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	resnet50-vicregl0p75	.955
11	antialiased-rnext101_32x8d	.950
12	vit_large_patch14_clip_336:laion2b_ft_in1k	.949
13	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.947
14	resnet-18-LC_1st_conv_m	.947
15	vgg_16	.944
16	resnet-152_v1-tf	.943
17	cv_18_dagger_408_pretrained	.943
18	RN50	.941
19	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.941
20	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.939
21	CLIP_resnet50_float32	.942
22	CLIP_resnet50	.940
23	nasnet_mobile	.940
24	vit_relpos_base_patch16_clsgap_224:sw_in1k	.939
25	BiT-S-R50x3	.939
26	my-model	.939
27	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.939
28	vonegrcnn_52e_full	.937
29	vision_transformer_vit_large_patch16_224	.937
30	resnet50_simclr	.936
31	vonegrcnn_47e	.935
32	convnext_tiny_sup	.935
33	grcnn_109	.934
34	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.933
35	resnet-18-LC_w_sh_10_iter_m	.931
36	AdvProp_efficientnet-b8	.931
37	cvt_cvt-13-384-in1k_4_LucyV4	.931
38	inception_v3	.929
39	grcnn_v2_text_noise	.927
40	alexnet_training_seed_04	.926
41	convnext_base:clip_laiona_augreg_ft_in1k_384	.925
42	resnet-50_v1-tf	.926
43	efficientnet-b7	.926
44	blt_vs	.924
45	custom_model_cv_18_dagger_408	.924
46	resnet50-SIN_IN_IN	.923
47	cvt_cvt-21-384-in1k_4_LucyV4	.921
48	CLIP-RN50	.922
49	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.921
50	resnet_50_v1	.921
51	cvt_cvt-13-224-in1k_4_LucyV4	.922
52	resnet_SIN_IN_FT_IN	.921
53	resnet50-sup	.921
54	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.920
55	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.920
56	resnet_50_v1_spiking_l4	.921
57	resnet50_tutorial	.920
58	resnet_50_v1_spiking	.919
59	resnet_50_v1_spiking_l3	.920
60	resnet50-barlow	.918
61	yudixie_resnet50_imagenet1kpret_0_240908	.919
62	resnet_50_v1_spiking_l4a	.918
63	resnet-50_v2-tf	.919
64	BiT-S-R101x3	.917
65	yudixie_resnet18_object_class_0_240719	.916
66	alexnet_training_seed_01	.915
67	BiT-S-R101x1	.914
68	mobilenet_v2_1.4_224-tf	.915
69	alexnet_training_seed_07	.914
70	alexnet_training_seed_02	.915
71	mobilenet_v2_1_4_224	.914
72	BiT-M-R152x4	.914
73	alexnet_training_seed_10	.915
74	efficientnet_b2_imagenet_full	.914
75	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.913
76	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.913
77	alexnet_training_seed_03	.912
78	alexnet_training_seed_09	.911
79	vonegrcnn_47e	.911
80	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.911
81	mobilenet_v2_1.3_224-tf	.909
82	resnet50_primary_visual_cortex	.908
83	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.906
84	0.5x_resnet-18_LC_w_sh_10_iter	.905
85	resnet50_random_l2_perturb	.905
86	SWSL_resnext101_32x8d	.902
87	resnet50_byol	.903
88	cvt_cvt-21-224-in1k_4_LucyV4	.903
89	deit_base_imagenet_full_seed-0	.902
90	focalnet_tiny_lrf_in1k	.900
91	effnetv2m_custom384	.899
92	resnet50_imagenet_full	.899
93	resnet50-vicreg	.898
94	xception	.899
95	BiT-M-R101x1	.897
96	nasnet_mobile-tf	.898
97	resnet-101_v1-tf	.898
98	mobilenet_v2_0.5_192-tf	.898
99	resnet-18-LC_w_sh_1_iter_conv_init_m	.898
100	omnivore_swinT	.896
101	convnext_small_imagenet_100_seed-0	.897
102	shufflenet_v2_x1_0	.896
103	resnet50_moco_v2	.894
104	AlexNet_SIN	.894
105	resnet50-moclr8deg	.894
106	grcnn	.893
107	resnet50_FractalDB	.893
108	inception_v4-tf	.892
109	BiT-M-R50x3	.891
110	r3m_resnet50_nocrop	.889
111	mobilenet_v2_1.0_160-tf	.889
112	konkle_alexnetgn_ipcl_ref01_primary_model	.888
113	efficientnet_b0_imagenet_full	.888
114	resnet-18_untrained	.887
115	AdvProp_efficientnet-b4	.886
116	r3m_resnet34_nocrop	.886
117	BiT-S-R152x4	.884
118	0.5x_resnet-18_LC_w_sh_100_iter	.884
119	yudixie_resnet50_distance_rotation_0_240908	.884
120	resnet50_imagenet_100_seed-0	.884
121	BiT-S-R50x1	.883
122	r3m_resnet50	.882
123	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
124	CLIP_ViT-B_32	.882
125	inception_v3-tf	.881
126	BiT-M-R50x1	.881
127	resnet50	.880
128	mobilenet_v2_0.75_192-tf	.880
129	vonegrcnn_62e_nobn	.879
130	inception_v2-tf	.878
131	mobilenet_v2_1.0_96	.878
132	yudixie_resnet18_random_0_240719	.877
133	yudixie_resnet18_imagenet1kpret_0_240719	.876
134	mobilenet_v2_1.0_224-tf	.876
135	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.875
136	SWSL_resnet50	.876
137	resnet50_random_linf8_perturb	.876
138	AdvProp_efficientnet-b6	.873
139	BiT-M-R152x2	.873
140	alexnet_training_seed_08	.872
141	omnivore_swinS	.871
142	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.871
143	resnet18_ecoset_full	.872
144	mobilenet_v2_0.5_224-tf	.872
145	mobilenet_v2_0_5_192	.871
146	CORnet-Z	.870
147	resnet50-SIN	.870
148	alexnet	.869
149	resnet50_linf_8_robust	.870
150	alexnet	.869
151	alexnet	.869
152	alexnet	.869
153	alexnet-baseline	.869
154	alexnet	.869
155	omnivore_swinB	.868
156	alexnet	.868
157	yudixie_resnet18_category_class_0_240719	.868
158	alexnet	.868
159	convnext_base_imagenet_full_seed-0	.868
160	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
161	convnext_large:fb_in22k_ft_in1k	.866
162	convnext_femto_ols:d1_in1k	.866
163	effnetb1_272x240	.863
164	convnext_tiny:in12k_ft_in1k	.863
165	antialiased-r50	.862
166	yudixie_resnet50_distance_reg_0_240908	.861
167	texture_shape_resnet50_trained_on_SIN	.861
168	resnet-50x2_untrained	.860
169	BiT-M-R101x3	.859
170	mobilenet_v2_0_75_224	.859
171	vit_large_patch14_clip_224:openai_ft_in1k	.856
172	mobilenet_v2_0_75_160	.854
173	vit_base_patch16_clip_224:openai_ft_in1k	.853
174	imagenet_l2_10_0	.853
175	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
176	inception_resnet_v2	.851
177	AdvProp_efficientnet-b7	.849
178	mobilenet_v2_1_0_224	.848
179	pnasnet_large-tf	.847
180	vonealexnet_gaussian_noise_std4_fixed	.847
181	resnet50-VITO-8deg-cc	.843
182	resnet-50-robust	.843
183	FrankRobWobv0	.842
184	yudixie_resnet18_translation_rotation_0_240719	.843
185	resnet-50_untrained	.839
186	yudixie_resnet50_distance_translation_rotation_0_240908	.838
187	resnet-152_v2-tf	.837
188	mobilenet_v2_1_0_160	.836
189	alexnet	.835
190	mobilenet_v2_0.75_224-tf	.834
191	deit_small_imagenet_100_seed-0	.833
192	mobilenet_v2_1.0_192-tf	.833
193	resnet-101_v2-tf	.830
194	yudixie_resnet50_rotation_reg_0_240908	.830
195	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
196	alexnet_reduced_aliasing_early_checkpoint	.829
197	vgg_19	.829
198	mobilenet_v2_1_0_192	.827
199	resnet50_linf_4_robust	.827
200	AdvProp_efficientnet-b0	.826
201	convnext_large_imagenet_full_seed-0	.821
202	grcnn_robust_v1	.821
203	yudixie_resnet50_category_class_0_240908	.821
204	r3m_resnet18	.819
205	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
206	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
207	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
208	efficientnet_b1_imagenet_full	.817
209	efficientnet_b0	.816
210	nasnet_large-tf	.814
211	resnet50_robust_l2_eps1	.813
212	resnet50_imagenet_10_seed-0	.814
213	yudixie_resnet50_translation_rotation_0_240908	.811
214	alexnet_random_linf8_perturb	.810
215	resnet-18-LC_w_sh_1_iter_m	.809
216	CORnetZ_CIFAR10	.808
217	deit_large_imagenet_full_seed-0	.806
218	r3m_resnet34	.804
219	resnet50_l2_3_robust	.805
220	alexnet_random_l2_3_perturb	.805
221	yudixie_resnet50_object_class_0_240908	.802
222	yudixie_resnet18_translation_reg_0_240719	.801
223	texture_shape_alexnet_trained_on_SIN	.800
224	resnet18_imagenet21kP	.800
225	resnet_101_v1	.797
226	BiT-S-R152x2	.797
227	densenet-169-keras	.797
228	resnet50_robust_l2_eps3	.796
229	antialias-resnet152	.794
230	convnext_xlarge:fb_in22k_ft_in1k	.793
231	mobilenet_v2_0.35_224	.792
232	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
233	yudixie_resnet18_distance_translation_rotation_0_240719	.789
234	resnet101	.787
235	resnet50_pretrained_with_retinal_waves	.787
236	resnet-18-LC_m	.786
237	inception_v1	.785
238	yudixie_resnet50_distance_translation_0_240908	.781
239	vgg-16-keras	.778
240	alexnet_robust_correct	.778
241	vgg-19-keras	.776
242	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
243	alexnet_training_seed_05	.773
244	voneresnet-50-non_stochastic	.772
245	yudixie_resnet18_distance_translation_0_240719	.771
246	mobilenet_v2_0_75_192	.769
247	VOneCORnet-S	.767
248	voneresnet-50	.766
249	alexnet_early_checkpoint	.764
250	voneresnet-50-robust	.763
251	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
252	convnext_tiny_imagenet_full_seed-0	.761
253	imagenet_l2_3_0	.757
254	mobilenet_v2_0.75_96	.757
255	artResNet18_1	.755
256	mobilenet_v2_1_3_224	.746
257	yudixie_resnet18_distance_rotation_0_240719	.746
258	resnet50-vicregl0p9	.746
259	yudixie_resnet50_random_0_240908	.746
260	deit_small_imagenet_full_seed-0	.745
261	resnet50_imagenet_1_seed-0	.746
262	alexnet_training_seed_06	.745
263	inception_v1-tf	.745
264	my-model	.745
265	resnet50-cifar	.745
266	resnet50-meshes-lt-100-original-pretrained	.745
267	mobilenet_v2_1.0_128-tf	.745
268	resnet50-vitoimagevidnet8	.742
269	alexnet_linf_8_robust	.742
270	0.5x_resnet-18_LC	.741
271	densenet-121	.738
272	densenet-121-keras	.737
273	resnet50	.736
274	densenet-169	.734
275	densenet-201	.733
276	yudixie_resnet50_translation_reg_0_240908	.732
277	briaai_rmbg_1_4	.731
278	resnext101_32x8d_wsl	.724
279	mobilenet_v1_0.25_128	.722
280	densenet-201-keras	.720
281	ViT-B/32	.715
282	ReAlnet01	.712
283	alexnet_l2_3_robust	.712
284	ReAlnet01_cornet	.710
285	resnet50_ecoset_full	.710
286	ReAlnet09_cornet	.707
287	barlow-twins-resnet50	.707
288	resnet50-simclr-vissl	.706
289	AdvProp_efficientnet-b2	.707
290	ReAlnet09	.705
291	AT_efficientnet-b2	.705
292	v1-pyr-nodown	.705
293	hmax	.700
294	Res2Net50_26w_4s	.693
295	resnet34_imagenet_full	.690
296	ReAlnet07	.688
297	ReAlnet07_cornet	.688
298	ReAlnet08	.688
299	ReAlnet08_cornet	.687
300	resnet50-SIN_IN	.685
301	ReAlnet04	.684
302	ReAlnet04_cornet	.684
303	ReAlnet03	.682
304	ReAlnet03_cornet	.681
305	ReAlnet10	.678
306	mobilenet_v2_1_0_128	.677
307	ReAlnet02_cornet	.677
308	ReAlnet02	.677
309	ReAlnet10_cornet	.677
310	yolos_tiny	.677
311	yudixie_resnet18_distance_reg_0_240719	.676
312	mobilenet_v2_0.75_128	.672
313	yudixie_resnet18_rotation_reg_0_240719	.667
314	CORnet-S	.667
315	resnet34_ecoset_full	.665
316	ReAlnet06	.665
317	ReAlnet06_cornet	.665
318	ReAlnet05	.664
319	ReAlnet05_cornet	.664
320	resnet101_imagenet_full	.661
321	resnet-18-LC_untrained	.658
322	0.5x_resnet-18_LC_w_sh_1_iter	.655
323	resnet_101_v2	.652
324	resnet152_ecoset_full	.650
325	vggface	.646
326	resnet18_imagenet_full	.646
327	resnet-18_test_m	.644
328	resnet101_ecoset_full	.644
329	resnet-18-LC_conv_init_m	.643
330	resnet-18-LC_w_sh_100_iter_m	.636
331	cornet_s	.635
332	0.5x_resnet-18	.633
333	resnet_50_v2	.631
334	resnet-152_v2_pytorch	.632
335	mobilenet_v2_1-4_224_pytorch	.616
336	mobilenet_v2_0_5_224	.597
337	convnext_small_imagenet_1_seed-0	.593
338	resnet152_imagenet_full	.590
339	mobilenet_v2_0.75_160-tf	.561
340	CORnetZ_CIFAR10_bs32_20_04	.507
341	convnext_small_imagenet_10_seed-0	.491
342	deit_small_imagenet_10_seed-0	.333
343	resnet50-meshes-lt-100-original-scratch	.333
344	mobilenet_v2_0.5_160	.288
345	mobilenet_v2_0.5_128	.288
346	pnasnet_large
347	deit_small_imagenet_1_seed-0
348	nasnet_large
349	resnet18-supervised
350	regnet_y_400mf
351	tv_efficientnet-b1
352	alexnet_ks_torevert
353	yudixie_resnet50_imagenet1kpret_0_240312
354	vonegrcnn_52e_full
355	vonegrcnn_62e_nobn
356	openclip
357	inception_v4
358	pnasnet_large_pytorch
359	resnet_152_v2
360	inception_v3_pytorch
361	densenet_201_pytorch
362	mobilenet_v2_0.35_160
363	resnext101_32x16d_wsl
364	fitvid_trained_on_physion
365	resnet_152_v1
366	resnext101_32x32d_wsl
367	resnext101_32x48d_wsl
368	resnet50_ImageNet

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio