Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_DeValois1982-pref_or")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50-SIN_IN	1.0
2	nasnet_large-tf	.998
3	resnet-18-LC_w_sh_10_iter_conv_init_m	.997
4	imagenet_l2_10_0	.997
5	resnet50-vicreg	.997
6	resnet50	.996
7	vgg_19	.995
8	inception_v4-tf	.996
9	antialiased-r50	.995
10	SWSL_resnet50	.995
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.993
12	resnet18_ecoset_full	.992
13	resnet50_random_linf8_perturb	.992
14	voneresnet-50-robust	.991
15	deit_small_imagenet_100_seed-0	.991
16	vit_large_patch14_clip_224:laion2b_ft_in1k	.991
17	voneresnet-50	.990
18	voneresnet-50-non_stochastic	.991
19	pnasnet_large-tf	.989
20	inception_v1	.989
21	resnet-50-robust	.988
22	resnet50-vicregl0p9	.988
23	SWSL_resnext101_32x8d	.987
24	mobilenet_v2_0_75_192	.989
25	vit_relpos_base_patch16_clsgap_224:sw_in1k	.988
26	vit_base_patch16_clip_224:openai_ft_in1k	.986
27	alexnet	.987
28	resnet50-barlow	.987
29	resnet50-vicregl0p75	.986
30	resnet50_ecoset_full	.987
31	resnet50_linf_8_robust	.987
32	resnet_SIN_IN_FT_IN	.987
33	v1-pyr-nodown	.986
34	ReAlnet06_cornet	.985
35	yudixie_resnet50_object_class_0_240908	.985
36	ReAlnet06	.985
37	resnet50_robust_l2_eps1	.984
38	vgg-16-keras	.984
39	resnet101_ecoset_full	.984
40	inception_v3-tf	.984
41	resnet_50_v2	.983
42	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.982
43	mobilenet_v2_1_0_192	.982
44	inception_v2-tf	.982
45	vit_large_patch14_clip_224:openai_ft_in1k	.983
46	hmax	.983
47	VOneCORnet-S	.983
48	resnet34_imagenet_full	.982
49	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.982
50	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.983
51	BiT-M-R50x3	.981
52	resnet50-sup	.980
53	alexnet_l2_3_robust	.980
54	resnet18_imagenet_full	.980
55	alexnet_linf_8_robust	.980
56	omnivore_swinB	.980
57	ReAlnet02	.979
58	yudixie_resnet50_imagenet1kpret_0_240908	.978
59	resnet_101_v2	.979
60	resnet_50_v1	.979
61	ReAlnet02_cornet	.979
62	resnet50_tutorial	.977
63	yudixie_resnet50_rotation_reg_0_240908	.978
64	alexnet_robust_correct	.976
65	antialias-resnet152	.977
66	mobilenet_v2_1_3_224	.977
67	vonegrcnn_47e	.976
68	antialiased-rnext101_32x8d	.974
69	inception_v3	.976
70	ReAlnet10	.975
71	resnet50-moclr8deg	.974
72	convnext_femto_ols:d1_in1k	.974
73	ReAlnet10_cornet	.975
74	resnet101	.972
75	resnet-18-LC_w_sh_1_iter_m	.973
76	cvt_cvt-21-224-in1k_4_LucyV4	.974
77	resnet50_linf_4_robust	.973
78	resnet152_ecoset_full	.973
79	cvt_cvt-13-384-in1k_4_LucyV4	.972
80	CLIP_ViT-B_32	.973
81	resnet-101_v1-tf	.972
82	omnivore_swinS	.973
83	ReAlnet_incorrect	.972
84	cvt_cvt-13-224-in1k_4_LucyV4	.971
85	alexnet_random_l2_3_perturb	.972
86	ReAlnet01_cornet	.971
87	resnet50-simclr-vissl	.972
88	ReAlnet01	.970
89	ReAlnet04_cornet	.970
90	0.5x_resnet-18	.970
91	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.969
92	ReAlnet04	.971
93	ReAlnet03_cornet	.970
94	resnet50_random_l2_perturb	.969
95	ReAlnet03	.969
96	resnet101_imagenet_full	.968
97	mobilenet_v2_1_4_224	.969
98	vit_large_patch14_clip_336:laion2b_ft_in1k	.967
99	barlow-twins-resnet50	.967
100	convnext_base_imagenet_full_seed-0	.968
101	vonegrcnn_52e_full	.968
102	ReAlnet09	.967
103	resnet50_l2_3_robust	.967
104	deit_base_imagenet_full_seed-0	.967
105	resnet50-meshes-lt-100-original-scratch	.966
106	ReAlnet09_cornet	.965
107	densenet-201	.965
108	CLIP-RN50	.965
109	resnet-50_v2-tf	.966
110	alexnet_early_checkpoint	.966
111	AlexNet_SIN	.965
112	resnet50-vitoimagevidnet8	.964
113	resnet-50_v1-tf	.964
114	resnet50_byol	.965
115	vision_transformer_vit_large_patch16_224	.963
116	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.963
117	convnext_base:clip_laiona_augreg_ft_in1k_384	.963
118	ReAlnet07	.964
119	r3m_resnet34_nocrop	.963
120	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.963
121	ReAlnet07_cornet	.963
122	vonegrcnn_62e_nobn	.963
123	vonealexnet_gaussian_noise_std4_fixed	.963
124	CORnet-S	.962
125	vgg-19-keras	.963
126	ReAlnet05	.963
127	ReAlnet05_cornet	.963
128	resnet-18-LC_w_sh_1_iter_conv_init_m	.962
129	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.960
130	ReAlnet08_cornet	.960
131	mobilenet_v2_1_0_224	.961
132	ReAlnet08	.960
133	imagenet_l2_3_0	.960
134	efficientnet_b1_imagenet_full	.959
135	BiT-S-R101x1	.959
136	resnet-101_v2-tf	.960
137	cvt_cvt-21-384-in1k_4_LucyV4	.960
138	grcnn_robust_v1	.958
139	resnet152_imagenet_full	.959
140	BiT-S-R50x1	.957
141	grcnn_v2_text_noise	.956
142	resnet-18_test_m	.956
143	resnet34_ecoset_full	.956
144	FrankRobWobv0	.954
145	resnext101_32x8d_wsl	.955
146	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.955
147	resnet50_moco_v2	.954
148	mobilenet_v2_0_75_160	.954
149	BiT-S-R50x3	.955
150	CORnet-Z	.954
151	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.954
152	BiT-S-R152x2	.953
153	omnivore_swinT	.954
154	cornet_s	.952
155	AdvProp_efficientnet-b7	.951
156	yudixie_resnet50_translation_reg_0_240908	.951
157	vgg_16	.950
158	cv_18_dagger_408_pretrained	.950
159	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.951
160	texture_shape_resnet50_trained_on_SIN	.950
161	CLIP_resnet50	.949
162	mobilenet_v2_1.0_192-tf	.948
163	convnext_large_imagenet_full_seed-0	.950
164	CLIP_resnet50_float32	.948
165	resnet50_robust_l2_eps3	.949
166	alexnet_reduced_aliasing_early_checkpoint	.947
167	resnet-18-LC_w_sh_100_iter_conv_init_m	.948
168	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.947
169	AdvProp_efficientnet-b8	.946
170	convnext_large:fb_in22k_ft_in1k	.947
171	densenet-169	.945
172	ViT_L_32_imagenet1k	.945
173	yudixie_resnet18_imagenet1kpret_0_240719	.943
174	blt_vs	.943
175	custom_model_cv_18_dagger_408	.943
176	inception_resnet_v2	.941
177	yudixie_resnet18_translation_rotation_0_240719	.941
178	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.940
179	effnetv2m_custom384	.939
180	resnet-152_v2-tf	.940
181	BiT-S-R152x4	.938
182	alexnet_training_seed_09	.938
183	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.939
184	resnet50_imagenet_100_seed-0	.938
185	BiT-S-R101x3	.938
186	yudixie_resnet50_distance_reg_0_240908	.936
187	mobilenet_v2_1.3_224-tf	.935
188	resnet50_simclr	.935
189	densenet-121-keras	.934
190	resnet50_FractalDB	.934
191	resnet50_imagenet_full	.933
192	grcnn	.933
193	grcnn_109	.933
194	yudixie_resnet50_random_0_240908	.932
195	convnext_small_imagenet_100_seed-0	.932
196	alexnet	.931
197	nasnet_mobile-tf	.931
198	resnet_101_v1	.931
199	alexnet	.931
200	vonegrcnn_52e_full	.930
201	alexnet	.930
202	alexnet	.931
203	densenet-169-keras	.931
204	alexnet	.931
205	alexnet	.931
206	texture_shape_alexnet_trained_on_SIN	.930
207	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.931
208	alexnet-baseline	.931
209	yudixie_resnet50_distance_rotation_0_240908	.930
210	alexnet	.929
211	effnetb1_272x240	.930
212	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.930
213	mobilenet_v2_0_75_224	.929
214	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.928
215	convnext_small_imagenet_1_seed-0	.928
216	0.5x_resnet-18_LC_w_sh_10_iter	.928
217	konkle_alexnetgn_ipcl_ref01_primary_model	.927
218	yudixie_resnet50_distance_translation_0_240908	.927
219	xception	.926
220	resnet50-SIN_IN_IN	.927
221	resnet-18-LC_d_w_sh_1x1_conv_init_m	.925
222	BiT-M-R101x3	.925
223	BiT-M-R50x1	.924
224	convnext_xlarge:fb_in22k_ft_in1k	.923
225	densenet-201-keras	.923
226	RN50	.923
227	my-model	.922
228	0.5x_resnet-18_LC_w_sh_1_iter	.921
229	alexnet_training_seed_02	.921
230	yudixie_resnet50_translation_rotation_0_240908	.922
231	yudixie_resnet18_distance_translation_0_240719	.921
232	resnet50-SIN	.921
233	convnext_small_imagenet_10_seed-0	.918
234	efficientnet_b0_imagenet_full	.918
235	yudixie_resnet18_translation_reg_0_240719	.916
236	BiT-M-R101x1	.917
237	alexnet_random_linf8_perturb	.916
238	resnet50-VITO-8deg-cc	.913
239	densenet-121	.913
240	nasnet_mobile	.911
241	Res2Net50_26w_4s	.911
242	resnet-18-LC_m	.911
243	convnext_tiny:in12k_ft_in1k	.910
244	mobilenet_v2_0_5_192	.908
245	AdvProp_efficientnet-b6	.907
246	resnet-50_untrained	.907
247	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.905
248	mobilenet_v2_1.0_160-tf	.905
249	alexnet_training_seed_05	.904
250	0.5x_resnet-18_LC	.901
251	mobilenet_v2_1_0_160	.900
252	resnet50-cifar	.900
253	resnet-18-LC_untrained	.899
254	convnext_small_imagenet_full_seed-0	.899
255	resnet-152_v1-tf	.897
256	resnet-18-LC_w_sh_100_iter_m	.896
257	focalnet_tiny_lrf_in1k	.895
258	inception_v1-tf	.894
259	efficientnet_b0	.893
260	resnet-18-LC_conv_init_m	.893
261	BiT-M-R152x4	.892
262	resnet-18-LC_w_sh_10_iter_m	.891
263	resnet-18_untrained	.890
264	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.891
265	vggface	.889
266	ViT-B/32	.886
267	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.885
268	CORnetZ_CIFAR10_bs32_20_04	.885
269	efficientnet-b7	.884
270	CORnetZ_CIFAR10	.883
271	artResNet18_1	.882
272	r3m_resnet34	.882
273	shufflenet_v2_x1_0	.880
274	yudixie_resnet18_category_class_0_240719	.879
275	mobilenet_v2_0.35_224	.877
276	r3m_resnet18	.876
277	yudixie_resnet50_distance_translation_rotation_0_240908	.874
278	yudixie_resnet18_rotation_reg_0_240719	.872
279	resnet-18-LC_1st_conv_m	.868
280	mobilenet_v2_1-4_224_pytorch	.867
281	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.868
282	resnet50	.867
283	resnet18_imagenet21kP	.868
284	yudixie_resnet50_category_class_0_240908	.859
285	mobilenet_v2_0_5_224	.857
286	mobilenet_v2_1.0_224-tf	.852
287	yudixie_resnet18_object_class_0_240719	.850
288	yudixie_resnet18_distance_reg_0_240719	.849
289	mobilenet_v2_1.4_224-tf	.848
290	efficientnet_b2_imagenet_full	.839
291	resnet-152_v2_pytorch	.832
292	mobilenet_v2_1.0_128-tf	.831
293	BiT-M-R152x2	.831
294	resnet-50x2_untrained	.832
295	alexnet_training_seed_04	.828
296	yudixie_resnet18_distance_rotation_0_240719	.822
297	alexnet_training_seed_01	.821
298	mobilenet_v2_1_0_128	.813
299	alexnet_training_seed_07	.812
300	mobilenet_v1_0.25_128	.812
301	yudixie_resnet18_random_0_240719	.809
302	r3m_resnet50_nocrop	.808
303	mobilenet_v2_0.5_224-tf	.803
304	r3m_resnet50	.803
305	alexnet_training_seed_08	.801
306	0.5x_resnet-18_LC_w_sh_100_iter	.793
307	resnet50_imagenet_10_seed-0	.786
308	mobilevit_small	.786
309	alexnet_training_seed_10	.786
310	convnext_tiny_sup	.784
311	AdvProp_efficientnet-b4	.780
312	convnext_tiny_imagenet_full_seed-0	.778
313	mobilenet_v2_0.5_192-tf	.774
314	yudixie_resnet18_distance_translation_rotation_0_240719	.771
315	deit_large_imagenet_full_seed-0	.769
316	AdvProp_efficientnet-b0	.768
317	resnet50-meshes-lt-100-original-pretrained	.752
318	mobilenet_v2_0.75_96	.745
319	resnet50_imagenet_1_seed-0	.735
320	resnet50_pretrained_with_retinal_waves	.731
321	mobilenet_v2_0.75_192-tf	.724
322	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.721
323	mobilenet_v2_1.0_96	.720
324	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.720
325	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.720
326	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.719
327	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.719
328	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.716
329	mobilenet_v2_0.75_128	.713
330	AT_efficientnet-b2	.697
331	mobilenet_v2_0.75_224-tf	.697
332	AdvProp_efficientnet-b2	.695
333	mobilenet_v2_0.5_160	.677
334	my-model	.674
335	alexnet_training_seed_03	.674
336	alexnet_training_seed_06	.649
337	deit_small_imagenet_full_seed-0	.354
338	deit_small_imagenet_10_seed-0	.355
339	mobilenet_v2_0.75_160-tf	.064
340	mobilenet_v2_0.5_128	.000
341	nasnet_large	X
342	fitvid_trained_on_physion	X
343	densenet_201_pytorch	X
344	vonegrcnn_47e	X
345	pnasnet_large_pytorch	X
346	deit_small_imagenet_1_seed-0	X
347	resnext101_32x16d_wsl	X
348	inception_v3_pytorch	X
349	resnet18-supervised	X
350	regnet_y_400mf	X
351	resnet50_ImageNet	X
352	resnext101_32x48d_wsl	X
353	inception_v4	X
354	mobilenet_v2_0.35_160	X
355	resnet_152_v1	X
356	resnext101_32x32d_wsl	X
357	tv_efficientnet-b1	X
358	openclip	X
359	yudixie_resnet50_imagenet1kpret_0_240312	X
360	resnet_152_v2	X
361	alexnet_ks_torevert	X
362	pnasnet_large	X
363	vonegrcnn_62e_nobn	X

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_DeValois1982

Metric: pref_or