Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024color-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
3	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
4	AT_efficientnet-b2	1.0
5	resnet50-vicregl0p9	1.0
6	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
7	BiT-S-R101x1	1.0
8	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
9	convnext_large:fb_in22k_ft_in1k	1.0
10	BiT-S-R50x1	1.0
11	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
12	BiT-S-R152x2	1.0
13	BiT-S-R101x3	1.0
14	convnext_xlarge:fb_in22k_ft_in1k	1.0
15	BiT-S-R152x4	1.0
16	vit_large_patch14_clip_224:openai_ft_in1k	1.0
17	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
18	convnext_tiny_sup	1.0
19	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
20	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
21	AdvProp_efficientnet-b8	1.0
22	BiT-S-R50x3	1.0
23	cvt_cvt-21-384-in1k_4_LucyV4	1.0
24	resnet50_robust_l2_eps3	1.0
25	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	1.0
26	imagenet_l2_3_0	1.0
27	AdvProp_efficientnet-b2	1.0
28	cvt_cvt-21-224-in1k_4_LucyV4	1.0
29	AdvProp_efficientnet-b4	1.0
30	cvt_cvt-13-224-in1k_4_LucyV4	1.0
31	AdvProp_efficientnet-b7	1.0
32	omnivore_swinS	1.0
33	convnext_base_imagenet_full_seed-0	1.0
34	convnext_large_imagenet_full_seed-0	1.0
35	AdvProp_efficientnet-b6	1.0
36	efficientnet_b2_imagenet_full	1.0
37	focalnet_tiny_lrf_in1k	1.0
38	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
39	custom_model_cv_18_dagger_408	1.0
40	ViT_L_32_imagenet1k	1.0
41	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
42	cvt_cvt-13-384-in1k_4_LucyV4	1.0
43	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
44	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
45	deit_base_imagenet_full_seed-0	1.0
46	efficientnet_b1_imagenet_full	1.0
47	vit_base_patch16_clip_224:openai_ft_in12k_in1k	1.0
48	efficientnet_b0	1.0
49	vit_base_patch16_clip_224:openai_ft_in1k	1.0
50	omnivore_swinB	1.0
51	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
52	yudixie_resnet18_translation_rotation_0_240719	1.0
53	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
54	efficientnet-b7	1.0
55	resnet-152_v2_pytorch	1.0
56	resnet_50_v1	.968
57	resnet50-VITO-8deg-cc	.968
58	resnet50-vitoimagevidnet8	.968
59	resnet50_imagenet_100_seed-0	.968
60	voneresnet-50	.968
61	densenet-169	.968
62	resnet50-sup	.968
63	resnet_152_v2	.952
64	pnasnet_large	.952
65	inception_v4	.952
66	yolos_tiny	.952
67	mobilenet_v2_1_0_192	.952
68	voneresnet-50-non_stochastic	.952
69	yudixie_resnet18_distance_rotation_0_240719	.952
70	mobilenet_v2_0_75_160	.952
71	resnext101_32x8d_wsl	.877
72	vonegrcnn_47e	.877
73	resnet_SIN_IN_FT_IN	.877
74	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.877
75	resnet50-SIN_IN_IN	.877
76	vonegrcnn_62e_nobn	.877
77	grcnn_109	.877
78	deit_large_imagenet_full_seed-0	.877
79	efficientnet_b0_imagenet_full	.877
80	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.877
81	ReAlnet10	.877
82	ReAlnet09	.877
83	ReAlnet09_cornet	.877
84	ReAlnet10_cornet	.877
85	blt_vs	.862
86	convnext_small_imagenet_full_seed-0	.862
87	voneresnet-50-robust	.862
88	inception_v3	.862
89	resnet50_imagenet_full	.862
90	resnet-18_test_m	.862
91	mobilenet_v2_0_75_224	.862
92	ReAlnet02_cornet	.794
93	resnet_50_v1_spiking_l3	.794
94	resnet50-vicreg	.794
95	grcnn	.794
96	effnetb1_272x240	.794
97	resnet34_imagenet_full	.794
98	ReAlnet02	.794
99	nasnet_large	.794
100	densenet-201	.780
101	mobilenet_v2_0_5_224	.780
102	resnet50_imagenet_1_seed-0	.780
103	deit_small_imagenet_full_seed-0	.719
104	resnext101_32x48d_wsl	.719
105	convnext_tiny_imagenet_full_seed-0	.719
106	resnet_152_v1	.719
107	resnext101_32x32d_wsl	.719
108	yudixie_resnet50_distance_rotation_0_240908	.706
109	convnext_small_imagenet_100_seed-0	.706
110	resnet_101_v2	.706
111	convnext_femto_ols:d1_in1k	.706
112	vonegrcnn_52e_full	.650
113	resnet50_robust_l2_eps1	.650
114	resnet152_imagenet_full	.650
115	yudixie_resnet50_random_0_240908	.650
116	mobilenet_v2_1_0_128	.640
117	antialiased-r50	.640
118	ReAlnet05	.640
119	ReAlnet05_cornet	.640
120	Res2Net50_26w_4s	.589
121	yudixie_resnet18_category_class_0_240719	.589
122	mobilenet_v2_1_3_224	.589
123	inception_v1	.589
124	convnext_tiny:in12k_ft_in1k	.589
125	resnet_50_v2	.579
126	yudixie_resnet50_imagenet1kpret_0_240908	.579
127	resnet_50_v1_spiking_l4a	.579
128	resnet34_ecoset_full	.579
129	yudixie_resnet18_imagenet1kpret_0_240719	.579
130	pnasnet_large_pytorch	.533
131	yudixie_resnet50_distance_translation_rotation_0_240908	.524
132	yudixie_resnet50_distance_reg_0_240908	.483
133	deit_small_imagenet_100_seed-0	.483
134	resnet101_imagenet_full	.483
135	ReAlnet08_cornet	.483
136	ReAlnet08	.483
137	mobilenet_v2_0_5_192	.474
138	ReAlnet03_cornet	.437
139	ReAlnet03	.437
140	resnet50-vicregl0p75	.437
141	resnet50-moclr8deg	.437
142	xception	.437
143	nasnet_mobile	.430
144	yudixie_resnet50_distance_translation_0_240908	.430
145	mobilenet_v2_1_4_224	.430
146	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.430
147	mobilenet_v2_1-4_224_pytorch	.430
148	resnet18_imagenet_full	.396
149	shufflenet_v2_x1_0	.396
150	vgg_16	.396
151	deit_small_imagenet_10_seed-0	.396
152	yudixie_resnet50_translation_rotation_0_240908	.389
153	resnet18_ecoset_full	.389
154	yudixie_resnet50_object_class_0_240908	.389
155	alexnet_training_seed_04	.358
156	ReAlnet07	.358
157	ReAlnet04_cornet	.358
158	ReAlnet04	.358
159	yudixie_resnet18_object_class_0_240719	.358
160	mobilenet_v2_1_0_224	.358
161	ReAlnet07_cornet	.358
162	yudixie_resnet18_distance_translation_rotation_0_240719	.358
163	resnet50-SIN_IN	.352
164	resnet_101_v1	.352
165	resnet_50_v1_spiking	.352
166	alexnet_training_seed_06	.324
167	resnet50_ecoset_full	.324
168	convnext_small_imagenet_10_seed-0	.319
169	yudixie_resnet50_rotation_reg_0_240908	.319
170	resnet50-SIN	.293
171	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.293
172	ReAlnet06	.293
173	ReAlnet06_cornet	.293
174	yudixie_resnet18_rotation_reg_0_240719	.266
175	densenet-121	.266
176	resnet50-barlow	.266
177	resnet_50_v1_spiking_l4	.266
178	yudixie_resnet50_translation_reg_0_240908	.266
179	VOneCORnet-S	.261
180	mobilenet_v2_0_75_192	.261
181	resnet50_tutorial	.261
182	yudixie_resnet18_distance_translation_0_240719	.261
183	resnet50_imagenet_10_seed-0	.240
184	yudixie_resnet18_distance_reg_0_240719	.236
185	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.236
186	resnet152_ecoset_full	.236
187	resnet101_ecoset_full	.218
188	resnet-50-robust	.218
189	ReAlnet01	.218
190	ReAlnet01_cornet	.218
191	yudixie_resnet50_category_class_0_240908	.214
192	alexnet_training_seed_07	.178
193	mobilenet_v2_1_0_160	.175
194	convnext_small_imagenet_1_seed-0	.175
195	yudixie_resnet18_translation_reg_0_240719	.159
196	resnet50_primary_visual_cortex	.159
197	deit_small_imagenet_1_seed-0	.159
198	alexnet_training_seed_01	.146
199	alexnet_training_seed_05	.146
200	yudixie_resnet18_random_0_240719	.146
201	alexnet_training_seed_08	.098
202	vgg_19	.098
203	alexnet_training_seed_02	.089
204	artResNet18_1	.089
205	alexnet_training_seed_09	.066
206	briaai_rmbg_1_4	.060
207	alexnet_training_seed_03	.022
208	hmax
209	alexnet_training_seed_10

Benchmark bibtex

        @misc{ferguson_ngo_lee_dicarlo_schrimpf_2024,
         title={How Well is Visual Search Asymmetry predicted by a Binary-Choice, Rapid, Accuracy-based Visual-search, Oddball-detection (BRAVO) task?},
         url={osf.io/5ba3n},
         DOI={10.17605/OSF.IO/5BA3N},
         publisher={OSF},
         author={Ferguson, Michael E, Jr and Ngo, Jerry and Lee, Michael and DiCarlo, James and Schrimpf, Martin},
         year={2024},
         month={Jun}
}

Ceiling

0.90.

Note that scores are relative to this ceiling.

Data: Ferguson2024color

Metric: value_delta