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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024color-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-21-384-in1k_4_LucyV4	1.0
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
3	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	1.0
4	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
5	convnext_large:fb_in22k_ft_in1k	1.0
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
7	convnext_xlarge:fb_in22k_ft_in1k	1.0
8	vit_large_patch14_clip_224:openai_ft_in1k	1.0
9	vit_large_patch14_clip_336:laion2b_ft_in1k	1.0
10	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
11	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
12	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	1.0
13	cvt_cvt-21-224-in1k_4_LucyV4	1.0
14	cvt_cvt-13-224-in1k_4_LucyV4	1.0
15	convnext_base_imagenet_full_seed-0	1.0
16	convnext_large_imagenet_full_seed-0	1.0
17	AdvProp_efficientnet-b6	1.0
18	efficientnet_b2_imagenet_full	1.0
19	focalnet_tiny_lrf_in1k	1.0
20	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
21	custom_model_cv_18_dagger_408	1.0
22	ViT_L_32_imagenet1k	1.0
23	cvt_cvt-w24-384-in22k_finetuned-in1k_4	1.0
24	cvt_cvt-13-384-in1k_4_LucyV4	1.0
25	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	1.0
26	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
27	deit_base_imagenet_full_seed-0	1.0
28	efficientnet_b1_imagenet_full	1.0
29	vit_base_patch16_clip_224:openai_ft_in12k_in1k	1.0
30	vit_base_patch16_clip_224:openai_ft_in1k	1.0
31	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
32	yudixie_resnet18_translation_rotation_0_240719	1.0
33	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.0
34	efficientnet-b7	1.0
35	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	1.0
36	omnivore_swinB	1.0
37	efficientnet_b0	1.0
38	omnivore_swinS	1.0
39	ReAlnet06_ALL	1.0
40	AdvProp_efficientnet-b7	1.0
41	AdvProp_efficientnet-b4	1.0
42	AdvProp_efficientnet-b2	1.0
43	imagenet_l2_3_0	1.0
44	resnet50_robust_l2_eps3	1.0
45	BiT-S-R50x3	1.0
46	AdvProp_efficientnet-b8	1.0
47	convnext_tiny_sup	1.0
48	BiT-S-R152x4	1.0
49	BiT-S-R101x3	1.0
50	BiT-S-R152x2	1.0
51	BiT-S-R50x1	1.0
52	BiT-S-R101x1	1.0
53	resnet50-vicregl0p9	1.0
54	AT_efficientnet-b2	1.0
55	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
56	resnet-152_v2_pytorch	1.0
57	resnet50_imagenet_100_seed-0	.968
58	resnet50-VITO-8deg-cc	.968
59	resnet50-vitoimagevidnet8	.968
60	voneresnet-50	.968
61	resnet_50_v1	.968
62	resnet50-sup	.968
63	densenet-169	.968
64	mobilenet_v2_1_0_192	.952
65	mobilenet_v2_0_75_160	.952
66	voneresnet-50-non_stochastic	.952
67	yolos_tiny	.952
68	inception_v4	.952
69	yudixie_resnet18_distance_rotation_0_240719	.952
70	pnasnet_large	.952
71	resnet_152_v2	.952
72	ReAlnet09	.877
73	ReAlnet10	.877
74	deit_large_imagenet_full_seed-0	.877
75	efficientnet_b0_imagenet_full	.877
76	resnext101_32x8d_wsl	.877
77	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.877
78	resnet50-SIN_IN_IN	.877
79	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.877
80	ReAlnet01_ALL	.877
81	vonegrcnn_62e_nobn	.877
82	ReAlnet09_cornet	.877
83	grcnn_109	.877
84	vonegrcnn_47e	.877
85	resnet_SIN_IN_FT_IN	.877
86	ReAlnet10_cornet	.877
87	inception_v3	.862
88	blt_vs	.862
89	mobilenet_v2_0_75_224	.862
90	convnext_small_imagenet_full_seed-0	.862
91	resnet50_imagenet_full	.862
92	resnet-18_test_m	.862
93	voneresnet-50-robust	.862
94	resnet34_imagenet_full	.794
95	effnetb1_272x240	.794
96	ReAlnet02_cornet	.794
97	nasnet_large	.794
98	resnet_50_v1_spiking_l3	.794
99	resnet50-vicreg	.794
100	ReAlnet04_ALL	.794
101	grcnn	.794
102	ReAlnet02	.794
103	densenet-201	.780
104	mobilenet_v2_0_5_224	.780
105	resnet50_imagenet_1_seed-0	.780
106	compact_resnet50_V4	.780
107	resnet_152_v1	.719
108	deit_small_imagenet_full_seed-0	.719
109	resnext101_32x32d_wsl	.719
110	convnext_tiny_imagenet_full_seed-0	.719
111	resnext101_32x48d_wsl	.719
112	ReAlnet03_ALL	.719
113	convnext_small_imagenet_100_seed-0	.706
114	yudixie_resnet50_distance_rotation_0_240908	.706
115	resnet_101_v2	.706
116	convnext_femto_ols:d1_in1k	.706
117	yudixie_resnet50_random_0_240908	.650
118	resnet152_imagenet_full	.650
119	compact_resnet50_robust_IT	.650
120	resnet50_robust_l2_eps1	.650
121	vonegrcnn_52e_full	.650
122	ReAlnet05	.640
123	mobilenet_v2_1_0_128	.640
124	ReAlnet05_cornet	.640
125	antialiased-r50	.640
126	ReAlnet09_ALL	.640
127	convnext_tiny:in12k_ft_in1k	.589
128	yudixie_resnet18_category_class_0_240719	.589
129	Res2Net50_26w_4s	.589
130	mobilenet_v2_1_3_224	.589
131	inception_v1	.589
132	yudixie_resnet50_imagenet1kpret_0_240908	.579
133	resnet50_ewconplus_wzc	.579
134	resnet_50_v1_spiking_l4a	.579
135	resnet34_ecoset_full	.579
136	compact_vgg19_V4	.579
137	yudixie_resnet18_imagenet1kpret_0_240719	.579
138	resnet_50_v2	.579
139	pnasnet_large_pytorch	.533
140	ReAlnet08_ALL	.533
141	yudixie_resnet50_distance_translation_rotation_0_240908	.524
142	ReAlnet10_ALL	.483
143	ReAlnet08	.483
144	yudixie_resnet50_distance_reg_0_240908	.483
145	deit_small_imagenet_100_seed-0	.483
146	resnet101_imagenet_full	.483
147	ReAlnet08_cornet	.483
148	mobilenet_v2_0_5_192	.474
149	ReAlnet05_ALL	.474
150	ReAlnet03	.437
151	ReAlnet03_cornet	.437
152	xception	.437
153	resnet50-moclr8deg	.437
154	resnet50-vicregl0p75	.437
155	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.430
156	mobilenet_v2_1-4_224_pytorch	.430
157	yudixie_resnet50_distance_translation_0_240908	.430
158	nasnet_mobile	.430
159	mobilenet_v2_1_4_224	.430
160	deit_small_imagenet_10_seed-0	.396
161	resnet18_imagenet_full	.396
162	shufflenet_v2_x1_0	.396
163	vgg_16	.396
164	yudixie_resnet50_object_class_0_240908	.389
165	resnet18_ecoset_full	.389
166	compact_resnet50_robust_V4	.389
167	yudixie_resnet50_translation_rotation_0_240908	.389
168	ReAlnet07	.358
169	alexnet_training_seed_04	.358
170	yudixie_resnet18_distance_translation_rotation_0_240719	.358
171	ReAlnet04_cornet	.358
172	yudixie_resnet18_object_class_0_240719	.358
173	ReAlnet07_cornet	.358
174	mobilenet_v2_1_0_224	.358
175	ReAlnet04	.358
176	resnet_50_v1_spiking	.352
177	resnet50-SIN_IN	.352
178	resnet_101_v1	.352
179	alexnet_training_seed_06	.324
180	resnet50_ecoset_full	.324
181	convnext_small_imagenet_10_seed-0	.319
182	yudixie_resnet50_rotation_reg_0_240908	.319
183	compact_vgg19_IT	.319
184	ReAlnet06	.293
185	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.293
186	resnet50-SIN	.293
187	ReAlnet06_cornet	.293
188	ReAlnet02_ALL	.293
189	ReAlnet07_ALL	.293
190	resnet_50_v1_spiking_l4	.266
191	yudixie_resnet18_rotation_reg_0_240719	.266
192	yudixie_resnet50_translation_reg_0_240908	.266
193	resnet50-barlow	.266
194	densenet-121	.266
195	mobilenet_v2_0_75_192	.261
196	resnet50_tutorial	.261
197	yudixie_resnet18_distance_translation_0_240719	.261
198	VOneCORnet-S	.261
199	resnet50_imagenet_10_seed-0	.240
200	compact_resnet50_IT	.240
201	resnet152_ecoset_full	.236
202	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.236
203	yudixie_resnet18_distance_reg_0_240719	.236
204	resnet101_ecoset_full	.218
205	resnet-50-robust	.218
206	ReAlnet01_cornet	.218
207	ReAlnet01	.218
208	yudixie_resnet50_category_class_0_240908	.214
209	alexnet_training_seed_07	.178
210	mobilenet_v2_1_0_160	.175
211	convnext_small_imagenet_1_seed-0	.175
212	deit_small_imagenet_1_seed-0	.159
213	yudixie_resnet18_translation_reg_0_240719	.159
214	resnet50_primary_visual_cortex	.159
215	alexnet_training_seed_05	.146
216	alexnet_training_seed_01	.146
217	yudixie_resnet18_random_0_240719	.146
218	alexnet_training_seed_08	.098
219	vgg_19	.098
220	compact_cornets_IT	.098
221	alexnet_training_seed_02	.089
222	artResNet18_1	.089
223	compact_cornets_V4	.087
224	alexnet_training_seed_09	.066
225	briaai_rmbg_1_4	.060
226	alexnet_training_seed_03	.022
227	alexnet_training_seed_10
228	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

0.90.

Note that scores are relative to this ceiling.

Data: Ferguson2024color

Metric: value_delta