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	Res2Net50_26w_4s	.896
66	BiT-S-R50x1	.895
67	resnet50_imagenet_100_seed-0	.894
68	mobilenet_v2_1.0_224-tf	.893
69	yudixie_resnet50_distance_translation_0_240908	.892
70	resnet-18-LC_w_sh_100_iter_conv_init_m	.893
71	grcnn_robust_v1	.887
72	yudixie_resnet18_distance_rotation_0_240719	.886
73	resnet101	.887
74	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.885
75	0.5x_resnet-18_LC_w_sh_100_iter	.884
76	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
77	resnet50-vitoimagevidnet8	.876
78	yudixie_resnet50_translation_reg_0_240908	.876
79	omnivore_swinB	.874
80	vgg-16-keras	.874
81	alexnet_reduced_aliasing_early_checkpoint	.874
82	barlow-twins-resnet50	.873
83	resnet18_ecoset_full	.872
84	vgg_16	.871
85	grcnn_109	.870
86	resnet50_byol	.868
87	resnet50-SIN_IN	.867
88	convnext_small_imagenet_full_seed-0	.867
89	CORnet-Z	.867
90	grcnn_v2_text_noise	.866
91	alexnet_random_linf8_perturb	.864
92	resnet_50_v2	.862
93	resnet-50_v1-tf	.861
94	alexnet_l2_3_robust	.860
95	resnet-18-LC_w_sh_10_iter_m	.859
96	0.5x_resnet-18_LC_w_sh_1_iter	.858
97	ReAlnet04	.857
98	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.857
99	ReAlnet04_cornet	.857
100	ReAlnet03	.855
101	ReAlnet03_cornet	.856
102	0.5x_resnet-18_LC	.853
103	BiT-M-R101x1	.853
104	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.852
105	ReAlnet_incorrect	.850
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	.824
145	yudixie_resnet50_imagenet1kpret_0_240908	.821
146	resnet50_tutorial	.822
147	resnet50-sup	.821
148	densenet-201-keras	.821
149	AdvProp_efficientnet-b7	.819
150	CORnet-S	.816
151	mobilenet_v2_0_75_192	.817
152	convnext_xlarge:fb_in22k_ft_in1k	.817
153	resnet50-moclr8deg	.814
154	resnet-50_untrained	.815
155	resnet50-vicregl0p75	.812
156	CLIP-RN50	.811
157	imagenet_l2_10_0	.810
158	vgg-19-keras	.810
159	imagenet_l2_3_0	.810
160	RN50	.807
161	resnet50	.807
162	resnet-50x2_untrained	.806
163	mobilenet_v2_1_4_224	.806
164	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.805
165	inception_v4-tf	.804
166	cornet_s	.803
167	resnet50-SIN_IN_IN	.800
168	resnet-50-robust	.800
169	mobilenet_v2_1_3_224	.799
170	resnet50_robust_l2_eps1	.796
171	resnet50_random_linf8_perturb	.795
172	deit_small_imagenet_full_seed-0	.795
173	r3m_resnet34_nocrop	.795
174	resnet50_l2_3_robust	.792
175	resnet50_linf_8_robust	.787
176	BiT-S-R152x2	.786
177	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.785
178	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.784
179	resnet50_robust_l2_eps3	.783
180	antialiased-r50	.782
181	resnet50_random_l2_perturb	.780
182	resnet_SIN_IN_FT_IN	.779
183	resnet50-vicreg	.779
184	yudixie_resnet18_imagenet1kpret_0_240719	.779
185	resnet34_imagenet_full	.778
186	resnet18_imagenet_full	.776
187	efficientnet_b0	.775
188	convnext_large_imagenet_full_seed-0	.775
189	convnext_base_imagenet_full_seed-0	.772
190	mobilenet_v2_1.0_96	.772
191	VOneCORnet-S	.772
192	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.769
193	texture_shape_resnet50_trained_on_SIN	.769
194	vit_base_patch16_clip_224:openai_ft_in1k	.769
195	densenet-121	.767
196	mobilenet_v2_0_5_224	.767
197	inception_v2-tf	.766
198	voneresnet-50-robust	.764
199	voneresnet-50	.758
200	mobilenet_v2_0.75_192-tf	.757
201	resnet50_linf_4_robust	.756
202	CLIP_resnet50	.756
203	convnext_tiny:in12k_ft_in1k	.755
204	CLIP_resnet50_float32	.755
205	AdvProp_efficientnet-b4	.754
206	mobilenet_v2_1.4_224-tf	.755
207	densenet-169	.754
208	resnet50_ecoset_full	.752
209	resnet-18_untrained	.749
210	resnet50-VITO-8deg-cc	.747
211	0.5x_resnet-18	.745
212	resnet50-cifar	.743
213	resnet50-SIN	.741
214	SWSL_resnet50	.741
215	resnet50_imagenet_full	.741
216	mobilenet_v2_0_75_224	.739
217	AdvProp_efficientnet-b0	.738
218	resnet152_imagenet_full	.738
219	resnet101_ecoset_full	.735
220	alexnet_training_seed_02	.735
221	resnet-101_v2-tf	.732
222	mobilenet_v2_1.0_192-tf	.730
223	mobilenet_v2_1-4_224_pytorch	.728
224	pnasnet_large-tf	.727
225	alexnet_training_seed_07	.727
226	cvt_cvt-13-384-in1k_4_LucyV4	.725
227	vggface	.725
228	resnet-50_v2-tf	.724
229	resnet-18_test_m	.720
230	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.719
231	alexnet_training_seed_04	.719
232	vonegrcnn_47e	.718
233	resnet-152_v1-tf	.716
234	yudixie_resnet18_distance_reg_0_240719	.715
235	alexnet_training_seed_01	.714
236	mobilenet_v2_0.5_192-tf	.715
237	resnet-18-LC_w_sh_100_iter_m	.715
238	voneresnet-50-non_stochastic	.712
239	mobilenet_v2_0.75_128	.713
240	yudixie_resnet50_random_0_240908	.711
241	alexnet_training_seed_09	.710
242	mobilenet_v1_0.25_128	.707
243	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.707
244	FrankRobWobv0	.705
245	antialiased-rnext101_32x8d	.705
246	resnet-101_v1-tf	.704
247	BiT-S-R152x4	.702
248	vit_large_patch14_clip_224:laion2b_ft_in1k	.701
249	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.701
250	resnet34_ecoset_full	.700
251	BiT-M-R101x3	.698
252	resnet152_ecoset_full	.697
253	BiT-M-R152x4	.697
254	mobilenet_v2_1_0_192	.696
255	SWSL_resnext101_32x8d	.696
256	convnext_small_imagenet_10_seed-0	.695
257	vonegrcnn_52e_full	.693
258	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.693
259	resnet-152_v2_pytorch	.693
260	densenet-121-keras	.692
261	v1-pyr-nodown	.689
262	efficientnet_b0_imagenet_full	.687
263	deit_small_imagenet_100_seed-0	.686
264	yudixie_resnet18_rotation_reg_0_240719	.687
265	yolos_tiny	.682
266	BiT-S-R101x1	.678
267	konkle_alexnetgn_ipcl_ref01_primary_model	.676
268	my-model	.677
269	mobilenet_v2_0.75_160-tf	.669
270	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.668
271	nasnet_mobile-tf	.664
272	resnet50-barlow	.662
273	shufflenet_v2_x1_0	.658
274	BiT-M-R152x2	.658
275	nasnet_mobile	.657
276	vision_transformer_vit_large_patch16_224	.656
277	resnet50_FractalDB	.655
278	CLIP_ViT-B_32	.653
279	densenet-169-keras	.652
280	BiT-M-R50x1	.648
281	effnetv2m_custom384	.640
282	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.639
283	vit_large_patch14_clip_336:laion2b_ft_in1k	.637
284	alexnet_training_seed_10	.636
285	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.634
286	cv_18_dagger_408_pretrained	.632
287	resnet50	.630
288	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.630
289	mobilenet_v2_0_75_160	.627
290	alexnet_training_seed_03	.627
291	resnet50_moco_v2	.624
292	deit_large_imagenet_full_seed-0	.624
293	omnivore_swinT	.623
294	mobilenet_v2_1_0_224	.622
295	efficientnet_b2_imagenet_full	.619
296	yudixie_resnet18_random_0_240719	.613
297	artResNet18_1	.612
298	custom_model_cv_18_dagger_408	.612
299	inception_v1-tf	.610
300	resnet50_simclr	.602
301	mobilenet_v2_0.35_224	.602
302	mobilenet_v2_1.0_128-tf	.596
303	mobilenet_v2_1_0_160	.594
304	effnetb1_272x240	.594
305	vit_large_patch14_clip_224:openai_ft_in1k	.589
306	r3m_resnet34	.585
307	mobilenet_v2_0.5_224-tf	.582
308	resnet-18-LC_conv_init_m	.583
309	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.578
310	convnext_small_imagenet_1_seed-0	.577
311	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.561
312	mobilenet_v2_0_5_192	.559
313	hmax	.553
314	deit_base_imagenet_full_seed-0	.551
315	vit_relpos_base_patch16_clsgap_224:sw_in1k	.550
316	r3m_resnet50_nocrop	.528
317	AT_efficientnet-b2	.526
318	AdvProp_efficientnet-b2	.525
319	inception_resnet_v2	.521
320	r3m_resnet18	.514
321	r3m_resnet50	.506
322	resnet50_pretrained_with_retinal_waves	.489
323	my-model	.484
324	mobilenet_v2_0.75_96	.481
325	alexnet_training_seed_08	.479
326	CORnetZ_CIFAR10	.422
327	mobilenet_v2_0.5_160	.398
328	mobilenet_v2_0.75_224-tf	.397
329	deit_small_imagenet_10_seed-0	.388
330	ViT-B/32	.387
331	resnet50_imagenet_1_seed-0	.325
332	alexnet_training_seed_05	.313
333	alexnet_training_seed_06	.233
334	resnet-18-LC_untrained	.233
335	resnet50-meshes-lt-100-original-pretrained	.177
336	resnet50-meshes-lt-100-original-scratch	.115
337	mobilenet_v2_0.5_128	.038
338	pnasnet_large	X
339	alexnet_ks_torevert	X
340	resnet_101_v1	X
341	yudixie_resnet50_imagenet1kpret_0_240312	X
342	resnet_152_v1	X
343	vonegrcnn_47e	X
344	vonegrcnn_52e_full	X
345	resnext101_32x48d_wsl	X
346	pnasnet_large_pytorch	X
347	resnet50_ImageNet	X
348	resnet18-supervised	X
349	inception_v3_pytorch	X
350	deit_small_imagenet_1_seed-0	X
351	densenet_201_pytorch	X
352	openclip	X
353	resnext101_32x16d_wsl	X
354	mobilenet_v2_0.35_160	X
355	resnet_152_v2	X
356	nasnet_large	X
357	ViT_L_32_imagenet1k	X
358	vonegrcnn_47e	X
359	resnext101_32x32d_wsl	X
360	tv_efficientnet-b1	X
361	inception_v4	X
362	vonegrcnn_62e_nobn	X
363	fitvid_trained_on_physion	X
364	regnet_y_400mf	X
365	resnet_101_v2	X

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: orth_pref_ratio