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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_Ringach2002-or_bandwidth")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet-18-LC_w_sh_1_iter_conv_init_m	.984
2	omnivore_swinS	.984
3	yudixie_resnet18_rotation_reg_0_240719	.982
4	efficientnet-b7	.981
5	resnet-18-LC_w_sh_10_iter_conv_init_m	.969
6	compact_vgg19_V4	.965
7	alexnet_robust_correct	.965
8	yudixie_resnet18_distance_reg_0_240719	.961
9	AdvProp_efficientnet-b6	.956
10	BiT-S-R50x1	.954
11	vonegrcnn_62e_nobn	.955
12	vggface	.952
13	convnext_large_imagenet_full_seed-0	.952
14	yudixie_resnet18_distance_rotation_0_240719	.950
15	resnet_101_v1	.949
16	convnext_base:clip_laiona_augreg_ft_in1k_384	.950
17	yudixie_resnet50_distance_translation_0_240908	.945
18	omnivore_swinB	.944
19	resnet-18-LC_w_sh_100_iter_m	.941
20	resnet34_imagenet_full	.941
21	nasnet_large-tf	.939
22	ReAlnet03_ALL	.939
23	yudixie_resnet18_distance_translation_rotation_0_240719	.938
24	cvt_cvt-13-224-in1k_4_LucyV4	.937
25	yudixie_resnet18_distance_translation_0_240719	.936
26	alexnet_l2_3_robust	.934
27	ReAlnet09_ALL	.933
28	resnet101	.932
29	ReAlnet01_ALL	.933
30	yudixie_resnet50_translation_rotation_0_240908	.932
31	mobilenet_v2_1.0_96	.932
32	ReAlnet06_ALL	.933
33	ReAlnet04_ALL	.931
34	resnet50	.930
35	resnet-50_untrained	.930
36	ReAlnet08_ALL	.931
37	cvt_cvt-21-224-in1k_4_LucyV4	.928
38	ReAlnet02_ALL	.930
39	resnet-18-LC_w_sh_1_iter_m	.928
40	AdvProp_efficientnet-b8	.928
41	resnet50-SIN_IN	.927
42	ReAlnet05_ALL	.927
43	resnet18_imagenet_full	.926
44	ReAlnet10_ALL	.927
45	cvt_cvt-21-384-in1k_4_LucyV4	.927
46	mobilenet_v2_1.3_224-tf	.925
47	resnet50-moclr8deg	.924
48	resnet-50_v1-tf	.923
49	BiT-S-R101x3	.923
50	vgg_19	.922
51	resnet-152_v2-tf	.922
52	compact_cornets_IT	.921
53	convnext_base_imagenet_full_seed-0	.920
54	xception	.920
55	voneresnet-50-non_stochastic	.920
56	compact_vgg19_IT	.919
57	alexnet_early_checkpoint	.917
58	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.917
59	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.917
60	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.916
61	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.916
62	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.915
63	alexnet_training_seed_01_fov12	.915
64	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.915
65	ReAlnet07_ALL	.916
66	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.915
67	mobilenet_v2_1.4_224-tf	.915
68	antialias-resnet152	.914
69	inception_v4-tf	.913
70	Res2Net50_26w_4s	.913
71	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.913
72	inception_v3-tf	.911
73	yudixie_resnet50_distance_rotation_0_240908	.911
74	yudixie_resnet50_translation_reg_0_240908	.911
75	focalnet_tiny_lrf_in1k	.911
76	yudixie_resnet50_distance_translation_rotation_0_240908	.910
77	BiT-M-R101x1	.910
78	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.910
79	resnet50-simclr-vissl	.909
80	yudixie_resnet50_rotation_reg_0_240908	.909
81	vgg_16	.907
82	resnet50_l2_3_robust	.907
83	resnet-18_test_m	.906
84	BiT-M-R50x3	.907
85	texture_shape_alexnet_trained_on_SIN	.906
86	inception_v2-tf	.905
87	CORnet-S	.905
88	blt_vs	.904
89	resnext101_32x8d_wsl	.904
90	omnivore_swinT	.904
91	0.5x_resnet-18_LC_w_sh_1_iter	.904
92	resnet34_ecoset_full	.903
93	alexnet_random_l2_3_perturb	.902
94	resnet-50x2_untrained	.903
95	resnet50_byol	.901
96	BiT-S-R50x3	.900
97	resnet50_random_linf8_perturb	.900
98	vonealexnet_gaussian_noise_std4_fixed	.899
99	compact_resnet50_IT	.899
100	resnet101_ecoset_full	.897
101	SWSL_resnet50	.896
102	mobilenet_v2_1.0_160-tf	.896
103	AdvProp_efficientnet-b7	.895
104	convnext_tiny:in12k_ft_in1k	.893
105	resnet50-vicregl0p9	.892
106	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.892
107	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.891
108	alexnet_linf_8_robust	.890
109	BiT-S-R152x2	.889
110	resnet-18-LC_1st_conv_m	.889
111	resnet50-vicregl0p75	.888
112	imagenet_l2_3_0	.888
113	barlow-twins-resnet50	.888
114	yudixie_resnet50_distance_reg_0_240908	.888
115	resnet50_robust_l2_eps3	.886
116	resnet152_ecoset_full	.887
117	BiT-M-R101x3	.885
118	resnet-18-LC_w_sh_100_iter_conv_init_m	.885
119	alexnet_random_linf8_perturb	.883
120	grcnn	.884
121	compact_resnet50_robust_IT	.882
122	ViT_L_32_imagenet1k	.882
123	resnet_50_v2	.881
124	grcnn_robust_v1	.883
125	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.881
126	yudixie_resnet50_random_0_240908	.879
127	CLIP-RN50	.879
128	ReAlnet01_cornet_fov12	.878
129	resnet50_imagenet_100_seed-0	.879
130	ReAlnet01_cornet_fov16	.879
131	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.878
132	resnet_50_v1_spiking	.877
133	resnet_50_v1_spiking_l4	.876
134	yudixie_resnet50_imagenet1kpret_0_240908	.876
135	resnet_50_v1_spiking_l4a	.876
136	alexnet	.876
137	alexnet_fov16	.876
138	resnet50_tutorial	.875
139	resnet_50_v1	.875
140	grcnn_v2_text_noise	.875
141	resnet_50_v1_spiking_l3	.874
142	resnet50	.874
143	resnet18_imagenet21kP	.874
144	yolos_tiny	.874
145	resnet50-sup	.874
146	grcnn_109	.872
147	mobilenet_v2_0.75_192-tf	.873
148	mobilenet_v2_1.0_224-tf	.871
149	resnet50_linf_8_robust	.871
150	CLIP_resnet50	.871
151	imagenet_l2_10_0	.870
152	resnet50-vitoimagevidnet8	.870
153	cornet_s	.869
154	resnet18_ecoset_full	.867
155	convnext_tiny_imagenet_full_seed-0	.868
156	CLIP_resnet50_float32	.867
157	mobilenet_v2_0_75_192	.867
158	resnet-101_v2-tf	.866
159	densenet-169	.866
160	convnext_tiny_sup	.865
161	resnet50_ewconplus_wzc	.865
162	CLIP_ViT-B_32	.864
163	resnet-18-LC_w_sh_10_iter_m	.863
164	RN50	.863
165	AdvProp_efficientnet-b4	.861
166	resnet152_imagenet_full	.861
167	convnext_xlarge:fb_in22k_ft_in1k	.861
168	resnet50-VITO-8deg-cc	.859
169	ReAlnet09	.860
170	convnext_small_imagenet_1_seed-0	.859
171	mobilenet_v2_1_4_224	.858
172	ReAlnet09_cornet	.858
173	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.857
174	resnet101_imagenet_full	.857
175	resnet-18_untrained	.857
176	resnet50_linf_4_robust	.857
177	resnet50_FractalDB	.856
178	ReAlnet01	.855
179	mobilenet_v2_1.0_192-tf	.855
180	AdvProp_efficientnet-b0	.854
181	densenet-121	.854
182	convnext_small_imagenet_100_seed-0	.854
183	resnet-50_v2-tf	.853
184	resnet50_random_l2_perturb	.853
185	resnet_SIN_IN_FT_IN	.852
186	ReAlnet01_cornet	.853
187	AdvProp_efficientnet-b2	.852
188	effnetv2m_custom384	.852
189	inception_v3	.851
190	antialiased-r50	.852
191	AT_efficientnet-b2	.849
192	inception_v1	.849
193	yudixie_resnet18_translation_reg_0_240719	.849
194	resnet50_primary_visual_cortex	.847
195	resnet50_robust_l2_eps1	.848
196	0.5x_resnet-18_LC_w_sh_100_iter	.843
197	ReAlnet07	.842
198	resnet50-vicreg	.842
199	ReAlnet03	.842
200	resnet50-SIN_IN_IN	.841
201	ReAlnet07_cornet	.840
202	ReAlnet03_cornet	.838
203	mobilenet_v2_0.75_224-tf	.838
204	compact_cornets_V4	.838
205	ReAlnet04	.838
206	yudixie_resnet18_translation_rotation_0_240719	.838
207	antialiased-rnext101_32x8d	.837
208	ReAlnet04_cornet	.836
209	vgg-16-keras	.836
210	SWSL_resnext101_32x8d	.836
211	ReAlnet10_cornet	.835
212	ReAlnet10	.834
213	AlexNet_SIN_fov16	.834
214	effnetb1_272x240	.833
215	compact_resnet50_robust_V4	.833
216	resnet-101_v1-tf	.832
217	resnet-152_v2_pytorch	.832
218	densenet-201	.831
219	cv_18_dagger_408_pretrained	.831
220	mobilenet_v2_1_0_160	.830
221	pnasnet_large_halfPrecision	.829
222	pnasnet_large-tf	.829
223	mobilenet_v2_1_3_224	.830
224	ReAlnet08_cornet	.829
225	ReAlnet08	.828
226	convnext_femto_ols:d1_in1k	.826
227	ReAlnet05_cornet	.826
228	ReAlnet05	.826
229	densenet-201-keras	.823
230	alexnet_fov12	.823
231	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.821
232	mobilenet_v2_1.0_128-tf	.822
233	mobilenet_v2_1_0_192	.820
234	vit_base_patch16_clip_224:openai_ft_in1k	.819
235	resnet50-barlow	.819
236	briaai_rmbg_1_4	.819
237	eBarlow_lmda_02_1000ep	.818
238	0.5x_resnet-18	.817
239	ReAlnet02	.816
240	BiT-S-R101x1	.816
241	ReAlnet02_cornet	.815
242	AlexNet_SIN_fov4	.814
243	konkle_alexnetgn_ipcl_ref01_primary_model	.813
244	resnet50-SIN	.813
245	ReAlnet06	.813
246	BiT-S-R152x4	.811
247	BiT-M-R152x2	.811
248	ReAlnet06_cornet	.811
249	resnet50_imagenet_10_seed-0	.811
250	densenet-169-keras	.810
251	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.810
252	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.807
253	0.5x_resnet-18_LC	.808
254	yudixie_resnet18_category_class_0_240719	.807
255	FrankRobWobv0	.807
256	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.806
257	mobilenet_v2_0_5_224	.804
258	AlexNet_SIN	.803
259	BiT-M-R152x4	.802
260	resnet-50-robust	.801
261	compact_resnet50_V4	.799
262	resnet50_simclr	.795
263	convnext_large:fb_in22k_ft_in1k	.794
264	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.794
265	resnet50_moco_v2	.794
266	resnet-18-LC_d_w_sh_1x1_conv_init_m	.794
267	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.790
268	mobilenet_v2_0_5_192	.790
269	AlexNet_SIN_fov12	.790
270	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.788
271	vit_large_patch14_clip_224:laion2b_ft_in1k	.788
272	alexnet_training_seed_07	.787
273	0.5x_resnet-18_LC_w_sh_10_iter	.787
274	texture_shape_resnet50_trained_on_SIN	.786
275	yudixie_resnet18_random_0_240719	.785
276	mobilenet_v2_0_75_160	.783
277	efficientnet_b1_imagenet_full	.784
278	my-model	.783
279	shufflenet_v2_x1_0	.782
280	ViT-B/32	.781
281	yudixie_resnet50_object_class_0_240908	.779
282	yudixie_resnet18_object_class_0_240719	.778
283	mobilenet_v2_0_75_224	.777
284	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.777
285	yudixie_resnet50_category_class_0_240908	.776
286	vgg-19-keras	.774
287	vit_relpos_base_patch16_clsgap_224:sw_in1k	.774
288	alexnet_training_seed_09	.774
289	nasnet_mobile-tf	.769
290	vit_large_patch14_clip_336:laion2b_ft_in1k	.764
291	vit_large_patch14_clip_224:openai_ft_in1k	.762
292	efficientnet_b0	.762
293	convnext_small_imagenet_full_seed-0	.761
294	alexnet_fov4	.762
295	cvt_cvt-13-384-in1k_4_LucyV4	.760
296	inception_v1-tf	.759
297	mobilenet_v2_1-4_224_pytorch	.753
298	alexnet_training_seed_04	.753
299	vit_base_patch14_reg4_dinov2-lvd142m	.751
300	custom_model_cv_18_dagger_408	.751
301	ReAlnet01_cornet_fov4	.749
302	alexnet_training_seed_01_fov16	.746
303	densenet-121-keras	.743
304	yudixie_resnet18_imagenet1kpret_0_240719	.741
305	mobilenet_v2_0.35_224	.740
306	deit_small_imagenet_full_seed-0	.739
307	mobilenet_v2_1_0_128	.739
308	alexnet_training_seed_01	.739
309	resnet50_ecoset_full	.739
310	alexnet_training_seed_02	.739
311	aughost_fair_resnet18	.734
312	nasnet_mobile	.734
313	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.730
314	inception_resnet_v2	.728
315	vision_transformer_vit_large_patch16_224	.728
316	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.724
317	resnet-152_v1-tf	.722
318	mobilenet_v2_0.75_96	.719
319	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.718
320	vonegrcnn_52e_full	.714
321	resnet-18-LC_m	.710
322	CORnet-Z	.706
323	vonegrcnn_47e	.705
324	efficientnet_b0_imagenet_full	.702
325	resnet18-supervised	.702
326	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.701
327	vonegrcnn_47e	.700
328	resnet50_imagenet_full	.698
329	convnext_small_imagenet_10_seed-0	.692
330	alexnet_training_seed_10	.691
331	alexnet_reduced_aliasing_early_checkpoint	.683
332	alexnet_training_seed_03	.674
333	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.671
334	mobilenet_v2_0.5_224-tf	.670
335	alexnet_training_seed_01_fov4	.669
336	vit_base_patch14_dinov2-lvd142m	.659
337	deit_base_imagenet_full_seed-0	.658
338	resnet-18-LC_conv_init_m	.648
339	efficientnet_b2_imagenet_full	.647
340	hmax	.644
341	mobilenet_v2_1_0_224	.641
342	mobilenet_v1_0.25_128	.612
343	mobilenet_v2_0.5_192-tf	.600
344	BiT-M-R50x1	.597
345	v1-pyr-nodown	.581
346	r3m_resnet34_nocrop	.566
347	CORnetZ_CIFAR10	.554
348	artResNet18_1	.548
349	mobilenet_v2_0.5_160	.518
350	r3m_resnet50_nocrop	.517
351	VOneCORnet-S	.511
352	r3m_resnet34	.505
353	vit_base_patch16_dinov3-lvd1689m	.504
354	voneresnet-50	.494
355	voneresnet-50-robust	.491
356	r3m_resnet50	.483
357	deit_small_imagenet_100_seed-0	.447
358	mobilenet_v2_0.75_128	.441
359	mobilenet_v2_0.75_160-tf	.440
360	alexnet_training_seed_08	.432
361	resnet50_pretrained_with_retinal_waves	.428
362	r3m_resnet18	.427
363	deit_small_imagenet_10_seed-0	.393
364	mobilenet_v2_0.5_128	.393
365	resnet50-cifar	.393
366	resnet50-meshes-lt-100-original-pretrained	.389
367	resnet50_imagenet_1_seed-0	.387
368	deit_large_imagenet_full_seed-0	.386
369	alexnet_training_seed_05	.297
370	resnet-18-LC_untrained	.262
371	alexnet_training_seed_06	.254
372	CORnetZ_CIFAR10_bs32_20_04	.229
373	my-model	.150
374	resnet50-meshes-lt-100-original-scratch	.108
375	mobilenet_v2_0.35_160
376	fitvid_trained_on_physion
377	openclip
378	resnext101_32x32d_wsl
379	resnext101_32x16d_wsl
380	resnext101_32x48d_wsl
381	fixres_resnext101_32x48d_wsl
382	resnet50_ImageNet
383	regnet_y_400mf
384	tv_efficientnet-b1
385	alexnet_ks_torevert
386	yudixie_resnet50_imagenet1kpret_0_240312
387	resnet_152_v1
388	densenet_201_pytorch
389	inception_v3_pytorch
390	pnasnet_large_pytorch
391	mobilevit_small
392	inception_v4
393	nasnet_large
394	deit_small_imagenet_1_seed-0
395	pnasnet_large
396	vonegrcnn_62e_nobn
397	resnet_101_v2
398	vonegrcnn_52e_full
399	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_bandwidth