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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	ReAlnet01_ALL	.978
55	resnet18_imagenet_full	.980
56	alexnet_linf_8_robust	.980
57	omnivore_swinB	.980
58	resnet_50_v1_spiking_l3	.978
59	ReAlnet02	.979
60	yudixie_resnet50_imagenet1kpret_0_240908	.978
61	resnet_50_v1_spiking_l4a	.978
62	resnet_101_v2	.979
63	resnet_50_v1	.979
64	ReAlnet02_cornet	.979
65	resnet_50_v1_spiking_l4	.979
66	resnet50_tutorial	.977
67	resnet_50_v1_spiking	.978
68	yudixie_resnet50_rotation_reg_0_240908	.978
69	alexnet_robust_correct	.976
70	antialias-resnet152	.977
71	mobilenet_v2_1_3_224	.977
72	vonegrcnn_47e	.976
73	ReAlnet09_ALL	.977
74	antialiased-rnext101_32x8d	.974
75	inception_v3	.976
76	ReAlnet02_ALL	.976
77	ReAlnet10	.975
78	resnet50-moclr8deg	.974
79	convnext_femto_ols:d1_in1k	.974
80	ReAlnet10_cornet	.975
81	resnet101	.972
82	resnet-18-LC_w_sh_1_iter_m	.973
83	cvt_cvt-21-224-in1k_4_LucyV4	.974
84	resnet50_linf_4_robust	.973
85	resnet152_ecoset_full	.973
86	cvt_cvt-13-384-in1k_4_LucyV4	.972
87	CLIP_ViT-B_32	.973
88	resnet-101_v1-tf	.972
89	omnivore_swinS	.973
90	cvt_cvt-13-224-in1k_4_LucyV4	.971
91	alexnet_random_l2_3_perturb	.972
92	ReAlnet01_cornet	.971
93	ReAlnet08_ALL	.970
94	resnet50-simclr-vissl	.972
95	ReAlnet01	.970
96	ReAlnet04_ALL	.972
97	ReAlnet04_cornet	.970
98	0.5x_resnet-18	.970
99	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.969
100	ReAlnet04	.971
101	ReAlnet03_cornet	.970
102	resnet50_random_l2_perturb	.969
103	ReAlnet10_ALL	.970
104	ReAlnet03	.969
105	resnet101_imagenet_full	.968
106	mobilenet_v2_1_4_224	.969
107	vit_large_patch14_clip_336:laion2b_ft_in1k	.967
108	barlow-twins-resnet50	.967
109	convnext_base_imagenet_full_seed-0	.968
110	vonegrcnn_52e_full	.968
111	ReAlnet09	.967
112	resnet50_l2_3_robust	.967
113	deit_base_imagenet_full_seed-0	.967
114	resnet50-meshes-lt-100-original-scratch	.966
115	ReAlnet09_cornet	.965
116	ReAlnet05_ALL	.966
117	densenet-201	.965
118	CLIP-RN50	.965
119	resnet-50_v2-tf	.966
120	alexnet_early_checkpoint	.966
121	AlexNet_SIN	.965
122	resnet50-vitoimagevidnet8	.964
123	resnet-50_v1-tf	.964
124	resnet50_byol	.965
125	vision_transformer_vit_large_patch16_224	.963
126	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.963
127	convnext_base:clip_laiona_augreg_ft_in1k_384	.963
128	ReAlnet07	.964
129	ReAlnet03_ALL	.964
130	r3m_resnet34_nocrop	.963
131	ReAlnet07_ALL	.963
132	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.963
133	ReAlnet07_cornet	.963
134	vonegrcnn_62e_nobn	.963
135	vonealexnet_gaussian_noise_std4_fixed	.963
136	CORnet-S	.962
137	vgg-19-keras	.963
138	ReAlnet05	.963
139	ReAlnet05_cornet	.963
140	resnet-18-LC_w_sh_1_iter_conv_init_m	.962
141	ReAlnet06_ALL	.959
142	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.960
143	ReAlnet08_cornet	.960
144	mobilenet_v2_1_0_224	.961
145	ReAlnet08	.960
146	imagenet_l2_3_0	.960
147	efficientnet_b1_imagenet_full	.959
148	BiT-S-R101x1	.959
149	resnet-101_v2-tf	.960
150	cvt_cvt-21-384-in1k_4_LucyV4	.960
151	grcnn_robust_v1	.958
152	resnet152_imagenet_full	.959
153	BiT-S-R50x1	.957
154	grcnn_v2_text_noise	.956
155	resnet-18_test_m	.956
156	resnet34_ecoset_full	.956
157	FrankRobWobv0	.954
158	resnext101_32x8d_wsl	.955
159	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.955
160	resnet50_moco_v2	.954
161	mobilenet_v2_0_75_160	.954
162	BiT-S-R50x3	.955
163	CORnet-Z	.954
164	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.954
165	BiT-S-R152x2	.953
166	omnivore_swinT	.954
167	cornet_s	.952
168	AdvProp_efficientnet-b7	.951
169	yudixie_resnet50_translation_reg_0_240908	.951
170	vgg_16	.950
171	cv_18_dagger_408_pretrained	.950
172	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.951
173	texture_shape_resnet50_trained_on_SIN	.950
174	CLIP_resnet50	.949
175	mobilenet_v2_1.0_192-tf	.948
176	convnext_large_imagenet_full_seed-0	.950
177	CLIP_resnet50_float32	.948
178	resnet50_robust_l2_eps3	.949
179	alexnet_reduced_aliasing_early_checkpoint	.947
180	resnet-18-LC_w_sh_100_iter_conv_init_m	.948
181	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.947
182	AdvProp_efficientnet-b8	.946
183	convnext_large:fb_in22k_ft_in1k	.947
184	densenet-169	.945
185	ViT_L_32_imagenet1k	.945
186	yudixie_resnet18_imagenet1kpret_0_240719	.943
187	blt_vs	.943
188	custom_model_cv_18_dagger_408	.943
189	inception_resnet_v2	.941
190	yudixie_resnet18_translation_rotation_0_240719	.941
191	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.940
192	effnetv2m_custom384	.939
193	resnet50_primary_visual_cortex	.940
194	resnet-152_v2-tf	.940
195	BiT-S-R152x4	.938
196	alexnet_training_seed_09	.938
197	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.939
198	resnet50_imagenet_100_seed-0	.938
199	BiT-S-R101x3	.938
200	yudixie_resnet50_distance_reg_0_240908	.936
201	mobilenet_v2_1.3_224-tf	.935
202	resnet50_simclr	.935
203	densenet-121-keras	.934
204	resnet50_FractalDB	.934
205	resnet50_imagenet_full	.933
206	grcnn	.933
207	grcnn_109	.933
208	yudixie_resnet50_random_0_240908	.932
209	convnext_small_imagenet_100_seed-0	.932
210	alexnet	.931
211	nasnet_mobile-tf	.931
212	resnet_101_v1	.931
213	alexnet	.931
214	vonegrcnn_52e_full	.930
215	alexnet	.931
216	alexnet	.930
217	densenet-169-keras	.931
218	alexnet	.931
219	alexnet	.931
220	texture_shape_alexnet_trained_on_SIN	.930
221	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.931
222	alexnet-baseline	.931
223	yudixie_resnet50_distance_rotation_0_240908	.930
224	alexnet	.929
225	effnetb1_272x240	.930
226	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.930
227	mobilenet_v2_0_75_224	.929
228	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.928
229	convnext_small_imagenet_1_seed-0	.928
230	0.5x_resnet-18_LC_w_sh_10_iter	.928
231	konkle_alexnetgn_ipcl_ref01_primary_model	.927
232	yudixie_resnet50_distance_translation_0_240908	.927
233	xception	.926
234	resnet50-SIN_IN_IN	.927
235	resnet-18-LC_d_w_sh_1x1_conv_init_m	.925
236	BiT-M-R101x3	.925
237	BiT-M-R50x1	.924
238	convnext_xlarge:fb_in22k_ft_in1k	.923
239	densenet-201-keras	.923
240	RN50	.923
241	my-model	.922
242	0.5x_resnet-18_LC_w_sh_1_iter	.921
243	alexnet_training_seed_02	.921
244	yudixie_resnet50_translation_rotation_0_240908	.922
245	yudixie_resnet18_distance_translation_0_240719	.921
246	resnet50-SIN	.921
247	convnext_small_imagenet_10_seed-0	.918
248	efficientnet_b0_imagenet_full	.918
249	yudixie_resnet18_translation_reg_0_240719	.916
250	BiT-M-R101x1	.917
251	alexnet_random_linf8_perturb	.916
252	resnet50-VITO-8deg-cc	.913
253	densenet-121	.913
254	nasnet_mobile	.911
255	Res2Net50_26w_4s	.911
256	resnet-18-LC_m	.911
257	convnext_tiny:in12k_ft_in1k	.910
258	mobilenet_v2_0_5_192	.908
259	AdvProp_efficientnet-b6	.907
260	resnet-50_untrained	.907
261	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.905
262	mobilenet_v2_1.0_160-tf	.905
263	alexnet_training_seed_05	.904
264	0.5x_resnet-18_LC	.901
265	mobilenet_v2_1_0_160	.900
266	resnet50-cifar	.900
267	resnet-18-LC_untrained	.899
268	convnext_small_imagenet_full_seed-0	.899
269	resnet-152_v1-tf	.897
270	resnet-18-LC_w_sh_100_iter_m	.896
271	focalnet_tiny_lrf_in1k	.895
272	inception_v1-tf	.894
273	efficientnet_b0	.893
274	resnet-18-LC_conv_init_m	.893
275	yolos_tiny	.894
276	BiT-M-R152x4	.892
277	resnet-18-LC_w_sh_10_iter_m	.891
278	resnet-18_untrained	.890
279	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.891
280	vggface	.889
281	ViT-B/32	.886
282	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.885
283	CORnetZ_CIFAR10_bs32_20_04	.885
284	efficientnet-b7	.884
285	CORnetZ_CIFAR10	.883
286	artResNet18_1	.882
287	r3m_resnet34	.882
288	shufflenet_v2_x1_0	.880
289	yudixie_resnet18_category_class_0_240719	.879
290	mobilenet_v2_0.35_224	.877
291	r3m_resnet18	.876
292	yudixie_resnet50_distance_translation_rotation_0_240908	.874
293	yudixie_resnet18_rotation_reg_0_240719	.872
294	resnet-18-LC_1st_conv_m	.868
295	mobilenet_v2_1-4_224_pytorch	.867
296	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.868
297	resnet50	.867
298	resnet18_imagenet21kP	.868
299	yudixie_resnet50_category_class_0_240908	.859
300	mobilenet_v2_0_5_224	.857
301	mobilenet_v2_1.0_224-tf	.852
302	yudixie_resnet18_object_class_0_240719	.850
303	yudixie_resnet18_distance_reg_0_240719	.849
304	mobilenet_v2_1.4_224-tf	.848
305	efficientnet_b2_imagenet_full	.839
306	briaai_rmbg_1_4	.833
307	resnet-152_v2_pytorch	.832
308	mobilenet_v2_1.0_128-tf	.831
309	BiT-M-R152x2	.831
310	resnet-50x2_untrained	.832
311	alexnet_training_seed_04	.828
312	yudixie_resnet18_distance_rotation_0_240719	.822
313	alexnet_training_seed_01	.821
314	mobilenet_v2_1_0_128	.813
315	alexnet_training_seed_07	.812
316	mobilenet_v1_0.25_128	.812
317	yudixie_resnet18_random_0_240719	.809
318	r3m_resnet50_nocrop	.808
319	mobilenet_v2_0.5_224-tf	.803
320	r3m_resnet50	.803
321	alexnet_training_seed_08	.801
322	0.5x_resnet-18_LC_w_sh_100_iter	.793
323	resnet50_imagenet_10_seed-0	.786
324	mobilevit_small	.786
325	alexnet_training_seed_10	.786
326	convnext_tiny_sup	.784
327	AdvProp_efficientnet-b4	.780
328	convnext_tiny_imagenet_full_seed-0	.778
329	mobilenet_v2_0.5_192-tf	.774
330	yudixie_resnet18_distance_translation_rotation_0_240719	.771
331	deit_large_imagenet_full_seed-0	.769
332	AdvProp_efficientnet-b0	.768
333	resnet50-meshes-lt-100-original-pretrained	.752
334	mobilenet_v2_0.75_96	.745
335	resnet50_imagenet_1_seed-0	.735
336	resnet50_pretrained_with_retinal_waves	.731
337	mobilenet_v2_0.75_192-tf	.724
338	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.721
339	mobilenet_v2_1.0_96	.720
340	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.720
341	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.720
342	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.719
343	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.719
344	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.716
345	mobilenet_v2_0.75_128	.713
346	AT_efficientnet-b2	.697
347	mobilenet_v2_0.75_224-tf	.697
348	AdvProp_efficientnet-b2	.695
349	mobilenet_v2_0.5_160	.677
350	my-model	.674
351	alexnet_training_seed_03	.674
352	alexnet_training_seed_06	.649
353	deit_small_imagenet_10_seed-0	.355
354	deit_small_imagenet_full_seed-0	.354
355	mobilenet_v2_0.75_160-tf	.064
356	mobilenet_v2_0.5_128	.000
357	mobilenet_v2_0.35_160
358	resnet18-supervised
359	fitvid_trained_on_physion
360	openclip
361	resnext101_32x32d_wsl
362	resnext101_32x48d_wsl
363	resnet50_ImageNet
364	regnet_y_400mf
365	tv_efficientnet-b1
366	alexnet_ks_torevert
367	yudixie_resnet50_imagenet1kpret_0_240312
368	resnet_152_v1
369	resnext101_32x16d_wsl
370	densenet_201_pytorch
371	pnasnet_large_pytorch
372	inception_v3_pytorch
373	inception_v4
374	deit_small_imagenet_1_seed-0
375	nasnet_large
376	pnasnet_large
377	vonegrcnn_62e_nobn
378	vonegrcnn_47e
379	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_DeValois1982

Metric: pref_or