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

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio