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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	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.915
64	ReAlnet07_ALL	.916
65	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.915
66	mobilenet_v2_1.4_224-tf	.915
67	antialias-resnet152	.914
68	inception_v4-tf	.913
69	Res2Net50_26w_4s	.913
70	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.913
71	inception_v3-tf	.911
72	yudixie_resnet50_distance_rotation_0_240908	.911
73	yudixie_resnet50_translation_reg_0_240908	.911
74	focalnet_tiny_lrf_in1k	.911
75	yudixie_resnet50_distance_translation_rotation_0_240908	.910
76	BiT-M-R101x1	.910
77	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.910
78	resnet50-simclr-vissl	.909
79	yudixie_resnet50_rotation_reg_0_240908	.909
80	vgg_16	.907
81	resnet50_l2_3_robust	.907
82	resnet-18_test_m	.906
83	BiT-M-R50x3	.907
84	texture_shape_alexnet_trained_on_SIN	.906
85	inception_v2-tf	.905
86	alexnet	.903
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	resnet50_imagenet_100_seed-0	.879
129	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.878
130	resnet_50_v1_spiking	.877
131	alexnet	.877
132	alexnet	.877
133	alexnet-baseline	.877
134	resnet_50_v1_spiking_l4	.876
135	yudixie_resnet50_imagenet1kpret_0_240908	.876
136	resnet_50_v1_spiking_l4a	.876
137	alexnet	.876
138	alexnet	.875
139	alexnet	.876
140	resnet50_tutorial	.875
141	resnet_50_v1	.875
142	alexnet	.875
143	grcnn_v2_text_noise	.875
144	resnet_50_v1_spiking_l3	.874
145	alexnet	.874
146	resnet50	.874
147	resnet18_imagenet21kP	.874
148	yolos_tiny	.874
149	resnet50-sup	.874
150	grcnn_109	.872
151	mobilenet_v2_0.75_192-tf	.873
152	mobilenet_v2_1.0_224-tf	.871
153	resnet50_linf_8_robust	.871
154	CLIP_resnet50	.871
155	imagenet_l2_10_0	.870
156	resnet50-vitoimagevidnet8	.870
157	cornet_s	.869
158	resnet18_ecoset_full	.867
159	convnext_tiny_imagenet_full_seed-0	.868
160	CLIP_resnet50_float32	.867
161	mobilenet_v2_0_75_192	.867
162	resnet-101_v2-tf	.866
163	densenet-169	.866
164	convnext_tiny_sup	.865
165	resnet50_ewconplus_wzc	.865
166	CLIP_ViT-B_32	.864
167	resnet-18-LC_w_sh_10_iter_m	.863
168	RN50	.863
169	AdvProp_efficientnet-b4	.861
170	resnet152_imagenet_full	.861
171	convnext_xlarge:fb_in22k_ft_in1k	.861
172	resnet50-VITO-8deg-cc	.859
173	ReAlnet09	.860
174	convnext_small_imagenet_1_seed-0	.859
175	mobilenet_v2_1_4_224	.858
176	ReAlnet09_cornet	.858
177	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.857
178	resnet101_imagenet_full	.857
179	resnet-18_untrained	.857
180	resnet50_linf_4_robust	.857
181	resnet50_FractalDB	.856
182	ReAlnet01	.855
183	mobilenet_v2_1.0_192-tf	.855
184	AdvProp_efficientnet-b0	.854
185	densenet-121	.854
186	convnext_small_imagenet_100_seed-0	.854
187	resnet-50_v2-tf	.853
188	resnet50_random_l2_perturb	.853
189	resnet_SIN_IN_FT_IN	.852
190	ReAlnet01_cornet	.853
191	AdvProp_efficientnet-b2	.852
192	effnetv2m_custom384	.852
193	inception_v3	.851
194	antialiased-r50	.852
195	AT_efficientnet-b2	.849
196	inception_v1	.849
197	yudixie_resnet18_translation_reg_0_240719	.849
198	resnet50_primary_visual_cortex	.847
199	resnet50_robust_l2_eps1	.848
200	0.5x_resnet-18_LC_w_sh_100_iter	.843
201	ReAlnet07	.842
202	resnet50-vicreg	.842
203	ReAlnet03	.842
204	resnet50-SIN_IN_IN	.841
205	ReAlnet07_cornet	.840
206	ReAlnet03_cornet	.838
207	mobilenet_v2_0.75_224-tf	.838
208	compact_cornets_V4	.838
209	ReAlnet04	.838
210	yudixie_resnet18_translation_rotation_0_240719	.838
211	antialiased-rnext101_32x8d	.837
212	ReAlnet04_cornet	.836
213	vgg-16-keras	.836
214	SWSL_resnext101_32x8d	.836
215	ReAlnet10_cornet	.835
216	ReAlnet10	.834
217	effnetb1_272x240	.833
218	compact_resnet50_robust_V4	.833
219	resnet-101_v1-tf	.832
220	resnet-152_v2_pytorch	.832
221	densenet-201	.831
222	cv_18_dagger_408_pretrained	.831
223	mobilenet_v2_1_0_160	.830
224	pnasnet_large-tf	.829
225	mobilenet_v2_1_3_224	.830
226	ReAlnet08_cornet	.829
227	ReAlnet08	.828
228	convnext_femto_ols:d1_in1k	.826
229	ReAlnet05_cornet	.826
230	ReAlnet05	.826
231	densenet-201-keras	.823
232	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.821
233	mobilenet_v2_1.0_128-tf	.822
234	mobilenet_v2_1_0_192	.820
235	vit_base_patch16_clip_224:openai_ft_in1k	.819
236	resnet50-barlow	.819
237	briaai_rmbg_1_4	.819
238	0.5x_resnet-18	.817
239	ReAlnet02	.816
240	BiT-S-R101x1	.816
241	ReAlnet02_cornet	.815
242	konkle_alexnetgn_ipcl_ref01_primary_model	.813
243	resnet50-SIN	.813
244	ReAlnet06	.813
245	BiT-S-R152x4	.811
246	BiT-M-R152x2	.811
247	ReAlnet06_cornet	.811
248	resnet50_imagenet_10_seed-0	.811
249	densenet-169-keras	.810
250	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.810
251	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.807
252	0.5x_resnet-18_LC	.808
253	yudixie_resnet18_category_class_0_240719	.807
254	FrankRobWobv0	.807
255	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.806
256	mobilenet_v2_0_5_224	.804
257	AlexNet_SIN	.803
258	BiT-M-R152x4	.802
259	resnet-50-robust	.801
260	compact_resnet50_V4	.799
261	resnet50_simclr	.795
262	convnext_large:fb_in22k_ft_in1k	.794
263	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.794
264	resnet50_moco_v2	.794
265	resnet-18-LC_d_w_sh_1x1_conv_init_m	.794
266	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.790
267	mobilenet_v2_0_5_192	.790
268	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.788
269	vit_large_patch14_clip_224:laion2b_ft_in1k	.788
270	alexnet_training_seed_07	.787
271	0.5x_resnet-18_LC_w_sh_10_iter	.787
272	texture_shape_resnet50_trained_on_SIN	.786
273	yudixie_resnet18_random_0_240719	.785
274	mobilenet_v2_0_75_160	.783
275	efficientnet_b1_imagenet_full	.784
276	my-model	.783
277	shufflenet_v2_x1_0	.782
278	ViT-B/32	.781
279	yudixie_resnet50_object_class_0_240908	.779
280	yudixie_resnet18_object_class_0_240719	.778
281	mobilenet_v2_0_75_224	.777
282	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.777
283	yudixie_resnet50_category_class_0_240908	.776
284	vgg-19-keras	.774
285	vit_relpos_base_patch16_clsgap_224:sw_in1k	.774
286	alexnet_training_seed_09	.774
287	nasnet_mobile-tf	.769
288	vit_large_patch14_clip_336:laion2b_ft_in1k	.764
289	vit_large_patch14_clip_224:openai_ft_in1k	.762
290	efficientnet_b0	.762
291	convnext_small_imagenet_full_seed-0	.761
292	cvt_cvt-13-384-in1k_4_LucyV4	.760
293	inception_v1-tf	.759
294	mobilenet_v2_1-4_224_pytorch	.753
295	alexnet_training_seed_04	.753
296	custom_model_cv_18_dagger_408	.751
297	densenet-121-keras	.743
298	yudixie_resnet18_imagenet1kpret_0_240719	.741
299	mobilenet_v2_0.35_224	.740
300	deit_small_imagenet_full_seed-0	.739
301	mobilenet_v2_1_0_128	.739
302	alexnet_training_seed_01	.739
303	resnet50_ecoset_full	.739
304	alexnet_training_seed_02	.739
305	nasnet_mobile	.734
306	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.730
307	inception_resnet_v2	.728
308	vision_transformer_vit_large_patch16_224	.728
309	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.724
310	resnet-152_v1-tf	.722
311	mobilenet_v2_0.75_96	.719
312	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.718
313	vonegrcnn_52e_full	.714
314	resnet-18-LC_m	.710
315	CORnet-Z	.706
316	vonegrcnn_47e	.705
317	efficientnet_b0_imagenet_full	.702
318	resnet18-supervised	.702
319	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.701
320	vonegrcnn_47e	.700
321	resnet50_imagenet_full	.698
322	convnext_small_imagenet_10_seed-0	.692
323	alexnet_training_seed_10	.691
324	alexnet_reduced_aliasing_early_checkpoint	.683
325	alexnet_training_seed_03	.674
326	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.671
327	mobilenet_v2_0.5_224-tf	.670
328	deit_base_imagenet_full_seed-0	.658
329	resnet-18-LC_conv_init_m	.648
330	efficientnet_b2_imagenet_full	.647
331	hmax	.644
332	mobilenet_v2_1_0_224	.641
333	mobilenet_v1_0.25_128	.612
334	mobilenet_v2_0.5_192-tf	.600
335	BiT-M-R50x1	.597
336	v1-pyr-nodown	.581
337	r3m_resnet34_nocrop	.566
338	CORnetZ_CIFAR10	.554
339	artResNet18_1	.548
340	mobilenet_v2_0.5_160	.518
341	r3m_resnet50_nocrop	.517
342	VOneCORnet-S	.511
343	r3m_resnet34	.505
344	voneresnet-50	.494
345	voneresnet-50-robust	.491
346	r3m_resnet50	.483
347	deit_small_imagenet_100_seed-0	.447
348	mobilenet_v2_0.75_128	.441
349	mobilenet_v2_0.75_160-tf	.440
350	alexnet_training_seed_08	.432
351	resnet50_pretrained_with_retinal_waves	.428
352	r3m_resnet18	.427
353	deit_small_imagenet_10_seed-0	.393
354	mobilenet_v2_0.5_128	.393
355	resnet50-cifar	.393
356	resnet50-meshes-lt-100-original-pretrained	.389
357	resnet50_imagenet_1_seed-0	.387
358	deit_large_imagenet_full_seed-0	.386
359	alexnet_training_seed_05	.297
360	resnet-18-LC_untrained	.262
361	alexnet_training_seed_06	.254
362	CORnetZ_CIFAR10_bs32_20_04	.229
363	my-model	.150
364	resnet50-meshes-lt-100-original-scratch	.108
365	mobilenet_v2_0.35_160
366	fitvid_trained_on_physion
367	openclip
368	resnext101_32x32d_wsl
369	resnext101_32x16d_wsl
370	resnext101_32x48d_wsl
371	fixres_resnext101_32x48d_wsl
372	resnet50_ImageNet
373	regnet_y_400mf
374	tv_efficientnet-b1
375	alexnet_ks_torevert
376	yudixie_resnet50_imagenet1kpret_0_240312
377	resnet_152_v1
378	densenet_201_pytorch
379	inception_v3_pytorch
380	pnasnet_large_pytorch
381	mobilevit_small
382	inception_v4
383	nasnet_large
384	deit_small_imagenet_1_seed-0
385	pnasnet_large
386	vonegrcnn_62e_nobn
387	resnet_101_v2
388	vonegrcnn_52e_full
389	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_bandwidth