	.921
132	resnet-18-LC_m	.921
133	yudixie_resnet18_distance_rotation_0_240719	.919
134	yudixie_resnet18_translation_reg_0_240719	.919
135	0.5x_resnet-18	.918
136	focalnet_tiny_lrf_in1k	.917
137	vonegrcnn_47e	.916
138	inception_v1-tf	.916
139	alexnet_training_seed_10	.914
140	alexnet_training_seed_01_fov4	.914
141	vision_transformer_vit_large_patch16_224	.915
142	resnet50_ewconplus_noseed_2_323	.914
143	resnet50_st_wzc_seed6_2_323	.913
144	alexnet_random_linf8_perturb	.912
145	yudixie_resnet18_distance_translation_rotation_0_240719	.912
146	alexnet_training_seed_02	.907
147	resnet50-SIN_IN	.907
148	vonealexnet_gaussian_noise_std4_fixed	.905
149	alexnet_fov4	.904
150	0.5x_resnet-18_LC_w_sh_100_iter	.904
151	resnet-50x2_untrained	.902
152	yudixie_resnet50_random_0_240908	.901
153	mobilenet_v2_0.75_96	.900
154	grcnn	.898
155	resnet50-vicregl0p75	.898
156	xception	.896
157	deepeisnn_ei_imagenet_resnet18	.896
158	alexnet_training_seed_01	.894
159	alexnet_early_checkpoint	.893
160	artResNet18_1	.893
161	alexnet_training_seed_01_fov12	.894
162	resnet_101_v2	.891
163	resnet152_ecoset_full	.890
164	resnet-50_v1-tf	.883
165	convnext_small_imagenet_1_seed-0	.883
166	densenet-169	.882
167	resnet50_st_wzc_seed7_2_323	.883
168	resnet-18-LC_conv_init_m	.879
169	ViT_L_32_imagenet1k	.881
170	vonegrcnn_47e	.879
171	resnet-18-LC_w_sh_10_iter_conv_init_m	.876
172	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.878
173	r3m_resnet34	.877
174	resnet50_imagenet_full	.876
175	aughost_fair_resnet18	.874
176	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
177	yudixie_wbh_object_class_scaling_0415_run_3	.872
178	texture_shape_alexnet_trained_on_SIN	.870
179	compact_resnet50_IT	.870
180	yudixie_wbh_object_class_scaling_0415_imagenet	.869
181	yudixie_resnet18_imagenet1kpret_0_240719	.870
182	compact_resnet50_V4	.867
183	resnet50_tutorial	.867
184	yudixie_resnet50_imagenet1kpret_0_240908	.866
185	ReAlnet01_cornet_fov16	.864
186	resnet_50_v1_spiking	.863
187	0.5x_resnet-18_LC	.865
188	resnet_50_v1_spiking_l4a	.865
189	resnet50_byol	.862
190	resnet_50_v1	.863
191	resnet_50_v1_spiking_l3	.861
192	resnet_50_v1_spiking_l4	.861
193	resnet50-sup	.861
194	alexnet_random_l2_3_perturb	.858
195	resnet_50_v2	.858
196	alexnet_training_seed_07	.858
197	resnet-18_test_m	.857
198	mobilenet_v2_1_0_192	.857
199	yudixie_resnet18_rotation_reg_0_240719	.857
200	vonegrcnn_62e_nobn	.856
201	ReAlnet01_cornet_fov12	.853
202	r3m_resnet50_nocrop	.852
203	convnext_femto_ols:d1_in1k	.852
204	resnet-18-LC_w_sh_1_iter_m	.845
205	yudixie_wbh_object_class_scaling_0415_run_2	.846
206	ReAlnet10	.845
207	ReAlnet10_cornet	.844
208	CLIP_resnet50	.841
209	BiT-M-R50x1	.841
210	CORnet-S	.839
211	CORnet-Z	.840
212	hmax	.839
213	mobilenet_v2_0.75_128	.840
214	mobilenet_v2_0.5_128	.840
215	0.5x_resnet-18_LC_w_sh_10_iter	.841
216	0.5x_resnet-18_LC_w_sh_1_iter	.840
217	alexnet	.839
218	resnet-18-LC_d_w_sh_1x1_conv_init_m	.838
219	CORnetZ_CIFAR10_bs32_20_04	.840
220	alexnet_reduced_aliasing_early_checkpoint	.839
221	ReAlnet02_ALL	.839
222	ReAlnet04_ALL	.840
223	ReAlnet03_ALL	.838
224	ReAlnet05_ALL	.838
225	ReAlnet06_ALL	.839
226	ReAlnet07_ALL	.839
227	ReAlnet09_ALL	.840
228	ReAlnet10_ALL	.839
229	ReAlnet08_ALL	.840
230	AlexNet_SIN	.839
231	ReAlnet04	.841
232	ReAlnet06	.840
233	ReAlnet07	.840
234	ReAlnet08	.840
