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	resnet50_tutorial	.920
57	resnet50-barlow	.918
58	yudixie_resnet50_imagenet1kpret_0_240908	.919
59	resnet-50_v2-tf	.919
60	BiT-S-R101x3	.917
61	yudixie_resnet18_object_class_0_240719	.916
62	alexnet_training_seed_01	.915
63	BiT-S-R101x1	.914
64	mobilenet_v2_1.4_224-tf	.915
65	alexnet_training_seed_07	.914
66	alexnet_training_seed_02	.915
67	mobilenet_v2_1_4_224	.914
68	BiT-M-R152x4	.914
69	alexnet_training_seed_10	.915
70	efficientnet_b2_imagenet_full	.914
71	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.913
72	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.913
73	alexnet_training_seed_03	.912
74	alexnet_training_seed_09	.911
75	vonegrcnn_47e	.911
76	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.911
77	mobilenet_v2_1.3_224-tf	.909
78	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.906
79	0.5x_resnet-18_LC_w_sh_10_iter	.905
80	resnet50_random_l2_perturb	.905
81	SWSL_resnext101_32x8d	.902
82	resnet50_byol	.903
83	cvt_cvt-21-224-in1k_4_LucyV4	.903
84	deit_base_imagenet_full_seed-0	.902
85	focalnet_tiny_lrf_in1k	.900
86	effnetv2m_custom384	.899
87	resnet50_imagenet_full	.899
88	resnet50-vicreg	.898
89	xception	.899
90	BiT-M-R101x1	.897
91	nasnet_mobile-tf	.898
92	resnet-101_v1-tf	.898
93	mobilenet_v2_0.5_192-tf	.898
94	resnet-18-LC_w_sh_1_iter_conv_init_m	.898
95	omnivore_swinT	.896
96	convnext_small_imagenet_100_seed-0	.897
97	shufflenet_v2_x1_0	.896
98	resnet50_moco_v2	.894
99	AlexNet_SIN	.894
100	resnet50-moclr8deg	.894
101	grcnn	.893
102	resnet50_FractalDB	.893
103	inception_v4-tf	.892
104	BiT-M-R50x3	.891
105	r3m_resnet50_nocrop	.889
106	mobilenet_v2_1.0_160-tf	.889
107	konkle_alexnetgn_ipcl_ref01_primary_model	.888
108	efficientnet_b0_imagenet_full	.888
109	resnet-18_untrained	.887
110	AdvProp_efficientnet-b4	.886
111	r3m_resnet34_nocrop	.886
112	BiT-S-R152x4	.884
113	0.5x_resnet-18_LC_w_sh_100_iter	.884
114	yudixie_resnet50_distance_rotation_0_240908	.884
115	resnet50_imagenet_100_seed-0	.884
116	BiT-S-R50x1	.883
117	r3m_resnet50	.882
118	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
119	CLIP_ViT-B_32	.882
120	inception_v3-tf	.881
121	BiT-M-R50x1	.881
122	resnet50	.880
123	mobilenet_v2_0.75_192-tf	.880
124	vonegrcnn_62e_nobn	.879
125	inception_v2-tf	.878
126	mobilenet_v2_1.0_96	.878
127	yudixie_resnet18_random_0_240719	.877
128	yudixie_resnet18_imagenet1kpret_0_240719	.876
129	mobilenet_v2_1.0_224-tf	.876
130	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.875
131	SWSL_resnet50	.876
132	resnet50_random_linf8_perturb	.876
133	AdvProp_efficientnet-b6	.873
134	BiT-M-R152x2	.873
135	alexnet_training_seed_08	.872
136	omnivore_swinS	.871
137	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.871
138	resnet18_ecoset_full	.872
139	mobilenet_v2_0.5_224-tf	.872
140	mobilenet_v2_0_5_192	.871
141	CORnet-Z	.870
142	resnet50-SIN	.870
143	alexnet	.869
144	resnet50_linf_8_robust	.870
145	alexnet	.869
146	alexnet	.869
147	alexnet	.869
148	alexnet-baseline	.869
149	alexnet	.869
150	omnivore_swinB	.868
151	alexnet	.868
152	yudixie_resnet18_category_class_0_240719	.868
153	alexnet	.868
154	convnext_base_imagenet_full_seed-0	.868
155	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
156	convnext_large:fb_in22k_ft_in1k	.866
157	convnext_femto_ols:d1_in1k	.866
158	effnetb1_272x240	.863
