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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024llh-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	alexnet_training_seed_10	1.0
2	alexnet_training_seed_06	1.0
3	alexnet_training_seed_09	1.0
4	mobilenet_v2_1_0_128	1.0
5	resnet50-SIN_IN_IN	1.0
6	vit_large_patch14_clip_224:openai_ft_in1k	1.0
7	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
8	convnext_femto_ols:d1_in1k	1.0
9	deit_small_imagenet_10_seed-0	1.0
10	deit_base_imagenet_full_seed-0	1.0
11	efficientnet_b1_imagenet_full	1.0
12	resnet50_imagenet_100_seed-0	1.0
13	ViT_L_32_imagenet1k	1.0
14	aughost_fair_resnet18	1.0
15	alexnet_training_seed_01_fov16	1.0
16	alexnet_fov4	1.0
17	resnet_50_v1_spiking_l4a	1.0
18	xception	1.0
19	alexnet_training_seed_02	1.0
20	resnet50-moclr8deg	1.0
21	mobilenet_v2_1_0_160	1.0
22	resnet-152_v2_pytorch	1.0
23	yudixie_resnet50_object_class_0_240908	1.0
24	alexnet_training_seed_01	1.0
25	mobilenet_v2_1_0_192	1.0
26	efficientnet-b7	1.0
27	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
28	resnet50_tutorial	1.0
29	resnet152_imagenet_full	1.0
30	resnet34_imagenet_full	1.0
31	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
32	vit_base_patch14_dinov2-lvd142m	1.0
33	compact_resnet50_robust_V4	1.0
34	resnet_50_v1_spiking	1.0
35	vgg_16	1.0
36	efficientnet_b0_imagenet_full	1.0
37	cvt_cvt-13-384-in1k_4_LucyV4	1.0
38	AdvProp_efficientnet-b8	.979
39	cvt_cvt-21-224-in1k_4_LucyV4	.979
40	vonegrcnn_52e_full	.951
41	AdvProp_efficientnet-b4	.951
42	antialiased-r50	.951
43	blt_vs	.951
44	yudixie_resnet18_category_class_0_240719	.951
45	resnet152_ecoset_full	.951
46	pnasnet_large_pytorch	.951
47	alexnet_training_seed_01_fov4	.888
48	mobilenet_v2_1_0_224	.888
49	mobilenet_v2_0_5_224	.888
50	BiT-S-R50x1	.888
51	BiT-S-R50x3	.888
52	AdvProp_efficientnet-b7	.888
53	yudixie_resnet18_distance_rotation_0_240719	.888
54	yudixie_resnet50_imagenet1kpret_0_240908	.862
55	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.862
56	AT_efficientnet-b2	.862
57	AdvProp_efficientnet-b2	.862
58	yudixie_resnet18_distance_translation_0_240719	.862
59	efficientnet_b2_imagenet_full	.862
60	focalnet_tiny_lrf_in1k	.862
61	resnet50_primary_visual_cortex	.805
62	mobilenet_v2_0_75_224	.805
63	inception_v1	.805
64	yudixie_resnet18_object_class_0_240719	.805
65	deit_small_imagenet_100_seed-0	.805
66	resnet18_ecoset_full	.805
67	alexnet	.805
68	resnext101_32x32d_wsl	.805
69	ReAlnet01_cornet_fov4	.782
70	resnet50_robust_l2_eps1	.782
71	resnet50-vicregl0p9	.782
72	BiT-S-R152x4	.782
73	artResNet18_1	.782
74	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.782
75	resnet18_imagenet_full	.782
76	ReAlnet01_cornet_fov16	.730
77	mobilenet_v2_0_75_192	.730
78	inception_v3	.730
79	omnivore_swinB	.730
80	yudixie_resnet18_rotation_reg_0_240719	.730
81	resnet50_imagenet_1_seed-0	.730
82	resnet50-vitoimagevidnet8	.709
83	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.709
84	omnivore_swinS	.709
85	efficientnet_b0	.709
86	resnet50-VITO-8deg-cc	.709
87	BiT-S-R101x1	.709
88	convnext_tiny_sup	.709
89	yudixie_resnet18_translation_rotation_0_240719	.709
90	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.709
91	resnext101_32x48d_wsl	.709
92	mobilenet_v2_1_3_224	.662
93	resnet_SIN_IN_FT_IN	.662
94	alexnet_training_seed_01_fov12	.643
95	alexnet_fov16	.643
96	ReAlnet01_ALL	.643
97	resnet_101_v1	.643
98	mobilenet_v2_0_5_192	.643
99	convnext_xlarge:fb_in22k_ft_in1k	.643
100	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.643
101	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.643
102	yudixie_resnet18_random_0_240719	.643
103	AlexNet_SIN_fov12	.601
104	compact_vgg19_IT	.601
105	alexnet_training_seed_05	.601
106	BiT-S-R152x2	.601
107	vit_base_patch16_dinov3-lvd1689m	.583
108	AlexNet_SIN_fov4	.583
109	alexnet_training_seed_08	.583
110	resnet_152_v1	.583
111	inception_v4	.583
112	yudixie_resnet50_translation_reg_0_240908	.583
113	shufflenet_v2_x1_0	.583
114	VOneCORnet-S	.583
115	densenet-169	.583
116	BiT-S-R101x3	.583
117	AdvProp_efficientnet-b6	.545
118	ReAlnet01_cornet_fov12	.529
119	AlexNet_SIN	.529
120	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.529
121	yudixie_resnet50_distance_reg_0_240908	.529
122	mobilenet_v2_1_4_224	.529
123	mobilenet_v2_0_75_160	.529
124	vonegrcnn_62e_nobn	.529
125	resnet50-vicreg	.529
126	resnet-18_test_m	.529
127	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
128	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.529
129	resnet50-vicregl0p75	.529
130	vonegrcnn_47e	.529
131	yudixie_resnet18_distance_translation_rotation_0_240719	.529
132	mobilenet_v2_1-4_224_pytorch	.529
133	alexnet_training_seed_04	.494
134	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.494
135	resnet_50_v1	.480
136	densenet-201	.480
137	resnet50-sup	.480
138	yudixie_resnet50_translation_rotation_0_240908	.448
139	yudixie_resnet18_distance_reg_0_240719	.448
140	resnet50_ewconplus_wzc	.435
141	compact_resnet50_IT	.435
142	ReAlnet02_ALL	.435
143	yudixie_resnet50_category_class_0_240908	.435
144	nasnet_mobile	.435
145	grcnn	.435
146	cvt_cvt-21-384-in1k_4_LucyV4	.435
147	resnet50_ecoset_full	.435
148	resnet_152_v2	.406
149	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.406
150	convnext_small_imagenet_10_seed-0	.406
151	alexnet_fov12	.395
152	compact_cornets_V4	.395
153	compact_resnet50_V4	.395
154	ReAlnet04_ALL	.395
155	yudixie_resnet50_distance_rotation_0_240908	.395
156	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.395
157	resnet_50_v1_spiking_l4	.369
158	alexnet_training_seed_07	.369
159	resnet50_imagenet_10_seed-0	.369
160	custom_model_cv_18_dagger_408	.369
161	vit_base_patch14_reg4_dinov2-lvd142m	.358
162	yudixie_resnet50_random_0_240908	.358
163	yudixie_resnet50_rotation_reg_0_240908	.358
164	resnet50-SIN	.358
165	effnetb1_272x240	.358
166	compact_resnet50_robust_IT	.334
167	briaai_rmbg_1_4	.334
168	grcnn_109	.325
169	vit_base_patch16_clip_224:openai_ft_in1k	.325
170	imagenet_l2_3_0	.325
171	resnet50_robust_l2_eps3	.325
172	deit_small_imagenet_full_seed-0	.325
173	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.303
174	Res2Net50_26w_4s	.294
175	densenet-121	.294
176	yudixie_resnet18_translation_reg_0_240719	.294
177	convnext_small_imagenet_full_seed-0	.294
178	deit_large_imagenet_full_seed-0	.275
179	AlexNet_SIN_fov16	.267
180	yolos_tiny	.267
181	resnet_50_v2	.267
182	convnext_tiny:in12k_ft_in1k	.267
183	convnext_base_imagenet_full_seed-0	.267
184	pnasnet_large_halfPrecision	.242
185	ReAlnet10_ALL	.242
186	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.242
187	pnasnet_large	.242
188	ReAlnet06_cornet	.242
189	ReAlnet02_cornet	.242
190	resnet_101_v2	.242
191	yudixie_resnet50_distance_translation_0_240908	.242
192	ReAlnet06	.242
193	ReAlnet02	.242
194	resnet50-barlow	.242
195	deit_small_imagenet_1_seed-0	.242
196	resnet50_imagenet_full	.242
197	alexnet_training_seed_03	.226
198	ReAlnet06_ALL	.220
199	resnet_50_v1_spiking_l3	.205
200	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.199
201	resnet101_imagenet_full	.199
202	voneresnet-50-non_stochastic	.199
203	ReAlnet09_ALL	.181
204	ReAlnet07_ALL	.181
205	convnext_large_imagenet_full_seed-0	.181
206	yudixie_resnet50_distance_translation_rotation_0_240908	.164
207	ReAlnet09_cornet	.164
208	nasnet_large	.164
209	ReAlnet09	.164
210	voneresnet-50	.164
211	resnet50-SIN_IN	.164
212	cvt_cvt-13-224-in1k_4_LucyV4	.164
213	resnet-50-robust	.164
214	resnet101_ecoset_full	.149
215	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.149
216	compact_cornets_IT	.135
217	ReAlnet05_cornet	.135
218	ReAlnet05	.135
219	vit_large_patch14_clip_336:laion2b_ft_in1k	.135
220	convnext_small_imagenet_1_seed-0	.126
221	vgg_19	.122
222	vit_large_patch14_clip_224:laion2b_ft_in1k	.122
223	voneresnet-50-robust	.111
224	convnext_small_imagenet_100_seed-0	.111
225	yudixie_resnet18_imagenet1kpret_0_240719	.111
226	compact_vgg19_V4	.101
227	ReAlnet08_ALL	.101
228	ReAlnet05_ALL	.101
229	ReAlnet01_cornet	.083
230	ReAlnet01	.083
231	ReAlnet03_ALL	.075
232	cv_18_dagger_408_pretrained	.068
233	convnext_base:clip_laiona_augreg_ft_in1k_384	.068
234	convnext_large:fb_in22k_ft_in1k	.068
235	resnext101_32x8d_wsl	.068
236	ReAlnet03_cornet	.062
237	ReAlnet03	.062
238	ReAlnet10_cornet	.051
239	ReAlnet10	.051
240	ReAlnet08_cornet	.046
241	ReAlnet08	.046
242	resnet34_ecoset_full	.046
243	ReAlnet07_cornet	.042
244	ReAlnet07	.042
245	convnext_tiny_imagenet_full_seed-0	.034
246	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.031
247	ReAlnet04_cornet	.016
248	ReAlnet04	.016
249	hmax

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

0.81.

Note that scores are relative to this ceiling.

Data: Ferguson2024llh

Metric: value_delta