Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024gray_hard-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	omnivore_swinB	1.0
2	deit_large_imagenet_full_seed-0	1.0
3	ReAlnet10	1.0
4	BiT-S-R101x1	1.0
5	pnasnet_large_pytorch	1.0
6	ReAlnet10_cornet	1.0
7	artResNet18_1	1.0
8	resnet101_ecoset_full	1.0
9	deit_small_imagenet_100_seed-0	1.0
10	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
11	convnext_femto_ols:d1_in1k	1.0
12	convnext_tiny:in12k_ft_in1k	1.0
13	yudixie_resnet18_object_class_0_240719	1.0
14	vgg_16	1.0
15	inception_v4	1.0
16	BiT-S-R152x2	.971
17	deit_small_imagenet_1_seed-0	.971
18	voneresnet-50-robust	.971
19	nasnet_mobile	.942
20	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
21	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.942
22	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.942
23	vit_large_patch14_clip_224:laion2b_ft_in1k	.942
24	vit_large_patch14_clip_336:laion2b_ft_in1k	.942
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.942
26	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
27	resnet50-vitoimagevidnet8	.942
28	resnet50-VITO-8deg-cc	.942
29	vit_large_patch14_clip_224:openai_ft_in1k	.942
30	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
31	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
32	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
33	convnext_large:fb_in22k_ft_in1k	.883
34	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.883
35	yudixie_resnet50_imagenet1kpret_0_240908	.883
36	nasnet_large	.883
37	deit_small_imagenet_10_seed-0	.883
38	ReAlnet05	.855
39	alexnet_training_seed_06	.855
40	ReAlnet05_cornet	.855
41	custom_model_cv_18_dagger_408	.855
42	resnet_SIN_IN_FT_IN	.802
43	resnet50-vicregl0p9	.802
44	briaai_rmbg_1_4	.777
45	resnet50_imagenet_full	.777
46	grcnn	.777
47	alexnet_training_seed_08	.777
48	ReAlnet07_cornet	.777
49	yudixie_resnet50_distance_translation_rotation_0_240908	.777
50	ReAlnet07	.777
51	yudixie_resnet50_object_class_0_240908	.728
52	cvt_cvt-21-224-in1k_4_LucyV4	.728
53	convnext_xlarge:fb_in22k_ft_in1k	.728
54	resnet50-vicregl0p75	.728
55	yudixie_resnet50_rotation_reg_0_240908	.728
56	yudixie_resnet18_distance_translation_rotation_0_240719	.728
57	focalnet_tiny_lrf_in1k	.728
58	mobilenet_v2_1_0_224	.706
59	VOneCORnet-S	.706
60	resnet50-vicreg	.706
61	cvt_cvt-13-224-in1k_4_LucyV4	.706
62	resnet_152_v2	.706
63	ViT_L_32_imagenet1k	.706
64	resnet50_tutorial	.662
65	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.662
66	inception_v1	.641
67	ReAlnet03	.641
68	Res2Net50_26w_4s	.641
69	mobilenet_v2_1_3_224	.641
70	yolos_tiny	.641
71	resnet-18_test_m	.641
72	resnet18_imagenet_full	.641
73	omnivore_swinS	.641
74	vit_base_patch16_clip_224:openai_ft_in1k	.641
75	ReAlnet03_cornet	.641
76	alexnet_training_seed_05	.601
77	yudixie_resnet18_distance_rotation_0_240719	.601
78	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.601
79	resnet18_ecoset_full	.583
80	AdvProp_efficientnet-b7	.583
81	resnet50_imagenet_1_seed-0	.583
82	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.583
83	mobilenet_v2_1_0_128	.583
84	cvt_cvt-13-384-in1k_4_LucyV4	.583
85	yudixie_resnet18_random_0_240719	.583
86	pnasnet_large	.546
87	resnet_101_v2	.546
88	BiT-S-R101x3	.529
89	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.529
90	alexnet_training_seed_07	.496
91	yudixie_resnet50_distance_reg_0_240908	.496
92	deit_base_imagenet_full_seed-0	.481
93	blt_vs	.481
94	ReAlnet09	.481
95	ReAlnet09_cornet	.481
96	vonegrcnn_47e	.481
97	yudixie_resnet18_translation_reg_0_240719	.481
98	alexnet_training_seed_01	.451
99	vgg_19	.437
100	convnext_base:clip_laiona_augreg_ft_in1k_384	.437
101	resnext101_32x8d_wsl	.437
102	convnext_tiny_imagenet_full_seed-0	.437
103	ReAlnet01_cornet	.437
104	alexnet_training_seed_03	.437
105	mobilenet_v2_1-4_224_pytorch	.437
106	densenet-201	.437
107	ReAlnet_incorrect	.437
108	resnet_101_v1	.437
109	hmax	.437
110	ReAlnet01	.437
111	mobilenet_v2_1_4_224	.437
112	xception	.410
113	resnet50-SIN_IN_IN	.410
114	mobilenet_v2_1_0_192	.410
115	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.397
116	convnext_small_imagenet_100_seed-0	.397
117	ReAlnet04	.397
118	alexnet_training_seed_10	.397
119	resnet50-moclr8deg	.397
120	alexnet_training_seed_04	.397
121	ReAlnet08	.397
122	resnet50-SIN	.397
123	ReAlnet04_cornet	.397
124	ReAlnet08_cornet	.397
125	resnet50-barlow	.397
126	yudixie_resnet50_random_0_240908	.397
127	resnet50_imagenet_10_seed-0	.372
128	resnext101_32x32d_wsl	.372
129	AdvProp_efficientnet-b6	.361
130	antialiased-r50	.361
131	deit_small_imagenet_full_seed-0	.361
132	densenet-169	.361
133	yudixie_resnet18_translation_rotation_0_240719	.338
134	vonegrcnn_62e_nobn	.328
135	efficientnet_b1_imagenet_full	.328
136	convnext_small_imagenet_1_seed-0	.307
137	yudixie_resnet50_distance_translation_0_240908	.298
138	resnet50_ecoset_full	.298
139	resnet34_ecoset_full	.298
140	resnext101_32x48d_wsl	.298
141	convnext_small_imagenet_10_seed-0	.279
142	ReAlnet06	.270
143	mobilenet_v2_0_75_160	.270
144	mobilenet_v2_0_5_224	.270
145	resnet50_imagenet_100_seed-0	.270
146	resnet34_imagenet_full	.270
147	alexnet_training_seed_02	.270
148	yudixie_resnet50_translation_reg_0_240908	.270
149	ReAlnet06_cornet	.270
150	voneresnet-50	.270
151	convnext_tiny_sup	.270
152	yudixie_resnet18_rotation_reg_0_240719	.253
153	ReAlnet02_cornet	.246
154	resnet152_ecoset_full	.246
155	ReAlnet02	.246
156	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.246
157	voneresnet-50-non_stochastic	.246
158	yudixie_resnet50_category_class_0_240908	.230
159	BiT-S-R50x1	.223
160	AdvProp_efficientnet-b8	.223
161	convnext_small_imagenet_full_seed-0	.223
162	densenet-121	.223
163	resnet101_imagenet_full	.223
164	resnet_50_v1	.203
165	resnet50-sup	.203
166	AdvProp_efficientnet-b4	.203
167	BiT-S-R152x4	.190
168	efficientnet_b0_imagenet_full	.184
169	inception_v3	.184
170	resnet152_imagenet_full	.184
171	yudixie_resnet18_category_class_0_240719	.184
172	yudixie_resnet50_translation_rotation_0_240908	.167
173	grcnn_109	.167
174	vonegrcnn_52e_full	.167
175	BiT-S-R50x3	.167
176	alexnet_training_seed_09	.167
177	cvt_cvt-21-384-in1k_4_LucyV4	.157
178	effnetb1_272x240	.152
179	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.152
180	yudixie_resnet18_distance_translation_0_240719	.152
181	shufflenet_v2_x1_0	.152
182	resnet_50_v2	.152
183	resnet_152_v1	.138
184	mobilenet_v2_1_0_160	.138
185	convnext_large_imagenet_full_seed-0	.126
186	yudixie_resnet18_imagenet1kpret_0_240719	.126
187	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.126
188	yudixie_resnet50_distance_rotation_0_240908	.126
189	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.114
190	mobilenet_v2_0_75_224	.114
191	yudixie_resnet18_distance_reg_0_240719	.104
192	efficientnet-b7	.104
193	efficientnet_b0	.086
194	convnext_base_imagenet_full_seed-0	.078
195	resnet-152_v2_pytorch	.078
196	resnet50_robust_l2_eps1	.073
197	resnet50-SIN_IN	.071
198	resnet50_robust_l2_eps3	.064
199	resnet-50-robust	.064
200	imagenet_l2_3_0	.064
201	mobilenet_v2_0_75_192	.048
202	mobilenet_v2_0_5_192	.048
203	AdvProp_efficientnet-b2	.033
204	AT_efficientnet-b2	.033
205	efficientnet_b2_imagenet_full	.020

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

0.86.

Note that scores are relative to this ceiling.

Data: Ferguson2024gray_hard

Metric: value_delta