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	BiT-S-R50x3	1.0
3	resnet_101_v2	1.0
4	densenet-121	1.0
5	custom_model_cv_18_dagger_408	1.0
6	resnet50-VITO-8deg-cc	.973
7	yudixie_resnet50_translation_rotation_0_240908	.973
8	resnet50-vitoimagevidnet8	.973
9	mobilenet_v2_1_3_224	.973
10	resnet_50_v1_spiking	.932
11	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.882
12	BiT-S-R152x2	.882
13	nasnet_large	.882
14	resnet50-moclr8deg	.882
15	deit_small_imagenet_full_seed-0	.882
16	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
17	resnet152_imagenet_full	.882
18	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.845
19	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.845
20	cvt_cvt-13-384-in1k_4_LucyV4	.845
21	resnet101_ecoset_full	.845
22	vit_relpos_base_patch16_clsgap_224:sw_in1k	.845
23	deit_large_imagenet_full_seed-0	.845
24	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.845
25	convnext_large:fb_in22k_ft_in1k	.845
26	cvt_cvt-21-384-in1k_4_LucyV4	.845
27	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.845
28	deit_base_imagenet_full_seed-0	.845
29	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.845
30	AdvProp_efficientnet-b7	.845
31	vit_base_patch16_clip_224:openai_ft_in1k	.845
32	vit_large_patch14_clip_224:openai_ft_in1k	.845
33	BiT-S-R152x4	.845
34	vit_large_patch14_clip_336:laion2b_ft_in1k	.845
35	AdvProp_efficientnet-b8	.845
36	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.845
37	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.845
38	ViT_L_32_imagenet1k	.845
39	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.845
40	vit_large_patch14_clip_224:laion2b_ft_in1k	.845
41	nasnet_mobile	.800
42	BiT-S-R101x3	.800
43	densenet-169	.767
44	grcnn	.767
45	vonegrcnn_62e_nobn	.767
46	omnivore_swinS	.725
47	resnet_152_v1	.725
48	convnext_xlarge:fb_in22k_ft_in1k	.725
49	yudixie_resnet18_translation_reg_0_240719	.725
50	resnet152_ecoset_full	.695
51	mobilenet_v2_1_0_128	.695
52	convnext_tiny:in12k_ft_in1k	.695
53	AdvProp_efficientnet-b6	.695
54	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.658
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.630
56	yudixie_resnet50_random_0_240908	.630
57	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.630
58	resnet-50-robust	.630
59	yudixie_resnet18_distance_translation_0_240719	.630
60	deit_small_imagenet_100_seed-0	.596
61	cvt_cvt-13-224-in1k_4_LucyV4	.596
62	resnet101_imagenet_full	.596
63	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.572
64	resnet50_robust_l2_eps3	.572
65	yolos_tiny	.572
66	imagenet_l2_3_0	.572
67	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
68	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.541
69	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.541
70	pnasnet_large	.518
71	mobilenet_v2_1_4_224	.518
72	resnet50-barlow	.518
73	resnet-152_v2_pytorch	.518
74	mobilenet_v2_1-4_224_pytorch	.518
75	focalnet_tiny_lrf_in1k	.518
76	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.518
77	alexnet_training_seed_01	.490
78	resnet50-vicregl0p75	.490
79	cvt_cvt-21-224-in1k_4_LucyV4	.470
80	effnetb1_272x240	.470
81	resnet_101_v1	.470
82	BiT-S-R50x1	.470
83	resnet50_imagenet_full	.470
84	BiT-S-R101x1	.470
85	resnet50_ecoset_full	.470
86	resnet34_ecoset_full	.470
87	alexnet_training_seed_10	.445
88	resnext101_32x8d_wsl	.445
89	yudixie_resnet18_random_0_240719	.426
90	pnasnet_large_pytorch	.426
91	ReAlnet10	.426
92	blt_vs	.426
93	ReAlnet09_cornet	.426
94	ReAlnet10_cornet	.426
95	ReAlnet09	.426
96	resnet50-vicreg	.426
97	resnext101_32x32d_wsl	.403
98	resnext101_32x48d_wsl	.403
99	resnet_50_v1_spiking_l4	.387
100	mobilenet_v2_1_0_192	.387
101	resnet_SIN_IN_FT_IN	.351
102	alexnet_training_seed_09	.332
103	alexnet_training_seed_07	.332
104	xception	.332
105	voneresnet-50-robust	.318
106	ReAlnet07	.318
107	ReAlnet07_cornet	.318
108	inception_v3	.318
109	convnext_small_imagenet_1_seed-0	.318
110	yudixie_resnet18_distance_reg_0_240719	.288
111	yudixie_resnet50_distance_rotation_0_240908	.288
112	antialiased-r50	.288
113	vonegrcnn_52e_full	.288
114	yudixie_resnet18_rotation_reg_0_240719	.288
115	artResNet18_1	.288
116	ReAlnet05	.261
117	ReAlnet05_cornet	.261
118	resnet_50_v1_spiking_l3	.261
119	yudixie_resnet18_distance_translation_rotation_0_240719	.261
120	alexnet_training_seed_02	.247
121	omnivore_swinB	.247
122	convnext_large_imagenet_full_seed-0	.237
123	resnet50_imagenet_1_seed-0	.237
124	resnet50_imagenet_100_seed-0	.237
125	efficientnet_b0	.237
126	densenet-201	.237
127	yudixie_resnet50_rotation_reg_0_240908	.215
128	efficientnet_b2_imagenet_full	.215
129	resnet34_imagenet_full	.215
130	vonegrcnn_47e	.215
131	convnext_femto_ols:d1_in1k	.195
132	resnet_50_v2	.195
133	alexnet_training_seed_05	.184
134	mobilenet_v2_0_75_160	.177
135	mobilenet_v2_0_5_224	.177
136	efficientnet-b7	.177
137	grcnn_109	.160
138	ReAlnet01_cornet	.160
139	ReAlnet01	.160
140	convnext_small_imagenet_full_seed-0	.160
141	resnet_152_v2	.160
142	resnet50_primary_visual_cortex	.160
143	resnet18_imagenet_full	.160
144	alexnet_training_seed_03	.152
145	deit_small_imagenet_10_seed-0	.152
146	ReAlnet02	.145
147	resnet50-vicregl0p9	.145
148	ReAlnet02_cornet	.145
149	hmax	.138
150	ReAlnet04	.132
151	ReAlnet04_cornet	.132
152	alexnet_training_seed_08	.125
153	yudixie_resnet50_category_class_0_240908	.120
154	deit_small_imagenet_1_seed-0	.120
155	yudixie_resnet18_distance_rotation_0_240719	.120
156	resnet_50_v1_spiking_l4a	.120
157	convnext_base_imagenet_full_seed-0	.108
158	convnext_small_imagenet_10_seed-0	.108
159	Res2Net50_26w_4s	.108
160	resnet50-SIN_IN_IN	.108
161	briaai_rmbg_1_4	.103
162	yudixie_resnet18_translation_rotation_0_240719	.098
163	ReAlnet03_cornet	.098
164	inception_v4	.098
165	ReAlnet03	.098
166	yudixie_resnet18_object_class_0_240719	.089
167	yudixie_resnet50_translation_reg_0_240908	.089
168	resnet50_imagenet_10_seed-0	.089
169	AT_efficientnet-b2	.089
170	AdvProp_efficientnet-b2	.089
171	mobilenet_v2_0_5_192	.089
172	resnet18_ecoset_full	.081
173	resnet-18_test_m	.081
174	resnet50_tutorial	.081
175	alexnet_training_seed_04	.076
176	yudixie_resnet50_distance_translation_0_240908	.073
177	resnet50-SIN	.073
178	resnet50-SIN_IN	.073
179	ReAlnet08	.073
180	vgg_19	.073
181	shufflenet_v2_x1_0	.073
182	ReAlnet08_cornet	.073
183	convnext_tiny_imagenet_full_seed-0	.066
184	voneresnet-50	.066
185	yudixie_resnet50_distance_translation_rotation_0_240908	.066
186	convnext_tiny_sup	.060
187	ReAlnet06_cornet	.060
188	VOneCORnet-S	.060
189	vgg_16	.060
190	yudixie_resnet50_object_class_0_240908	.060
191	ReAlnet06	.060
192	yudixie_resnet50_imagenet1kpret_0_240908	.060
193	yudixie_resnet50_distance_reg_0_240908	.055
194	convnext_small_imagenet_100_seed-0	.055
195	mobilenet_v2_0_75_224	.055
196	yudixie_resnet18_imagenet1kpret_0_240719	.055
197	alexnet_training_seed_06	.052
198	mobilenet_v2_1_0_160	.050
199	yudixie_resnet18_category_class_0_240719	.050
200	voneresnet-50-non_stochastic	.050
201	efficientnet_b0_imagenet_full	.041
202	inception_v1	.041
203	efficientnet_b1_imagenet_full	.041
204	mobilenet_v2_1_0_224	.037
205	resnet50-sup	.034
206	resnet_50_v1	.034
207	AdvProp_efficientnet-b4	.028
208	mobilenet_v2_0_75_192	.025
209	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