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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	resnet-152_v1-tf	.943
18	cv_18_dagger_408_pretrained	.943
19	RN50	.941
20	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.941
21	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.939
22	CLIP_resnet50_float32	.942
23	CLIP_resnet50	.940
24	nasnet_mobile	.940
25	vit_relpos_base_patch16_clsgap_224:sw_in1k	.939
26	BiT-S-R50x3	.939
27	my-model	.939
28	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.939
29	vonegrcnn_52e_full	.937
30	vision_transformer_vit_large_patch16_224	.937
31	resnet50_simclr	.936
32	vonegrcnn_47e	.935
33	convnext_tiny_sup	.935
34	grcnn_109	.934
35	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.933
36	resnet-18-LC_w_sh_10_iter_m	.931
37	AdvProp_efficientnet-b8	.931
38	cvt_cvt-13-384-in1k_4_LucyV4	.931
39	inception_v3	.929
40	grcnn_v2_text_noise	.927
41	alexnet_training_seed_04	.926
42	convnext_base:clip_laiona_augreg_ft_in1k_384	.925
43	resnet-50_v1-tf	.926
44	efficientnet-b7	.926
45	blt_vs	.924
46	custom_model_cv_18_dagger_408	.924
47	resnet50-SIN_IN_IN	.923
48	cvt_cvt-21-384-in1k_4_LucyV4	.921
49	CLIP-RN50	.922
50	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.921
51	resnet_50_v1	.921
52	cvt_cvt-13-224-in1k_4_LucyV4	.922
53	resnet_SIN_IN_FT_IN	.921
54	resnet50-sup	.921
55	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.920
56	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.920
57	resnet_50_v1_spiking_l4	.921
58	deepeisnn_ei_imagenet_resnet18	.920
59	resnet50_tutorial	.920
60	resnet_50_v1_spiking	.919
61	resnet_50_v1_spiking_l3	.920
62	resnet50-barlow	.918
63	yudixie_resnet50_imagenet1kpret_0_240908	.919
64	resnet_50_v1_spiking_l4a	.918
65	resnet-50_v2-tf	.919
66	BiT-S-R101x3	.917
67	vit_base_patch14_dinov2-lvd142m	.917
68	yudixie_resnet18_object_class_0_240719	.916
69	alexnet_training_seed_01	.915
70	alexnet_training_seed_01_fov4	.914
71	vit_base_patch14_reg4_dinov2-lvd142m	.915
72	BiT-S-R101x1	.914
73	mobilenet_v2_1.4_224-tf	.915
74	alexnet_training_seed_07	.914
75	alexnet_training_seed_02	.915
76	compact_resnet50_robust_IT	.915
77	alexnet_training_seed_01_fov16	.914
78	mobilenet_v2_1_4_224	.914
79	BiT-M-R152x4	.914
80	alexnet_training_seed_10	.915
81	efficientnet_b2_imagenet_full	.914
82	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.913
83	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.913
84	alexnet_training_seed_03	.912
85	alexnet_training_seed_09	.911
86	vonegrcnn_47e	.911
87	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.911
88	mobilenet_v2_1.3_224-tf	.909
89	alexnet_fov16	.908
90	resnet50_primary_visual_cortex	.908
91	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.906
92	0.5x_resnet-18_LC_w_sh_10_iter	.905
93	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.905
94	resnet50_random_l2_perturb	.905
95	convnext_base_fb_in22k_ft_in1k	.904
96	compact_cornets_IT	.904
97	SWSL_resnext101_32x8d	.902
98	resnet50_byol	.903
99	cvt_cvt-21-224-in1k_4_LucyV4	.903
100	deit_base_imagenet_full_seed-0	.902
101	focalnet_tiny_lrf_in1k	.900
102	effnetv2m_custom384	.899
103	resnet50_imagenet_full	.899
104	resnet50-vicreg	.898
105	xception	.899
106	BiT-M-R101x1	.897
107	nasnet_mobile-tf	.898
108	resnet-101_v1-tf	.898
109	mobilenet_v2_0.5_192-tf	.898
110	resnet-18-LC_w_sh_1_iter_conv_init_m	.898
111	omnivore_swinT	.896
112	convnext_small_imagenet_100_seed-0	.897
113	shufflenet_v2_x1_0	.896
114	resnet50_moco_v2	.894
115	AlexNet_SIN	.894
116	resnet50-moclr8deg	.894
117	grcnn	.893
118	resnet50_FractalDB	.893
119	inception_v4-tf	.892
120	BiT-M-R50x3	.891
121	r3m_resnet50_nocrop	.889
122	mobilenet_v2_1.0_160-tf	.889
123	konkle_alexnetgn_ipcl_ref01_primary_model	.888
124	efficientnet_b0_imagenet_full	.888
125	resnet-18_untrained	.887
126	AdvProp_efficientnet-b4	.886
127	r3m_resnet34_nocrop	.886
128	BiT-S-R152x4	.884
129	0.5x_resnet-18_LC_w_sh_100_iter	.884
130	yudixie_resnet50_distance_rotation_0_240908	.884
131	resnet50_imagenet_100_seed-0	.884
132	BiT-S-R50x1	.883
133	r3m_resnet50	.882
134	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
135	CLIP_ViT-B_32	.882
136	inception_v3-tf	.881
137	BiT-M-R50x1	.881
138	resnet50	.880
139	mobilenet_v2_0.75_192-tf	.880
140	vonegrcnn_62e_nobn	.879
141	inception_v2-tf	.878
142	mobilenet_v2_1.0_96	.878
143	yudixie_wbh_object_class_scaling_0415_imagenet	.878
144	yudixie_resnet18_random_0_240719	.877
145	yudixie_resnet18_imagenet1kpret_0_240719	.876
146	mobilenet_v2_1.0_224-tf	.876
147	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.875
148	SWSL_resnet50	.876
149	resnet50_random_linf8_perturb	.876
150	AdvProp_efficientnet-b6	.873
151	BiT-M-R152x2	.873
152	alexnet_training_seed_08	.872
153	omnivore_swinS	.871
154	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.871
155	resnet18_ecoset_full	.872
156	mobilenet_v2_0.5_224-tf	.872
157	mobilenet_v2_0_5_192	.871
158	alexnet	.871
159	CORnet-Z	.870
160	vit_base_patch16_dinov3-lvd1689m	.870
161	resnet50-SIN	.870
162	resnet50_linf_8_robust	.870
163	omnivore_swinB	.868
164	yudixie_resnet18_category_class_0_240719	.868
165	convnext_base_imagenet_full_seed-0	.868
166	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
167	convnext_large:fb_in22k_ft_in1k	.866
168	convnext_femto_ols:d1_in1k	.866
169	pnasnet_large_halfPrecision	.865
170	eBarlow_lmda_02_1000ep	.865
171	effnetb1_272x240	.863
172	convnext_tiny:in12k_ft_in1k	.863
173	antialiased-r50	.862
174	yudixie_resnet50_distance_reg_0_240908	.861
175	texture_shape_resnet50_trained_on_SIN	.861
176	resnet-50x2_untrained	.860
177	BiT-M-R101x3	.859
178	mobilenet_v2_0_75_224	.859
179	vit_large_patch14_clip_224:openai_ft_in1k	.856
180	mobilenet_v2_0_75_160	.854
181	vit_base_patch16_clip_224:openai_ft_in1k	.853
182	imagenet_l2_10_0	.853
183	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
184	inception_resnet_v2	.851
185	AlexNet_SIN_fov16	.849
186	AdvProp_efficientnet-b7	.849
187	mobilenet_v2_1_0_224	.848
188	pnasnet_large-tf	.847
189	vonealexnet_gaussian_noise_std4_fixed	.847
190	AlexNet_SIN_fov12	.844
191	resnet50-VITO-8deg-cc	.843
192	resnet-50-robust	.843
193	FrankRobWobv0	.842
194	yudixie_resnet18_translation_rotation_0_240719	.843
195	resnet-50_untrained	.839
196	yudixie_resnet50_distance_translation_rotation_0_240908	.838
197	alexnet_fov12	.837
198	resnet-152_v2-tf	.837
199	mobilenet_v2_1_0_160	.836
200	mobilenet_v2_0.75_224-tf	.834
201	deit_small_imagenet_100_seed-0	.833
202	mobilenet_v2_1.0_192-tf	.833
203	compact_cornets_V4	.833
204	aughost_fair_resnet18	.830
205	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.830
206	resnet-101_v2-tf	.830
207	yudixie_resnet50_rotation_reg_0_240908	.830
208	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
209	deit3_base_patch16_224_fb_in1k	.828
210	alexnet_reduced_aliasing_early_checkpoint	.829
211	vgg_19	.829
212	mobilenet_v2_1_0_192	.827
213	resnet50_linf_4_robust	.827
214	compact_resnet50_V4	.826
215	alexnet_fov4	.826
216	AdvProp_efficientnet-b0	.826
217	convnext_large_imagenet_full_seed-0	.821
218	grcnn_robust_v1	.821
219	yudixie_resnet50_category_class_0_240908	.821
220	r3m_resnet18	.819
221	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
222	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
223	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
224	yudixie_wbh_object_class_scaling_0415_run_3	.817
225	efficientnet_b1_imagenet_full	.817
226	efficientnet_b0	.816
227	nasnet_large-tf	.814
228	resnet50_robust_l2_eps1	.813
229	resnet50_imagenet_10_seed-0	.814
230	yudixie_resnet50_translation_rotation_0_240908	.811
231	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.811
232	deit3_base_patch16_224_fb_in22k_ft_in1k	.811
233	alexnet_random_linf8_perturb	.810
234	resnet-18-LC_w_sh_1_iter_m	.809
235	CORnetZ_CIFAR10	.808
236	deit_large_imagenet_full_seed-0	.806
237	r3m_resnet34	.804
238	resnet50_l2_3_robust	.805
239	alexnet_random_l2_3_perturb	.805
240	yudixie_resnet50_object_class_0_240908	.802
241	yudixie_resnet18_translation_reg_0_240719	.801
242	texture_shape_alexnet_trained_on_SIN	.800
243	resnet18_imagenet21kP	.800
244	resnet_101_v1	.797
245	BiT-S-R152x2	.797
246	densenet-169-keras	.797
247	resnet50_robust_l2_eps3	.796
248	antialias-resnet152	.794
249	yudixie_wbh_object_class_scaling_0415_run_0	.794
250	convnext_xlarge:fb_in22k_ft_in1k	.793
251	mobilenet_v2_0.35_224	.792
252	compact_resnet50_IT	.790
253	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
254	yudixie_resnet18_distance_translation_rotation_0_240719	.789
255	resnet101	.787
256	resnet50_pretrained_with_retinal_waves	.787
257	AlexNet_SIN_fov4	.787
258	resnet-18-LC_m	.786
259	compact_resnet50_robust_V4	.784
260	inception_v1	.785
261	yudixie_resnet50_distance_translation_0_240908	.781
262	compact_vgg19_IT	.777
263	vgg-16-keras	.778
264	alexnet_robust_correct	.778
265	vgg-19-keras	.776
266	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
267	alexnet_training_seed_05	.773
268	voneresnet-50-non_stochastic	.772
269	yudixie_resnet18_distance_translation_0_240719	.771
270	ReAlnet01_cornet_fov16	.770
271	mobilenet_v2_0_75_192	.769
272	VOneCORnet-S	.767
273	voneresnet-50	.766
274	alexnet_early_checkpoint	.764
275	voneresnet-50-robust	.763
276	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
277	convnext_tiny_imagenet_full_seed-0	.761
278	alexnet_training_seed_01_fov12	.760
279	imagenet_l2_3_0	.757
280	mobilenet_v2_0.75_96	.757
281	artResNet18_1	.755
282	mobilenet_v2_1_3_224	.746
283	yudixie_resnet18_distance_rotation_0_240719	.746
284	resnet50-vicregl0p9	.746
285	yudixie_resnet50_random_0_240908	.746
286	alexnet_training_seed_06	.745
287	deit_small_imagenet_full_seed-0	.745
288	resnet50_imagenet_1_seed-0	.746
289	inception_v1-tf	.745
290	my-model	.745
291	resnet50-cifar	.745
292	resnet50-meshes-lt-100-original-pretrained	.745
293	mobilenet_v2_1.0_128-tf	.745
294	resnet50-vitoimagevidnet8	.742
295	alexnet_linf_8_robust	.742
296	0.5x_resnet-18_LC	.741
297	resnet50_ewconplus_wzc_seed4_2_323	.740
298	densenet-121	.738
299	densenet-121-keras	.737
300	resnet50	.736
301	densenet-169	.734
302	densenet-201	.733
303	yudixie_resnet50_translation_reg_0_240908	.732
304	briaai_rmbg_1_4	.731
305	deepeisnn_ei_cifar10_vgg19	.730
306	resnet50_st_wzc_seed6_2_323	.725
307	resnext101_32x8d_wsl	.724
308	mobilenet_v1_0.25_128	.722
309	yudixie_wbh_object_class_scaling_0415_run_2	.721
310	densenet-201-keras	.720
311	ViT-B/32	.715
312	resnet50_ewconplus_wzc_seed5_2__323	.713
313	ReAlnet01	.712
314	alexnet_l2_3_robust	.712
315	ReAlnet01_cornet	.710
316	resnet50_ecoset_full	.710
317	resnet50_ewconplus_wzc_seed7_2_323	.710
318	ReAlnet09_cornet	.707
319	barlow-twins-resnet50	.707
320	resnet50-simclr-vissl	.706
321	AdvProp_efficientnet-b2	.707
322	ReAlnet09	.705
323	AT_efficientnet-b2	.705
324	v1-pyr-nodown	.705
325	ReAlnet03_ALL	.704
326	resnet50_st_wzc_seed5_2_323	.700
327	hmax	.700
328	ReAlnet08_ALL	.696
329	yudixie_wbh_object_class_scaling_0415_run_1	.695
330	convnext_base_fb_in1k_gram_aligned_dinov3	.693
331	Res2Net50_26w_4s	.693
332	deepeisnn_ei_cifar10_resnet18	.691
333	resnet50_st_wzc_seed4_2_323	.691
334	resnet34_imagenet_full	.690
335	resnet50_st_wzc_seed7_2_323	.688
336	ReAlnet07	.688
337	ReAlnet07_cornet	.688
338	ReAlnet08	.688
339	ReAlnet08_cornet	.687
340	resnet50-SIN_IN	.685
341	ReAlnet04	.684
342	ReAlnet04_cornet	.684
343	ReAlnet03	.682
344	ReAlnet03_cornet	.681
345	ReAlnet02_ALL	.680
346	ReAlnet04_ALL	.679
347	resnet50_st_wzc_noseed_2_323	.679
348	resnet50_st_wzc_2_323	.678
349	ReAlnet10	.678
350	mobilenet_v2_1_0_128	.677
351	ReAlnet02_cornet	.677
352	ReAlnet02	.677
353	ReAlnet10_cornet	.677
354	yolos_tiny	.677
355	yudixie_resnet18_distance_reg_0_240719	.676
356	mobilenet_v2_0.75_128	.672
357	ReAlnet05_ALL	.670
358	resnet18_fair	.670
359	ReAlnet06_ALL	.669
360	ReAlnet07_ALL	.667
361	yudixie_resnet18_rotation_reg_0_240719	.667
362	CORnet-S	.667
363	resnet34_ecoset_full	.665
364	ReAlnet06	.665
365	ReAlnet06_cornet	.665
366	ReAlnet01_cornet_fov4	.664
367	ReAlnet05	.664
368	ReAlnet05_cornet	.664
369	ReAlnet01_ALL	.663
370	ReAlnet09_ALL	.661
371	resnet101_imagenet_full	.661
372	ReAlnet10_ALL	.661
373	resnet-18-LC_untrained	.658
374	0.5x_resnet-18_LC_w_sh_1_iter	.655
375	resnet50_st_wzc_2	.654
376	resnet_101_v2	.652
377	resnet152_ecoset_full	.650
378	resnet50_ewconplus_noseed_2_323	.647
379	ReAlnet01_cornet_fov12	.647
380	vggface	.646
381	resnet18_imagenet_full	.646
382	resnet50_ewconplus_wzc_seed2_2__323	.645
383	resnet-18_test_m	.644
384	resnet50_st_wzc_seed3_2_323	.644
385	resnet101_ecoset_full	.644
386	resnet-18-LC_conv_init_m	.643
387	resnet50_ewconplus_wzc_2_323	.640
388	convnext_base_fb_in1k	.639
389	resnet-18-LC_w_sh_100_iter_m	.636
390	cornet_s	.635
391	0.5x_resnet-18	.633
392	resnet_50_v2	.631
393	resnet-152_v2_pytorch	.632
394	resnet50_ewconplus_wzc	.618
395	mobilenet_v2_1-4_224_pytorch	.616
396	resnet50_ewconplus_wzc_seed6_2_323	.604
397	resnet50_ewconplus_wzc_seed3_2_323	.597
398	mobilenet_v2_0_5_224	.597
399	convnext_small_imagenet_1_seed-0	.593
400	resnet152_imagenet_full	.590
401	mobilenet_v2_0.75_160-tf	.561
402	CORnetZ_CIFAR10_bs32_20_04	.507
403	convnext_small_imagenet_10_seed-0	.491
404	deit_small_imagenet_10_seed-0	.333
405	resnet50-meshes-lt-100-original-scratch	.333
406	mobilenet_v2_0.5_160	.288
407	mobilenet_v2_0.5_128	.288
408	mobilenet_v2_0.35_160
409	resnet18-supervised
410	fitvid_trained_on_physion
411	openclip
412	resnext101_32x32d_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	resnext101_32x16d_wsl
421	densenet_201_pytorch
422	inception_v3_pytorch
423	pnasnet_large_pytorch
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

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio