Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024gray_easy-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50_robust_l2_eps1	1.0
2	resnet_101_v2	1.0
3	BiT-S-R50x3	1.0
4	densenet-121	1.0
5	custom_model_cv_18_dagger_408	1.0
6	mobilenet_v2_1_3_224	.973
7	yudixie_resnet50_translation_rotation_0_240908	.973
8	resnet50-vitoimagevidnet8	.973
9	resnet50-VITO-8deg-cc	.973
10	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.882
11	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
12	nasnet_large	.882
13	resnet152_imagenet_full	.882
14	resnet50-moclr8deg	.882
15	BiT-S-R152x2	.882
16	deit_small_imagenet_full_seed-0	.882
17	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.845
18	resnet101_ecoset_full	.845
19	vit_large_patch14_clip_224:laion2b_ft_in1k	.845
20	cvt_cvt-21-384-in1k_4_LucyV4	.845
21	convnext_large:fb_in22k_ft_in1k	.845
22	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.845
23	BiT-S-R152x4	.845
24	AdvProp_efficientnet-b8	.845
25	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.845
26	deit_large_imagenet_full_seed-0	.845
27	AdvProp_efficientnet-b7	.845
28	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.845
29	vit_large_patch14_clip_224:openai_ft_in1k	.845
30	cvt_cvt-13-384-in1k_4_LucyV4	.845
31	vit_large_patch14_clip_336:laion2b_ft_in1k	.845
32	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.845
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.845
34	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.845
35	ViT_L_32_imagenet1k	.845
36	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.845
37	vit_relpos_base_patch16_clsgap_224:sw_in1k	.845
38	deit_base_imagenet_full_seed-0	.845
39	vit_base_patch16_clip_224:openai_ft_in1k	.845
40	BiT-S-R101x3	.800
41	nasnet_mobile	.800
42	grcnn	.767
43	densenet-169	.767
44	vonegrcnn_62e_nobn	.767
45	convnext_xlarge:fb_in22k_ft_in1k	.725
46	omnivore_swinS	.725
47	yudixie_resnet18_translation_reg_0_240719	.725
48	resnet_152_v1	.725
49	mobilenet_v2_1_0_128	.695
50	AdvProp_efficientnet-b6	.695
51	resnet152_ecoset_full	.695
52	convnext_tiny:in12k_ft_in1k	.695
53	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.658
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.630
55	resnet-50-robust	.630
56	yudixie_resnet50_random_0_240908	.630
57	yudixie_resnet18_distance_translation_0_240719	.630
58	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.630
59	deit_small_imagenet_100_seed-0	.596
60	cvt_cvt-13-224-in1k_4_LucyV4	.596
61	resnet101_imagenet_full	.596
62	yolos_tiny	.572
63	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.572
64	imagenet_l2_3_0	.572
65	resnet50_robust_l2_eps3	.572
66	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.541
67	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
68	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.541
69	mobilenet_v2_1_4_224	.518
70	pnasnet_large	.518
71	resnet50-barlow	.518
72	mobilenet_v2_1-4_224_pytorch	.518
73	resnet-152_v2_pytorch	.518
74	focalnet_tiny_lrf_in1k	.518
75	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.518
76	resnet50-vicregl0p75	.490
77	alexnet_training_seed_01	.490
78	BiT-S-R50x1	.470
79	resnet50_ecoset_full	.470
80	effnetb1_272x240	.470
81	resnet34_ecoset_full	.470
82	resnet_101_v1	.470
83	resnet50_imagenet_full	.470
84	BiT-S-R101x1	.470
85	cvt_cvt-21-224-in1k_4_LucyV4	.470
86	resnext101_32x8d_wsl	.445
87	alexnet_training_seed_10	.445
88	resnet50-vicreg	.426
89	pnasnet_large_pytorch	.426
90	ReAlnet10	.426
91	blt_vs	.426
92	yudixie_resnet18_random_0_240719	.426
93	ReAlnet10_cornet	.426
94	ReAlnet09	.426
95	ReAlnet09_cornet	.426
96	resnext101_32x32d_wsl	.403
97	resnext101_32x48d_wsl	.403
98	mobilenet_v2_1_0_192	.387
99	resnet_SIN_IN_FT_IN	.351
100	alexnet_training_seed_09	.332
101	xception	.332
102	alexnet_training_seed_07	.332
103	convnext_small_imagenet_1_seed-0	.318
104	inception_v3	.318
105	ReAlnet07_cornet	.318
106	ReAlnet07	.318
107	voneresnet-50-robust	.318
108	antialiased-r50	.288
109	artResNet18_1	.288
110	vonegrcnn_52e_full	.288
111	yudixie_resnet50_distance_rotation_0_240908	.288
112	yudixie_resnet18_distance_reg_0_240719	.288
113	yudixie_resnet18_rotation_reg_0_240719	.288
114	ReAlnet05_cornet	.261
115	ReAlnet05	.261
116	yudixie_resnet18_distance_translation_rotation_0_240719	.261
117	omnivore_swinB	.247
118	alexnet_training_seed_02	.247
119	efficientnet_b0	.237
120	resnet50_imagenet_1_seed-0	.237
121	convnext_large_imagenet_full_seed-0	.237
122	resnet50_imagenet_100_seed-0	.237
123	densenet-201	.237
124	vonegrcnn_47e	.215
125	yudixie_resnet50_rotation_reg_0_240908	.215
126	resnet34_imagenet_full	.215
127	efficientnet_b2_imagenet_full	.215
128	convnext_femto_ols:d1_in1k	.195
129	resnet_50_v2	.195
130	alexnet_training_seed_05	.184
131	mobilenet_v2_0_75_160	.177
132	mobilenet_v2_0_5_224	.177
133	efficientnet-b7	.177
134	resnet_152_v2	.160
135	ReAlnet01	.160
136	resnet18_imagenet_full	.160
137	convnext_small_imagenet_full_seed-0	.160
138	ReAlnet01_cornet	.160
139	ReAlnet_incorrect	.160
140	grcnn_109	.160
141	deit_small_imagenet_10_seed-0	.152
142	alexnet_training_seed_03	.152
143	ReAlnet02_cornet	.145
144	ReAlnet02	.145
145	resnet50-vicregl0p9	.145
146	hmax	.138
147	ReAlnet04	.132
148	ReAlnet04_cornet	.132
149	alexnet_training_seed_08	.125
150	yudixie_resnet50_category_class_0_240908	.120
151	deit_small_imagenet_1_seed-0	.120
152	yudixie_resnet18_distance_rotation_0_240719	.120
153	convnext_base_imagenet_full_seed-0	.108
154	resnet50-SIN_IN_IN	.108
155	Res2Net50_26w_4s	.108
156	convnext_small_imagenet_10_seed-0	.108
157	briaai_rmbg_1_4	.103
158	ReAlnet03	.098
159	ReAlnet03_cornet	.098
160	inception_v4	.098
161	yudixie_resnet18_translation_rotation_0_240719	.098
162	resnet50_imagenet_10_seed-0	.089
163	mobilenet_v2_0_5_192	.089
164	AT_efficientnet-b2	.089
165	yudixie_resnet50_translation_reg_0_240908	.089
166	yudixie_resnet18_object_class_0_240719	.089
167	AdvProp_efficientnet-b2	.089
168	resnet50_tutorial	.081
169	resnet18_ecoset_full	.081
170	resnet-18_test_m	.081
171	alexnet_training_seed_04	.076
172	ReAlnet08_cornet	.073
173	resnet50-SIN_IN	.073
174	resnet50-SIN	.073
175	ReAlnet08	.073
176	vgg_19	.073
177	yudixie_resnet50_distance_translation_0_240908	.073
178	shufflenet_v2_x1_0	.073
179	convnext_tiny_imagenet_full_seed-0	.066
180	voneresnet-50	.066
181	yudixie_resnet50_distance_translation_rotation_0_240908	.066
182	vgg_16	.060
183	yudixie_resnet50_object_class_0_240908	.060
184	convnext_tiny_sup	.060
185	ReAlnet06	.060
186	yudixie_resnet50_imagenet1kpret_0_240908	.060
187	ReAlnet06_cornet	.060
188	VOneCORnet-S	.060
189	mobilenet_v2_0_75_224	.055
190	yudixie_resnet50_distance_reg_0_240908	.055
191	convnext_small_imagenet_100_seed-0	.055
192	yudixie_resnet18_imagenet1kpret_0_240719	.055
193	alexnet_training_seed_06	.052
194	mobilenet_v2_1_0_160	.050
195	yudixie_resnet18_category_class_0_240719	.050
196	voneresnet-50-non_stochastic	.050
197	inception_v1	.041
198	efficientnet_b0_imagenet_full	.041
199	efficientnet_b1_imagenet_full	.041
200	mobilenet_v2_1_0_224	.037
201	resnet50-sup	.034
202	resnet_50_v1	.034
203	AdvProp_efficientnet-b4	.028
204	mobilenet_v2_0_75_192	.025
205	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.019

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

0.91.

Note that scores are relative to this ceiling.

Data: Ferguson2024gray_easy

Metric: value_delta