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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	alexnet	.903
88	CORnet-S	.905
89	blt_vs	.904
90	resnext101_32x8d_wsl	.904
91	omnivore_swinT	.904
92	0.5x_resnet-18_LC_w_sh_1_iter	.904
93	resnet34_ecoset_full	.903
94	alexnet_random_l2_3_perturb	.902
95	resnet-50x2_untrained	.903
96	resnet50_byol	.901
97	BiT-S-R50x3	.900
98	resnet50_random_linf8_perturb	.900
99	vonealexnet_gaussian_noise_std4_fixed	.899
100	compact_resnet50_IT	.899
101	resnet101_ecoset_full	.897
102	SWSL_resnet50	.896
103	mobilenet_v2_1.0_160-tf	.896
104	AdvProp_efficientnet-b7	.895
105	convnext_tiny:in12k_ft_in1k	.893
106	resnet50-vicregl0p9	.892
107	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.892
108	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.891
109	alexnet_linf_8_robust	.890
110	BiT-S-R152x2	.889
111	resnet-18-LC_1st_conv_m	.889
112	resnet50-vicregl0p75	.888
113	imagenet_l2_3_0	.888
114	barlow-twins-resnet50	.888
115	yudixie_resnet50_distance_reg_0_240908	.888
116	resnet50_robust_l2_eps3	.886
117	resnet152_ecoset_full	.887
118	BiT-M-R101x3	.885
119	resnet-18-LC_w_sh_100_iter_conv_init_m	.885
120	alexnet_random_linf8_perturb	.883
121	grcnn	.884
122	compact_resnet50_robust_IT	.882
123	ViT_L_32_imagenet1k	.882
124	resnet_50_v2	.881
125	grcnn_robust_v1	.883
126	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.881
127	yudixie_resnet50_random_0_240908	.879
128	CLIP-RN50	.879
129	ReAlnet01_cornet_fov12	.878
130	resnet50_imagenet_100_seed-0	.879
131	ReAlnet01_cornet_fov16	.879
132	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.878
133	resnet_50_v1_spiking	.877
134	alexnet	.877
135	alexnet	.877
136	alexnet-baseline	.877
137	resnet_50_v1_spiking_l4	.876
138	yudixie_resnet50_imagenet1kpret_0_240908	.876
139	alexnet_workflow_trigger	.876
140	resnet_50_v1_spiking_l4a	.876
141	alexnet	.876
142	alexnet_fov16	.876
143	alexnet	.876
144	alexnet	.875
145	alexnet	.876
146	resnet50_tutorial	.875
147	resnet_50_v1	.875
148	alexnet	.875
149	grcnn_v2_text_noise	.875
150	resnet_50_v1_spiking_l3	.874
151	alexnet	.874
152	resnet50	.874
153	resnet18_imagenet21kP	.874
154	yolos_tiny	.874
155	resnet50-sup	.874
156	grcnn_109	.872
157	mobilenet_v2_0.75_192-tf	.873
158	mobilenet_v2_1.0_224-tf	.871
159	resnet50_linf_8_robust	.871
160	CLIP_resnet50	.871
161	imagenet_l2_10_0	.870
162	resnet50-vitoimagevidnet8	.870
163	cornet_s	.869
164	resnet18_ecoset_full	.867
165	convnext_tiny_imagenet_full_seed-0	.868
166	CLIP_resnet50_float32	.867
167	mobilenet_v2_0_75_192	.867
168	resnet-101_v2-tf	.866
169	densenet-169	.866
170	convnext_tiny_sup	.865
171	resnet50_ewconplus_wzc	.865
172	CLIP_ViT-B_32	.864
173	resnet-18-LC_w_sh_10_iter_m	.863
174	RN50	.863
175	AdvProp_efficientnet-b4	.861
176	resnet152_imagenet_full	.861
177	convnext_xlarge:fb_in22k_ft_in1k	.861
178	resnet50-VITO-8deg-cc	.859
179	ReAlnet09	.860
180	convnext_small_imagenet_1_seed-0	.859
181	mobilenet_v2_1_4_224	.858
182	ReAlnet09_cornet	.858
183	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.857
184	resnet101_imagenet_full	.857
185	resnet-18_untrained	.857
186	resnet50_linf_4_robust	.857
187	resnet50_FractalDB	.856
188	ReAlnet01	.855
189	mobilenet_v2_1.0_192-tf	.855
190	AdvProp_efficientnet-b0	.854
191	densenet-121	.854
192	convnext_small_imagenet_100_seed-0	.854
193	resnet-50_v2-tf	.853
194	resnet50_random_l2_perturb	.853
195	resnet_SIN_IN_FT_IN	.852
196	ReAlnet01_cornet	.853
197	AdvProp_efficientnet-b2	.852
198	effnetv2m_custom384	.852
199	inception_v3	.851
200	antialiased-r50	.852
201	AT_efficientnet-b2	.849
202	inception_v1	.849
203	yudixie_resnet18_translation_reg_0_240719	.849
204	resnet50_primary_visual_cortex	.847
205	resnet50_robust_l2_eps1	.848
206	0.5x_resnet-18_LC_w_sh_100_iter	.843
207	ReAlnet07	.842
208	resnet50-vicreg	.842
209	ReAlnet03	.842
210	resnet50-SIN_IN_IN	.841
211	ReAlnet07_cornet	.840
212	ReAlnet03_cornet	.838
213	mobilenet_v2_0.75_224-tf	.838
214	compact_cornets_V4	.838
215	ReAlnet04	.838
216	yudixie_resnet18_translation_rotation_0_240719	.838
217	antialiased-rnext101_32x8d	.837
218	ReAlnet04_cornet	.836
219	vgg-16-keras	.836
220	SWSL_resnext101_32x8d	.836
221	ReAlnet10_cornet	.835
222	ReAlnet10	.834
223	AlexNet_SIN_fov16	.834
224	effnetb1_272x240	.833
225	compact_resnet50_robust_V4	.833
226	resnet-101_v1-tf	.832
227	resnet-152_v2_pytorch	.832
228	densenet-201	.831
229	cv_18_dagger_408_pretrained	.831
230	mobilenet_v2_1_0_160	.830
231	pnasnet_large-tf	.829
232	mobilenet_v2_1_3_224	.830
233	ReAlnet08_cornet	.829
234	ReAlnet08	.828
235	convnext_femto_ols:d1_in1k	.826
236	ReAlnet05_cornet	.826
237	ReAlnet05	.826
238	densenet-201-keras	.823
239	alexnet_fov12	.823
240	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.821
241	mobilenet_v2_1.0_128-tf	.822
242	mobilenet_v2_1_0_192	.820
243	vit_base_patch16_clip_224:openai_ft_in1k	.819
244	resnet50-barlow	.819
245	briaai_rmbg_1_4	.819
246	0.5x_resnet-18	.817
247	ReAlnet02	.816
248	BiT-S-R101x1	.816
249	ReAlnet02_cornet	.815
250	AlexNet_SIN_fov4	.814
251	konkle_alexnetgn_ipcl_ref01_primary_model	.813
252	resnet50-SIN	.813
253	ReAlnet06	.813
254	BiT-S-R152x4	.811
255	BiT-M-R152x2	.811
256	ReAlnet06_cornet	.811
257	resnet50_imagenet_10_seed-0	.811
258	densenet-169-keras	.810
259	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.810
260	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.807
261	0.5x_resnet-18_LC	.808
262	yudixie_resnet18_category_class_0_240719	.807
263	FrankRobWobv0	.807
264	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.806
265	mobilenet_v2_0_5_224	.804
266	AlexNet_SIN	.803
267	BiT-M-R152x4	.802
268	resnet-50-robust	.801
269	compact_resnet50_V4	.799
270	resnet50_simclr	.795
271	convnext_large:fb_in22k_ft_in1k	.794
272	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.794
273	resnet50_moco_v2	.794
274	resnet-18-LC_d_w_sh_1x1_conv_init_m	.794
275	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.790
276	mobilenet_v2_0_5_192	.790
277	AlexNet_SIN_fov12	.790
278	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.788
279	vit_large_patch14_clip_224:laion2b_ft_in1k	.788
280	alexnet_training_seed_07	.787
281	0.5x_resnet-18_LC_w_sh_10_iter	.787
282	texture_shape_resnet50_trained_on_SIN	.786
283	yudixie_resnet18_random_0_240719	.785
284	mobilenet_v2_0_75_160	.783
285	efficientnet_b1_imagenet_full	.784
286	my-model	.783
287	shufflenet_v2_x1_0	.782
288	ViT-B/32	.781
289	yudixie_resnet50_object_class_0_240908	.779
290	yudixie_resnet18_object_class_0_240719	.778
291	mobilenet_v2_0_75_224	.777
292	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.777
293	yudixie_resnet50_category_class_0_240908	.776
294	vgg-19-keras	.774
295	vit_relpos_base_patch16_clsgap_224:sw_in1k	.774
296	alexnet_training_seed_09	.774
297	nasnet_mobile-tf	.769
298	vit_large_patch14_clip_336:laion2b_ft_in1k	.764
299	vit_large_patch14_clip_224:openai_ft_in1k	.762
300	efficientnet_b0	.762
301	convnext_small_imagenet_full_seed-0	.761
302	alexnet_fov4	.762
303	cvt_cvt-13-384-in1k_4_LucyV4	.760
304	inception_v1-tf	.759
305	mobilenet_v2_1-4_224_pytorch	.753
306	alexnet_training_seed_04	.753
307	vit_base_patch14_reg4_dinov2:lvd142m	.751
308	custom_model_cv_18_dagger_408	.751
309	ReAlnet01_cornet_fov4	.749
310	alexnet_training_seed_01_fov16	.746
311	densenet-121-keras	.743
312	yudixie_resnet18_imagenet1kpret_0_240719	.741
313	mobilenet_v2_0.35_224	.740
314	deit_small_imagenet_full_seed-0	.739
315	mobilenet_v2_1_0_128	.739
316	alexnet_training_seed_01	.739
317	resnet50_ecoset_full	.739
318	alexnet_training_seed_02	.739
319	nasnet_mobile	.734
320	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.730
321	inception_resnet_v2	.728
322	vision_transformer_vit_large_patch16_224	.728
323	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.724
324	resnet-152_v1-tf	.722
325	mobilenet_v2_0.75_96	.719
326	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.718
327	vonegrcnn_52e_full	.714
328	resnet-18-LC_m	.710
329	CORnet-Z	.706
330	vonegrcnn_47e	.705
331	efficientnet_b0_imagenet_full	.702
332	resnet18-supervised	.702
333	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.701
334	vonegrcnn_47e	.700
335	resnet50_imagenet_full	.698
336	convnext_small_imagenet_10_seed-0	.692
337	alexnet_training_seed_10	.691
338	alexnet_reduced_aliasing_early_checkpoint	.683
339	alexnet_training_seed_03	.674
340	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.671
341	mobilenet_v2_0.5_224-tf	.670
342	alexnet_training_seed_01_fov4	.669
343	vit_base_patch14_dinov2:lvd142m	.660
344	deit_base_imagenet_full_seed-0	.658
345	resnet-18-LC_conv_init_m	.648
346	efficientnet_b2_imagenet_full	.647
347	hmax	.644
348	mobilenet_v2_1_0_224	.641
349	mobilenet_v1_0.25_128	.612
350	mobilenet_v2_0.5_192-tf	.600
351	BiT-M-R50x1	.597
352	v1-pyr-nodown	.581
353	r3m_resnet34_nocrop	.566
354	CORnetZ_CIFAR10	.554
355	artResNet18_1	.548
356	mobilenet_v2_0.5_160	.518
357	r3m_resnet50_nocrop	.517
358	VOneCORnet-S	.511
359	r3m_resnet34	.505
360	vit_base_patch16_dinov3:lvd1689m	.504
361	voneresnet-50	.494
362	voneresnet-50-robust	.491
363	r3m_resnet50	.483
364	deit_small_imagenet_100_seed-0	.447
365	mobilenet_v2_0.75_128	.441
366	mobilenet_v2_0.75_160-tf	.440
367	alexnet_training_seed_08	.432
368	resnet50_pretrained_with_retinal_waves	.428
369	r3m_resnet18	.427
370	deit_small_imagenet_10_seed-0	.393
371	mobilenet_v2_0.5_128	.393
372	resnet50-cifar	.393
373	resnet50-meshes-lt-100-original-pretrained	.389
374	resnet50_imagenet_1_seed-0	.387
375	deit_large_imagenet_full_seed-0	.386
376	alexnet_training_seed_05	.297
377	resnet-18-LC_untrained	.262
378	alexnet_training_seed_06	.254
379	CORnetZ_CIFAR10_bs32_20_04	.229
380	my-model	.150
381	resnet50-meshes-lt-100-original-scratch	.108
382	mobilenet_v2_0.35_160
383	fitvid_trained_on_physion
384	openclip
385	resnext101_32x32d_wsl
386	resnext101_32x16d_wsl
387	resnext101_32x48d_wsl
388	fixres_resnext101_32x48d_wsl
389	resnet50_ImageNet
390	regnet_y_400mf
391	tv_efficientnet-b1
392	alexnet_ks_torevert
393	yudixie_resnet50_imagenet1kpret_0_240312
394	resnet_152_v1
395	densenet_201_pytorch
396	inception_v3_pytorch
397	pnasnet_large_pytorch
398	mobilevit_small
399	inception_v4
400	nasnet_large
401	deit_small_imagenet_1_seed-0
402	pnasnet_large
403	vonegrcnn_62e_nobn
404	resnet_101_v2
405	vonegrcnn_52e_full
406	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_bandwidth