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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	compact_vgg19_V4	.932
26	blt_vs	.931
27	yudixie_resnet50_translation_rotation_0_240908	.928
28	mobilenet_v2_1.3_224-tf	.930
29	mobilenet_v2_1_0_128	.929
30	inception_v1	.926
31	resnet-18-LC_w_sh_1_iter_m	.926
32	resnet-18-LC_1st_conv_m	.926
33	briaai_rmbg_1_4	.926
34	resnet-152_v2-tf	.922
35	vgg_19	.921
36	AdvProp_efficientnet-b6	.921
37	compact_cornets_V4	.921
38	alexnet	.919
39	efficientnet-b7	.918
40	AlexNet_SIN	.919
41	BiT-M-R50x3	.916
42	resnet-18-LC_d_w_sh_1x1_conv_init_m	.916
43	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.915
44	compact_resnet50_V4	.915
45	compact_cornets_IT	.914
46	ReAlnet01_cornet_fov4	.914
47	alexnet-baseline	.912
48	alexnet	.912
49	alexnet	.912
50	alexnet	.911
51	alexnet	.911
52	yudixie_resnet18_translation_rotation_0_240719	.911
53	alexnet	.911
54	alexnet	.911
55	convnext_base:clip_laiona_augreg_ft_in1k_384	.911
56	alexnet	.910
57	alexnet	.910
58	grcnn	.911
59	alexnet_robust_correct	.909
60	texture_shape_alexnet_trained_on_SIN	.906
61	resnext101_32x8d_wsl	.905
62	alexnet_random_l2_3_perturb	.903
63	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.903
64	resnet50_ewconplus_wzc	.902
65	resnet-18-LC_w_sh_10_iter_conv_init_m	.903
66	focalnet_tiny_lrf_in1k	.901
67	yudixie_resnet18_object_class_0_240719	.901
68	mobilenet_v2_1.0_160-tf	.901
69	yudixie_resnet50_distance_reg_0_240908	.901
70	AdvProp_efficientnet-b8	.900
71	omnivore_swinS	.898
72	compact_resnet50_robust_V4	.895
73	resnet50_primary_visual_cortex	.896
74	Res2Net50_26w_4s	.896
75	BiT-S-R50x1	.895
76	resnet50_imagenet_100_seed-0	.894
77	mobilenet_v2_1.0_224-tf	.893
78	yudixie_resnet50_distance_translation_0_240908	.892
79	resnet-18-LC_w_sh_100_iter_conv_init_m	.893
80	alexnet_fov4	.892
81	grcnn_robust_v1	.887
82	yudixie_resnet18_distance_rotation_0_240719	.886
83	resnet101	.887
84	compact_vgg19_IT	.885
85	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.885
86	0.5x_resnet-18_LC_w_sh_100_iter	.884
87	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
88	resnet50-vitoimagevidnet8	.876
89	yudixie_resnet50_translation_reg_0_240908	.876
90	omnivore_swinB	.874
91	vgg-16-keras	.874
92	alexnet_reduced_aliasing_early_checkpoint	.874
93	barlow-twins-resnet50	.873
94	resnet18_ecoset_full	.872
95	compact_resnet50_IT	.872
96	vgg_16	.871
97	grcnn_109	.870
98	resnet50_byol	.868
99	resnet50-SIN_IN	.867
100	convnext_small_imagenet_full_seed-0	.867
101	CORnet-Z	.867
102	grcnn_v2_text_noise	.866
103	alexnet_random_linf8_perturb	.864
104	resnet_50_v2	.862
105	resnet-50_v1-tf	.861
106	alexnet_l2_3_robust	.860
107	resnet-18-LC_w_sh_10_iter_m	.859
108	0.5x_resnet-18_LC_w_sh_1_iter	.858
109	ReAlnet04	.857
110	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.857
111	ReAlnet04_cornet	.857
112	ReAlnet03	.855
113	ReAlnet03_cornet	.856
114	ReAlnet01_cornet_fov16	.855
115	0.5x_resnet-18_LC	.853
116	BiT-M-R101x1	.853
117	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.852
118	inception_v3-tf	.850
119	CORnetZ_CIFAR10_bs32_20_04	.850
120	ReAlnet01	.850
121	resnet50-vicregl0p9	.850
122	ReAlnet08_cornet	.849
123	ReAlnet08	.850
124	ReAlnet01_cornet	.847
125	ReAlnet09	.846
126	ReAlnet09_cornet	.847
127	convnext_tiny_imagenet_full_seed-0	.847
128	resnet50-simclr-vissl	.847
129	ReAlnet02	.847
130	ReAlnet07	.847
131	ReAlnet02_cornet	.846
132	yudixie_resnet50_object_class_0_240908	.846
133	ReAlnet07_cornet	.846
134	inception_v3	.845
135	alexnet_linf_8_robust	.844
136	yudixie_resnet18_distance_translation_rotation_0_240719	.843
137	BiT-S-R50x3	.844
138	ReAlnet10	.843
139	nasnet_large-tf	.842
140	resnet18_imagenet21kP	.841
141	vonealexnet_gaussian_noise_std4_fixed	.839
142	ReAlnet10_cornet	.839
143	alexnet_early_checkpoint	.838
144	compact_resnet50_robust_IT	.837
145	BiT-S-R101x3	.837
146	resnet50_imagenet_10_seed-0	.837
147	ReAlnet06_cornet	.835
148	ReAlnet06	.836
149	AlexNet_SIN_fov4	.835
150	ReAlnet05	.833
151	ReAlnet05_cornet	.832
152	resnet101_imagenet_full	.829
153	convnext_tiny_sup	.829
154	antialias-resnet152	.827
155	yudixie_resnet18_distance_translation_0_240719	.826
156	densenet-201	.826
157	convnext_small_imagenet_100_seed-0	.825
158	resnet_50_v1_spiking_l4a	.824
159	resnet_50_v1	.824
160	resnet_50_v1_spiking	.823
161	yudixie_resnet50_imagenet1kpret_0_240908	.821
162	resnet50_tutorial	.822
163	resnet50-sup	.821
164	resnet_50_v1_spiking_l4	.821
165	alexnet_fov12	.821
166	densenet-201-keras	.821
167	AlexNet_SIN_fov12	.820
168	resnet_50_v1_spiking_l3	.820
169	AdvProp_efficientnet-b7	.819
170	CORnet-S	.816
171	mobilenet_v2_0_75_192	.817
172	convnext_xlarge:fb_in22k_ft_in1k	.817
173	resnet50-moclr8deg	.814
174	resnet-50_untrained	.815
175	ReAlnet07_ALL	.814
176	resnet50-vicregl0p75	.812
177	CLIP-RN50	.811
178	imagenet_l2_10_0	.810
179	ReAlnet03_ALL	.810
180	vgg-19-keras	.810
181	imagenet_l2_3_0	.810
182	ReAlnet04_ALL	.809
183	ReAlnet08_ALL	.808
184	RN50	.807
185	resnet50	.807
186	ReAlnet10_ALL	.807
187	resnet-50x2_untrained	.806
188	mobilenet_v2_1_4_224	.806
189	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.805
190	inception_v4-tf	.804
191	ReAlnet05_ALL	.804
192	cornet_s	.803
193	ReAlnet09_ALL	.802
194	ReAlnet02_ALL	.802
195	ReAlnet01_ALL	.802
196	resnet50-SIN_IN_IN	.800
197	resnet-50-robust	.800
198	mobilenet_v2_1_3_224	.799
199	resnet50_robust_l2_eps1	.796
200	resnet50_random_linf8_perturb	.795
201	deit_small_imagenet_full_seed-0	.795
202	r3m_resnet34_nocrop	.795
203	ReAlnet06_ALL	.795
204	alexnet_fov16	.793
205	resnet50_l2_3_robust	.792
206	resnet50_linf_8_robust	.787
207	BiT-S-R152x2	.786
208	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.785
209	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.784
210	resnet50_robust_l2_eps3	.783
211	antialiased-r50	.782
212	resnet50_random_l2_perturb	.780
213	resnet_SIN_IN_FT_IN	.779
214	resnet50-vicreg	.779
215	yudixie_resnet18_imagenet1kpret_0_240719	.779
216	resnet34_imagenet_full	.778
217	resnet18_imagenet_full	.776
218	efficientnet_b0	.775
219	convnext_large_imagenet_full_seed-0	.775
220	convnext_base_imagenet_full_seed-0	.772
221	mobilenet_v2_1.0_96	.772
222	VOneCORnet-S	.772
223	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.769
224	texture_shape_resnet50_trained_on_SIN	.769
225	vit_base_patch16_clip_224:openai_ft_in1k	.769
226	densenet-121	.767
227	mobilenet_v2_0_5_224	.767
228	inception_v2-tf	.766
229	voneresnet-50-robust	.764
230	voneresnet-50	.758
231	mobilenet_v2_0.75_192-tf	.757
232	resnet50_linf_4_robust	.756
233	CLIP_resnet50	.756
234	ReAlnet01_cornet_fov12	.755
235	convnext_tiny:in12k_ft_in1k	.755
236	CLIP_resnet50_float32	.755
237	AdvProp_efficientnet-b4	.754
238	mobilenet_v2_1.4_224-tf	.755
239	densenet-169	.754
240	resnet50_ecoset_full	.752
241	resnet-18_untrained	.749
242	resnet50-VITO-8deg-cc	.747
243	0.5x_resnet-18	.745
244	resnet50-cifar	.743
245	resnet50-SIN	.741
246	SWSL_resnet50	.741
247	resnet50_imagenet_full	.741
248	alexnet_training_seed_01_fov16	.741
249	alexnet_training_seed_01_fov12	.738
250	mobilenet_v2_0_75_224	.739
251	AdvProp_efficientnet-b0	.738
252	resnet152_imagenet_full	.738
253	resnet101_ecoset_full	.735
254	alexnet_training_seed_02	.735
255	resnet-101_v2-tf	.732
256	mobilenet_v2_1.0_192-tf	.730
257	mobilenet_v2_1-4_224_pytorch	.728
258	pnasnet_large-tf	.727
259	alexnet_training_seed_07	.727
260	cvt_cvt-13-384-in1k_4_LucyV4	.725
261	vggface	.725
262	resnet-50_v2-tf	.724
263	resnet-18_test_m	.720
264	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.719
265	alexnet_training_seed_04	.719
266	vonegrcnn_47e	.718
267	resnet-152_v1-tf	.716
268	yudixie_resnet18_distance_reg_0_240719	.715
269	alexnet_training_seed_01	.714
270	mobilenet_v2_0.5_192-tf	.715
271	resnet-18-LC_w_sh_100_iter_m	.715
272	voneresnet-50-non_stochastic	.712
273	mobilenet_v2_0.75_128	.713
274	yudixie_resnet50_random_0_240908	.711
275	alexnet_training_seed_09	.710
276	mobilenet_v1_0.25_128	.707
277	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.707
278	FrankRobWobv0	.705
279	antialiased-rnext101_32x8d	.705
280	AlexNet_SIN_fov16	.705
281	resnet-101_v1-tf	.704
282	BiT-S-R152x4	.702
283	vit_large_patch14_clip_224:laion2b_ft_in1k	.701
284	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.701
285	resnet34_ecoset_full	.700
286	BiT-M-R101x3	.698
287	resnet152_ecoset_full	.697
288	BiT-M-R152x4	.697
289	mobilenet_v2_1_0_192	.696
290	SWSL_resnext101_32x8d	.696
291	convnext_small_imagenet_10_seed-0	.695
292	vonegrcnn_52e_full	.693
293	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.693
294	resnet-152_v2_pytorch	.693
295	densenet-121-keras	.692
296	v1-pyr-nodown	.689
297	efficientnet_b0_imagenet_full	.687
298	deit_small_imagenet_100_seed-0	.686
299	yudixie_resnet18_rotation_reg_0_240719	.687
300	yolos_tiny	.682
301	BiT-S-R101x1	.678
302	konkle_alexnetgn_ipcl_ref01_primary_model	.676
303	my-model	.677
304	mobilenet_v2_0.75_160-tf	.669
305	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.668
306	nasnet_mobile-tf	.664
307	resnet50-barlow	.662
308	shufflenet_v2_x1_0	.658
309	BiT-M-R152x2	.658
310	nasnet_mobile	.657
311	vision_transformer_vit_large_patch16_224	.656
312	resnet50_FractalDB	.655
313	CLIP_ViT-B_32	.653
314	densenet-169-keras	.652
315	BiT-M-R50x1	.648
316	effnetv2m_custom384	.640
317	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.639
318	vit_large_patch14_clip_336:laion2b_ft_in1k	.637
319	alexnet_training_seed_10	.636
320	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.634
321	cv_18_dagger_408_pretrained	.632
322	resnet50	.630
323	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.630
324	mobilenet_v2_0_75_160	.627
325	alexnet_training_seed_03	.627
326	resnet50_moco_v2	.624
327	deit_large_imagenet_full_seed-0	.624
328	omnivore_swinT	.623
329	mobilenet_v2_1_0_224	.622
330	efficientnet_b2_imagenet_full	.619
331	yudixie_resnet18_random_0_240719	.613
332	artResNet18_1	.612
333	custom_model_cv_18_dagger_408	.612
334	inception_v1-tf	.610
335	resnet50_simclr	.602
336	mobilenet_v2_0.35_224	.602
337	mobilenet_v2_1.0_128-tf	.596
338	mobilenet_v2_1_0_160	.594
339	effnetb1_272x240	.594
340	vit_large_patch14_clip_224:openai_ft_in1k	.589
341	r3m_resnet34	.585
342	mobilenet_v2_0.5_224-tf	.582
343	resnet-18-LC_conv_init_m	.583
344	alexnet_training_seed_01_fov4	.581
345	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.578
346	convnext_small_imagenet_1_seed-0	.577
347	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.561
348	mobilenet_v2_0_5_192	.559
349	hmax	.553
350	deit_base_imagenet_full_seed-0	.551
351	vit_relpos_base_patch16_clsgap_224:sw_in1k	.550
352	r3m_resnet50_nocrop	.528
353	AT_efficientnet-b2	.526
354	AdvProp_efficientnet-b2	.525
355	inception_resnet_v2	.521
356	r3m_resnet18	.514
357	r3m_resnet50	.506
358	resnet50_pretrained_with_retinal_waves	.489
359	my-model	.484
360	mobilenet_v2_0.75_96	.481
361	alexnet_training_seed_08	.479
362	CORnetZ_CIFAR10	.422
363	mobilenet_v2_0.5_160	.398
364	mobilenet_v2_0.75_224-tf	.397
365	deit_small_imagenet_10_seed-0	.388
366	ViT-B/32	.387
367	resnet50_imagenet_1_seed-0	.325
368	alexnet_training_seed_05	.313
369	alexnet_training_seed_06	.233
370	resnet-18-LC_untrained	.233
371	resnet50-meshes-lt-100-original-pretrained	.177
372	resnet50-meshes-lt-100-original-scratch	.115
373	mobilenet_v2_0.5_128	.038
374	mobilenet_v2_0.35_160
375	resnet18-supervised
376	fitvid_trained_on_physion
377	openclip
378	resnext101_32x32d_wsl
379	resnext101_32x48d_wsl
380	resnet50_ImageNet
381	resnet_101_v1
382	regnet_y_400mf
383	tv_efficientnet-b1
384	alexnet_ks_torevert
385	yudixie_resnet50_imagenet1kpret_0_240312
386	resnet_152_v1
387	ViT_L_32_imagenet1k
388	resnext101_32x16d_wsl
389	densenet_201_pytorch
390	inception_v3_pytorch
391	pnasnet_large_pytorch
392	inception_v4
393	nasnet_large
394	deit_small_imagenet_1_seed-0
395	pnasnet_large
396	vonegrcnn_62e_nobn
397	resnet_101_v2
398	vonegrcnn_47e
399	vonegrcnn_52e_full
400	vonegrcnn_47e
401	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: orth_pref_ratio