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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	vit_base_patch16_dinov3-lvd1689m	.870
157	resnet50-SIN	.870
158	resnet50_linf_8_robust	.870
159	omnivore_swinB	.868
160	yudixie_resnet18_category_class_0_240719	.868
161	convnext_base_imagenet_full_seed-0	.868
162	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
163	convnext_large:fb_in22k_ft_in1k	.866
164	convnext_femto_ols:d1_in1k	.866
165	pnasnet_large_halfPrecision	.865
166	eBarlow_lmda_02_1000ep	.865
167	effnetb1_272x240	.863
168	convnext_tiny:in12k_ft_in1k	.863
169	antialiased-r50	.862
170	yudixie_resnet50_distance_reg_0_240908	.861
171	texture_shape_resnet50_trained_on_SIN	.861
172	resnet-50x2_untrained	.860
173	BiT-M-R101x3	.859
174	mobilenet_v2_0_75_224	.859
175	vit_large_patch14_clip_224:openai_ft_in1k	.856
176	mobilenet_v2_0_75_160	.854
177	vit_base_patch16_clip_224:openai_ft_in1k	.853
178	imagenet_l2_10_0	.853
179	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
180	inception_resnet_v2	.851
181	AlexNet_SIN_fov16	.849
182	AdvProp_efficientnet-b7	.849
183	mobilenet_v2_1_0_224	.848
184	pnasnet_large-tf	.847
185	vonealexnet_gaussian_noise_std4_fixed	.847
186	AlexNet_SIN_fov12	.844
187	resnet50-VITO-8deg-cc	.843
188	resnet-50-robust	.843
189	FrankRobWobv0	.842
190	yudixie_resnet18_translation_rotation_0_240719	.843
191	resnet-50_untrained	.839
192	yudixie_resnet50_distance_translation_rotation_0_240908	.838
193	alexnet_fov12	.837
194	resnet-152_v2-tf	.837
195	mobilenet_v2_1_0_160	.836
196	mobilenet_v2_0.75_224-tf	.834
197	deit_small_imagenet_100_seed-0	.833
198	mobilenet_v2_1.0_192-tf	.833
199	compact_cornets_V4	.833
200	aughost_fair_resnet18	.830
201	resnet-101_v2-tf	.830
202	yudixie_resnet50_rotation_reg_0_240908	.830
203	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
204	alexnet_reduced_aliasing_early_checkpoint	.829
205	vgg_19	.829
206	mobilenet_v2_1_0_192	.827
207	resnet50_linf_4_robust	.827
208	compact_resnet50_V4	.826
209	alexnet_fov4	.826
210	AdvProp_efficientnet-b0	.826
211	convnext_large_imagenet_full_seed-0	.821
212	grcnn_robust_v1	.821
213	yudixie_resnet50_category_class_0_240908	.821
214	r3m_resnet18	.819
215	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
216	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
217	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
218	efficientnet_b1_imagenet_full	.817
219	efficientnet_b0	.816
220	nasnet_large-tf	.814
221	resnet50_robust_l2_eps1	.813
222	resnet50_imagenet_10_seed-0	.814
223	yudixie_resnet50_translation_rotation_0_240908	.811
224	alexnet_random_linf8_perturb	.810
225	resnet-18-LC_w_sh_1_iter_m	.809
226	CORnetZ_CIFAR10	.808
227	deit_large_imagenet_full_seed-0	.806
228	r3m_resnet34	.804
229	resnet50_l2_3_robust	.805
230	alexnet_random_l2_3_perturb	.805
231	yudixie_resnet50_object_class_0_240908	.802
232	yudixie_resnet18_translation_reg_0_240719	.801
233	texture_shape_alexnet_trained_on_SIN	.800
234	resnet18_imagenet21kP	.800
235	resnet_101_v1	.797
236	BiT-S-R152x2	.797
237	densenet-169-keras	.797
238	resnet50_robust_l2_eps3	.796
239	antialias-resnet152	.794
240	convnext_xlarge:fb_in22k_ft_in1k	.793
241	mobilenet_v2_0.35_224	.792
242	compact_resnet50_IT	.790
243	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
244	yudixie_resnet18_distance_translation_rotation_0_240719	.789
245	resnet101	.787
246	resnet50_pretrained_with_retinal_waves	.787
247	AlexNet_SIN_fov4	.787
248	resnet-18-LC_m	.786
249	compact_resnet50_robust_V4	.784
250	inception_v1	.785
251	yudixie_resnet50_distance_translation_0_240908	.781
252	compact_vgg19_IT	.777
253	vgg-16-keras	.778
254	alexnet_robust_correct	.778
255	vgg-19-keras	.776
256	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
257	alexnet_training_seed_05	.773
258	voneresnet-50-non_stochastic	.772
259	yudixie_resnet18_distance_translation_0_240719	.771
260	ReAlnet01_cornet_fov16	.770
261	mobilenet_v2_0_75_192	.769
262	VOneCORnet-S	.767
263	voneresnet-50	.766
264	alexnet_early_checkpoint	.764
265	voneresnet-50-robust	.763
266	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
267	convnext_tiny_imagenet_full_seed-0	.761
268	alexnet_training_seed_01_fov12	.760
269	imagenet_l2_3_0	.757
270	mobilenet_v2_0.75_96	.757
271	artResNet18_1	.755
272	mobilenet_v2_1_3_224	.746
273	yudixie_resnet18_distance_rotation_0_240719	.746
274	resnet50-vicregl0p9	.746
275	yudixie_resnet50_random_0_240908	.746
276	alexnet_training_seed_06	.745
277	deit_small_imagenet_full_seed-0	.745
278	resnet50_imagenet_1_seed-0	.746
279	inception_v1-tf	.745
280	my-model	.745
281	resnet50-cifar	.745
282	resnet50-meshes-lt-100-original-pretrained	.745
283	mobilenet_v2_1.0_128-tf	.745
284	resnet50-vitoimagevidnet8	.742
285	alexnet_linf_8_robust	.742
286	0.5x_resnet-18_LC	.741
287	densenet-121	.738
288	densenet-121-keras	.737
289	resnet50	.736
290	densenet-169	.734
291	densenet-201	.733
292	yudixie_resnet50_translation_reg_0_240908	.732
293	briaai_rmbg_1_4	.731
294	resnext101_32x8d_wsl	.724
295	mobilenet_v1_0.25_128	.722
296	densenet-201-keras	.720
297	ViT-B/32	.715
298	ReAlnet01	.712
299	alexnet_l2_3_robust	.712
300	ReAlnet01_cornet	.710
301	resnet50_ecoset_full	.710
302	ReAlnet09_cornet	.707
303	barlow-twins-resnet50	.707
304	resnet50-simclr-vissl	.706
305	AdvProp_efficientnet-b2	.707
306	ReAlnet09	.705
307	AT_efficientnet-b2	.705
308	v1-pyr-nodown	.705
309	ReAlnet03_ALL	.704
310	hmax	.700
311	ReAlnet08_ALL	.696
312	Res2Net50_26w_4s	.693
313	resnet34_imagenet_full	.690
314	ReAlnet07	.688
315	ReAlnet07_cornet	.688
316	ReAlnet08	.688
317	ReAlnet08_cornet	.687
318	resnet50-SIN_IN	.685
319	ReAlnet04	.684
320	ReAlnet04_cornet	.684
321	ReAlnet03	.682
322	ReAlnet03_cornet	.681
323	ReAlnet02_ALL	.680
324	ReAlnet04_ALL	.679
325	ReAlnet10	.678
326	mobilenet_v2_1_0_128	.677
327	ReAlnet02_cornet	.677
328	ReAlnet02	.677
329	ReAlnet10_cornet	.677
330	yolos_tiny	.677
331	yudixie_resnet18_distance_reg_0_240719	.676
332	mobilenet_v2_0.75_128	.672
333	ReAlnet05_ALL	.670
334	ReAlnet06_ALL	.669
335	ReAlnet07_ALL	.667
336	yudixie_resnet18_rotation_reg_0_240719	.667
337	CORnet-S	.667
338	resnet34_ecoset_full	.665
339	ReAlnet06	.665
340	ReAlnet06_cornet	.665
341	ReAlnet01_cornet_fov4	.664
342	ReAlnet05	.664
343	ReAlnet05_cornet	.664
344	ReAlnet01_ALL	.663
345	ReAlnet09_ALL	.661
346	resnet101_imagenet_full	.661
347	ReAlnet10_ALL	.661
348	resnet-18-LC_untrained	.658
349	0.5x_resnet-18_LC_w_sh_1_iter	.655
350	resnet_101_v2	.652
351	resnet152_ecoset_full	.650
352	ReAlnet01_cornet_fov12	.647
353	vggface	.646
354	resnet18_imagenet_full	.646
355	resnet-18_test_m	.644
356	resnet101_ecoset_full	.644
357	resnet-18-LC_conv_init_m	.643
358	resnet-18-LC_w_sh_100_iter_m	.636
359	cornet_s	.635
360	0.5x_resnet-18	.633
361	resnet_50_v2	.631
362	resnet-152_v2_pytorch	.632
363	resnet50_ewconplus_wzc	.618
364	mobilenet_v2_1-4_224_pytorch	.616
365	mobilenet_v2_0_5_224	.597
366	convnext_small_imagenet_1_seed-0	.593
367	resnet152_imagenet_full	.590
368	mobilenet_v2_0.75_160-tf	.561
369	CORnetZ_CIFAR10_bs32_20_04	.507
370	convnext_small_imagenet_10_seed-0	.491
371	deit_small_imagenet_10_seed-0	.333
372	resnet50-meshes-lt-100-original-scratch	.333
373	mobilenet_v2_0.5_160	.288
374	mobilenet_v2_0.5_128	.288
375	mobilenet_v2_0.35_160
376	resnet18-supervised
377	fitvid_trained_on_physion
378	openclip
379	resnext101_32x32d_wsl
380	resnext101_32x48d_wsl
381	resnet50_ImageNet
382	regnet_y_400mf
383	tv_efficientnet-b1
384	alexnet_ks_torevert
385	yudixie_resnet50_imagenet1kpret_0_240312
386	resnet_152_v1
387	resnext101_32x16d_wsl
388	densenet_201_pytorch
389	inception_v3_pytorch
390	pnasnet_large_pytorch
391	inception_v4
392	nasnet_large
393	deit_small_imagenet_1_seed-0
394	pnasnet_large
395	vonegrcnn_62e_nobn
396	vonegrcnn_52e_full
397	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio