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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	resnet_101_v2	1.0
3	BiT-S-R50x3	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	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
12	resnet152_imagenet_full	.882
13	deit_small_imagenet_full_seed-0	.882
14	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.882
15	nasnet_large	.882
16	resnet50-moclr8deg	.882
17	BiT-S-R152x2	.882
18	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.845
19	cvt_cvt-21-384-in1k_4_LucyV4	.845
20	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.845
21	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.845
22	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.845
23	vit_large_patch14_clip_224:openai_ft_in1k	.845
24	vit_large_patch14_clip_336:laion2b_ft_in1k	.845
25	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.845
26	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.845
27	ViT_L_32_imagenet1k	.845
28	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.845
29	deit_large_imagenet_full_seed-0	.845
30	cvt_cvt-13-384-in1k_4_LucyV4	.845
31	compact_resnet50_V4	.845
32	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.845
33	deit_base_imagenet_full_seed-0	.845
34	vit_base_patch16_clip_224:openai_ft_in1k	.845
35	vit_large_patch14_clip_224:laion2b_ft_in1k	.845
36	resnet101_ecoset_full	.845
37	vit_relpos_base_patch16_clsgap_224:sw_in1k	.845
38	AdvProp_efficientnet-b7	.845
39	AdvProp_efficientnet-b8	.845
40	BiT-S-R152x4	.845
41	convnext_large:fb_in22k_ft_in1k	.845
42	compact_resnet50_IT	.800
43	compact_vgg19_V4	.800
44	nasnet_mobile	.800
45	BiT-S-R101x3	.800
46	grcnn	.767
47	vonegrcnn_62e_nobn	.767
48	densenet-169	.767
49	convnext_xlarge:fb_in22k_ft_in1k	.725
50	yudixie_resnet18_translation_reg_0_240719	.725
51	omnivore_swinS	.725
52	resnet_152_v1	.725
53	convnext_tiny:in12k_ft_in1k	.695
54	mobilenet_v2_1_0_128	.695
55	resnet152_ecoset_full	.695
56	AdvProp_efficientnet-b6	.695
57	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.658
58	yudixie_resnet50_random_0_240908	.630
59	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.630
60	resnet-50-robust	.630
61	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.630
62	yudixie_resnet18_distance_translation_0_240719	.630
63	deit_small_imagenet_100_seed-0	.596
64	cvt_cvt-13-224-in1k_4_LucyV4	.596
65	resnet101_imagenet_full	.596
66	yolos_tiny	.572
67	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.572
68	imagenet_l2_3_0	.572
69	resnet50_robust_l2_eps3	.572
70	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.541
71	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.541
72	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
73	pnasnet_large	.518
74	resnet50-barlow	.518
75	mobilenet_v2_1_4_224	.518
76	resnet-152_v2_pytorch	.518
77	focalnet_tiny_lrf_in1k	.518
78	mobilenet_v2_1-4_224_pytorch	.518
79	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.518
80	alexnet_training_seed_01	.490
81	compact_cornets_IT	.490
82	resnet50-vicregl0p75	.490
83	resnet50_imagenet_full	.470
84	cvt_cvt-21-224-in1k_4_LucyV4	.470
85	effnetb1_272x240	.470
86	resnet34_ecoset_full	.470
87	resnet50_ecoset_full	.470
88	BiT-S-R101x1	.470
89	resnet_101_v1	.470
