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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_workflow_trigger	.912
50	alexnet	.912
51	alexnet	.911
52	alexnet	.911
53	yudixie_resnet18_translation_rotation_0_240719	.911
54	alexnet	.911
55	alexnet	.911
56	convnext_base:clip_laiona_augreg_ft_in1k_384	.911
57	alexnet	.910
58	alexnet	.910
59	grcnn	.911
60	alexnet_robust_correct	.909
61	texture_shape_alexnet_trained_on_SIN	.906
62	resnext101_32x8d_wsl	.905
63	alexnet_random_l2_3_perturb	.903
64	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.903
65	resnet50_ewconplus_wzc	.902
66	resnet-18-LC_w_sh_10_iter_conv_init_m	.903
67	focalnet_tiny_lrf_in1k	.901
68	yudixie_resnet18_object_class_0_240719	.901
69	mobilenet_v2_1.0_160-tf	.901
70	yudixie_resnet50_distance_reg_0_240908	.901
71	AdvProp_efficientnet-b8	.900
72	omnivore_swinS	.898
73	compact_resnet50_robust_V4	.895
74	resnet50_primary_visual_cortex	.896
75	Res2Net50_26w_4s	.896
76	BiT-S-R50x1	.895
77	resnet50_imagenet_100_seed-0	.894
78	mobilenet_v2_1.0_224-tf	.893
79	yudixie_resnet50_distance_translation_0_240908	.892
80	resnet-18-LC_w_sh_100_iter_conv_init_m	.893
81	alexnet_fov4	.892
82	grcnn_robust_v1	.887
83	yudixie_resnet18_distance_rotation_0_240719	.886
84	resnet101	.887
85	compact_vgg19_IT	.885
86	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.885
87	0.5x_resnet-18_LC_w_sh_100_iter	.884
88	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
89	resnet50-vitoimagevidnet8	.876
90	yudixie_resnet50_translation_reg_0_240908	.876
91	omnivore_swinB	.874
92	vgg-16-keras	.874
93	alexnet_reduced_aliasing_early_checkpoint	.874
94	barlow-twins-resnet50	.873
95	resnet18_ecoset_full	.872
96	compact_resnet50_IT	.872
97	vgg_16	.871
98	grcnn_109	.870
99	resnet50_byol	.868
100	resnet50-SIN_IN	.867
101	convnext_small_imagenet_full_seed-0	.867
102	CORnet-Z	.867
103	grcnn_v2_text_noise	.866
104	alexnet_random_linf8_perturb	.864
105	resnet_50_v2	.862
106	resnet-50_v1-tf	.861
107	alexnet_l2_3_robust	.860
108	resnet-18-LC_w_sh_10_iter_m	.859
109	0.5x_resnet-18_LC_w_sh_1_iter	.858
110	ReAlnet04	.857
111	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.857
112	ReAlnet04_cornet	.857
113	ReAlnet03	.855
114	ReAlnet03_cornet	.856
115	ReAlnet01_cornet_fov16	.855
116	0.5x_resnet-18_LC	.853
117	BiT-M-R101x1	.853
118	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.852
119	inception_v3-tf	.850
120	CORnetZ_CIFAR10_bs32_20_04	.850
121	ReAlnet01	.850
122	resnet50-vicregl0p9	.850
123	ReAlnet08_cornet	.849
124	ReAlnet08	.850
125	ReAlnet01_cornet	.847
126	ReAlnet09	.846
127	ReAlnet09_cornet	.847
128	convnext_tiny_imagenet_full_seed-0	.847
129	resnet50-simclr-vissl	.847
130	ReAlnet02	.847
131	ReAlnet07	.847
132	ReAlnet02_cornet	.846
133	yudixie_resnet50_object_class_0_240908	.846
134	ReAlnet07_cornet	.846
135	inception_v3	.845
136	alexnet_linf_8_robust	.844
137	yudixie_resnet18_distance_translation_rotation_0_240719	.843
138	BiT-S-R50x3	.844
139	ReAlnet10	.843
140	nasnet_large-tf	.842
141	resnet18_imagenet21kP	.841
142	vonealexnet_gaussian_noise_std4_fixed	.839
143	ReAlnet10_cornet	.839
144	alexnet_early_checkpoint	.838
145	compact_resnet50_robust_IT	.837
146	BiT-S-R101x3	.837
147	resnet50_imagenet_10_seed-0	.837
148	ReAlnet06_cornet	.835
149	ReAlnet06	.836
150	AlexNet_SIN_fov4	.835
151	ReAlnet05	.833
152	ReAlnet05_cornet	.832
153	resnet101_imagenet_full	.829
154	convnext_tiny_sup	.829
155	antialias-resnet152	.827
156	yudixie_resnet18_distance_translation_0_240719	.826
157	densenet-201	.826
158	convnext_small_imagenet_100_seed-0	.825
159	resnet_50_v1_spiking_l4a	.824
160	resnet_50_v1	.824
161	resnet_50_v1_spiking	.823
162	yudixie_resnet50_imagenet1kpret_0_240908	.821
163	resnet50_tutorial	.822
164	resnet50-sup	.821
165	resnet_50_v1_spiking_l4	.821
166	alexnet_fov12	.821
167	densenet-201-keras	.821
168	AlexNet_SIN_fov12	.820
169	resnet_50_v1_spiking_l3	.820
170	AdvProp_efficientnet-b7	.819
171	CORnet-S	.816
172	mobilenet_v2_0_75_192	.817
173	convnext_xlarge:fb_in22k_ft_in1k	.817
174	resnet50-moclr8deg	.814
175	resnet-50_untrained	.815
176	ReAlnet07_ALL	.814
177	resnet50-vicregl0p75	.812
178	CLIP-RN50	.811
179	imagenet_l2_10_0	.810
180	ReAlnet03_ALL	.810
181	vgg-19-keras	.810
182	imagenet_l2_3_0	.810
183	ReAlnet04_ALL	.809
184	ReAlnet08_ALL	.808
185	RN50	.807
186	resnet50	.807
187	ReAlnet10_ALL	.807
188	resnet-50x2_untrained	.806
189	mobilenet_v2_1_4_224	.806
190	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.805
191	inception_v4-tf	.804
192	ReAlnet05_ALL	.804
193	cornet_s	.803
194	ReAlnet09_ALL	.802
195	ReAlnet02_ALL	.802
196	ReAlnet01_ALL	.802
197	resnet50-SIN_IN_IN	.800
198	resnet-50-robust	.800
199	mobilenet_v2_1_3_224	.799
200	resnet50_robust_l2_eps1	.796
201	resnet50_random_linf8_perturb	.795
202	deit_small_imagenet_full_seed-0	.795
203	r3m_resnet34_nocrop	.795
204	ReAlnet06_ALL	.795
205	alexnet_fov16	.793
206	resnet50_l2_3_robust	.792
207	resnet50_linf_8_robust	.787
208	BiT-S-R152x2	.786
209	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.785
210	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.784
211	resnet50_robust_l2_eps3	.783
212	antialiased-r50	.782
213	resnet50_random_l2_perturb	.780
214	resnet_SIN_IN_FT_IN	.779
215	resnet50-vicreg	.779
216	yudixie_resnet18_imagenet1kpret_0_240719	.779
217	resnet34_imagenet_full	.778
218	resnet18_imagenet_full	.776
219	efficientnet_b0	.775
220	convnext_large_imagenet_full_seed-0	.775
221	convnext_base_imagenet_full_seed-0	.772
222	mobilenet_v2_1.0_96	.772
223	VOneCORnet-S	.772
224	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.769
225	texture_shape_resnet50_trained_on_SIN	.769
226	vit_base_patch16_clip_224:openai_ft_in1k	.769
227	densenet-121	.767
228	mobilenet_v2_0_5_224	.767
229	inception_v2-tf	.766
230	voneresnet-50-robust	.764
231	voneresnet-50	.758
232	mobilenet_v2_0.75_192-tf	.757
233	resnet50_linf_4_robust	.756
234	CLIP_resnet50	.756
235	ReAlnet01_cornet_fov12	.755
236	convnext_tiny:in12k_ft_in1k	.755
237	CLIP_resnet50_float32	.755
238	AdvProp_efficientnet-b4	.754
239	mobilenet_v2_1.4_224-tf	.755
240	densenet-169	.754
241	resnet50_ecoset_full	.752
242	resnet-18_untrained	.749
243	resnet50-VITO-8deg-cc	.747
244	0.5x_resnet-18	.745
245	resnet50-cifar	.743
246	resnet50-SIN	.741
247	SWSL_resnet50	.741
248	resnet50_imagenet_full	.741
249	alexnet_training_seed_01_fov16	.741
250	alexnet_training_seed_01_fov12	.738
251	mobilenet_v2_0_75_224	.739
252	AdvProp_efficientnet-b0	.738
253	resnet152_imagenet_full	.738
254	resnet101_ecoset_full	.735
255	alexnet_training_seed_02	.735
256	resnet-101_v2-tf	.732
257	mobilenet_v2_1.0_192-tf	.730
258	mobilenet_v2_1-4_224_pytorch	.728
259	pnasnet_large-tf	.727
260	alexnet_training_seed_07	.727
261	cvt_cvt-13-384-in1k_4_LucyV4	.725
262	vggface	.725
263	resnet-50_v2-tf	.724
264	resnet-18_test_m	.720
265	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.719
266	alexnet_training_seed_04	.719
267	vonegrcnn_47e	.718
268	resnet-152_v1-tf	.716
269	yudixie_resnet18_distance_reg_0_240719	.715
270	alexnet_training_seed_01	.714
271	mobilenet_v2_0.5_192-tf	.715
272	resnet-18-LC_w_sh_100_iter_m	.715
273	voneresnet-50-non_stochastic	.712
274	mobilenet_v2_0.75_128	.713
275	yudixie_resnet50_random_0_240908	.711
276	alexnet_training_seed_09	.710
277	mobilenet_v1_0.25_128	.707
278	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.707
279	FrankRobWobv0	.705
280	antialiased-rnext101_32x8d	.705
281	AlexNet_SIN_fov16	.705
282	vit_base_patch14_reg4_dinov2:lvd142m	.705
283	resnet-101_v1-tf	.704
284	BiT-S-R152x4	.702
285	vit_large_patch14_clip_224:laion2b_ft_in1k	.701
286	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.701
287	resnet34_ecoset_full	.700
288	BiT-M-R101x3	.698
289	resnet152_ecoset_full	.697
290	BiT-M-R152x4	.697
291	mobilenet_v2_1_0_192	.696
292	SWSL_resnext101_32x8d	.696
293	convnext_small_imagenet_10_seed-0	.695
294	vonegrcnn_52e_full	.693
295	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.693
296	resnet-152_v2_pytorch	.693
297	densenet-121-keras	.692
298	v1-pyr-nodown	.689
299	efficientnet_b0_imagenet_full	.687
300	deit_small_imagenet_100_seed-0	.686
301	yudixie_resnet18_rotation_reg_0_240719	.687
302	yolos_tiny	.682
303	vit_base_patch14_dinov2:lvd142m	.681
304	BiT-S-R101x1	.678
305	konkle_alexnetgn_ipcl_ref01_primary_model	.676
306	my-model	.677
307	mobilenet_v2_0.75_160-tf	.669
308	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.668
309	nasnet_mobile-tf	.664
310	resnet50-barlow	.662
311	shufflenet_v2_x1_0	.658
312	BiT-M-R152x2	.658
313	nasnet_mobile	.657
314	vision_transformer_vit_large_patch16_224	.656
315	resnet50_FractalDB	.655
316	CLIP_ViT-B_32	.653
317	densenet-169-keras	.652
318	BiT-M-R50x1	.648
319	effnetv2m_custom384	.640
320	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.639
321	vit_large_patch14_clip_336:laion2b_ft_in1k	.637
322	alexnet_training_seed_10	.636
323	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.634
324	cv_18_dagger_408_pretrained	.632
325	resnet50	.630
326	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.630
327	mobilenet_v2_0_75_160	.627
328	alexnet_training_seed_03	.627
329	resnet50_moco_v2	.624
330	deit_large_imagenet_full_seed-0	.624
331	omnivore_swinT	.623
332	mobilenet_v2_1_0_224	.622
333	efficientnet_b2_imagenet_full	.619
334	yudixie_resnet18_random_0_240719	.613
335	artResNet18_1	.612
336	custom_model_cv_18_dagger_408	.612
337	inception_v1-tf	.610
338	resnet50_simclr	.602
339	mobilenet_v2_0.35_224	.602
340	mobilenet_v2_1.0_128-tf	.596
341	mobilenet_v2_1_0_160	.594
342	effnetb1_272x240	.594
343	vit_large_patch14_clip_224:openai_ft_in1k	.589
344	r3m_resnet34	.585
345	mobilenet_v2_0.5_224-tf	.582
346	resnet-18-LC_conv_init_m	.583
347	alexnet_training_seed_01_fov4	.581
348	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.578
349	convnext_small_imagenet_1_seed-0	.577
350	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.561
351	mobilenet_v2_0_5_192	.559
352	hmax	.553
353	deit_base_imagenet_full_seed-0	.551
354	vit_relpos_base_patch16_clsgap_224:sw_in1k	.550
355	r3m_resnet50_nocrop	.528
356	AT_efficientnet-b2	.526
357	AdvProp_efficientnet-b2	.525
358	inception_resnet_v2	.521
359	r3m_resnet18	.514
360	r3m_resnet50	.506
361	resnet50_pretrained_with_retinal_waves	.489
362	my-model	.484
363	mobilenet_v2_0.75_96	.481
364	alexnet_training_seed_08	.479
365	vit_base_patch16_dinov3:lvd1689m	.474
366	CORnetZ_CIFAR10	.422
367	mobilenet_v2_0.5_160	.398
368	mobilenet_v2_0.75_224-tf	.397
369	deit_small_imagenet_10_seed-0	.388
370	ViT-B/32	.387
371	resnet50_imagenet_1_seed-0	.325
372	alexnet_training_seed_05	.313
373	alexnet_training_seed_06	.233
374	resnet-18-LC_untrained	.233
375	resnet50-meshes-lt-100-original-pretrained	.177
376	resnet50-meshes-lt-100-original-scratch	.115
377	mobilenet_v2_0.5_128	.038
378	mobilenet_v2_0.35_160
379	resnet18-supervised
380	fitvid_trained_on_physion
381	openclip
382	resnext101_32x32d_wsl
383	resnext101_32x48d_wsl
384	resnet50_ImageNet
385	resnet_101_v1
386	regnet_y_400mf
387	tv_efficientnet-b1
388	alexnet_ks_torevert
389	yudixie_resnet50_imagenet1kpret_0_240312
390	resnet_152_v1
391	ViT_L_32_imagenet1k
392	resnext101_32x16d_wsl
393	densenet_201_pytorch
394	inception_v3_pytorch
395	pnasnet_large_pytorch
396	inception_v4
397	nasnet_large
398	deit_small_imagenet_1_seed-0
399	pnasnet_large
400	vonegrcnn_62e_nobn
401	resnet_101_v2
402	vonegrcnn_47e
403	vonegrcnn_52e_full
404	vonegrcnn_47e
405	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: orth_pref_ratio