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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	BiT-S-R101x1	.914
69	mobilenet_v2_1.4_224-tf	.915
70	alexnet_training_seed_07	.914
71	alexnet_training_seed_02	.915
72	compact_resnet50_robust_IT	.915
73	mobilenet_v2_1_4_224	.914
74	BiT-M-R152x4	.914
75	alexnet_training_seed_10	.915
76	efficientnet_b2_imagenet_full	.914
77	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.913
78	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.913
79	alexnet_training_seed_03	.912
80	alexnet_training_seed_09	.911
81	vonegrcnn_47e	.911
82	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.911
83	mobilenet_v2_1.3_224-tf	.909
84	resnet50_primary_visual_cortex	.908
85	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.906
86	0.5x_resnet-18_LC_w_sh_10_iter	.905
87	resnet50_random_l2_perturb	.905
88	compact_cornets_IT	.904
89	SWSL_resnext101_32x8d	.902
90	resnet50_byol	.903
91	cvt_cvt-21-224-in1k_4_LucyV4	.903
92	deit_base_imagenet_full_seed-0	.902
93	focalnet_tiny_lrf_in1k	.900
94	effnetv2m_custom384	.899
95	resnet50_imagenet_full	.899
96	resnet50-vicreg	.898
97	xception	.899
98	BiT-M-R101x1	.897
99	nasnet_mobile-tf	.898
100	resnet-101_v1-tf	.898
101	mobilenet_v2_0.5_192-tf	.898
102	resnet-18-LC_w_sh_1_iter_conv_init_m	.898
103	omnivore_swinT	.896
104	convnext_small_imagenet_100_seed-0	.897
105	shufflenet_v2_x1_0	.896
106	resnet50_moco_v2	.894
107	AlexNet_SIN	.894
108	resnet50-moclr8deg	.894
109	grcnn	.893
110	resnet50_FractalDB	.893
111	inception_v4-tf	.892
112	BiT-M-R50x3	.891
113	r3m_resnet50_nocrop	.889
114	mobilenet_v2_1.0_160-tf	.889
115	konkle_alexnetgn_ipcl_ref01_primary_model	.888
116	efficientnet_b0_imagenet_full	.888
117	resnet-18_untrained	.887
118	AdvProp_efficientnet-b4	.886
119	r3m_resnet34_nocrop	.886
120	BiT-S-R152x4	.884
121	0.5x_resnet-18_LC_w_sh_100_iter	.884
122	yudixie_resnet50_distance_rotation_0_240908	.884
123	resnet50_imagenet_100_seed-0	.884
124	BiT-S-R50x1	.883
125	r3m_resnet50	.882
126	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
127	CLIP_ViT-B_32	.882
128	inception_v3-tf	.881
129	BiT-M-R50x1	.881
130	resnet50	.880
131	mobilenet_v2_0.75_192-tf	.880
132	vonegrcnn_62e_nobn	.879
133	inception_v2-tf	.878
134	mobilenet_v2_1.0_96	.878
135	yudixie_resnet18_random_0_240719	.877
136	yudixie_resnet18_imagenet1kpret_0_240719	.876
137	mobilenet_v2_1.0_224-tf	.876
138	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.875
139	SWSL_resnet50	.876
140	resnet50_random_linf8_perturb	.876
141	AdvProp_efficientnet-b6	.873
142	BiT-M-R152x2	.873
143	alexnet_training_seed_08	.872
144	omnivore_swinS	.871
145	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.871
146	resnet18_ecoset_full	.872
147	mobilenet_v2_0.5_224-tf	.872
148	mobilenet_v2_0_5_192	.871
149	CORnet-Z	.870
150	resnet50-SIN	.870
151	alexnet	.869
152	resnet50_linf_8_robust	.870
153	alexnet	.869
154	alexnet	.869
155	alexnet	.869
156	alexnet-baseline	.869
157	alexnet	.869
158	omnivore_swinB	.868
159	alexnet	.868
160	yudixie_resnet18_category_class_0_240719	.868
161	alexnet	.868
162	convnext_base_imagenet_full_seed-0	.868
163	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.868
164	convnext_large:fb_in22k_ft_in1k	.866
165	convnext_femto_ols:d1_in1k	.866
166	effnetb1_272x240	.863
167	convnext_tiny:in12k_ft_in1k	.863
168	antialiased-r50	.862
169	yudixie_resnet50_distance_reg_0_240908	.861
170	texture_shape_resnet50_trained_on_SIN	.861
171	resnet-50x2_untrained	.860
172	BiT-M-R101x3	.859
173	mobilenet_v2_0_75_224	.859
174	vit_large_patch14_clip_224:openai_ft_in1k	.856
175	mobilenet_v2_0_75_160	.854
176	vit_base_patch16_clip_224:openai_ft_in1k	.853
177	imagenet_l2_10_0	.853
178	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.853
179	inception_resnet_v2	.851
180	AdvProp_efficientnet-b7	.849
181	mobilenet_v2_1_0_224	.848
182	pnasnet_large-tf	.847
183	vonealexnet_gaussian_noise_std4_fixed	.847
184	resnet50-VITO-8deg-cc	.843
185	resnet-50-robust	.843
186	FrankRobWobv0	.842
187	yudixie_resnet18_translation_rotation_0_240719	.843
188	resnet-50_untrained	.839
189	yudixie_resnet50_distance_translation_rotation_0_240908	.838
190	resnet-152_v2-tf	.837
191	mobilenet_v2_1_0_160	.836
192	alexnet	.835
193	mobilenet_v2_0.75_224-tf	.834
194	deit_small_imagenet_100_seed-0	.833
195	mobilenet_v2_1.0_192-tf	.833
196	compact_cornets_V4	.833
197	resnet-101_v2-tf	.830
198	yudixie_resnet50_rotation_reg_0_240908	.830
199	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.829
200	alexnet_reduced_aliasing_early_checkpoint	.829
201	vgg_19	.829
202	mobilenet_v2_1_0_192	.827
203	resnet50_linf_4_robust	.827
204	compact_resnet50_V4	.826
205	AdvProp_efficientnet-b0	.826
206	convnext_large_imagenet_full_seed-0	.821
207	grcnn_robust_v1	.821
208	yudixie_resnet50_category_class_0_240908	.821
209	r3m_resnet18	.819
210	vit_large_patch14_clip_224:laion2b_ft_in1k	.819
211	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.818
212	resnet-18-LC_w_sh_100_iter_conv_init_m	.819
213	efficientnet_b1_imagenet_full	.817
214	efficientnet_b0	.816
215	nasnet_large-tf	.814
216	resnet50_robust_l2_eps1	.813
217	resnet50_imagenet_10_seed-0	.814
218	yudixie_resnet50_translation_rotation_0_240908	.811
219	alexnet_random_linf8_perturb	.810
220	resnet-18-LC_w_sh_1_iter_m	.809
221	CORnetZ_CIFAR10	.808
222	deit_large_imagenet_full_seed-0	.806
223	r3m_resnet34	.804
224	resnet50_l2_3_robust	.805
225	alexnet_random_l2_3_perturb	.805
226	yudixie_resnet50_object_class_0_240908	.802
227	yudixie_resnet18_translation_reg_0_240719	.801
228	texture_shape_alexnet_trained_on_SIN	.800
229	resnet18_imagenet21kP	.800
230	resnet_101_v1	.797
231	BiT-S-R152x2	.797
232	densenet-169-keras	.797
233	resnet50_robust_l2_eps3	.796
234	antialias-resnet152	.794
235	convnext_xlarge:fb_in22k_ft_in1k	.793
236	mobilenet_v2_0.35_224	.792
237	compact_resnet50_IT	.790
238	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.789
239	yudixie_resnet18_distance_translation_rotation_0_240719	.789
240	resnet101	.787
241	resnet50_pretrained_with_retinal_waves	.787
242	resnet-18-LC_m	.786
243	compact_resnet50_robust_V4	.784
244	inception_v1	.785
245	yudixie_resnet50_distance_translation_0_240908	.781
246	compact_vgg19_IT	.777
247	vgg-16-keras	.778
248	alexnet_robust_correct	.778
249	vgg-19-keras	.776
250	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.775
251	alexnet_training_seed_05	.773
252	voneresnet-50-non_stochastic	.772
253	yudixie_resnet18_distance_translation_0_240719	.771
254	mobilenet_v2_0_75_192	.769
255	VOneCORnet-S	.767
256	voneresnet-50	.766
257	alexnet_early_checkpoint	.764
258	voneresnet-50-robust	.763
259	resnet-18-LC_d_w_sh_1x1_conv_init_m	.761
260	convnext_tiny_imagenet_full_seed-0	.761
261	imagenet_l2_3_0	.757
262	mobilenet_v2_0.75_96	.757
263	artResNet18_1	.755
264	mobilenet_v2_1_3_224	.746
265	yudixie_resnet18_distance_rotation_0_240719	.746
266	resnet50-vicregl0p9	.746
267	yudixie_resnet50_random_0_240908	.746
268	alexnet_training_seed_06	.745
269	deit_small_imagenet_full_seed-0	.745
270	resnet50_imagenet_1_seed-0	.746
271	inception_v1-tf	.745
272	my-model	.745
273	resnet50-cifar	.745
274	resnet50-meshes-lt-100-original-pretrained	.745
275	mobilenet_v2_1.0_128-tf	.745
276	resnet50-vitoimagevidnet8	.742
277	alexnet_linf_8_robust	.742
278	0.5x_resnet-18_LC	.741
279	densenet-121	.738
280	densenet-121-keras	.737
281	resnet50	.736
282	densenet-169	.734
283	densenet-201	.733
284	yudixie_resnet50_translation_reg_0_240908	.732
285	briaai_rmbg_1_4	.731
286	resnext101_32x8d_wsl	.724
287	mobilenet_v1_0.25_128	.722
288	densenet-201-keras	.720
289	ViT-B/32	.715
290	ReAlnet01	.712
291	alexnet_l2_3_robust	.712
292	ReAlnet01_cornet	.710
293	resnet50_ecoset_full	.710
294	ReAlnet09_cornet	.707
295	barlow-twins-resnet50	.707
296	resnet50-simclr-vissl	.706
297	AdvProp_efficientnet-b2	.707
298	ReAlnet09	.705
299	AT_efficientnet-b2	.705
300	v1-pyr-nodown	.705
301	ReAlnet03_ALL	.704
302	hmax	.700
303	ReAlnet08_ALL	.696
304	Res2Net50_26w_4s	.693
305	resnet34_imagenet_full	.690
306	ReAlnet07	.688
307	ReAlnet07_cornet	.688
308	ReAlnet08	.688
309	ReAlnet08_cornet	.687
310	resnet50-SIN_IN	.685
311	ReAlnet04	.684
312	ReAlnet04_cornet	.684
313	ReAlnet03	.682
314	ReAlnet03_cornet	.681
315	ReAlnet02_ALL	.680
316	ReAlnet04_ALL	.679
317	ReAlnet10	.678
318	mobilenet_v2_1_0_128	.677
319	ReAlnet02_cornet	.677
320	ReAlnet02	.677
321	ReAlnet10_cornet	.677
322	yolos_tiny	.677
323	yudixie_resnet18_distance_reg_0_240719	.676
324	mobilenet_v2_0.75_128	.672
325	ReAlnet05_ALL	.670
326	ReAlnet06_ALL	.669
327	ReAlnet07_ALL	.667
328	yudixie_resnet18_rotation_reg_0_240719	.667
329	CORnet-S	.667
330	resnet34_ecoset_full	.665
331	ReAlnet06	.665
332	ReAlnet06_cornet	.665
333	ReAlnet05	.664
334	ReAlnet05_cornet	.664
335	ReAlnet01_ALL	.663
336	ReAlnet09_ALL	.661
337	resnet101_imagenet_full	.661
338	ReAlnet10_ALL	.661
339	resnet-18-LC_untrained	.658
340	0.5x_resnet-18_LC_w_sh_1_iter	.655
341	resnet_101_v2	.652
342	resnet152_ecoset_full	.650
343	vggface	.646
344	resnet18_imagenet_full	.646
345	resnet-18_test_m	.644
346	resnet101_ecoset_full	.644
347	resnet-18-LC_conv_init_m	.643
348	resnet-18-LC_w_sh_100_iter_m	.636
349	cornet_s	.635
350	0.5x_resnet-18	.633
351	resnet_50_v2	.631
352	resnet-152_v2_pytorch	.632
353	resnet50_ewconplus_wzc	.618
354	mobilenet_v2_1-4_224_pytorch	.616
355	mobilenet_v2_0_5_224	.597
356	convnext_small_imagenet_1_seed-0	.593
357	resnet152_imagenet_full	.590
358	mobilenet_v2_0.75_160-tf	.561
359	CORnetZ_CIFAR10_bs32_20_04	.507
360	convnext_small_imagenet_10_seed-0	.491
361	deit_small_imagenet_10_seed-0	.333
362	resnet50-meshes-lt-100-original-scratch	.333
363	mobilenet_v2_0.5_160	.288
364	mobilenet_v2_0.5_128	.288
365	mobilenet_v2_0.35_160
366	resnet18-supervised
367	fitvid_trained_on_physion
368	openclip
369	resnext101_32x32d_wsl
370	resnext101_32x48d_wsl
371	resnet50_ImageNet
372	regnet_y_400mf
373	tv_efficientnet-b1
374	alexnet_ks_torevert
375	yudixie_resnet50_imagenet1kpret_0_240312
376	resnet_152_v1
377	resnext101_32x16d_wsl
378	densenet_201_pytorch
379	inception_v3_pytorch
380	pnasnet_large_pytorch
381	inception_v4
382	nasnet_large
383	deit_small_imagenet_1_seed-0
384	pnasnet_large
385	vonegrcnn_62e_nobn
386	vonegrcnn_52e_full
387	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: cv_bandwidth_ratio