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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_Ringach2002-cv_bandwidth_ratio")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ViT_L_32_imagenet1k	.966
2	convnext_small_imagenet_full_seed-0	.963
3	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.961
4	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.962
5	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.961
6	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.960
7	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.961
8	resnet-18-LC_w_sh_10_iter_conv_init_m	.961
9	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.959
10	compact_vgg19_V4	.956
11	resnet50-vicregl0p75	.955
12	antialiased-rnext101_32x8d	.950
13	vit_large_patch14_clip_336:laion2b_ft_in1k	.949
14	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.947
15	resnet-18-LC_1st_conv_m	.947
16	vgg_16	.944
17	vit_huge_clip336_laion	.943
18	resnet-152_v1-tf	.943
19	cv_18_dagger_408_pretrained	.943
20	RN50	.941
21	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.941
22	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.939
23	CLIP_resnet50_float32	.942
24	CLIP_resnet50	.940
25	nasnet_mobile	.940
26	vit_relpos_base_patch16_clsgap_224:sw_in1k	.939
27	BiT-S-R50x3	.939
28	my-model	.939
29	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.939
30	vonegrcnn_52e_full	.937
31	vision_transformer_vit_large_patch16_224	.937
32	resnet50_simclr	.936
33	vonegrcnn_47e	.935
34	convnext_tiny_sup	.935
35	grcnn_109	.934
36	regnety_032	.934
37	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.933
38	resnet-18-LC_w_sh_10_iter_m	.931
39	AdvProp_efficientnet-b8	.931
40	cvt_cvt-13-384-in1k_4_LucyV4	.931
41	inception_v3	.929
42	grcnn_v2_text_noise	.927
43	alexnet_training_seed_04	.926
44	convnext_base:clip_laiona_augreg_ft_in1k_384	.925
45	dinov2_vitg14_lc	.926
46	resnet-50_v1-tf	.926
47	efficientnet-b7	.926
48	blt_vs	.924
49	custom_model_cv_18_dagger_408	.924
50	resnet50-SIN_IN_IN	.923
51	cvt_cvt-21-384-in1k_4_LucyV4	.921
52	CLIP-RN50	.922
53	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.921
54	resnet_50_v1	.921
55	cvt_cvt-13-224-in1k_4_LucyV4	.922
56	resnet_SIN_IN_FT_IN	.921
57	dinov2_vitl14_lc_res336	.921
58	resnet50-sup	.921
59	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.920
60	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.920
61	resnet_50_v1_spiking_l4	.921
62	deepeisnn_ei_imagenet_resnet18	.920
63	resnet50_tutorial	.920
64	resnet_50_v1_spiking	.919
65	resnet_50_v1_spiking_l3	.920
66	resnet50-barlow	.918
67	yudixie_resnet50_imagenet1kpret_0_240908	.919
68	resnet_50_v1_spiking_l4a	.918
69	resnet-50_v2-tf	.919
70	BiT-S-R101x3	.917
71	vit_base_patch14_dinov2-lvd142m	.917
72	yudixie_resnet18_object_class_0_240719	.916
73	alexnet_training_seed_01	.915
74	alexnet_training_seed_01_fov4	.914
75	vit_base_patch14_reg4_dinov2-lvd142m	.915
76	BiT-S-R101x1	.914
77	mobilenet_v2_1.4_224-tf	.915
78	alexnet_training_seed_07	.914
79	alexnet_training_seed_02	.915
80	compact_resnet50_robust_IT	.915
81	alexnet_training_seed_01_fov16	.914
82	mobilenet_v2_1_4_224	.914
83	BiT-M-R152x4	.914
84	alexnet_training_seed_10	.915
85	efficientnet_b2_imagenet_full	.914
86	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.913
87	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.913
88	alexnet_training_seed_03	.912
89	dinov2_vitl14_lc	.912
90	alexnet_training_seed_09	.911
91	vonegrcnn_47e	.911
92	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.911
93	mobilenet_v2_1.3_224-tf	.909
94	alexnet_fov16	.908
95	seresnext50_32x4d	.908
96	resnet50_primary_visual_cortex	.908
97	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.906
98	0.5x_resnet-18_LC_w_sh_10_iter	.905
99	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.905
100	resnet50_random_l2_perturb	.905
101	convnext_base_fb_in22k_ft_in1k	.904
102	compact_cornets_IT	.904
103	SWSL_resnext101_32x8d	.902
104	resnet50_byol	.903
105	cvt_cvt-21-224-in1k_4_LucyV4	.903
106	deit_base_imagenet_full_seed-0	.902
107	focalnet_tiny_lrf_in1k	.900
108	effnetv2m_custom384	.899
109	resnet50_imagenet_full	.899
110	resnet50-vicreg	.898
111	xception	.899
112	BiT-M-R101x1	.897
113	nasnet_mobile-tf	.898
114	resnet-101_v1-tf	.898
115	mobilenet_v2_0.5_192-tf	.898
116	dinov2_vitl14_reg_lc_featread	.897
117	resnet-18-LC_w_sh_1_iter_conv_init_m	.898
118	omnivore_swinT	.896
119	convnext_small_imagenet_100_seed-0	.897
120	shufflenet_v2_x1_0	.896
121	dinov2_vitg14_reg_lc	.896
122	dinov2_vitl14_reg_lc	.896
123	resnet50_moco_v2	.894
124	AlexNet_SIN	.894
125	resnet50-moclr8deg	.894
126	grcnn	.893
127	resnet50_FractalDB	.893
128	inception_v4-tf	.892
129	BiT-M-R50x3	.891
130	r3m_resnet50_nocrop	.889
131	mobilenet_v2_1.0_160-tf	.889
132	konkle_alexnetgn_ipcl_ref01_primary_model	.888
133	efficientnet_b0_imagenet_full	.888
134	resnet-18_untrained	.887
135	AdvProp_efficientnet-b4	.886
136	r3m_resnet34_nocrop	.886
137	BiT-S-R152x4	.884
138	0.5x_resnet-18_LC_w_sh_100_iter	.884
139	yudixie_resnet50_distance_rotation_0_240908	.884
140	resnet50_imagenet_100_seed-0	.884
141	BiT-S-R50x1	.883
142	r3m_resnet50	.882
143	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
144	CLIP_ViT-B_32	.882
145	inception_v3-tf	.881
146	BiT-M-R50x1	.881
147	resnet50	.880
148	mobilenet_v2_0.75_192-tf	.880
149	vonegrcnn_62e_nobn	.879
150	inception_v2-tf	.878
151	mobilenet_v2_1.0_96	.878
152	yudixie_wbh_object_class_scaling_0415_imagenet	.878
153	yudixie_resnet18_random_0_240719	.877
154	convnext_large_clip_laion2b_finelayers	.876
155	yudixie_resnet18_imagenet1kpret_0_240719	.876
156	mobilenet_v2_1.0_224-tf	.876
157	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.875
158	SWSL_resnet50	.876
159	resnet50_random_linf8_perturb	.876
160	vit_large_clip336_openai	.873
161	AdvProp_efficientnet-b6	.873
162	BiT-M-R152x2	.873
163	alexnet_training_seed_08	.872
164	omnivore_swinS	.871
165	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.871
