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