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

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