166	resnet18_ecoset_full	.872
167	mobilenet_v2_0.5_224-tf	.872
168	mobilenet_v2_0_5_192	.871
169	alexnet	.871
170	CORnet-Z	.870
171	vit_base_patch16_dinov3-lvd1689m	.870
172	resnet50-SIN	.870
173	resnet50_linf_8_robust	.870
174	omnivore_swinB	.868
175	yudixie_resnet18_category_class_0_240719	.868
176	convnext_base_imagenet_full_seed-0	.868
177	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
178	convnext_large:fb_in22k_ft_in1k	.866
179	convnext_femto_ols:d1_in1k	.866
180	pnasnet_large_halfPrecision	.865
181	eBarlow_lmda_02_1000ep	.865
182	gabor_filter_mean_abs_4x4_v2	.865
183	effnetb1_272x240	.863
184	convnext_tiny:in12k_ft_in1k	.863
185	antialiased-r50	.862
186	yudixie_resnet50_distance_reg_0_240908	.861
187	texture_shape_resnet50_trained_on_SIN	.861
188	resnet-50x2_untrained	.860
189	BiT-M-R101x3	.859
190	mobilenet_v2_0_75_224	.859
191	vit_large_patch14_clip_224:openai_ft_in1k	.856
192	mobilenet_v2_0_75_160	.854
193	vit_base_patch16_clip_224:openai_ft_in1k	.853
194	imagenet_l2_10_0	.853
195	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
196	inception_resnet_v2	.851
197	eicircuit_resnet18_bn_20260919	.850
198	AlexNet_SIN_fov16	.849
199	AdvProp_efficientnet-b7	.849
200	mobilenet_v2_1_0_224	.848
201	pnasnet_large-tf	.847
202	vonealexnet_gaussian_noise_std4_fixed	.847
203	AlexNet_SIN_fov12	.844
204	resnet50-VITO-8deg-cc	.843
205	resnet-50-robust	.843
206	FrankRobWobv0	.842
207	yudixie_resnet18_translation_rotation_0_240719	.843
208	resnet-50_untrained	.839
209	yudixie_resnet50_distance_translation_rotation_0_240908	.838
210	alexnet_fov12	.837
211	resnet-152_v2-tf	.837
212	mobilenet_v2_1_0_160	.836
213	mobilenet_v2_0.75_224-tf	.834
214	deit_small_imagenet_100_seed-0	.833
215	mobilenet_v2_1.0_192-tf	.833
216	compact_cornets_V4	.833
217	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.830
218	resnet-101_v2-tf	.830
219	yudixie_resnet50_rotation_reg_0_240908	.830
220	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
221	deit3_base_patch16_224_fb_in1k	.828
222	alexnet_reduced_aliasing_early_checkpoint	.829
223	vgg_19	.829
224	mobilenet_v2_1_0_192	.827
225	resnet50_linf_4_robust	.827
226	compact_resnet50_V4	.826
227	alexnet_fov4	.826
228	AdvProp_efficientnet-b0	.826
229	convnext_large_imagenet_full_seed-0	.821
230	grcnn_robust_v1	.821
231	yudixie_resnet50_category_class_0_240908	.821
232	r3m_resnet18	.819
233	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
234	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
235	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
236	yudixie_wbh_object_class_scaling_0415_run_3	.817
237	efficientnet_b1_imagenet_full	.817
238	efficientnet_b0	.816
239	nasnet_large-tf	.814
240	resnet50_robust_l2_eps1	.813
241	resnet50_imagenet_10_seed-0	.814
242	yudixie_resnet50_translation_rotation_0_240908	.811
243	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.811
244	deit3_base_patch16_224_fb_in22k_ft_in1k	.811
245	alexnet_random_linf8_perturb	.810
246	resnet-18-LC_w_sh_1_iter_m	.809
247	CORnetZ_CIFAR10	.808
248	deit_large_imagenet_full_seed-0	.806
249	r3m_resnet34	.804
250	resnet50_l2_3_robust	.805
251	alexnet_random_l2_3_perturb	.805
252	yudixie_resnet50_object_class_0_240908	.802
253	yudixie_resnet18_translation_reg_0_240719	.801
254	texture_shape_alexnet_trained_on_SIN	.800
255	resnet18_imagenet21kP	.800
256	resnet_101_v1	.797
257	BiT-S-R152x2	.797
258	densenet-169-keras	.797
259	resnet50_robust_l2_eps3	.796
260	antialias-resnet152	.794
261	yudixie_wbh_object_class_scaling_0415_run_0	.794
262	convnext_xlarge:fb_in22k_ft_in1k	.793
263	mobilenet_v2_0.35_224	.792
264	compact_resnet50_IT	.790
265	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
266	yudixie_resnet18_distance_translation_rotation_0_240719	.789
267	resnet101	.787
268	resnet50_pretrained_with_retinal_waves	.787
269	AlexNet_SIN_fov4	.787
270	resnet-18-LC_m	.786
271	compact_resnet50_robust_V4	.784
272	inception_v1	.785
273	yudixie_resnet50_distance_translation_0_240908	.781
274	compact_vgg19_IT	.777
275	vgg-16-keras	.778
276	alexnet_robust_correct	.778
277	vgg-19-keras	.776
278	convnext_large_in22k_supervised	.775
279	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
280	alexnet_training_seed_05	.773
281	voneresnet-50-non_stochastic	.772
282	yudixie_resnet18_distance_translation_0_240719	.771
283	ReAlnet01_cornet_fov16	.770
284	mobilenet_v2_0_75_192	.769
285	VOneCORnet-S	.767
286	voneresnet-50	.766
287	alexnet_early_checkpoint	.764
288	voneresnet-50-robust	.763
289	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
290	convnext_tiny_imagenet_full_seed-0	.761
291	alexnet_training_seed_01_fov12	.760
292	imagenet_l2_3_0	.757
293	mobilenet_v2_0.75_96	.757
294	artResNet18_1	.755
295	mobilenet_v2_1_3_224	.746
296	yudixie_resnet18_distance_rotation_0_240719	.746
297	resnet50-vicregl0p9	.746
298	yudixie_resnet50_random_0_240908	.746
299	alexnet_training_seed_06	.745
300	deit_small_imagenet_full_seed-0	.745
301	resnet50_imagenet_1_seed-0	.746
302	gabor_filter_entropy_4x4_v2_forcedCosine	.746
303	inception_v1-tf	.745
304	my-model	.745
305	resnet50-cifar	.745
306	mobilenet_v2_1.0_128-tf	.745
307	resnet50-vitoimagevidnet8	.742
308	alexnet_linf_8_robust	.742
309	0.5x_resnet-18_LC	.741
310	densenet-121	.738
311	densenet-121-keras	.737
312	densenet-169	.734
313	densenet-201	.733
314	yudixie_resnet50_translation_reg_0_240908	.732
315	briaai_rmbg_1_4	.731
316	deepeisnn_ei_cifar10_vgg19	.730
317	resnet50_st_wzc_seed6_2_323	.725
318	resnext101_32x8d_wsl	.724
319	mobilenet_v1_0.25_128	.722
320	yudixie_wbh_object_class_scaling_0415_run_2	.721
321	densenet-201-keras	.720
322	ViT-B/32	.715
323	resnet50_ewconplus_wzc_seed5_2__323	.713
324	ReAlnet01	.712
325	alexnet_l2_3_robust	.712
326	ReAlnet01_cornet	.710
327	resnet50_ecoset_full	.710
328	ReAlnet09_cornet	.707
329	barlow-twins-resnet50	.707
330	resnet50-simclr-vissl	.706
331	AdvProp_efficientnet-b2	.707
332	ReAlnet09	.705
333	AT_efficientnet-b2	.705
334	v1-pyr-nodown	.705
335	ReAlnet03_ALL	.704
336	hmax	.700
337	ReAlnet08_ALL	.696
338	yudixie_wbh_object_class_scaling_0415_run_1	.695
339	convnext_base_fb_in1k_gram_aligned_dinov3	.693
340	Res2Net50_26w_4s	.693
341	deepeisnn_ei_cifar10_resnet18	.691
342	resnet34_imagenet_full	.690
343	resnet50_st_wzc_seed7_2_323	.688
344	ReAlnet07	.688
345	ReAlnet07_cornet	.688
346	ReAlnet08	.688
347	ReAlnet08_cornet	.687
348	resnet50-SIN_IN	.685
349	ReAlnet04	.684
350	ReAlnet04_cornet	.684
351	ReAlnet03	.682
352	ReAlnet03_cornet	.681
353	ReAlnet02_ALL	.680
354	ReAlnet04_ALL	.679
355	ReAlnet10	.678
356	mobilenet_v2_1_0_128	.677
357	ReAlnet02_cornet	.677
358	ReAlnet02	.677
359	ReAlnet10_cornet	.677
360	yolos_tiny	.677
361	yudixie_resnet18_distance_reg_0_240719	.676
362	mobilenet_v2_0.75_128	.672
363	ReAlnet05_ALL	.670
364	resnet18_fair	.670
365	ReAlnet06_ALL	.669
366	ReAlnet07_ALL	.667
367	yudixie_resnet18_rotation_reg_0_240719	.667
368	CORnet-S	.667
369	resnet34_ecoset_full	.665
370	ReAlnet06	.665
371	ReAlnet06_cornet	.665
372	ReAlnet01_cornet_fov4	.664
373	ReAlnet05	.664
374	ReAlnet05_cornet	.664
375	ReAlnet01_ALL	.663
376	ReAlnet09_ALL	.661
377	resnet101_imagenet_full	.661
378	ReAlnet10_ALL	.661
379	resnet-18-LC_untrained	.658
380	0.5x_resnet-18_LC_w_sh_1_iter	.655
381	resnet50_st_wzc_2	.654
382	resnet_101_v2	.652
383	resnet152_ecoset_full	.650
384	ReAlnet01_cornet_fov12	.647
385	vggface	.646
386	resnet18_imagenet_full	.646
387	resnet50_ewconplus_wzc_seed2_2__323	.645
388	resnet-18_test_m	.644
389	resnet50_st_wzc_seed3_2_323	.644
390	resnet101_ecoset_full	.644
391	resnet-18-LC_conv_init_m	.643
392	convnext_base_fb_in1k	.639
393	resnet-18-LC_w_sh_100_iter_m	.636
394	cornet_s	.635
395	0.5x_resnet-18	.633
396	resnet_50_v2	.631
397	resnet-152_v2_pytorch	.632
398	resnet50_hvs_wzc	.627
399	resnet50_ewconplus_wzc	.618
400	mobilenet_v2_1-4_224_pytorch	.616
401	resnet50_ewconplus_wzc_seed6_2_323	.604
402	mobilenet_v2_0_5_224	.597
403	convnext_small_imagenet_1_seed-0	.593
404	resnet152_imagenet_full	.590
405	mobilenet_v2_0.75_160-tf	.561
406	CORnetZ_CIFAR10_bs32_20_04	.507
407	convnext_small_imagenet_10_seed-0	.491
408	deit_small_imagenet_10_seed-0	.333
409	mobilenet_v2_0.5_160	.288
410	mobilenet_v2_0.5_128	.288
411	gabor_filter_entropy_4x4	.000
412	gabor_filter_entropy_4x4_v2	.000
413	mobilenet_v2_0.35_160
414	resnet18-supervised
415	fitvid_trained_on_physion
416	openclip
417	resnext101_32x32d_wsl
418	resnext101_32x48d_wsl
419	resnet50_ImageNet
420	regnet_y_400mf
421	tv_efficientnet-b1
422	alexnet_ks_torevert
423	yudixie_resnet50_imagenet1kpret_0_240312
424	resnet_152_v1
425	resnext101_32x16d_wsl
426	densenet_201_pytorch
427	inception_v3_pytorch
428	pnasnet_large_pytorch
429	inception_v4
430	nasnet_large
431	deit_small_imagenet_1_seed-0
432	pnasnet_large
433	vonegrcnn_62e_nobn
434	vonegrcnn_52e_full
435	resnet_152_v2
436	pr1_stl10_disentangled_alpha04
437	predictive_coding_pc
438	eva02_large_in22k
439	convnext_xxlarge_clip

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio