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

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_bandwidth