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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024round_v-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
2	mobilenet_v2_0_75_192	1.0
3	ReAlnet01_cornet	1.0
4	resnet50_ecoset_full	1.0
5	grcnn	1.0
6	voneresnet-50-robust	1.0
7	ReAlnet01	1.0
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
9	resnet152_ecoset_full	1.0
10	yudixie_resnet18_distance_translation_0_240719	1.0
11	yudixie_resnet18_object_class_0_240719	1.0
12	ReAlnet08	1.0
13	resnet50-SIN	1.0
14	nasnet_large	1.0
15	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
16	ReAlnet08_cornet	1.0
17	resnet_152_v2	1.0
18	convnext_femto_ols:d1_in1k	1.0
19	convnext_small_imagenet_10_seed-0	1.000
20	ReAlnet06	1.000
21	yudixie_resnet50_category_class_0_240908	1.000
22	alexnet_training_seed_04	1.000
23	inception_v4	1.000
24	resnet34_ecoset_full	1.000
25	vit_large_patch14_clip_224:laion2b_ft_in1k	1.000
26	ReAlnet04_cornet	1.000
27	Res2Net50_26w_4s	1.000
28	ReAlnet06_cornet	1.000
29	ReAlnet04	1.000
30	convnext_xlarge:fb_in22k_ft_in1k	.924
31	convnext_base_imagenet_full_seed-0	.924
32	resnet101_imagenet_full	.924
33	mobilenet_v2_0_5_224	.924
34	mobilenet_v2_1-4_224_pytorch	.924
35	ReAlnet01_ALL	.924
36	omnivore_swinB	.924
37	mobilenet_v2_1_4_224	.924
38	vit_large_patch14_clip_336:laion2b_ft_in1k	.905
39	convnext_tiny_imagenet_full_seed-0	.905
40	efficientnet-b7	.905
41	compact_vgg19_IT	.905
42	voneresnet-50	.905
43	deit_small_imagenet_100_seed-0	.836
44	deit_large_imagenet_full_seed-0	.836
45	ReAlnet09	.836
46	resnet101_ecoset_full	.836
47	nasnet_mobile	.836
48	ReAlnet09_cornet	.836
49	inception_v1	.836
50	vit_large_patch14_clip_224:openai_ft_in1k	.820
51	resnet18_imagenet_full	.820
52	resnext101_32x32d_wsl	.820
53	focalnet_tiny_lrf_in1k	.820
54	resnet152_imagenet_full	.820
55	blt_vs	.820
56	yudixie_resnet18_distance_reg_0_240719	.820
57	resnet50-vicreg	.820
58	resnet34_imagenet_full	.757
59	yudixie_resnet50_distance_reg_0_240908	.757
60	ViT_L_32_imagenet1k	.757
61	alexnet_training_seed_10	.757
62	vgg_16	.757
63	vonegrcnn_62e_nobn	.757
64	ReAlnet05	.742
65	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.742
66	alexnet_training_seed_01	.742
67	ReAlnet05_cornet	.742
68	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.686
69	shufflenet_v2_x1_0	.686
70	ReAlnet02_ALL	.686
71	ReAlnet05_ALL	.686
72	ReAlnet10_ALL	.686
73	efficientnet_b2_imagenet_full	.672
74	custom_model_cv_18_dagger_408	.672
75	efficientnet_b0	.672
76	resnet_50_v1_spiking_l3	.672
77	vgg_19	.672
78	AdvProp_efficientnet-b7	.672
79	ReAlnet07_ALL	.672
80	resnet_50_v2	.672
81	inception_v3	.621
82	convnext_small_imagenet_full_seed-0	.621
83	deit_small_imagenet_10_seed-0	.621
84	deit_base_imagenet_full_seed-0	.621
85	resnet18_ecoset_full	.621
86	ReAlnet03_ALL	.621
87	omnivore_swinS	.621
88	AdvProp_efficientnet-b4	.621
89	ReAlnet08_ALL	.621
90	densenet-169	.621
91	resnext101_32x48d_wsl	.621
92	yudixie_resnet50_distance_translation_rotation_0_240908	.609
93	ReAlnet03	.609
94	ReAlnet07	.609
95	voneresnet-50-non_stochastic	.609
96	ReAlnet03_cornet	.609
97	ReAlnet07_cornet	.609
98	compact_cornets_V4	.609
99	compact_cornets_IT	.609
100	grcnn_109	.609
101	vonegrcnn_47e	.609
102	yudixie_resnet18_imagenet1kpret_0_240719	.562
103	AdvProp_efficientnet-b8	.562
104	convnext_tiny_sup	.562
105	alexnet_training_seed_05	.551
106	resnet-50-robust	.551
107	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.551
108	ReAlnet02_cornet	.551
109	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.551
110	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.551
111	yudixie_resnet18_translation_reg_0_240719	.551
112	resnet_SIN_IN_FT_IN	.551
113	ReAlnet02	.551
114	alexnet_training_seed_03	.509
115	cvt_cvt-21-384-in1k_4_LucyV4	.509
116	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.509
117	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
118	hmax	.509
119	mobilenet_v2_0_75_224	.499
120	resnet_50_v1_spiking_l4a	.499
121	convnext_small_imagenet_1_seed-0	.499
122	compact_resnet50_robust_IT	.499
123	BiT-S-R50x1	.499
124	convnext_large_imagenet_full_seed-0	.461
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.461
126	resnet50_imagenet_100_seed-0	.461
127	vit_base_patch16_clip_224:openai_ft_in1k	.461
128	ReAlnet06_ALL	.461
129	convnext_base:clip_laiona_augreg_ft_in1k_384	.417
130	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.417
131	cvt_cvt-13-224-in1k_4_LucyV4	.417
132	resnet50-SIN_IN	.417
133	resnet-18_test_m	.417
134	resnet50_primary_visual_cortex	.417
135	mobilenet_v2_0_75_160	.409
136	yudixie_resnet18_distance_rotation_0_240719	.409
137	vit_relpos_base_patch16_clsgap_224:sw_in1k	.409
138	resnet50-vitoimagevidnet8	.409
139	mobilenet_v2_1_3_224	.409
140	resnet50-VITO-8deg-cc	.409
141	resnet_50_v1	.409
142	resnet_101_v2	.409
143	resnet50-sup	.409
144	resnet_152_v1	.409
145	effnetb1_272x240	.378
146	VOneCORnet-S	.378
147	imagenet_l2_3_0	.378
148	resnet50_robust_l2_eps3	.378
149	BiT-S-R50x3	.378
150	ReAlnet09_ALL	.378
151	resnet50_imagenet_full	.370
152	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.370
153	ReAlnet10	.370
154	briaai_rmbg_1_4	.370
155	resnet50-SIN_IN_IN	.370
156	xception	.370
157	ReAlnet10_cornet	.370
158	convnext_large:fb_in22k_ft_in1k	.370
159	cvt_cvt-13-384-in1k_4_LucyV4	.342
160	artResNet18_1	.342
161	resnet50-vicregl0p9	.342
162	yudixie_resnet50_random_0_240908	.335
163	yudixie_resnet50_object_class_0_240908	.335
164	resnet50_ewconplus_wzc	.335
165	yudixie_resnet50_distance_translation_0_240908	.310
166	compact_resnet50_IT	.304
167	yudixie_resnet50_distance_rotation_0_240908	.304
168	resnet50-moclr8deg	.304
169	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.281
170	efficientnet_b0_imagenet_full	.281
171	yudixie_resnet50_imagenet1kpret_0_240908	.275
172	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.275
173	resnet50_imagenet_10_seed-0	.275
174	mobilenet_v2_1_0_224	.275
175	densenet-121	.275
176	deit_small_imagenet_full_seed-0	.254
177	densenet-201	.254
178	AdvProp_efficientnet-b6	.254
179	deit_small_imagenet_1_seed-0	.249
180	resnet50_robust_l2_eps1	.249
181	BiT-S-R152x2	.249
182	resnet50-barlow	.230
183	ReAlnet04_ALL	.230
184	vonegrcnn_52e_full	.230
185	AdvProp_efficientnet-b2	.230
186	BiT-S-R152x4	.230
187	AT_efficientnet-b2	.230
188	alexnet_training_seed_09	.225
189	resnet_50_v1_spiking_l4	.225
190	alexnet_training_seed_02	.225
191	alexnet_training_seed_08	.225
192	yudixie_resnet18_translation_rotation_0_240719	.225
193	yudixie_resnet18_distance_translation_rotation_0_240719	.225
194	yudixie_resnet50_translation_rotation_0_240908	.225
195	resnet_101_v1	.225
196	mobilenet_v2_1_0_128	.208
197	resnet-152_v2_pytorch	.208
198	mobilenet_v2_1_0_160	.204
199	mobilenet_v2_1_0_192	.204
200	compact_vgg19_V4	.204
201	antialiased-r50	.204
202	mobilenet_v2_0_5_192	.189
203	convnext_tiny:in12k_ft_in1k	.185
204	alexnet_training_seed_06	.185
205	yudixie_resnet50_rotation_reg_0_240908	.185
206	cvt_cvt-21-224-in1k_4_LucyV4	.171
207	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.155
208	resnet50-vicregl0p75	.155
209	efficientnet_b1_imagenet_full	.140
210	resnet50_tutorial	.137
211	alexnet_training_seed_07	.137
212	pnasnet_large_pytorch	.124
213	resnext101_32x8d_wsl	.102
214	BiT-S-R101x3	.083
215	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.076
216	convnext_small_imagenet_100_seed-0	.070
217	resnet50_imagenet_1_seed-0	.068
218	yudixie_resnet18_category_class_0_240719	.068
219	yudixie_resnet18_rotation_reg_0_240719	.068
220	pnasnet_large	.062
221	compact_resnet50_V4	.051
222	compact_resnet50_robust_V4	.051
223	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.035
224	yudixie_resnet50_translation_reg_0_240908	.034
225	resnet_50_v1_spiking	.025
226	yolos_tiny	.025
227	BiT-S-R101x1	.025
228	yudixie_resnet18_random_0_240719	.023

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

0.85.

Note that scores are relative to this ceiling.

Data: Ferguson2024round_v

Metric: value_delta