159	convnext_tiny:in12k_ft_in1k	.863
160	antialiased-r50	.862
161	yudixie_resnet50_distance_reg_0_240908	.861
162	texture_shape_resnet50_trained_on_SIN	.861
163	resnet-50x2_untrained	.860
164	BiT-M-R101x3	.859
165	mobilenet_v2_0_75_224	.859
166	vit_large_patch14_clip_224:openai_ft_in1k	.856
167	mobilenet_v2_0_75_160	.854
168	vit_base_patch16_clip_224:openai_ft_in1k	.853
169	imagenet_l2_10_0	.853
170	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
171	inception_resnet_v2	.851
172	AdvProp_efficientnet-b7	.849
173	mobilenet_v2_1_0_224	.848
174	pnasnet_large-tf	.847
175	vonealexnet_gaussian_noise_std4_fixed	.847
176	resnet50-VITO-8deg-cc	.843
177	resnet-50-robust	.843
178	FrankRobWobv0	.842
179	yudixie_resnet18_translation_rotation_0_240719	.843
180	resnet-50_untrained	.839
181	yudixie_resnet50_distance_translation_rotation_0_240908	.838
182	resnet-152_v2-tf	.837
183	mobilenet_v2_1_0_160	.836
184	alexnet	.835
185	mobilenet_v2_0.75_224-tf	.834
186	deit_small_imagenet_100_seed-0	.833
187	mobilenet_v2_1.0_192-tf	.833
188	resnet-101_v2-tf	.830
189	yudixie_resnet50_rotation_reg_0_240908	.830
190	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
191	alexnet_reduced_aliasing_early_checkpoint	.829
192	vgg_19	.829
193	mobilenet_v2_1_0_192	.827
194	resnet50_linf_4_robust	.827
195	AdvProp_efficientnet-b0	.826
196	convnext_large_imagenet_full_seed-0	.821
197	grcnn_robust_v1	.821
198	yudixie_resnet50_category_class_0_240908	.821
199	r3m_resnet18	.819
200	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
201	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
202	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
203	efficientnet_b1_imagenet_full	.817
204	efficientnet_b0	.816
205	nasnet_large-tf	.814
206	resnet50_robust_l2_eps1	.813
207	resnet50_imagenet_10_seed-0	.814
208	yudixie_resnet50_translation_rotation_0_240908	.811
209	alexnet_random_linf8_perturb	.810
210	resnet-18-LC_w_sh_1_iter_m	.809
211	CORnetZ_CIFAR10	.808
212	deit_large_imagenet_full_seed-0	.806
213	r3m_resnet34	.804
214	resnet50_l2_3_robust	.805
215	alexnet_random_l2_3_perturb	.805
216	yudixie_resnet50_object_class_0_240908	.802
217	yudixie_resnet18_translation_reg_0_240719	.801
218	texture_shape_alexnet_trained_on_SIN	.800
219	resnet18_imagenet21kP	.800
220	resnet_101_v1	.797
221	BiT-S-R152x2	.797
222	densenet-169-keras	.797
223	resnet50_robust_l2_eps3	.796
224	antialias-resnet152	.794
225	convnext_xlarge:fb_in22k_ft_in1k	.793
226	mobilenet_v2_0.35_224	.792
227	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
228	yudixie_resnet18_distance_translation_rotation_0_240719	.789
229	resnet101	.787
230	resnet50_pretrained_with_retinal_waves	.787
231	resnet-18-LC_m	.786
232	inception_v1	.785
233	yudixie_resnet50_distance_translation_0_240908	.781
234	vgg-16-keras	.778
235	alexnet_robust_correct	.778
236	vgg-19-keras	.776
237	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
238	alexnet_training_seed_05	.773
239	voneresnet-50-non_stochastic	.772
240	yudixie_resnet18_distance_translation_0_240719	.771
241	mobilenet_v2_0_75_192	.769
242	VOneCORnet-S	.767
243	voneresnet-50	.766
244	alexnet_early_checkpoint	.764
245	voneresnet-50-robust	.763
246	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
247	convnext_tiny_imagenet_full_seed-0	.761
248	imagenet_l2_3_0	.757
249	mobilenet_v2_0.75_96	.757
250	artResNet18_1	.755
251	mobilenet_v2_1_3_224	.746
252	yudixie_resnet18_distance_rotation_0_240719	.746
253	resnet50-vicregl0p9	.746
254	yudixie_resnet50_random_0_240908	.746
255	alexnet_training_seed_06	.745
256	resnet50_imagenet_1_seed-0	.746
257	deit_small_imagenet_full_seed-0	.745
258	my-model	.745
259	inception_v1-tf	.745
260	resnet50-cifar	.745
261	resnet50-meshes-lt-100-original-pretrained	.745
262	mobilenet_v2_1.0_128-tf	.745
263	resnet50-vitoimagevidnet8	.742
264	alexnet_linf_8_robust	.742
265	0.5x_resnet-18_LC	.741
266	densenet-121	.738
267	densenet-121-keras	.737
268	resnet50	.736
269	densenet-169	.734
270	densenet-201	.733
271	yudixie_resnet50_translation_reg_0_240908	.732
272	briaai_rmbg_1_4	.731
273	resnext101_32x8d_wsl	.724
274	mobilenet_v1_0.25_128	.722
275	densenet-201-keras	.720
276	ViT-B/32	.715
277	ReAlnet01	.712
278	alexnet_l2_3_robust	.712
279	ReAlnet_incorrect	.711
280	ReAlnet01_cornet	.710
281	resnet50_ecoset_full	.710
282	ReAlnet09_cornet	.707
283	barlow-twins-resnet50	.707
284	resnet50-simclr-vissl	.706
285	AdvProp_efficientnet-b2	.707
286	ReAlnet09	.705
287	AT_efficientnet-b2	.705
288	v1-pyr-nodown	.705
289	hmax	.700
290	Res2Net50_26w_4s	.693
291	resnet34_imagenet_full	.690
292	ReAlnet07	.688
293	ReAlnet07_cornet	.688
294	ReAlnet08	.688
295	ReAlnet08_cornet	.687
296	resnet50-SIN_IN	.685
297	ReAlnet04	.684
298	ReAlnet04_cornet	.684
299	ReAlnet03	.682
300	ReAlnet03_cornet	.681
301	ReAlnet10	.678
302	mobilenet_v2_1_0_128	.677
303	ReAlnet02_cornet	.677
304	ReAlnet02	.677
305	ReAlnet10_cornet	.677
306	yolos_tiny	.677
307	yudixie_resnet18_distance_reg_0_240719	.676
308	mobilenet_v2_0.75_128	.672
309	yudixie_resnet18_rotation_reg_0_240719	.667
310	CORnet-S	.667
311	resnet34_ecoset_full	.665
312	ReAlnet06	.665
313	ReAlnet06_cornet	.665
314	ReAlnet05	.664
315	ReAlnet05_cornet	.664
316	resnet101_imagenet_full	.661
317	resnet-18-LC_untrained	.658
318	0.5x_resnet-18_LC_w_sh_1_iter	.655
319	resnet_101_v2	.652
320	resnet152_ecoset_full	.650
321	vggface	.646
322	resnet18_imagenet_full	.646
323	resnet-18_test_m	.644
324	resnet101_ecoset_full	.644
325	resnet-18-LC_conv_init_m	.643
326	resnet-18-LC_w_sh_100_iter_m	.636
327	cornet_s	.635
328	0.5x_resnet-18	.633
329	resnet_50_v2	.631
330	resnet-152_v2_pytorch	.632
331	mobilenet_v2_1-4_224_pytorch	.616
332	mobilenet_v2_0_5_224	.597
333	convnext_small_imagenet_1_seed-0	.593
334	resnet152_imagenet_full	.590
335	mobilenet_v2_0.75_160-tf	.561
336	CORnetZ_CIFAR10_bs32_20_04	.507
337	convnext_small_imagenet_10_seed-0	.491
338	deit_small_imagenet_10_seed-0	.333
339	resnet50-meshes-lt-100-original-scratch	.333
340	mobilenet_v2_0.5_160	.288
341	mobilenet_v2_0.5_128	.288
342	yudixie_resnet50_imagenet1kpret_0_240312	X
343	pnasnet_large	X
344	deit_small_imagenet_1_seed-0	X
345	nasnet_large	X
346	vonegrcnn_52e_full	X
347	resnext101_32x16d_wsl	X
348	alexnet_ks_torevert	X
349	tv_efficientnet-b1	X
350	densenet_201_pytorch	X
351	inception_v3_pytorch	X
352	regnet_y_400mf	X
353	resnet_152_v2	X
354	mobilenet_v2_0.35_160	X
355	resnext101_32x32d_wsl	X
356	resnet50_ImageNet	X
357	pnasnet_large_pytorch	X
358	resnext101_32x48d_wsl	X
359	vonegrcnn_62e_nobn	X
360	inception_v4	X
361	openclip	X
362	resnet_152_v1	X
363	resnet18-supervised	X
364	fitvid_trained_on_physion	X

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio