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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	resnet50_hvs_wzc	.917
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	.912
48	yudixie_resnet18_translation_rotation_0_240719	.911
49	convnext_base:clip_laiona_augreg_ft_in1k_384	.911
50	grcnn	.911
51	alexnet_robust_correct	.909
52	texture_shape_alexnet_trained_on_SIN	.906
53	resnext101_32x8d_wsl	.905
54	alexnet_random_l2_3_perturb	.903
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.903
56	resnet50_ewconplus_wzc	.902
57	resnet-18-LC_w_sh_10_iter_conv_init_m	.903
58	focalnet_tiny_lrf_in1k	.901
59	yudixie_resnet18_object_class_0_240719	.901
60	mobilenet_v2_1.0_160-tf	.901
61	yudixie_resnet50_distance_reg_0_240908	.901
62	AdvProp_efficientnet-b8	.900
63	resnet50_st_wzc_seed7_2_323	.898
64	omnivore_swinS	.898
65	compact_resnet50_robust_V4	.895
66	resnet50_primary_visual_cortex	.896
67	Res2Net50_26w_4s	.896
68	BiT-S-R50x1	.895
69	resnet50_imagenet_100_seed-0	.894
70	mobilenet_v2_1.0_224-tf	.893
71	yudixie_resnet50_distance_translation_0_240908	.892
72	resnet-18-LC_w_sh_100_iter_conv_init_m	.893
73	alexnet_fov4	.892
74	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.889
75	convnext_base_fb_in22k_ft_in1k	.889
76	grcnn_robust_v1	.887
77	yudixie_wbh_object_class_scaling_0415_run_0	.888
78	yudixie_resnet18_distance_rotation_0_240719	.886
79	deepeisnn_ei_cifar10_vgg19	.885
80	resnet101	.887
81	compact_vgg19_IT	.885
82	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.885
83	resnet50_st_wzc_seed6_2_323	.885
84	0.5x_resnet-18_LC_w_sh_100_iter	.884
85	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
86	resnet50-vitoimagevidnet8	.876
87	yudixie_resnet50_translation_reg_0_240908	.876
88	resnet50_ewconplus_wzc_seed6_2_323	.875
89	omnivore_swinB	.874
90	vgg-16-keras	.874
91	alexnet_reduced_aliasing_early_checkpoint	.874
92	barlow-twins-resnet50	.873
93	resnet50_ewconplus_wzc_seed5_2__323	.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	resnet18_fair	.868
102	CORnet-Z	.867
103	grcnn_v2_text_noise	.866
104	alexnet_random_linf8_perturb	.864
105	resnet50_st_wzc_seed3_2_323	.862
106	resnet_50_v2	.862
107	resnet-50_v1-tf	.861
108	alexnet_l2_3_robust	.860
109	resnet-18-LC_w_sh_10_iter_m	.859
110	0.5x_resnet-18_LC_w_sh_1_iter	.858
111	ReAlnet04	.857
112	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.857
113	ReAlnet04_cornet	.857
114	ReAlnet03	.855
115	ReAlnet03_cornet	.856
116	ReAlnet01_cornet_fov16	.855
117	0.5x_resnet-18_LC	.853
118	BiT-M-R101x1	.853
119	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.852
120	inception_v3-tf	.850
121	CORnetZ_CIFAR10_bs32_20_04	.850
122	ReAlnet01	.850
123	resnet50-vicregl0p9	.850
124	ReAlnet08_cornet	.849
125	ReAlnet08	.850
126	ReAlnet01_cornet	.847
127	ReAlnet09	.846
128	ReAlnet09_cornet	.847
129	convnext_tiny_imagenet_full_seed-0	.847
130	resnet50-simclr-vissl	.847
131	ReAlnet02	.847
132	ReAlnet07	.847
133	ReAlnet02_cornet	.846
134	yudixie_resnet50_object_class_0_240908	.846
135	ReAlnet07_cornet	.846
136	inception_v3	.845
137	yudixie_wbh_object_class_scaling_0415_run_3	.844
138	alexnet_linf_8_robust	.844
139	yudixie_resnet18_distance_translation_rotation_0_240719	.843
140	BiT-S-R50x3	.844
141	ReAlnet10	.843
142	nasnet_large-tf	.842
143	resnet18_imagenet21kP	.841
144	vonealexnet_gaussian_noise_std4_fixed	.839
145	ReAlnet10_cornet	.839
146	alexnet_early_checkpoint	.838
147	compact_resnet50_robust_IT	.837
148	BiT-S-R101x3	.837
149	resnet50_imagenet_10_seed-0	.837
150	ReAlnet06_cornet	.835
151	ReAlnet06	.836
152	AlexNet_SIN_fov4	.835
153	convnext_xxlarge_clip	.836
154	ReAlnet05	.833
155	convnext_large_clip_laion2b_finelayers	.833
156	ReAlnet05_cornet	.832
157	yudixie_wbh_object_class_scaling_0415_run_1	.832
158	resnet101_imagenet_full	.829
159	convnext_tiny_sup	.829
160	antialias-resnet152	.827
161	yudixie_resnet18_distance_translation_0_240719	.826
162	densenet-201	.826
163	convnext_small_imagenet_100_seed-0	.825
164	resnet_50_v1_spiking_l4a	.824
165	resnet_50_v1	.824
166	resnet_50_v1_spiking	.823
167	yudixie_wbh_object_class_scaling_0415_run_2	.823
168	resnet50_ewconplus_wzc_seed2_2__323	.822
169	yudixie_resnet50_imagenet1kpret_0_240908	.821
170	resnet50_tutorial	.822
171	resnet50-sup	.821
172	resnet_50_v1_spiking_l4	.821
173	alexnet_fov12	.821
174	densenet-201-keras	.821
175	AlexNet_SIN_fov12	.820
176	resnet_50_v1_spiking_l3	.820
177	AdvProp_efficientnet-b7	.819
178	CORnet-S	.816
179	mobilenet_v2_0_75_192	.817
180	convnext_xlarge:fb_in22k_ft_in1k	.817
181	resnet50-moclr8deg	.814
182	resnet-50_untrained	.815
183	ReAlnet07_ALL	.814
184	resnet50-vicregl0p75	.812
185	CLIP-RN50	.811
186	imagenet_l2_10_0	.810
187	ReAlnet03_ALL	.810
188	vgg-19-keras	.810
189	imagenet_l2_3_0	.810
190	ReAlnet04_ALL	.809
191	ReAlnet08_ALL	.808
192	RN50	.807
193	resnet50	.807
194	ReAlnet10_ALL	.807
195	resnet-50x2_untrained	.806
196	mobilenet_v2_1_4_224	.806
197	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.805
198	inception_v4-tf	.804
199	ReAlnet05_ALL	.804
200	resnet50_st_wzc_2	.804
201	cornet_s	.803
202	ReAlnet09_ALL	.802
203	ReAlnet02_ALL	.802
204	convnext_large_in22k_supervised	.802
205	ReAlnet01_ALL	.802
206	resnet50-SIN_IN_IN	.800
207	resnet-50-robust	.800
208	mobilenet_v2_1_3_224	.799
209	resnet50_robust_l2_eps1	.796
210	resnet50_random_linf8_perturb	.795
211	deit_small_imagenet_full_seed-0	.795
212	r3m_resnet34_nocrop	.795
213	ReAlnet06_ALL	.795
214	alexnet_fov16	.793
215	resnet50_l2_3_robust	.792
216	resnet50_linf_8_robust	.787
217	deepeisnn_ei_cifar10_resnet18	.786
218	BiT-S-R152x2	.786
219	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.785
220	convnext_base_fb_in1k	.784
221	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.784
222	resnet50_robust_l2_eps3	.783
223	antialiased-r50	.782
224	resnet50_random_l2_perturb	.780
225	resnet_SIN_IN_FT_IN	.779
226	yudixie_wbh_object_class_scaling_0415_imagenet	.779
227	resnet50-vicreg	.779
228	yudixie_resnet18_imagenet1kpret_0_240719	.779
229	resnet34_imagenet_full	.778
230	resnet18_imagenet_full	.776
231	efficientnet_b0	.775
232	convnext_large_imagenet_full_seed-0	.775
233	convnext_base_imagenet_full_seed-0	.772
234	mobilenet_v2_1.0_96	.772
235	VOneCORnet-S	.772
236	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.769
237	texture_shape_resnet50_trained_on_SIN	.769
238	vit_base_patch16_clip_224:openai_ft_in1k	.769
239	densenet-121	.767
240	mobilenet_v2_0_5_224	.767
241	inception_v2-tf	.766
242	voneresnet-50-robust	.764
243	voneresnet-50	.758
244	mobilenet_v2_0.75_192-tf	.757
245	resnet50_linf_4_robust	.756
246	CLIP_resnet50	.756
247	ReAlnet01_cornet_fov12	.755
248	convnext_tiny:in12k_ft_in1k	.755
249	CLIP_resnet50_float32	.755
250	AdvProp_efficientnet-b4	.754
251	mobilenet_v2_1.4_224-tf	.755
252	densenet-169	.754
253	resnet50_ecoset_full	.752
254	resnet-18_untrained	.749
255	deepeisnn_ei_imagenet_resnet18	.750
256	resnet50-VITO-8deg-cc	.747
257	0.5x_resnet-18	.745
258	resnet50-cifar	.743
259	resnet50-SIN	.741
260	SWSL_resnet50	.741
261	dinov2_vitl14_lc_res336	.742
262	resnet50_imagenet_full	.741
263	alexnet_training_seed_01_fov16	.741
264	alexnet_training_seed_01_fov12	.738
265	mobilenet_v2_0_75_224	.739
266	AdvProp_efficientnet-b0	.738
267	resnet152_imagenet_full	.738
268	resnet101_ecoset_full	.735
269	alexnet_training_seed_02	.735
270	resnet-101_v2-tf	.732
271	mobilenet_v2_1.0_192-tf	.730
272	mobilenet_v2_1-4_224_pytorch	.728
273	pnasnet_large-tf	.727
274	alexnet_training_seed_07	.727
275	cvt_cvt-13-384-in1k_4_LucyV4	.725
276	vggface	.725
277	resnet-50_v2-tf	.724
278	resnet-18_test_m	.720
279	pnasnet_large_halfPrecision	.719
280	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.719
281	alexnet_training_seed_04	.719
282	vonegrcnn_47e	.718
283	resnet-152_v1-tf	.716
284	yudixie_resnet18_distance_reg_0_240719	.715
285	alexnet_training_seed_01	.714
286	mobilenet_v2_0.5_192-tf	.715
287	resnet-18-LC_w_sh_100_iter_m	.715
288	voneresnet-50-non_stochastic	.712
289	mobilenet_v2_0.75_128	.713
290	yudixie_resnet50_random_0_240908	.711
291	alexnet_training_seed_09	.710
292	mobilenet_v1_0.25_128	.707
293	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.707
294	FrankRobWobv0	.705
295	antialiased-rnext101_32x8d	.705
296	AlexNet_SIN_fov16	.705
297	vit_base_patch14_reg4_dinov2-lvd142m	.705
298	resnet-101_v1-tf	.704
299	BiT-S-R152x4	.702
300	vit_large_patch14_clip_224:laion2b_ft_in1k	.701
301	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.701
302	resnet34_ecoset_full	.700
303	BiT-M-R101x3	.698
304	resnet152_ecoset_full	.697
305	BiT-M-R152x4	.697
306	mobilenet_v2_1_0_192	.696
307	SWSL_resnext101_32x8d	.696
308	convnext_small_imagenet_10_seed-0	.695
309	vonegrcnn_52e_full	.693
310	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.693
311	resnet-152_v2_pytorch	.693
312	densenet-121-keras	.692
313	v1-pyr-nodown	.689
314	efficientnet_b0_imagenet_full	.687
315	deit_small_imagenet_100_seed-0	.686
316	yudixie_resnet18_rotation_reg_0_240719	.687
317	yolos_tiny	.682
318	vit_base_patch14_dinov2-lvd142m	.680
319	BiT-S-R101x1	.678
320	konkle_alexnetgn_ipcl_ref01_primary_model	.676
321	my-model	.677
322	mobilenet_v2_0.75_160-tf	.669
323	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.668
324	nasnet_mobile-tf	.664
325	resnet50-barlow	.662
326	shufflenet_v2_x1_0	.658
327	BiT-M-R152x2	.658
328	nasnet_mobile	.657
329	vision_transformer_vit_large_patch16_224	.656
330	resnet50_FractalDB	.655
331	vit_huge_clip336_laion	.654
332	CLIP_ViT-B_32	.653
333	densenet-169-keras	.652
334	BiT-M-R50x1	.648
335	effnetv2m_custom384	.640
336	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.639
337	vit_large_patch14_clip_336:laion2b_ft_in1k	.637
338	alexnet_training_seed_10	.636
339	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.634
340	eBarlow_lmda_02_1000ep	.633
341	cv_18_dagger_408_pretrained	.632
342	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.630
343	dinov2_vitg14_lc	.628
344	mobilenet_v2_0_75_160	.627
345	alexnet_training_seed_03	.627
346	resnet50_moco_v2	.624
347	deit_large_imagenet_full_seed-0	.624
348	omnivore_swinT	.623
349	mobilenet_v2_1_0_224	.622
350	efficientnet_b2_imagenet_full	.619
351	dinov2_vitl14_reg_lc_featread	.615
352	dinov2_vitl14_reg_lc	.615
353	yudixie_resnet18_random_0_240719	.613
354	artResNet18_1	.612
355	custom_model_cv_18_dagger_408	.612
356	eva02_large_in22k	.610
357	inception_v1-tf	.610
358	convnext_base_fb_in1k_gram_aligned_dinov3	.606
359	vit_large_clip336_openai	.602
360	resnet50_simclr	.602
361	mobilenet_v2_0.35_224	.602
362	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.598
363	deit3_base_patch16_224_fb_in22k_ft_in1k	.597
364	mobilenet_v2_1.0_128-tf	.596
365	mobilenet_v2_1_0_160	.594
366	effnetb1_272x240	.594
367	vit_large_patch14_clip_224:openai_ft_in1k	.589
368	r3m_resnet34	.585
369	mobilenet_v2_0.5_224-tf	.582
370	resnet-18-LC_conv_init_m	.583
371	alexnet_training_seed_01_fov4	.581
372	dinov2_vitl14_lc	.581
373	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.578
374	convnext_small_imagenet_1_seed-0	.577
375	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.561
376	mobilenet_v2_0_5_192	.559
377	hmax	.553
378	deit_base_imagenet_full_seed-0	.551
379	vit_relpos_base_patch16_clsgap_224:sw_in1k	.550
380	deit3_base_patch16_224_fb_in1k	.547
381	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.545
382	r3m_resnet50_nocrop	.528
383	AT_efficientnet-b2	.526
384	AdvProp_efficientnet-b2	.525
385	inception_resnet_v2	.521
386	r3m_resnet18	.514
387	r3m_resnet50	.506
388	resnet50_pretrained_with_retinal_waves	.489
389	my-model	.484
390	mobilenet_v2_0.75_96	.481
391	alexnet_training_seed_08	.479
392	vit_base_patch16_dinov3-lvd1689m	.474
393	CORnetZ_CIFAR10	.422
394	mobilenet_v2_0.5_160	.398
395	mobilenet_v2_0.75_224-tf	.397
396	deit_small_imagenet_10_seed-0	.388
397	ViT-B/32	.387
398	resnet50_imagenet_1_seed-0	.325
399	alexnet_training_seed_05	.313
400	alexnet_training_seed_06	.233
401	resnet-18-LC_untrained	.233
402	mobilenet_v2_0.5_128	.038
403	mobilenet_v2_0.35_160
404	resnet18-supervised
405	fitvid_trained_on_physion
406	openclip
407	resnext101_32x32d_wsl
408	resnext101_32x48d_wsl
409	resnet50_ImageNet
410	resnet_101_v1
411	regnet_y_400mf
412	tv_efficientnet-b1
413	alexnet_ks_torevert
414	yudixie_resnet50_imagenet1kpret_0_240312
415	resnet_152_v1
416	ViT_L_32_imagenet1k
417	resnext101_32x16d_wsl
418	densenet_201_pytorch
419	inception_v3_pytorch
420	pnasnet_large_pytorch
421	inception_v4
422	nasnet_large
423	deit_small_imagenet_1_seed-0
424	pnasnet_large
425	vonegrcnn_62e_nobn
426	resnet_101_v2
427	vonegrcnn_47e
428	vonegrcnn_52e_full
429	vonegrcnn_47e
430	resnet_152_v2
431	pr1_stl10_disentangled_alpha04
432	predictive_coding_pc
433	dinov2_vitg14_reg_lc

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: orth_pref_ratio