Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024round_f-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50-vicreg	1.0
2	convnext_small_imagenet_10_seed-0	1.0
3	yudixie_resnet50_translation_reg_0_240908	1.0
4	resnet-18_test_m	1.0
5	alexnet_training_seed_01	1.0
6	yudixie_resnet18_translation_rotation_0_240719	1.0
7	convnext_tiny:in12k_ft_in1k	1.0
8	briaai_rmbg_1_4	1.0
9	VOneCORnet-S	1.0
10	vit_base_patch16_clip_224:openai_ft_in1k	1.0
11	cvt_cvt-13-224-in1k_4_LucyV4	.961
12	convnext_base:clip_laiona_augreg_ft_in1k_384	.961
13	inception_v4	.961
14	resnet-152_v2_pytorch	.961
15	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.961
16	resnext101_32x32d_wsl	.961
17	resnet_101_v1	.961
18	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.906
19	inception_v1	.906
20	yudixie_resnet50_random_0_240908	.906
21	yudixie_resnet50_distance_rotation_0_240908	.906
22	mobilenet_v2_1_0_160	.867
23	yudixie_resnet18_rotation_reg_0_240719	.867
24	resnet_50_v2	.867
25	resnet50_ecoset_full	.867
26	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.867
27	alexnet_training_seed_10	.867
28	convnext_xlarge:fb_in22k_ft_in1k	.818
29	yudixie_resnet18_distance_rotation_0_240719	.818
30	inception_v3	.818
31	alexnet_training_seed_04	.783
32	AdvProp_efficientnet-b4	.783
33	alexnet_training_seed_07	.783
34	resnet18_imagenet_full	.783
35	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.783
36	resnet50_imagenet_10_seed-0	.783
37	deit_small_imagenet_full_seed-0	.783
38	resnet_152_v2	.783
39	resnet_101_v2	.739
40	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.739
41	deit_base_imagenet_full_seed-0	.707
42	BiT-S-R152x2	.707
43	efficientnet_b2_imagenet_full	.707
44	ReAlnet02	.707
45	yolos_tiny	.707
46	ReAlnet02_cornet	.707
47	resnet101_imagenet_full	.667
48	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.667
49	yudixie_resnet50_distance_translation_rotation_0_240908	.667
50	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.667
51	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.667
52	yudixie_resnet18_random_0_240719	.667
53	vit_large_patch14_clip_224:openai_ft_in1k	.638
54	alexnet_training_seed_02	.638
55	cvt_cvt-21-224-in1k_4_LucyV4	.638
56	resnet_50_v1_spiking_l4a	.638
57	yudixie_resnet50_object_class_0_240908	.638
58	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.638
59	resnet50_robust_l2_eps3	.638
60	BiT-S-R101x1	.638
61	imagenet_l2_3_0	.638
62	BiT-S-R101x3	.638
63	deit_small_imagenet_100_seed-0	.602
64	resnext101_32x8d_wsl	.576
65	alexnet_training_seed_06	.576
66	BiT-S-R50x1	.576
67	yudixie_resnet50_translation_rotation_0_240908	.576
68	pnasnet_large	.576
69	efficientnet_b0	.576
70	mobilenet_v2_1_4_224	.544
71	mobilenet_v2_1-4_224_pytorch	.544
72	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.544
73	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.544
74	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.544
75	vit_large_patch14_clip_224:laion2b_ft_in1k	.544
76	convnext_large:fb_in22k_ft_in1k	.544
77	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.544
78	deit_small_imagenet_10_seed-0	.520
79	alexnet_training_seed_09	.520
80	yudixie_resnet18_translation_reg_0_240719	.470
81	ReAlnet09	.470
82	convnext_base_imagenet_full_seed-0	.470
83	ReAlnet09_cornet	.470
84	resnet50-moclr8deg	.470
85	convnext_small_imagenet_1_seed-0	.443
86	yudixie_resnet18_distance_reg_0_240719	.443
87	voneresnet-50	.424
88	efficientnet_b1_imagenet_full	.424
89	xception	.424
90	mobilenet_v2_0_75_224	.424
91	efficientnet_b0_imagenet_full	.424
92	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.424
93	deit_small_imagenet_1_seed-0	.400
94	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.400
95	vit_large_patch14_clip_336:laion2b_ft_in1k	.400
96	resnet18_ecoset_full	.383
97	voneresnet-50-non_stochastic	.383
98	pnasnet_large_pytorch	.383
99	resnet_50_v1_spiking	.383
100	convnext_tiny_imagenet_full_seed-0	.383
101	yudixie_resnet50_rotation_reg_0_240908	.346
102	hmax	.346
103	artResNet18_1	.346
104	convnext_small_imagenet_100_seed-0	.346
105	omnivore_swinS	.346
106	yudixie_resnet18_imagenet1kpret_0_240719	.346
107	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.346
108	resnext101_32x48d_wsl	.326
109	convnext_femto_ols:d1_in1k	.326
110	resnet_50_v1_spiking_l4	.326
111	resnet34_imagenet_full	.312
112	mobilenet_v2_0_5_224	.312
113	alexnet_training_seed_03	.312
114	resnet152_ecoset_full	.312
115	nasnet_large	.312
116	cvt_cvt-21-384-in1k_4_LucyV4	.282
117	deit_large_imagenet_full_seed-0	.282
118	ReAlnet01	.282
119	ReAlnet01_cornet	.282
120	nasnet_mobile	.282
121	vonegrcnn_52e_full	.255
122	resnet50-SIN_IN	.255
123	resnet50-SIN	.255
124	grcnn	.255
125	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.255
126	resnet50_imagenet_100_seed-0	.255
127	custom_model_cv_18_dagger_408	.255
128	shufflenet_v2_x1_0	.255
129	resnet_SIN_IN_FT_IN	.230
130	yudixie_resnet18_distance_translation_rotation_0_240719	.230
131	yudixie_resnet50_distance_reg_0_240908	.230
132	yudixie_resnet50_distance_translation_0_240908	.230
133	yudixie_resnet18_category_class_0_240719	.230
134	densenet-121	.230
135	omnivore_swinB	.230
136	convnext_small_imagenet_full_seed-0	.208
137	ReAlnet03_cornet	.208
138	ReAlnet03	.208
139	ReAlnet04_cornet	.208
140	ReAlnet04	.208
141	ReAlnet07_cornet	.208
142	ReAlnet07	.208
143	resnet_50_v1_spiking_l3	.208
144	AdvProp_efficientnet-b2	.187
145	ReAlnet08	.187
146	AdvProp_efficientnet-b6	.187
147	resnet50-VITO-8deg-cc	.187
148	vgg_16	.187
149	ReAlnet08_cornet	.187
150	resnet50-vitoimagevidnet8	.187
151	resnet50_robust_l2_eps1	.187
152	resnet50-vicregl0p9	.187
153	AT_efficientnet-b2	.187
154	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.169
155	convnext_large_imagenet_full_seed-0	.169
156	yudixie_resnet18_object_class_0_240719	.160
157	blt_vs	.160
158	resnet101_ecoset_full	.153
159	ReAlnet05	.153
160	mobilenet_v2_0_5_192	.153
161	voneresnet-50-robust	.153
162	vonegrcnn_47e	.153
163	AdvProp_efficientnet-b8	.153
164	ReAlnet05_cornet	.153
165	ReAlnet10_cornet	.138
166	ReAlnet10	.138
167	densenet-201	.138
168	vonegrcnn_62e_nobn	.138
169	cvt_cvt-13-384-in1k_4_LucyV4	.138
170	mobilenet_v2_0_75_160	.138
171	yudixie_resnet50_category_class_0_240908	.138
172	resnet50_imagenet_1_seed-0	.125
173	ReAlnet06	.113
174	BiT-S-R50x3	.113
175	yudixie_resnet18_distance_translation_0_240719	.113
176	ReAlnet06_cornet	.113
177	resnet152_imagenet_full	.113
178	ViT_L_32_imagenet1k	.102
179	resnet-50-robust	.102
180	BiT-S-R152x4	.102
181	resnet_152_v1	.102
182	alexnet_training_seed_08	.102
183	grcnn_109	.102
184	vit_relpos_base_patch16_clsgap_224:sw_in1k	.092
185	resnet50_imagenet_full	.092
186	mobilenet_v2_0_75_192	.092
187	resnet34_ecoset_full	.092
188	efficientnet-b7	.092
189	resnet50_primary_visual_cortex	.092
190	mobilenet_v2_1_0_128	.083
191	resnet50-barlow	.075
192	mobilenet_v2_1_0_224	.075
193	convnext_tiny_sup	.075
194	resnet50-vicregl0p75	.075
195	densenet-169	.075
196	resnet50_tutorial	.075
197	effnetb1_272x240	.068
198	yudixie_resnet50_imagenet1kpret_0_240908	.061
199	mobilenet_v2_1_3_224	.061
200	AdvProp_efficientnet-b7	.055
201	focalnet_tiny_lrf_in1k	.055
202	Res2Net50_26w_4s	.050
203	alexnet_training_seed_05	.050
204	resnet50-sup	.033
205	resnet_50_v1	.033
206	mobilenet_v2_1_0_192	.030
207	antialiased-r50	.027
208	resnet50-SIN_IN_IN	.018
209	vgg_19	.012

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

0.87.

Note that scores are relative to this ceiling.

Data: Ferguson2024round_f

Metric: value_delta