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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	yudixie_resnet18_object_class_0_240719	.916
67	alexnet_training_seed_01	.915
68	alexnet_training_seed_01_fov4	.914
69	BiT-S-R101x1	.914
70	mobilenet_v2_1.4_224-tf	.915
71	alexnet_training_seed_07	.914
72	alexnet_training_seed_02	.915
73	compact_resnet50_robust_IT	.915
74	alexnet_training_seed_01_fov16	.914
75	mobilenet_v2_1_4_224	.914
76	BiT-M-R152x4	.914
77	alexnet_training_seed_10	.915
78	efficientnet_b2_imagenet_full	.914
79	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.913
80	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.913
81	alexnet_training_seed_03	.912
82	alexnet_training_seed_09	.911
83	vonegrcnn_47e	.911
84	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.911
85	mobilenet_v2_1.3_224-tf	.909
86	alexnet_fov16	.908
87	resnet50_primary_visual_cortex	.908
88	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.906
89	0.5x_resnet-18_LC_w_sh_10_iter	.905
90	resnet50_random_l2_perturb	.905
91	compact_cornets_IT	.904
92	SWSL_resnext101_32x8d	.902
93	resnet50_byol	.903
94	cvt_cvt-21-224-in1k_4_LucyV4	.903
95	deit_base_imagenet_full_seed-0	.902
96	focalnet_tiny_lrf_in1k	.900
97	effnetv2m_custom384	.899
98	resnet50_imagenet_full	.899
99	resnet50-vicreg	.898
100	xception	.899
101	BiT-M-R101x1	.897
102	nasnet_mobile-tf	.898
103	resnet-101_v1-tf	.898
104	mobilenet_v2_0.5_192-tf	.898
105	resnet-18-LC_w_sh_1_iter_conv_init_m	.898
106	omnivore_swinT	.896
107	convnext_small_imagenet_100_seed-0	.897
108	shufflenet_v2_x1_0	.896
109	resnet50_moco_v2	.894
110	AlexNet_SIN	.894
111	resnet50-moclr8deg	.894
112	grcnn	.893
113	resnet50_FractalDB	.893
114	inception_v4-tf	.892
115	BiT-M-R50x3	.891
116	r3m_resnet50_nocrop	.889
117	mobilenet_v2_1.0_160-tf	.889
118	konkle_alexnetgn_ipcl_ref01_primary_model	.888
119	efficientnet_b0_imagenet_full	.888
120	resnet-18_untrained	.887
121	AdvProp_efficientnet-b4	.886
122	r3m_resnet34_nocrop	.886
123	BiT-S-R152x4	.884
124	0.5x_resnet-18_LC_w_sh_100_iter	.884
125	yudixie_resnet50_distance_rotation_0_240908	.884
126	resnet50_imagenet_100_seed-0	.884
127	BiT-S-R50x1	.883
128	r3m_resnet50	.882
129	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
130	CLIP_ViT-B_32	.882
131	inception_v3-tf	.881
132	BiT-M-R50x1	.881
133	resnet50	.880
134	mobilenet_v2_0.75_192-tf	.880
135	vonegrcnn_62e_nobn	.879
136	inception_v2-tf	.878
137	mobilenet_v2_1.0_96	.878
138	yudixie_resnet18_random_0_240719	.877
139	yudixie_resnet18_imagenet1kpret_0_240719	.876
140	mobilenet_v2_1.0_224-tf	.876
141	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.875
142	SWSL_resnet50	.876
143	resnet50_random_linf8_perturb	.876
144	AdvProp_efficientnet-b6	.873
145	BiT-M-R152x2	.873
146	alexnet_training_seed_08	.872
147	omnivore_swinS	.871
148	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.871
149	resnet18_ecoset_full	.872
150	mobilenet_v2_0.5_224-tf	.872
151	mobilenet_v2_0_5_192	.871
152	alexnet	.871
153	CORnet-Z	.870
154	resnet50-SIN	.870
155	alexnet	.869
156	resnet50_linf_8_robust	.870
157	alexnet	.869
158	alexnet	.869
159	alexnet	.869
160	alexnet-baseline	.869
161	alexnet	.869
162	omnivore_swinB	.868
163	alexnet	.868
164	yudixie_resnet18_category_class_0_240719	.868
165	alexnet	.868
166	convnext_base_imagenet_full_seed-0	.868
167	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
168	convnext_large:fb_in22k_ft_in1k	.866
169	convnext_femto_ols:d1_in1k	.866
170	effnetb1_272x240	.863
171	convnext_tiny:in12k_ft_in1k	.863
172	antialiased-r50	.862
173	yudixie_resnet50_distance_reg_0_240908	.861
174	texture_shape_resnet50_trained_on_SIN	.861
175	resnet-50x2_untrained	.860
176	BiT-M-R101x3	.859
177	mobilenet_v2_0_75_224	.859
178	vit_large_patch14_clip_224:openai_ft_in1k	.856
179	mobilenet_v2_0_75_160	.854
180	vit_base_patch16_clip_224:openai_ft_in1k	.853
181	imagenet_l2_10_0	.853
182	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
183	inception_resnet_v2	.851
184	AlexNet_SIN_fov16	.849
185	AdvProp_efficientnet-b7	.849
186	mobilenet_v2_1_0_224	.848
187	pnasnet_large-tf	.847
188	vonealexnet_gaussian_noise_std4_fixed	.847
189	AlexNet_SIN_fov12	.844
190	resnet50-VITO-8deg-cc	.843
191	resnet-50-robust	.843
192	FrankRobWobv0	.842
193	yudixie_resnet18_translation_rotation_0_240719	.843
194	resnet-50_untrained	.839
195	yudixie_resnet50_distance_translation_rotation_0_240908	.838
196	alexnet_fov12	.837
197	resnet-152_v2-tf	.837
198	mobilenet_v2_1_0_160	.836
199	alexnet	.835
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	resnet-101_v2-tf	.830
205	yudixie_resnet50_rotation_reg_0_240908	.830
206	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
207	alexnet_reduced_aliasing_early_checkpoint	.829
208	vgg_19	.829
209	mobilenet_v2_1_0_192	.827
210	resnet50_linf_4_robust	.827
211	compact_resnet50_V4	.826
212	alexnet_fov4	.826
213	AdvProp_efficientnet-b0	.826
214	convnext_large_imagenet_full_seed-0	.821
215	grcnn_robust_v1	.821
216	yudixie_resnet50_category_class_0_240908	.821
217	r3m_resnet18	.819
218	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
219	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
220	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
221	efficientnet_b1_imagenet_full	.817
222	efficientnet_b0	.816
223	nasnet_large-tf	.814
224	resnet50_robust_l2_eps1	.813
225	resnet50_imagenet_10_seed-0	.814
226	yudixie_resnet50_translation_rotation_0_240908	.811
227	alexnet_random_linf8_perturb	.810
228	resnet-18-LC_w_sh_1_iter_m	.809
229	CORnetZ_CIFAR10	.808
230	deit_large_imagenet_full_seed-0	.806
231	r3m_resnet34	.804
232	resnet50_l2_3_robust	.805
233	alexnet_random_l2_3_perturb	.805
234	yudixie_resnet50_object_class_0_240908	.802
235	yudixie_resnet18_translation_reg_0_240719	.801
236	texture_shape_alexnet_trained_on_SIN	.800
237	resnet18_imagenet21kP	.800
238	resnet_101_v1	.797
239	BiT-S-R152x2	.797
240	densenet-169-keras	.797
241	resnet50_robust_l2_eps3	.796
242	antialias-resnet152	.794
243	convnext_xlarge:fb_in22k_ft_in1k	.793
244	mobilenet_v2_0.35_224	.792
245	compact_resnet50_IT	.790
246	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
247	yudixie_resnet18_distance_translation_rotation_0_240719	.789
248	resnet101	.787
249	resnet50_pretrained_with_retinal_waves	.787
250	AlexNet_SIN_fov4	.787
251	resnet-18-LC_m	.786
252	compact_resnet50_robust_V4	.784
253	inception_v1	.785
254	yudixie_resnet50_distance_translation_0_240908	.781
255	compact_vgg19_IT	.777
256	vgg-16-keras	.778
257	alexnet_robust_correct	.778
258	vgg-19-keras	.776
259	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
260	alexnet_training_seed_05	.773
261	voneresnet-50-non_stochastic	.772
262	yudixie_resnet18_distance_translation_0_240719	.771
263	ReAlnet01_cornet_fov16	.770
264	mobilenet_v2_0_75_192	.769
265	VOneCORnet-S	.767
266	voneresnet-50	.766
267	alexnet_early_checkpoint	.764
268	voneresnet-50-robust	.763
269	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
270	convnext_tiny_imagenet_full_seed-0	.761
271	alexnet_training_seed_01_fov12	.760
272	imagenet_l2_3_0	.757
273	mobilenet_v2_0.75_96	.757
274	artResNet18_1	.755
275	mobilenet_v2_1_3_224	.746
276	yudixie_resnet18_distance_rotation_0_240719	.746
277	resnet50-vicregl0p9	.746
278	yudixie_resnet50_random_0_240908	.746
279	alexnet_training_seed_06	.745
280	deit_small_imagenet_full_seed-0	.745
281	resnet50_imagenet_1_seed-0	.746
282	inception_v1-tf	.745
283	my-model	.745
284	resnet50-cifar	.745
285	resnet50-meshes-lt-100-original-pretrained	.745
286	mobilenet_v2_1.0_128-tf	.745
287	resnet50-vitoimagevidnet8	.742
288	alexnet_linf_8_robust	.742
289	0.5x_resnet-18_LC	.741
290	densenet-121	.738
291	densenet-121-keras	.737
292	resnet50	.736
293	densenet-169	.734
294	densenet-201	.733
295	yudixie_resnet50_translation_reg_0_240908	.732
296	briaai_rmbg_1_4	.731
297	resnext101_32x8d_wsl	.724
298	mobilenet_v1_0.25_128	.722
299	densenet-201-keras	.720
300	ViT-B/32	.715
301	ReAlnet01	.712
302	alexnet_l2_3_robust	.712
303	ReAlnet01_cornet	.710
304	resnet50_ecoset_full	.710
305	ReAlnet09_cornet	.707
306	barlow-twins-resnet50	.707
307	resnet50-simclr-vissl	.706
308	AdvProp_efficientnet-b2	.707
309	ReAlnet09	.705
310	AT_efficientnet-b2	.705
311	v1-pyr-nodown	.705
312	ReAlnet03_ALL	.704
313	hmax	.700
314	ReAlnet08_ALL	.696
315	Res2Net50_26w_4s	.693
316	resnet34_imagenet_full	.690
317	ReAlnet07	.688
318	ReAlnet07_cornet	.688
319	ReAlnet08	.688
320	ReAlnet08_cornet	.687
321	resnet50-SIN_IN	.685
322	ReAlnet04	.684
323	ReAlnet04_cornet	.684
324	ReAlnet03	.682
325	ReAlnet03_cornet	.681
326	ReAlnet02_ALL	.680
327	ReAlnet04_ALL	.679
328	ReAlnet10	.678
329	mobilenet_v2_1_0_128	.677
330	ReAlnet02_cornet	.677
331	ReAlnet02	.677
332	ReAlnet10_cornet	.677
333	yolos_tiny	.677
334	yudixie_resnet18_distance_reg_0_240719	.676
335	mobilenet_v2_0.75_128	.672
336	ReAlnet05_ALL	.670
337	ReAlnet06_ALL	.669
338	ReAlnet07_ALL	.667
339	yudixie_resnet18_rotation_reg_0_240719	.667
340	CORnet-S	.667
341	resnet34_ecoset_full	.665
342	ReAlnet06	.665
343	ReAlnet06_cornet	.665
344	ReAlnet01_cornet_fov4	.664
345	ReAlnet05	.664
346	ReAlnet05_cornet	.664
347	ReAlnet01_ALL	.663
348	ReAlnet09_ALL	.661
349	resnet101_imagenet_full	.661
350	ReAlnet10_ALL	.661
351	resnet-18-LC_untrained	.658
352	0.5x_resnet-18_LC_w_sh_1_iter	.655
353	resnet_101_v2	.652
354	resnet152_ecoset_full	.650
355	ReAlnet01_cornet_fov12	.647
356	vggface	.646
357	resnet18_imagenet_full	.646
358	resnet-18_test_m	.644
359	resnet101_ecoset_full	.644
360	resnet-18-LC_conv_init_m	.643
361	resnet-18-LC_w_sh_100_iter_m	.636
362	cornet_s	.635
363	0.5x_resnet-18	.633
364	resnet_50_v2	.631
365	resnet-152_v2_pytorch	.632
366	resnet50_ewconplus_wzc	.618
367	mobilenet_v2_1-4_224_pytorch	.616
368	mobilenet_v2_0_5_224	.597
369	convnext_small_imagenet_1_seed-0	.593
370	resnet152_imagenet_full	.590
371	mobilenet_v2_0.75_160-tf	.561
372	CORnetZ_CIFAR10_bs32_20_04	.507
373	convnext_small_imagenet_10_seed-0	.491
374	deit_small_imagenet_10_seed-0	.333
375	resnet50-meshes-lt-100-original-scratch	.333
376	mobilenet_v2_0.5_160	.288
377	mobilenet_v2_0.5_128	.288
378	mobilenet_v2_0.35_160
379	resnet18-supervised
380	fitvid_trained_on_physion
381	openclip
382	resnext101_32x32d_wsl
383	resnext101_32x48d_wsl
384	resnet50_ImageNet
385	regnet_y_400mf
386	tv_efficientnet-b1
387	alexnet_ks_torevert
388	yudixie_resnet50_imagenet1kpret_0_240312
389	resnet_152_v1
390	resnext101_32x16d_wsl
391	densenet_201_pytorch
392	inception_v3_pytorch
393	pnasnet_large_pytorch
394	inception_v4
395	nasnet_large
396	deit_small_imagenet_1_seed-0
397	pnasnet_large
398	vonegrcnn_62e_nobn
399	vonegrcnn_52e_full
400	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio