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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	resnet50_tutorial	.920
59	resnet_50_v1_spiking	.919
60	resnet_50_v1_spiking_l3	.920
61	resnet50-barlow	.918
62	yudixie_resnet50_imagenet1kpret_0_240908	.919
63	resnet_50_v1_spiking_l4a	.918
64	resnet-50_v2-tf	.919
65	BiT-S-R101x3	.917
66	vit_base_patch14_dinov2:lvd142m	.917
67	yudixie_resnet18_object_class_0_240719	.916
68	alexnet_training_seed_01	.915
69	alexnet_training_seed_01_fov4	.914
70	vit_base_patch14_reg4_dinov2:lvd142m	.915
71	BiT-S-R101x1	.914
72	mobilenet_v2_1.4_224-tf	.915
73	alexnet_training_seed_07	.914
74	alexnet_training_seed_02	.915
75	compact_resnet50_robust_IT	.915
76	alexnet_training_seed_01_fov16	.914
77	mobilenet_v2_1_4_224	.914
78	BiT-M-R152x4	.914
79	alexnet_training_seed_10	.915
80	efficientnet_b2_imagenet_full	.914
81	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.913
82	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.913
83	alexnet_training_seed_03	.912
84	alexnet_training_seed_09	.911
85	vonegrcnn_47e	.911
86	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.911
87	mobilenet_v2_1.3_224-tf	.909
88	alexnet_fov16	.908
89	resnet50_primary_visual_cortex	.908
90	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.906
91	0.5x_resnet-18_LC_w_sh_10_iter	.905
92	resnet50_random_l2_perturb	.905
93	compact_cornets_IT	.904
94	SWSL_resnext101_32x8d	.902
95	resnet50_byol	.903
96	cvt_cvt-21-224-in1k_4_LucyV4	.903
97	deit_base_imagenet_full_seed-0	.902
98	focalnet_tiny_lrf_in1k	.900
99	effnetv2m_custom384	.899
100	resnet50_imagenet_full	.899
101	resnet50-vicreg	.898
102	xception	.899
103	BiT-M-R101x1	.897
104	nasnet_mobile-tf	.898
105	resnet-101_v1-tf	.898
106	mobilenet_v2_0.5_192-tf	.898
107	resnet-18-LC_w_sh_1_iter_conv_init_m	.898
108	omnivore_swinT	.896
109	convnext_small_imagenet_100_seed-0	.897
110	shufflenet_v2_x1_0	.896
111	resnet50_moco_v2	.894
112	AlexNet_SIN	.894
113	resnet50-moclr8deg	.894
114	grcnn	.893
115	resnet50_FractalDB	.893
116	inception_v4-tf	.892
117	BiT-M-R50x3	.891
118	r3m_resnet50_nocrop	.889
119	mobilenet_v2_1.0_160-tf	.889
120	konkle_alexnetgn_ipcl_ref01_primary_model	.888
121	efficientnet_b0_imagenet_full	.888
122	resnet-18_untrained	.887
123	AdvProp_efficientnet-b4	.886
124	r3m_resnet34_nocrop	.886
125	BiT-S-R152x4	.884
126	0.5x_resnet-18_LC_w_sh_100_iter	.884
127	yudixie_resnet50_distance_rotation_0_240908	.884
128	resnet50_imagenet_100_seed-0	.884
129	BiT-S-R50x1	.883
130	r3m_resnet50	.882
131	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
132	CLIP_ViT-B_32	.882
133	inception_v3-tf	.881
134	BiT-M-R50x1	.881
135	resnet50	.880
136	mobilenet_v2_0.75_192-tf	.880
137	vonegrcnn_62e_nobn	.879
138	inception_v2-tf	.878
139	mobilenet_v2_1.0_96	.878
140	yudixie_resnet18_random_0_240719	.877
141	yudixie_resnet18_imagenet1kpret_0_240719	.876
142	mobilenet_v2_1.0_224-tf	.876
143	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.875
144	SWSL_resnet50	.876
145	resnet50_random_linf8_perturb	.876
146	AdvProp_efficientnet-b6	.873
147	BiT-M-R152x2	.873
148	alexnet_training_seed_08	.872
149	omnivore_swinS	.871
150	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.871
151	resnet18_ecoset_full	.872
152	mobilenet_v2_0.5_224-tf	.872
153	mobilenet_v2_0_5_192	.871
154	alexnet	.871
155	CORnet-Z	.870
156	alexnet_workflow_trigger	.870
157	vit_base_patch16_dinov3:lvd1689m	.870
158	resnet50-SIN	.870
159	alexnet	.869
160	resnet50_linf_8_robust	.870
161	alexnet	.869
162	alexnet	.869
163	alexnet	.869
164	alexnet-baseline	.869
165	alexnet	.869
166	omnivore_swinB	.868
167	alexnet	.868
168	yudixie_resnet18_category_class_0_240719	.868
169	alexnet	.868
170	convnext_base_imagenet_full_seed-0	.868
171	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
172	convnext_large:fb_in22k_ft_in1k	.866
173	convnext_femto_ols:d1_in1k	.866
174	effnetb1_272x240	.863
175	convnext_tiny:in12k_ft_in1k	.863
176	antialiased-r50	.862
177	yudixie_resnet50_distance_reg_0_240908	.861
178	texture_shape_resnet50_trained_on_SIN	.861
179	resnet-50x2_untrained	.860
180	BiT-M-R101x3	.859
181	mobilenet_v2_0_75_224	.859
182	vit_large_patch14_clip_224:openai_ft_in1k	.856
183	mobilenet_v2_0_75_160	.854
184	vit_base_patch16_clip_224:openai_ft_in1k	.853
185	imagenet_l2_10_0	.853
186	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
187	inception_resnet_v2	.851
188	AlexNet_SIN_fov16	.849
189	AdvProp_efficientnet-b7	.849
190	mobilenet_v2_1_0_224	.848
191	pnasnet_large-tf	.847
192	vonealexnet_gaussian_noise_std4_fixed	.847
193	AlexNet_SIN_fov12	.844
194	resnet50-VITO-8deg-cc	.843
195	resnet-50-robust	.843
196	FrankRobWobv0	.842
197	yudixie_resnet18_translation_rotation_0_240719	.843
198	resnet-50_untrained	.839
199	yudixie_resnet50_distance_translation_rotation_0_240908	.838
200	alexnet_fov12	.837
201	resnet-152_v2-tf	.837
202	mobilenet_v2_1_0_160	.836
203	alexnet	.835
204	mobilenet_v2_0.75_224-tf	.834
205	deit_small_imagenet_100_seed-0	.833
206	mobilenet_v2_1.0_192-tf	.833
207	compact_cornets_V4	.833
208	resnet-101_v2-tf	.830
209	yudixie_resnet50_rotation_reg_0_240908	.830
210	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
211	alexnet_reduced_aliasing_early_checkpoint	.829
212	vgg_19	.829
213	mobilenet_v2_1_0_192	.827
214	resnet50_linf_4_robust	.827
215	compact_resnet50_V4	.826
216	alexnet_fov4	.826
217	AdvProp_efficientnet-b0	.826
218	convnext_large_imagenet_full_seed-0	.821
219	grcnn_robust_v1	.821
220	yudixie_resnet50_category_class_0_240908	.821
221	r3m_resnet18	.819
222	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
223	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
224	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
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	alexnet_random_linf8_perturb	.810
232	resnet-18-LC_w_sh_1_iter_m	.809
233	CORnetZ_CIFAR10	.808
234	deit_large_imagenet_full_seed-0	.806
235	r3m_resnet34	.804
236	resnet50_l2_3_robust	.805
237	alexnet_random_l2_3_perturb	.805
238	yudixie_resnet50_object_class_0_240908	.802
239	yudixie_resnet18_translation_reg_0_240719	.801
240	texture_shape_alexnet_trained_on_SIN	.800
241	resnet18_imagenet21kP	.800
242	resnet_101_v1	.797
243	BiT-S-R152x2	.797
244	densenet-169-keras	.797
245	resnet50_robust_l2_eps3	.796
246	antialias-resnet152	.794
247	convnext_xlarge:fb_in22k_ft_in1k	.793
248	mobilenet_v2_0.35_224	.792
249	compact_resnet50_IT	.790
250	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
251	yudixie_resnet18_distance_translation_rotation_0_240719	.789
252	resnet101	.787
253	resnet50_pretrained_with_retinal_waves	.787
254	AlexNet_SIN_fov4	.787
255	resnet-18-LC_m	.786
256	compact_resnet50_robust_V4	.784
257	inception_v1	.785
258	yudixie_resnet50_distance_translation_0_240908	.781
259	compact_vgg19_IT	.777
260	vgg-16-keras	.778
261	alexnet_robust_correct	.778
262	vgg-19-keras	.776
263	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
264	alexnet_training_seed_05	.773
265	voneresnet-50-non_stochastic	.772
266	yudixie_resnet18_distance_translation_0_240719	.771
267	ReAlnet01_cornet_fov16	.770
268	mobilenet_v2_0_75_192	.769
269	VOneCORnet-S	.767
270	voneresnet-50	.766
271	alexnet_early_checkpoint	.764
272	voneresnet-50-robust	.763
273	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
274	convnext_tiny_imagenet_full_seed-0	.761
275	alexnet_training_seed_01_fov12	.760
276	imagenet_l2_3_0	.757
277	mobilenet_v2_0.75_96	.757
278	artResNet18_1	.755
279	mobilenet_v2_1_3_224	.746
280	yudixie_resnet18_distance_rotation_0_240719	.746
281	resnet50-vicregl0p9	.746
282	yudixie_resnet50_random_0_240908	.746
283	alexnet_training_seed_06	.745
284	deit_small_imagenet_full_seed-0	.745
285	resnet50_imagenet_1_seed-0	.746
286	inception_v1-tf	.745
287	my-model	.745
288	resnet50-cifar	.745
289	resnet50-meshes-lt-100-original-pretrained	.745
290	mobilenet_v2_1.0_128-tf	.745
291	resnet50-vitoimagevidnet8	.742
292	alexnet_linf_8_robust	.742
293	0.5x_resnet-18_LC	.741
294	densenet-121	.738
295	densenet-121-keras	.737
296	resnet50	.736
297	densenet-169	.734
298	densenet-201	.733
299	yudixie_resnet50_translation_reg_0_240908	.732
300	briaai_rmbg_1_4	.731
301	resnext101_32x8d_wsl	.724
302	mobilenet_v1_0.25_128	.722
303	densenet-201-keras	.720
304	ViT-B/32	.715
305	ReAlnet01	.712
306	alexnet_l2_3_robust	.712
307	ReAlnet01_cornet	.710
308	resnet50_ecoset_full	.710
309	ReAlnet09_cornet	.707
310	barlow-twins-resnet50	.707
311	resnet50-simclr-vissl	.706
312	AdvProp_efficientnet-b2	.707
313	ReAlnet09	.705
314	AT_efficientnet-b2	.705
315	v1-pyr-nodown	.705
316	ReAlnet03_ALL	.704
317	hmax	.700
318	ReAlnet08_ALL	.696
319	Res2Net50_26w_4s	.693
320	resnet34_imagenet_full	.690
321	ReAlnet07	.688
322	ReAlnet07_cornet	.688
323	ReAlnet08	.688
324	ReAlnet08_cornet	.687
325	resnet50-SIN_IN	.685
326	ReAlnet04	.684
327	ReAlnet04_cornet	.684
328	ReAlnet03	.682
329	ReAlnet03_cornet	.681
330	ReAlnet02_ALL	.680
331	ReAlnet04_ALL	.679
332	ReAlnet10	.678
333	mobilenet_v2_1_0_128	.677
334	ReAlnet02_cornet	.677
335	ReAlnet02	.677
336	ReAlnet10_cornet	.677
337	yolos_tiny	.677
338	yudixie_resnet18_distance_reg_0_240719	.676
339	mobilenet_v2_0.75_128	.672
340	ReAlnet05_ALL	.670
341	ReAlnet06_ALL	.669
342	ReAlnet07_ALL	.667
343	yudixie_resnet18_rotation_reg_0_240719	.667
344	CORnet-S	.667
345	resnet34_ecoset_full	.665
346	ReAlnet06	.665
347	ReAlnet06_cornet	.665
348	ReAlnet01_cornet_fov4	.664
349	ReAlnet05	.664
350	ReAlnet05_cornet	.664
351	ReAlnet01_ALL	.663
352	ReAlnet09_ALL	.661
353	resnet101_imagenet_full	.661
354	ReAlnet10_ALL	.661
355	resnet-18-LC_untrained	.658
356	0.5x_resnet-18_LC_w_sh_1_iter	.655
357	resnet_101_v2	.652
358	resnet152_ecoset_full	.650
359	ReAlnet01_cornet_fov12	.647
360	vggface	.646
361	resnet18_imagenet_full	.646
362	resnet-18_test_m	.644
363	resnet101_ecoset_full	.644
364	resnet-18-LC_conv_init_m	.643
365	resnet-18-LC_w_sh_100_iter_m	.636
366	cornet_s	.635
367	0.5x_resnet-18	.633
368	resnet_50_v2	.631
369	resnet-152_v2_pytorch	.632
370	resnet50_ewconplus_wzc	.618
371	mobilenet_v2_1-4_224_pytorch	.616
372	mobilenet_v2_0_5_224	.597
373	convnext_small_imagenet_1_seed-0	.593
374	resnet152_imagenet_full	.590
375	mobilenet_v2_0.75_160-tf	.561
376	CORnetZ_CIFAR10_bs32_20_04	.507
377	convnext_small_imagenet_10_seed-0	.491
378	deit_small_imagenet_10_seed-0	.333
379	resnet50-meshes-lt-100-original-scratch	.333
380	mobilenet_v2_0.5_160	.288
381	mobilenet_v2_0.5_128	.288
382	mobilenet_v2_0.35_160
383	resnet18-supervised
384	fitvid_trained_on_physion
385	openclip
386	resnext101_32x32d_wsl
387	resnext101_32x48d_wsl
388	resnet50_ImageNet
389	regnet_y_400mf
390	tv_efficientnet-b1
391	alexnet_ks_torevert
392	yudixie_resnet50_imagenet1kpret_0_240312
393	resnet_152_v1
394	resnext101_32x16d_wsl
395	densenet_201_pytorch
396	inception_v3_pytorch
397	pnasnet_large_pytorch
398	inception_v4
399	nasnet_large
400	deit_small_imagenet_1_seed-0
401	pnasnet_large
402	vonegrcnn_62e_nobn
403	vonegrcnn_52e_full
404	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio