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

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: orth_pref_ratio