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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	efficientnet-b7	.918
39	AlexNet_SIN	.919
40	BiT-M-R50x3	.916
41	resnet-18-LC_d_w_sh_1x1_conv_init_m	.916
42	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.915
43	compact_resnet50_V4	.915
44	compact_cornets_IT	.914
45	ReAlnet01_cornet_fov4	.914
46	alexnet	.912
47	yudixie_resnet18_translation_rotation_0_240719	.911
48	convnext_base:clip_laiona_augreg_ft_in1k_384	.911
49	grcnn	.911
50	alexnet_robust_correct	.909
51	texture_shape_alexnet_trained_on_SIN	.906
52	resnext101_32x8d_wsl	.905
53	alexnet_random_l2_3_perturb	.903
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.903
55	resnet50_ewconplus_wzc	.902
56	resnet-18-LC_w_sh_10_iter_conv_init_m	.903
57	focalnet_tiny_lrf_in1k	.901
58	yudixie_resnet18_object_class_0_240719	.901
59	mobilenet_v2_1.0_160-tf	.901
60	yudixie_resnet50_distance_reg_0_240908	.901
61	AdvProp_efficientnet-b8	.900
62	omnivore_swinS	.898
63	compact_resnet50_robust_V4	.895
64	resnet50_primary_visual_cortex	.896
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	alexnet_fov4	.892
72	grcnn_robust_v1	.887
73	yudixie_resnet18_distance_rotation_0_240719	.886
74	resnet101	.887
75	compact_vgg19_IT	.885
76	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.885
77	0.5x_resnet-18_LC_w_sh_100_iter	.884
78	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
79	resnet50-vitoimagevidnet8	.876
80	yudixie_resnet50_translation_reg_0_240908	.876
81	omnivore_swinB	.874
82	vgg-16-keras	.874
83	alexnet_reduced_aliasing_early_checkpoint	.874
84	barlow-twins-resnet50	.873
85	resnet18_ecoset_full	.872
86	compact_resnet50_IT	.872
87	vgg_16	.871
88	grcnn_109	.870
89	resnet50_byol	.868
90	resnet50-SIN_IN	.867
91	convnext_small_imagenet_full_seed-0	.867
92	CORnet-Z	.867
93	grcnn_v2_text_noise	.866
94	alexnet_random_linf8_perturb	.864
95	resnet_50_v2	.862
96	resnet-50_v1-tf	.861
97	alexnet_l2_3_robust	.860
98	resnet-18-LC_w_sh_10_iter_m	.859
99	0.5x_resnet-18_LC_w_sh_1_iter	.858
100	ReAlnet04	.857
101	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.857
102	ReAlnet04_cornet	.857
103	ReAlnet03	.855
104	ReAlnet03_cornet	.856
105	ReAlnet01_cornet_fov16	.855
106	0.5x_resnet-18_LC	.853
107	BiT-M-R101x1	.853
108	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.852
109	inception_v3-tf	.850
110	CORnetZ_CIFAR10_bs32_20_04	.850
111	ReAlnet01	.850
112	resnet50-vicregl0p9	.850
113	ReAlnet08_cornet	.849
114	ReAlnet08	.850
115	ReAlnet01_cornet	.847
116	ReAlnet09	.846
117	ReAlnet09_cornet	.847
118	convnext_tiny_imagenet_full_seed-0	.847
119	resnet50-simclr-vissl	.847
120	ReAlnet02	.847
121	ReAlnet07	.847
122	ReAlnet02_cornet	.846
123	yudixie_resnet50_object_class_0_240908	.846
124	ReAlnet07_cornet	.846
125	inception_v3	.845
126	alexnet_linf_8_robust	.844
127	yudixie_resnet18_distance_translation_rotation_0_240719	.843
128	BiT-S-R50x3	.844
129	ReAlnet10	.843
130	nasnet_large-tf	.842
131	resnet18_imagenet21kP	.841
132	vonealexnet_gaussian_noise_std4_fixed	.839
133	ReAlnet10_cornet	.839
134	alexnet_early_checkpoint	.838
135	compact_resnet50_robust_IT	.837
136	BiT-S-R101x3	.837
137	resnet50_imagenet_10_seed-0	.837
138	ReAlnet06_cornet	.835
139	ReAlnet06	.836
140	AlexNet_SIN_fov4	.835
141	ReAlnet05	.833
142	ReAlnet05_cornet	.832
143	resnet101_imagenet_full	.829
144	convnext_tiny_sup	.829
145	antialias-resnet152	.827
146	yudixie_resnet18_distance_translation_0_240719	.826
147	densenet-201	.826
148	convnext_small_imagenet_100_seed-0	.825
149	resnet_50_v1_spiking_l4a	.824
150	resnet_50_v1	.824
151	resnet_50_v1_spiking	.823
152	yudixie_resnet50_imagenet1kpret_0_240908	.821
153	resnet50_tutorial	.822
154	resnet50-sup	.821
155	resnet_50_v1_spiking_l4	.821
156	alexnet_fov12	.821
157	densenet-201-keras	.821
158	AlexNet_SIN_fov12	.820
159	resnet_50_v1_spiking_l3	.820
160	AdvProp_efficientnet-b7	.819
161	CORnet-S	.816
162	mobilenet_v2_0_75_192	.817
163	convnext_xlarge:fb_in22k_ft_in1k	.817
164	resnet50-moclr8deg	.814
165	resnet-50_untrained	.815
166	ReAlnet07_ALL	.814
167	resnet50-vicregl0p75	.812
168	CLIP-RN50	.811
169	imagenet_l2_10_0	.810
170	ReAlnet03_ALL	.810
171	vgg-19-keras	.810
172	imagenet_l2_3_0	.810
173	ReAlnet04_ALL	.809
174	ReAlnet08_ALL	.808
175	RN50	.807
176	resnet50	.807
177	ReAlnet10_ALL	.807
178	resnet-50x2_untrained	.806
179	mobilenet_v2_1_4_224	.806
180	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.805
181	inception_v4-tf	.804
182	ReAlnet05_ALL	.804
183	cornet_s	.803
184	ReAlnet09_ALL	.802
185	ReAlnet02_ALL	.802
186	ReAlnet01_ALL	.802
187	resnet50-SIN_IN_IN	.800
188	resnet-50-robust	.800
189	mobilenet_v2_1_3_224	.799
190	resnet50_robust_l2_eps1	.796
191	resnet50_random_linf8_perturb	.795
192	deit_small_imagenet_full_seed-0	.795
193	r3m_resnet34_nocrop	.795
194	ReAlnet06_ALL	.795
195	alexnet_fov16	.793
196	resnet50_l2_3_robust	.792
197	resnet50_linf_8_robust	.787
198	BiT-S-R152x2	.786
199	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.785
200	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.784
201	resnet50_robust_l2_eps3	.783
202	antialiased-r50	.782
203	resnet50_random_l2_perturb	.780
204	resnet_SIN_IN_FT_IN	.779
205	resnet50-vicreg	.779
206	yudixie_resnet18_imagenet1kpret_0_240719	.779
207	resnet34_imagenet_full	.778
208	resnet18_imagenet_full	.776
209	efficientnet_b0	.775
210	convnext_large_imagenet_full_seed-0	.775
211	convnext_base_imagenet_full_seed-0	.772
212	mobilenet_v2_1.0_96	.772
213	VOneCORnet-S	.772
214	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.769
215	texture_shape_resnet50_trained_on_SIN	.769
216	vit_base_patch16_clip_224:openai_ft_in1k	.769
217	densenet-121	.767
218	mobilenet_v2_0_5_224	.767
219	inception_v2-tf	.766
220	voneresnet-50-robust	.764
221	voneresnet-50	.758
222	mobilenet_v2_0.75_192-tf	.757
223	resnet50_linf_4_robust	.756
224	CLIP_resnet50	.756
225	ReAlnet01_cornet_fov12	.755
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	aughost_fair_resnet18	.753
232	resnet50_ecoset_full	.752
233	resnet-18_untrained	.749
234	resnet50-VITO-8deg-cc	.747
235	0.5x_resnet-18	.745
236	resnet50-cifar	.743
237	resnet50-SIN	.741
238	SWSL_resnet50	.741
239	resnet50_imagenet_full	.741
240	alexnet_training_seed_01_fov16	.741
241	alexnet_training_seed_01_fov12	.738
242	mobilenet_v2_0_75_224	.739
243	AdvProp_efficientnet-b0	.738
244	resnet152_imagenet_full	.738
245	resnet101_ecoset_full	.735
246	alexnet_training_seed_02	.735
247	resnet-101_v2-tf	.732
248	mobilenet_v2_1.0_192-tf	.730
249	mobilenet_v2_1-4_224_pytorch	.728
250	pnasnet_large-tf	.727
251	alexnet_training_seed_07	.727
252	cvt_cvt-13-384-in1k_4_LucyV4	.725
253	vggface	.725
254	resnet-50_v2-tf	.724
255	resnet-18_test_m	.720
256	pnasnet_large_halfPrecision	.719
257	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.719
258	alexnet_training_seed_04	.719
259	vonegrcnn_47e	.718
260	resnet-152_v1-tf	.716
261	yudixie_resnet18_distance_reg_0_240719	.715
262	alexnet_training_seed_01	.714
263	mobilenet_v2_0.5_192-tf	.715
264	resnet-18-LC_w_sh_100_iter_m	.715
265	voneresnet-50-non_stochastic	.712
266	mobilenet_v2_0.75_128	.713
267	yudixie_resnet50_random_0_240908	.711
268	alexnet_training_seed_09	.710
269	mobilenet_v1_0.25_128	.707
270	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.707
271	FrankRobWobv0	.705
272	antialiased-rnext101_32x8d	.705
273	AlexNet_SIN_fov16	.705
274	vit_base_patch14_reg4_dinov2-lvd142m	.705
275	resnet-101_v1-tf	.704
276	BiT-S-R152x4	.702
277	vit_large_patch14_clip_224:laion2b_ft_in1k	.701
278	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.701
279	resnet34_ecoset_full	.700
280	BiT-M-R101x3	.698
281	resnet152_ecoset_full	.697
282	BiT-M-R152x4	.697
283	mobilenet_v2_1_0_192	.696
284	SWSL_resnext101_32x8d	.696
285	convnext_small_imagenet_10_seed-0	.695
286	vonegrcnn_52e_full	.693
287	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.693
288	resnet-152_v2_pytorch	.693
289	densenet-121-keras	.692
290	v1-pyr-nodown	.689
291	efficientnet_b0_imagenet_full	.687
292	deit_small_imagenet_100_seed-0	.686
293	yudixie_resnet18_rotation_reg_0_240719	.687
294	yolos_tiny	.682
295	vit_base_patch14_dinov2-lvd142m	.680
296	BiT-S-R101x1	.678
297	konkle_alexnetgn_ipcl_ref01_primary_model	.676
298	my-model	.677
299	mobilenet_v2_0.75_160-tf	.669
300	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.668
301	nasnet_mobile-tf	.664
302	resnet50-barlow	.662
303	shufflenet_v2_x1_0	.658
304	BiT-M-R152x2	.658
305	nasnet_mobile	.657
306	vision_transformer_vit_large_patch16_224	.656
307	resnet50_FractalDB	.655
308	CLIP_ViT-B_32	.653
309	densenet-169-keras	.652
310	BiT-M-R50x1	.648
311	effnetv2m_custom384	.640
312	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.639
313	vit_large_patch14_clip_336:laion2b_ft_in1k	.637
314	alexnet_training_seed_10	.636
315	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.634
316	eBarlow_lmda_02_1000ep	.633
317	cv_18_dagger_408_pretrained	.632
318	resnet50	.630
319	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.630
320	mobilenet_v2_0_75_160	.627
321	alexnet_training_seed_03	.627
322	resnet50_moco_v2	.624
323	deit_large_imagenet_full_seed-0	.624
324	omnivore_swinT	.623
325	mobilenet_v2_1_0_224	.622
326	efficientnet_b2_imagenet_full	.619
327	yudixie_resnet18_random_0_240719	.613
328	artResNet18_1	.612
329	custom_model_cv_18_dagger_408	.612
330	inception_v1-tf	.610
331	resnet50_simclr	.602
332	mobilenet_v2_0.35_224	.602
333	mobilenet_v2_1.0_128-tf	.596
334	mobilenet_v2_1_0_160	.594
335	effnetb1_272x240	.594
336	vit_large_patch14_clip_224:openai_ft_in1k	.589
337	r3m_resnet34	.585
338	mobilenet_v2_0.5_224-tf	.582
339	resnet-18-LC_conv_init_m	.583
340	alexnet_training_seed_01_fov4	.581
341	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.578
342	convnext_small_imagenet_1_seed-0	.577
343	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.561
344	mobilenet_v2_0_5_192	.559
345	hmax	.553
346	deit_base_imagenet_full_seed-0	.551
347	vit_relpos_base_patch16_clsgap_224:sw_in1k	.550
348	r3m_resnet50_nocrop	.528
349	AT_efficientnet-b2	.526
350	AdvProp_efficientnet-b2	.525
351	inception_resnet_v2	.521
352	r3m_resnet18	.514
353	r3m_resnet50	.506
354	resnet50_pretrained_with_retinal_waves	.489
355	my-model	.484
356	mobilenet_v2_0.75_96	.481
357	alexnet_training_seed_08	.479
358	vit_base_patch16_dinov3-lvd1689m	.474
359	CORnetZ_CIFAR10	.422
360	mobilenet_v2_0.5_160	.398
361	mobilenet_v2_0.75_224-tf	.397
362	deit_small_imagenet_10_seed-0	.388
363	ViT-B/32	.387
364	resnet50_imagenet_1_seed-0	.325
365	alexnet_training_seed_05	.313
366	alexnet_training_seed_06	.233
367	resnet-18-LC_untrained	.233
368	resnet50-meshes-lt-100-original-pretrained	.177
369	resnet50-meshes-lt-100-original-scratch	.115
370	mobilenet_v2_0.5_128	.038
371	mobilenet_v2_0.35_160
372	resnet18-supervised
373	fitvid_trained_on_physion
374	openclip
375	resnext101_32x32d_wsl
376	resnext101_32x48d_wsl
377	resnet50_ImageNet
378	resnet_101_v1
379	regnet_y_400mf
380	tv_efficientnet-b1
381	alexnet_ks_torevert
382	yudixie_resnet50_imagenet1kpret_0_240312
383	resnet_152_v1
384	ViT_L_32_imagenet1k
385	resnext101_32x16d_wsl
386	densenet_201_pytorch
387	inception_v3_pytorch
388	pnasnet_large_pytorch
389	inception_v4
390	nasnet_large
391	deit_small_imagenet_1_seed-0
392	pnasnet_large
393	vonegrcnn_62e_nobn
394	resnet_101_v2
395	vonegrcnn_47e
396	vonegrcnn_52e_full
397	vonegrcnn_47e
398	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: orth_pref_ratio