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	resnet_50_v1_spiking_l3	.978
58	ReAlnet02	.979
59	yudixie_resnet50_imagenet1kpret_0_240908	.978
60	resnet_50_v1_spiking_l4a	.978
61	resnet_101_v2	.979
62	resnet_50_v1	.979
63	ReAlnet02_cornet	.979
64	resnet_50_v1_spiking_l4	.979
65	resnet50_tutorial	.977
66	resnet_50_v1_spiking	.978
67	yudixie_resnet50_rotation_reg_0_240908	.978
68	alexnet_robust_correct	.976
69	antialias-resnet152	.977
70	mobilenet_v2_1_3_224	.977
71	vonegrcnn_47e	.976
72	antialiased-rnext101_32x8d	.974
73	inception_v3	.976
74	ReAlnet10	.975
75	resnet50-moclr8deg	.974
76	convnext_femto_ols:d1_in1k	.974
77	ReAlnet10_cornet	.975
78	resnet101	.972
79	resnet-18-LC_w_sh_1_iter_m	.973
80	cvt_cvt-21-224-in1k_4_LucyV4	.974
81	resnet50_linf_4_robust	.973
82	resnet152_ecoset_full	.973
83	cvt_cvt-13-384-in1k_4_LucyV4	.972
84	CLIP_ViT-B_32	.973
85	resnet-101_v1-tf	.972
86	omnivore_swinS	.973
87	cvt_cvt-13-224-in1k_4_LucyV4	.971
88	alexnet_random_l2_3_perturb	.972
89	ReAlnet01_cornet	.971
90	resnet50-simclr-vissl	.972
91	ReAlnet01	.970
92	ReAlnet04_cornet	.970
93	0.5x_resnet-18	.970
94	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.969
95	ReAlnet04	.971
96	ReAlnet03_cornet	.970
97	resnet50_random_l2_perturb	.969
98	ReAlnet03	.969
99	resnet101_imagenet_full	.968
100	mobilenet_v2_1_4_224	.969
101	vit_large_patch14_clip_336:laion2b_ft_in1k	.967
102	barlow-twins-resnet50	.967
103	convnext_base_imagenet_full_seed-0	.968
104	vonegrcnn_52e_full	.968
105	ReAlnet09	.967
106	resnet50_l2_3_robust	.967
107	deit_base_imagenet_full_seed-0	.967
108	resnet50-meshes-lt-100-original-scratch	.966
109	ReAlnet09_cornet	.965
110	densenet-201	.965
111	CLIP-RN50	.965
112	resnet-50_v2-tf	.966
113	alexnet_early_checkpoint	.966
114	AlexNet_SIN	.965
115	resnet50-vitoimagevidnet8	.964
116	resnet-50_v1-tf	.964
117	resnet50_byol	.965
118	vision_transformer_vit_large_patch16_224	.963
119	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.963
120	convnext_base:clip_laiona_augreg_ft_in1k_384	.963
121	ReAlnet07	.964
122	r3m_resnet34_nocrop	.963
123	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.963
124	ReAlnet07_cornet	.963
125	vonegrcnn_62e_nobn	.963
126	vonealexnet_gaussian_noise_std4_fixed	.963
127	CORnet-S	.962
128	vgg-19-keras	.963
129	ReAlnet05	.963
130	ReAlnet05_cornet	.963
131	resnet-18-LC_w_sh_1_iter_conv_init_m	.962
132	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.960
133	ReAlnet08_cornet	.960
134	mobilenet_v2_1_0_224	.961
135	ReAlnet08	.960
136	imagenet_l2_3_0	.960
137	efficientnet_b1_imagenet_full	.959
138	BiT-S-R101x1	.959
139	resnet-101_v2-tf	.960
140	cvt_cvt-21-384-in1k_4_LucyV4	.960
141	grcnn_robust_v1	.958
142	resnet152_imagenet_full	.959
143	BiT-S-R50x1	.957
144	grcnn_v2_text_noise	.956
145	resnet-18_test_m	.956
146	resnet34_ecoset_full	.956
147	FrankRobWobv0	.954
148	resnext101_32x8d_wsl	.955
149	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.955
150	resnet50_moco_v2	.954
151	mobilenet_v2_0_75_160	.954
152	BiT-S-R50x3	.955
153	CORnet-Z	.954
154	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.954
155	BiT-S-R152x2	.953
156	omnivore_swinT	.954
157	cornet_s	.952
158	AdvProp_efficientnet-b7	.951
159	yudixie_resnet50_translation_reg_0_240908	.951
160	vgg_16	.950
161	cv_18_dagger_408_pretrained	.950
162	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.951
163	texture_shape_resnet50_trained_on_SIN	.950
164	CLIP_resnet50	.949
165	mobilenet_v2_1.0_192-tf	.948
166	convnext_large_imagenet_full_seed-0	.950
167	CLIP_resnet50_float32	.948
168	resnet50_robust_l2_eps3	.949
169	alexnet_reduced_aliasing_early_checkpoint	.947
170	resnet-18-LC_w_sh_100_iter_conv_init_m	.948
171	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.947
172	AdvProp_efficientnet-b8	.946
173	convnext_large:fb_in22k_ft_in1k	.947
174	densenet-169	.945
175	ViT_L_32_imagenet1k	.945
176	yudixie_resnet18_imagenet1kpret_0_240719	.943
177	blt_vs	.943
178	custom_model_cv_18_dagger_408	.943
179	inception_resnet_v2	.941
180	yudixie_resnet18_translation_rotation_0_240719	.941
181	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.940
182	effnetv2m_custom384	.939
183	resnet50_primary_visual_cortex	.940
184	resnet-152_v2-tf	.940
185	BiT-S-R152x4	.938
186	alexnet_training_seed_09	.938
187	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.939
188	resnet50_imagenet_100_seed-0	.938
189	BiT-S-R101x3	.938
190	yudixie_resnet50_distance_reg_0_240908	.936
191	mobilenet_v2_1.3_224-tf	.935
192	resnet50_simclr	.935
193	resnet50_FractalDB	.934
194	densenet-121-keras	.934
195	resnet50_imagenet_full	.933
196	grcnn	.933
197	grcnn_109	.933
198	yudixie_resnet50_random_0_240908	.932
199	convnext_small_imagenet_100_seed-0	.932
200	alexnet	.931
201	nasnet_mobile-tf	.931
202	resnet_101_v1	.931
203	alexnet	.931
204	vonegrcnn_52e_full	.930
205	alexnet	.931
206	alexnet	.930
207	densenet-169-keras	.931
208	alexnet	.931
209	alexnet	.931
210	texture_shape_alexnet_trained_on_SIN	.930
211	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.931
212	alexnet-baseline	.931
213	yudixie_resnet50_distance_rotation_0_240908	.930
214	alexnet	.929
215	effnetb1_272x240	.930
216	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.930
217	mobilenet_v2_0_75_224	.929
218	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.928
219	convnext_small_imagenet_1_seed-0	.928
220	0.5x_resnet-18_LC_w_sh_10_iter	.928
221	konkle_alexnetgn_ipcl_ref01_primary_model	.927
222	yudixie_resnet50_distance_translation_0_240908	.927
223	xception	.926
224	resnet50-SIN_IN_IN	.927
225	resnet-18-LC_d_w_sh_1x1_conv_init_m	.925
226	BiT-M-R101x3	.925
227	BiT-M-R50x1	.924
228	convnext_xlarge:fb_in22k_ft_in1k	.923
229	densenet-201-keras	.923
230	RN50	.923
231	my-model	.922
232	0.5x_resnet-18_LC_w_sh_1_iter	.921
233	alexnet_training_seed_02	.921
234	yudixie_resnet50_translation_rotation_0_240908	.922
235	yudixie_resnet18_distance_translation_0_240719	.921
236	resnet50-SIN	.921
237	convnext_small_imagenet_10_seed-0	.918
238	efficientnet_b0_imagenet_full	.918
239	yudixie_resnet18_translation_reg_0_240719	.916
240	BiT-M-R101x1	.917
241	alexnet_random_linf8_perturb	.916
242	resnet50-VITO-8deg-cc	.913
243	densenet-121	.913
244	nasnet_mobile	.911
245	Res2Net50_26w_4s	.911
246	resnet-18-LC_m	.911
247	convnext_tiny:in12k_ft_in1k	.910
248	mobilenet_v2_0_5_192	.908
249	AdvProp_efficientnet-b6	.907
250	resnet-50_untrained	.907
251	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.905
252	mobilenet_v2_1.0_160-tf	.905
253	alexnet_training_seed_05	.904
254	0.5x_resnet-18_LC	.901
255	mobilenet_v2_1_0_160	.900
256	resnet50-cifar	.900
257	resnet-18-LC_untrained	.899
258	convnext_small_imagenet_full_seed-0	.899
259	resnet-152_v1-tf	.897
260	resnet-18-LC_w_sh_100_iter_m	.896
261	focalnet_tiny_lrf_in1k	.895
262	inception_v1-tf	.894
263	efficientnet_b0	.893
264	resnet-18-LC_conv_init_m	.893
265	yolos_tiny	.894
266	BiT-M-R152x4	.892
267	resnet-18-LC_w_sh_10_iter_m	.891
268	resnet-18_untrained	.890
269	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.891
270	vggface	.889
271	ViT-B/32	.886
272	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.885
273	CORnetZ_CIFAR10_bs32_20_04	.885
274	efficientnet-b7	.884
275	CORnetZ_CIFAR10	.883
276	artResNet18_1	.882
277	r3m_resnet34	.882
278	shufflenet_v2_x1_0	.880
279	yudixie_resnet18_category_class_0_240719	.879
280	mobilenet_v2_0.35_224	.877
281	r3m_resnet18	.876
282	yudixie_resnet50_distance_translation_rotation_0_240908	.874
283	yudixie_resnet18_rotation_reg_0_240719	.872
284	resnet-18-LC_1st_conv_m	.868
285	mobilenet_v2_1-4_224_pytorch	.867
286	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.868
287	resnet50	.867
288	resnet18_imagenet21kP	.868
289	yudixie_resnet50_category_class_0_240908	.859
290	mobilenet_v2_0_5_224	.857
291	mobilenet_v2_1.0_224-tf	.852
292	yudixie_resnet18_object_class_0_240719	.850
293	yudixie_resnet18_distance_reg_0_240719	.849
294	mobilenet_v2_1.4_224-tf	.848
295	efficientnet_b2_imagenet_full	.839
296	briaai_rmbg_1_4	.833
297	resnet-152_v2_pytorch	.832
298	mobilenet_v2_1.0_128-tf	.831
299	BiT-M-R152x2	.831
300	resnet-50x2_untrained	.832
301	alexnet_training_seed_04	.828
302	yudixie_resnet18_distance_rotation_0_240719	.822
303	alexnet_training_seed_01	.821
304	mobilenet_v2_1_0_128	.813
305	alexnet_training_seed_07	.812
306	mobilenet_v1_0.25_128	.812
307	yudixie_resnet18_random_0_240719	.809
308	r3m_resnet50_nocrop	.808
309	mobilenet_v2_0.5_224-tf	.803
310	r3m_resnet50	.803
311	alexnet_training_seed_08	.801
312	0.5x_resnet-18_LC_w_sh_100_iter	.793
313	resnet50_imagenet_10_seed-0	.786
314	mobilevit_small	.786
315	alexnet_training_seed_10	.786
316	convnext_tiny_sup	.784
317	AdvProp_efficientnet-b4	.780
318	convnext_tiny_imagenet_full_seed-0	.778
319	mobilenet_v2_0.5_192-tf	.774
320	yudixie_resnet18_distance_translation_rotation_0_240719	.771
321	deit_large_imagenet_full_seed-0	.769
322	AdvProp_efficientnet-b0	.768
323	resnet50-meshes-lt-100-original-pretrained	.752
324	mobilenet_v2_0.75_96	.745
325	resnet50_imagenet_1_seed-0	.735
326	resnet50_pretrained_with_retinal_waves	.731
327	mobilenet_v2_0.75_192-tf	.724
328	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.721
329	mobilenet_v2_1.0_96	.720
330	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.720
331	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.720
332	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.719
333	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.719
334	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.716
335	mobilenet_v2_0.75_128	.713
336	AT_efficientnet-b2	.697
337	mobilenet_v2_0.75_224-tf	.697
338	AdvProp_efficientnet-b2	.695
339	mobilenet_v2_0.5_160	.677
340	my-model	.674
341	alexnet_training_seed_03	.674
342	alexnet_training_seed_06	.649
343	deit_small_imagenet_full_seed-0	.354
344	deit_small_imagenet_10_seed-0	.355
345	mobilenet_v2_0.75_160-tf	.064
346	mobilenet_v2_0.5_128	.000
347	densenet_201_pytorch
348	vonegrcnn_47e
349	resnet50_ImageNet
350	pnasnet_large
351	nasnet_large
352	fitvid_trained_on_physion
353	resnext101_32x48d_wsl
354	deit_small_imagenet_1_seed-0
355	pnasnet_large_pytorch
356	inception_v3_pytorch
357	alexnet_ks_torevert
358	openclip
359	tv_efficientnet-b1
360	resnet_152_v2
361	inception_v4
362	vonegrcnn_62e_nobn
363	regnet_y_400mf
364	resnet_152_v1
365	yudixie_resnet50_imagenet1kpret_0_240312
366	resnext101_32x32d_wsl
367	mobilenet_v2_0.35_160
368	resnext101_32x16d_wsl
369	resnet18-supervised

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_DeValois1982

Metric: pref_or