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

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_bandwidth