Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet-18-LC_m	.983
2	yudixie_resnet50_rotation_reg_0_240908	.974
3	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.973
4	yudixie_resnet18_category_class_0_240719	.971
5	vonegrcnn_62e_nobn	.958
6	cvt_cvt-21-384-in1k_4_LucyV4	.954
7	0.5x_resnet-18_LC_w_sh_10_iter	.954
8	yudixie_resnet50_distance_translation_rotation_0_240908	.953
9	efficientnet_b1_imagenet_full	.949
10	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.949
11	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.943
12	convnext_large:fb_in22k_ft_in1k	.944
13	convnext_femto_ols:d1_in1k	.941
14	yudixie_resnet50_category_class_0_240908	.938
15	yudixie_resnet18_translation_reg_0_240719	.938
16	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.937
17	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.937
18	xception	.937
19	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.937
20	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.936
21	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.936
22	cvt_cvt-21-224-in1k_4_LucyV4	.935
23	yudixie_resnet50_distance_rotation_0_240908	.932
24	cvt_cvt-13-224-in1k_4_LucyV4	.933
25	blt_vs	.931
26	yudixie_resnet50_translation_rotation_0_240908	.928
27	mobilenet_v2_1.3_224-tf	.930
28	mobilenet_v2_1_0_128	.929
29	inception_v1	.926
30	resnet-18-LC_w_sh_1_iter_m	.926
31	resnet-18-LC_1st_conv_m	.926
32	briaai_rmbg_1_4	.926
33	resnet-152_v2-tf	.922
34	vgg_19	.921
35	AdvProp_efficientnet-b6	.921
36	alexnet	.919
37	efficientnet-b7	.918
38	AlexNet_SIN	.919
39	BiT-M-R50x3	.916
40	resnet-18-LC_d_w_sh_1x1_conv_init_m	.916
41	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.915
42	alexnet-baseline	.912
43	alexnet	.912
44	alexnet	.911
45	alexnet	.911
46	yudixie_resnet18_translation_rotation_0_240719	.911
47	alexnet	.911
48	alexnet	.911
49	convnext_base:clip_laiona_augreg_ft_in1k_384	.911
50	alexnet	.910
51	alexnet	.910
52	grcnn	.911
53	alexnet_robust_correct	.909
54	texture_shape_alexnet_trained_on_SIN	.906
55	resnext101_32x8d_wsl	.905
56	alexnet_random_l2_3_perturb	.903
57	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.903
58	resnet-18-LC_w_sh_10_iter_conv_init_m	.903
59	focalnet_tiny_lrf_in1k	.901
60	yudixie_resnet18_object_class_0_240719	.901
61	mobilenet_v2_1.0_160-tf	.901
62	yudixie_resnet50_distance_reg_0_240908	.901
63	AdvProp_efficientnet-b8	.900
64	omnivore_swinS	.898
65	resnet50_primary_visual_cortex	.896
66	Res2Net50_26w_4s	.896
67	BiT-S-R50x1	.895
68	resnet50_imagenet_100_seed-0	.894
69	mobilenet_v2_1.0_224-tf	.893
70	yudixie_resnet50_distance_translation_0_240908	.892
71	resnet-18-LC_w_sh_100_iter_conv_init_m	.893
72	grcnn_robust_v1	.887
73	yudixie_resnet18_distance_rotation_0_240719	.886
74	resnet101	.887
75	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.885
76	0.5x_resnet-18_LC_w_sh_100_iter	.884
77	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
78	resnet50-vitoimagevidnet8	.876
79	yudixie_resnet50_translation_reg_0_240908	.876
80	omnivore_swinB	.874
81	vgg-16-keras	.874
82	alexnet_reduced_aliasing_early_checkpoint	.874
83	barlow-twins-resnet50	.873
84	resnet18_ecoset_full	.872
85	vgg_16	.871
86	grcnn_109	.870
87	resnet50_byol	.868
88	resnet50-SIN_IN	.867
89	convnext_small_imagenet_full_seed-0	.867
90	CORnet-Z	.867
91	grcnn_v2_text_noise	.866
92	alexnet_random_linf8_perturb	.864
93	resnet_50_v2	.862
94	resnet-50_v1-tf	.861
95	alexnet_l2_3_robust	.860
96	resnet-18-LC_w_sh_10_iter_m	.859
97	0.5x_resnet-18_LC_w_sh_1_iter	.858
98	ReAlnet04	.857
99	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.857
100	ReAlnet04_cornet	.857
101	ReAlnet03	.855
102	ReAlnet03_cornet	.856
103	0.5x_resnet-18_LC	.853
104	BiT-M-R101x1	.853
105	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.852
106	inception_v3-tf	.850
107	CORnetZ_CIFAR10_bs32_20_04	.850
108	ReAlnet01	.850
109	resnet50-vicregl0p9	.850
110	ReAlnet08_cornet	.849
111	ReAlnet08	.850
112	ReAlnet01_cornet	.847
113	ReAlnet09	.846
114	ReAlnet09_cornet	.847
115	convnext_tiny_imagenet_full_seed-0	.847
116	resnet50-simclr-vissl	.847
117	ReAlnet02	.847
118	ReAlnet07	.847
119	ReAlnet02_cornet	.846
120	yudixie_resnet50_object_class_0_240908	.846
121	ReAlnet07_cornet	.846
122	inception_v3	.845
123	alexnet_linf_8_robust	.844
124	yudixie_resnet18_distance_translation_rotation_0_240719	.843
125	BiT-S-R50x3	.844
126	ReAlnet10	.843
127	nasnet_large-tf	.842
128	resnet18_imagenet21kP	.841
129	vonealexnet_gaussian_noise_std4_fixed	.839
130	ReAlnet10_cornet	.839
131	alexnet_early_checkpoint	.838
132	BiT-S-R101x3	.837
133	resnet50_imagenet_10_seed-0	.837
134	ReAlnet06_cornet	.835
135	ReAlnet06	.836
136	ReAlnet05	.833
137	ReAlnet05_cornet	.832
138	resnet101_imagenet_full	.829
139	convnext_tiny_sup	.829
140	antialias-resnet152	.827
141	yudixie_resnet18_distance_translation_0_240719	.826
142	densenet-201	.826
143	convnext_small_imagenet_100_seed-0	.825
144	resnet_50_v1_spiking_l4a	.824
145	resnet_50_v1	.824
146	resnet_50_v1_spiking	.823
147	yudixie_resnet50_imagenet1kpret_0_240908	.821
148	resnet50_tutorial	.822
149	resnet50-sup	.821
150	resnet_50_v1_spiking_l4	.821
151	densenet-201-keras	.821
152	resnet_50_v1_spiking_l3	.820
153	AdvProp_efficientnet-b7	.819
154	CORnet-S	.816
155	mobilenet_v2_0_75_192	.817
156	convnext_xlarge:fb_in22k_ft_in1k	.817
157	resnet50-moclr8deg	.814
158	resnet-50_untrained	.815
159	resnet50-vicregl0p75	.812
160	CLIP-RN50	.811
161	imagenet_l2_10_0	.810
162	vgg-19-keras	.810
163	imagenet_l2_3_0	.810
164	RN50	.807
165	resnet50	.807
166	resnet-50x2_untrained	.806
167	mobilenet_v2_1_4_224	.806
168	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.805
169	inception_v4-tf	.804
170	cornet_s	.803
171	resnet50-SIN_IN_IN	.800
172	resnet-50-robust	.800
173	mobilenet_v2_1_3_224	.799
174	resnet50_robust_l2_eps1	.796
175	resnet50_random_linf8_perturb	.795
176	deit_small_imagenet_full_seed-0	.795
177	r3m_resnet34_nocrop	.795
178	resnet50_l2_3_robust	.792
179	resnet50_linf_8_robust	.787
180	BiT-S-R152x2	.786
181	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.785
182	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.784
183	resnet50_robust_l2_eps3	.783
184	antialiased-r50	.782
185	resnet50_random_l2_perturb	.780
186	resnet_SIN_IN_FT_IN	.779
187	resnet50-vicreg	.779
188	yudixie_resnet18_imagenet1kpret_0_240719	.779
189	resnet34_imagenet_full	.778
190	resnet18_imagenet_full	.776
191	efficientnet_b0	.775
192	convnext_large_imagenet_full_seed-0	.775
193	convnext_base_imagenet_full_seed-0	.772
194	mobilenet_v2_1.0_96	.772
195	VOneCORnet-S	.772
196	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.769
197	texture_shape_resnet50_trained_on_SIN	.769
198	vit_base_patch16_clip_224:openai_ft_in1k	.769
199	densenet-121	.767
200	mobilenet_v2_0_5_224	.767
201	inception_v2-tf	.766
202	voneresnet-50-robust	.764
203	voneresnet-50	.758
204	mobilenet_v2_0.75_192-tf	.757
205	resnet50_linf_4_robust	.756
206	CLIP_resnet50	.756
207	convnext_tiny:in12k_ft_in1k	.755
208	CLIP_resnet50_float32	.755
209	AdvProp_efficientnet-b4	.754
210	mobilenet_v2_1.4_224-tf	.755
211	densenet-169	.754
212	resnet50_ecoset_full	.752
213	resnet-18_untrained	.749
214	resnet50-VITO-8deg-cc	.747
215	0.5x_resnet-18	.745
216	resnet50-cifar	.743
217	resnet50-SIN	.741
218	SWSL_resnet50	.741
219	resnet50_imagenet_full	.741
220	mobilenet_v2_0_75_224	.739
221	AdvProp_efficientnet-b0	.738
222	resnet152_imagenet_full	.738
223	resnet101_ecoset_full	.735
224	alexnet_training_seed_02	.735
225	resnet-101_v2-tf	.732
226	mobilenet_v2_1.0_192-tf	.730
227	mobilenet_v2_1-4_224_pytorch	.728
228	pnasnet_large-tf	.727
229	alexnet_training_seed_07	.727
230	cvt_cvt-13-384-in1k_4_LucyV4	.725
231	vggface	.725
232	resnet-50_v2-tf	.724
233	resnet-18_test_m	.720
234	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.719
235	alexnet_training_seed_04	.719
236	vonegrcnn_47e	.718
237	resnet-152_v1-tf	.716
238	yudixie_resnet18_distance_reg_0_240719	.715
239	alexnet_training_seed_01	.714
240	mobilenet_v2_0.5_192-tf	.715
241	resnet-18-LC_w_sh_100_iter_m	.715
242	voneresnet-50-non_stochastic	.712
243	mobilenet_v2_0.75_128	.713
244	yudixie_resnet50_random_0_240908	.711
245	alexnet_training_seed_09	.710
246	mobilenet_v1_0.25_128	.707
247	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.707
248	FrankRobWobv0	.705
249	antialiased-rnext101_32x8d	.705
250	resnet-101_v1-tf	.704
251	BiT-S-R152x4	.702
252	vit_large_patch14_clip_224:laion2b_ft_in1k	.701
253	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.701
254	resnet34_ecoset_full	.700
255	BiT-M-R101x3	.698
256	resnet152_ecoset_full	.697
257	BiT-M-R152x4	.697
258	mobilenet_v2_1_0_192	.696
259	SWSL_resnext101_32x8d	.696
260	convnext_small_imagenet_10_seed-0	.695
261	vonegrcnn_52e_full	.693
262	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.693
263	resnet-152_v2_pytorch	.693
264	densenet-121-keras	.692
265	v1-pyr-nodown	.689
266	efficientnet_b0_imagenet_full	.687
267	deit_small_imagenet_100_seed-0	.686
268	yudixie_resnet18_rotation_reg_0_240719	.687
269	yolos_tiny	.682
270	BiT-S-R101x1	.678
271	konkle_alexnetgn_ipcl_ref01_primary_model	.676
272	my-model	.677
273	mobilenet_v2_0.75_160-tf	.669
274	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.668
275	nasnet_mobile-tf	.664
276	resnet50-barlow	.662
277	shufflenet_v2_x1_0	.658
278	BiT-M-R152x2	.658
279	nasnet_mobile	.657
280	vision_transformer_vit_large_patch16_224	.656
281	resnet50_FractalDB	.655
282	CLIP_ViT-B_32	.653
283	densenet-169-keras	.652
284	BiT-M-R50x1	.648
285	effnetv2m_custom384	.640
286	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.639
287	vit_large_patch14_clip_336:laion2b_ft_in1k	.637
288	alexnet_training_seed_10	.636
289	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.634
290	cv_18_dagger_408_pretrained	.632
291	resnet50	.630
292	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.630
293	mobilenet_v2_0_75_160	.627
294	alexnet_training_seed_03	.627
295	resnet50_moco_v2	.624
296	deit_large_imagenet_full_seed-0	.624
297	omnivore_swinT	.623
298	mobilenet_v2_1_0_224	.622
299	efficientnet_b2_imagenet_full	.619
300	yudixie_resnet18_random_0_240719	.613
301	artResNet18_1	.612
302	custom_model_cv_18_dagger_408	.612
303	inception_v1-tf	.610
304	resnet50_simclr	.602
305	mobilenet_v2_0.35_224	.602
306	mobilenet_v2_1.0_128-tf	.596
307	mobilenet_v2_1_0_160	.594
308	effnetb1_272x240	.594
309	vit_large_patch14_clip_224:openai_ft_in1k	.589
310	r3m_resnet34	.585
311	mobilenet_v2_0.5_224-tf	.582
312	resnet-18-LC_conv_init_m	.583
313	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.578
314	convnext_small_imagenet_1_seed-0	.577
315	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.561
316	mobilenet_v2_0_5_192	.559
317	hmax	.553
318	deit_base_imagenet_full_seed-0	.551
319	vit_relpos_base_patch16_clsgap_224:sw_in1k	.550
320	r3m_resnet50_nocrop	.528
321	AT_efficientnet-b2	.526
322	AdvProp_efficientnet-b2	.525
323	inception_resnet_v2	.521
324	r3m_resnet18	.514
325	r3m_resnet50	.506
326	resnet50_pretrained_with_retinal_waves	.489
327	my-model	.484
328	mobilenet_v2_0.75_96	.481
329	alexnet_training_seed_08	.479
330	CORnetZ_CIFAR10	.422
331	mobilenet_v2_0.5_160	.398
332	mobilenet_v2_0.75_224-tf	.397
333	deit_small_imagenet_10_seed-0	.388
334	ViT-B/32	.387
335	resnet50_imagenet_1_seed-0	.325
336	alexnet_training_seed_05	.313
337	alexnet_training_seed_06	.233
338	resnet-18-LC_untrained	.233
339	resnet50-meshes-lt-100-original-pretrained	.177
340	resnet50-meshes-lt-100-original-scratch	.115
341	mobilenet_v2_0.5_128	.038
342	resnet18-supervised
343	tv_efficientnet-b1
344	regnet_y_400mf
345	mobilenet_v2_0.35_160
346	resnet_101_v1
347	alexnet_ks_torevert
348	nasnet_large
349	deit_small_imagenet_1_seed-0
350	fitvid_trained_on_physion
351	yudixie_resnet50_imagenet1kpret_0_240312
352	resnet_152_v1
353	pnasnet_large
354	resnet50_ImageNet
355	vonegrcnn_62e_nobn
356	resnet_101_v2
357	pnasnet_large_pytorch
358	resnext101_32x48d_wsl
359	inception_v3_pytorch
360	resnext101_32x32d_wsl
361	densenet_201_pytorch
362	vonegrcnn_47e
363	openclip
364	vonegrcnn_52e_full
365	vonegrcnn_47e
366	resnext101_32x16d_wsl
367	ViT_L_32_imagenet1k
368	resnet_152_v2
369	inception_v4

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: orth_pref_ratio