235	alexnet_fov16	.839
236	ReAlnet09	.838
237	ReAlnet05	.839
238	alexnet_fov12	.838
239	ReAlnet01_cornet	.839
240	ReAlnet02_cornet	.839
241	ReAlnet03_cornet	.840
242	ReAlnet04_cornet	.841
243	ReAlnet01_ALL	.839
244	compact_resnet50_robust_V4	.839
245	compact_vgg19_IT	.838
246	ReAlnet05_cornet	.840
247	ReAlnet06_cornet	.839
248	compact_cornets_V4	.839
249	ReAlnet07_cornet	.840
250	ReAlnet08_cornet	.838
251	ReAlnet01	.840
252	ReAlnet09_cornet	.839
253	ReAlnet02	.840
254	ReAlnet03	.839
255	AlexNet_SIN_fov12	.839
256	yudixie_wbh_object_class_scaling_0415_run_0	.839
257	resnet50_pretrained_with_retinal_waves	.838
258	CLIP_resnet50_float32	.837
259	mobilenet_v2_1_4_224	.835
260	mobilenet_v2_1_0_160	.836
261	densenet-169-keras	.831
262	resnet50-SIN_IN_IN	.827
263	resnet50-vicreg	.824
264	BiT-M-R152x2	.825
265	AdvProp_efficientnet-b0	.825
266	mobilenet_v1_0.25_128	.821
267	resnet50-barlow	.820
268	cvt_cvt-13-224-in1k_4_LucyV4	.816
269	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.814
270	efficientnet_b2_imagenet_full	.814
271	RN50	.813
272	vgg_19	.805
273	efficientnet-b7	.804
274	nasnet_large-tf	.804
275	BiT-M-R152x4	.802
276	vit_base_patch16_dinov3-lvd1689m	.799
277	mobilenet_v2_0_75_192	.800
278	BiT-S-R101x1	.800
279	mobilenet_v2_0.75_224-tf	.799
280	resnet50-VITO-8deg-cc	.799
281	AdvProp_efficientnet-b7	.800
282	AdvProp_efficientnet-b6	.796
283	mobilenet_v2_1.0_224-tf	.791
284	SWSL_resnext101_32x8d	.782
285	my-model	.782
286	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.781
287	convnext_base_fb_in22k_ft_in1k	.780
288	mobilenet_v2_1_0_224	.773
289	pnasnet_large_halfPrecision	.773
290	resnet50_simclr	.774
291	BiT-M-R101x3	.773
292	resnet50	.767
293	effnetb1_272x240	.768
294	inception_v4-tf	.769
295	resnet50	.767
296	resnet-152_v1-tf	.763
297	SWSL_resnet50	.759
298	densenet-201	.761
299	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.756
300	densenet-121	.754
301	inception_v3	.749
302	omnivore_swinS	.748
303	resnet50_random_linf8_perturb	.743
304	resnet152_imagenet_full	.740
305	convnext_small_imagenet_10_seed-0	.735
306	r3m_resnet50	.733
307	convnext_small_imagenet_full_seed-0	.732
308	v1-pyr-nodown	.731
309	resnet18_imagenet21kP	.728
310	convnext_large_imagenet_full_seed-0	.726
311	resnet50_random_l2_perturb	.721
312	pnasnet_large-tf	.722
313	mobilenet_v2_0.75_192-tf	.720
314	nasnet_mobile-tf	.720
315	konkle_alexnetgn_ipcl_ref01_primary_model	.720
316	mobilenet_v2_0_5_224	.718
317	alexnet_training_seed_05	.710
318	CLIP_ViT-B_32	.708
319	resnet_SIN_IN_FT_IN	.694
320	mobilenet_v2_0.5_224-tf	.694
321	nasnet_mobile	.694
322	efficientnet_b0	.691
323	deit_base_imagenet_full_seed-0	.692
324	deit_small_imagenet_full_seed-0	.688
325	inception_v3-tf	.688
326	BiT-S-R50x3	.687
327	yolos_tiny	.685
328	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.683
329	convnext_tiny_imagenet_full_seed-0	.679
330	efficientnet_b1_imagenet_full	.679
331	my-model	.672
332	vit_base_patch16_clip_224:openai_ft_in1k	.671
333	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.664
334	BiT-S-R152x4	.661
335	mobilenet_v2_1.4_224-tf	.660
336	mobilenet_v2_1.0_192-tf	.653
337	convnext_large:fb_in22k_ft_in1k	.647
338	densenet-201-keras	.643
339	densenet-121-keras	.641
340	convnext_xlarge:fb_in22k_ft_in1k	.640
341	mobilenet_v2_1-4_224_pytorch	.639
342	cvt_cvt-21-224-in1k_4_LucyV4	.638
343	resnet50_moco_v2	.637
344	mobilenet_v2_1.3_224-tf	.636
345	mobilenet_v2_0_5_192	.636
346	FrankRobWobv0	.623
347	mobilenet_v2_0.5_160	.623
348	resnet-101_v2-tf	.620
349	deit_small_imagenet_10_seed-0	.618
350	mobilenet_v2_1_0_128	.617
351	convnext_tiny_sup	.618
352	antialiased-rnext101_32x8d	.613
353	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.608
354	vit_relpos_base_patch16_clsgap_224:sw_in1k	.605
355	convnext_tiny:in12k_ft_in1k	.603
356	vit_base_patch14_dinov2-lvd142m	.601
357	efficientnet_b0_imagenet_full	.600
358	effnetv2m_custom384	.599
359	convnext_base_fb_in1k	.592
360	convnext_base_fb_in1k_gram_aligned_dinov3	.585
361	resnet-101_v1-tf	.584
362	mobilenet_v2_1_3_224	.585
363	mobilenet_v2_0_75_160	.583
364	deit_small_imagenet_100_seed-0	.580
365	resnet-50_v2-tf	.579
366	BiT-S-R101x3	.575
367	cvt_cvt-21-384-in1k_4_LucyV4	.575
368	cvt_cvt-13-384-in1k_4_LucyV4	.571
369	AlexNet_SIN_fov4	.558
370	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.557
371	resnet-152_v2-tf	.557
372	AT_efficientnet-b2	.550
373	AdvProp_efficientnet-b2	.549
374	convnext_base_imagenet_full_seed-0	.543
375	deit_large_imagenet_full_seed-0	.543
376	inception_resnet_v2	.541
377	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.540
378	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.535
379	shufflenet_v2_x1_0	.534
380	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.532
381	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.527
382	ViT-B/32	.520
383	custom_model_cv_18_dagger_408	.513
384	mobilenet_v2_0.35_224	.498
385	deit3_base_patch16_224_fb_in22k_ft_in1k	.481
386	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.476
387	vit_large_patch14_clip_336:laion2b_ft_in1k	.467
388	vit_large_patch14_clip_224:laion2b_ft_in1k	.465
389	omnivore_swinT	.465
390	cv_18_dagger_408_pretrained	.462
391	mobilenet_v2_0_75_224	.456
392	convnext_small_imagenet_100_seed-0	.457
393	resnet50_imagenet_1_seed-0	.443
394	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.437
395	resnet-18-LC_untrained	.414
396	convnext_base:clip_laiona_augreg_ft_in1k_384	.413
397	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.409
398	vit_base_patch14_reg4_dinov2-lvd142m	.406
399	vit_large_patch14_clip_224:openai_ft_in1k	.404
400	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.395
401	deit3_base_patch16_224_fb_in1k	.374
402	ReAlnet01_cornet_fov4	.370
403	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.369
404	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.346
405	resnet50-meshes-lt-100-original-pretrained	.161
406	resnet50-meshes-lt-100-original-scratch	.063
407	mobilenet_v2_0.35_160
408	resnet18-supervised
409	fitvid_trained_on_physion
410	openclip
411	resnext101_32x32d_wsl
412	resnext101_32x16d_wsl
413	resnext101_32x48d_wsl
414	resnet50_ImageNet
415	regnet_y_400mf
416	tv_efficientnet-b1
417	alexnet_ks_torevert
418	yudixie_resnet50_imagenet1kpret_0_240312
419	resnet_152_v1
420	densenet_201_pytorch
421	pnasnet_large_pytorch
422	inception_v3_pytorch
423	eBarlow_lmda_02_1000ep
424	inception_v4
425	nasnet_large
426	deit_small_imagenet_1_seed-0
427	pnasnet_large
428	vonegrcnn_62e_nobn
429	vonegrcnn_52e_full
430	resnet_152_v2
431	pr1_stl10_disentangled_alpha04
432	predictive_coding_pc

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