90	BiT-S-R50x1	.470
91	resnext101_32x8d_wsl	.445
92	alexnet_training_seed_10	.445
93	compact_vgg19_IT	.445
94	blt_vs	.426
95	ReAlnet09	.426
96	ReAlnet10	.426
97	pnasnet_large_pytorch	.426
98	yudixie_resnet18_random_0_240719	.426
99	resnet50-vicreg	.426
100	ReAlnet09_cornet	.426
101	ReAlnet10_cornet	.426
102	resnext101_32x32d_wsl	.403
103	resnext101_32x48d_wsl	.403
104	resnet_50_v1_spiking_l4	.387
105	mobilenet_v2_1_0_192	.387
106	ReAlnet07_ALL	.351
107	resnet_SIN_IN_FT_IN	.351
108	alexnet_training_seed_09	.332
109	alexnet_training_seed_07	.332
110	xception	.332
111	ReAlnet07	.318
112	inception_v3	.318
113	convnext_small_imagenet_1_seed-0	.318
114	ReAlnet07_cornet	.318
115	voneresnet-50-robust	.318
116	compact_resnet50_robust_V4	.301
117	yudixie_resnet50_distance_rotation_0_240908	.288
118	yudixie_resnet18_rotation_reg_0_240719	.288
119	yudixie_resnet18_distance_reg_0_240719	.288
120	artResNet18_1	.288
121	antialiased-r50	.288
122	vonegrcnn_52e_full	.288
123	ReAlnet05	.261
124	ReAlnet05_cornet	.261
125	yudixie_resnet18_distance_translation_rotation_0_240719	.261
126	ReAlnet01_ALL	.261
127	resnet_50_v1_spiking_l3	.261
128	alexnet_training_seed_02	.247
129	omnivore_swinB	.247
130	densenet-201	.237
131	resnet50_imagenet_100_seed-0	.237
132	convnext_large_imagenet_full_seed-0	.237
133	resnet50_imagenet_1_seed-0	.237
134	efficientnet_b0	.237
135	ReAlnet10_ALL	.237
136	resnet34_imagenet_full	.215
137	efficientnet_b2_imagenet_full	.215
138	yudixie_resnet50_rotation_reg_0_240908	.215
139	vonegrcnn_47e	.215
140	ReAlnet02_ALL	.195
141	convnext_femto_ols:d1_in1k	.195
142	resnet_50_v2	.195
143	alexnet_training_seed_05	.184
144	compact_resnet50_robust_IT	.184
145	mobilenet_v2_0_5_224	.177
146	mobilenet_v2_0_75_160	.177
147	efficientnet-b7	.177
148	ReAlnet03_ALL	.177
149	convnext_small_imagenet_full_seed-0	.160
150	ReAlnet01_cornet	.160
151	resnet18_imagenet_full	.160
152	grcnn_109	.160
153	ReAlnet01	.160
154	resnet_152_v2	.160
155	resnet50_primary_visual_cortex	.160
156	deit_small_imagenet_10_seed-0	.152
157	compact_cornets_V4	.152
158	alexnet_training_seed_03	.152
159	ReAlnet02_cornet	.145
160	ReAlnet02	.145
161	resnet50-vicregl0p9	.145
162	hmax	.138
163	ReAlnet04_cornet	.132
164	ReAlnet04	.132
165	alexnet_training_seed_08	.125
166	yudixie_resnet50_category_class_0_240908	.120
167	resnet_50_v1_spiking_l4a	.120
168	yudixie_resnet18_distance_rotation_0_240719	.120
169	deit_small_imagenet_1_seed-0	.120
170	ReAlnet04_ALL	.120
171	convnext_small_imagenet_10_seed-0	.108
172	convnext_base_imagenet_full_seed-0	.108
173	resnet50-SIN_IN_IN	.108
174	Res2Net50_26w_4s	.108
175	ReAlnet05_ALL	.108
176	briaai_rmbg_1_4	.103
177	ReAlnet03	.098
178	ReAlnet03_cornet	.098
179	inception_v4	.098
180	yudixie_resnet18_translation_rotation_0_240719	.098
181	ReAlnet06_ALL	.098
182	resnet50_imagenet_10_seed-0	.089
183	mobilenet_v2_0_5_192	.089
184	yudixie_resnet50_translation_reg_0_240908	.089
185	yudixie_resnet18_object_class_0_240719	.089
186	AdvProp_efficientnet-b2	.089
187	AT_efficientnet-b2	.089
188	resnet50_tutorial	.081
189	resnet18_ecoset_full	.081
190	resnet-18_test_m	.081
191	alexnet_training_seed_04	.076
192	resnet50_ewconplus_wzc	.073
193	ReAlnet08	.073
194	resnet50-SIN_IN	.073
195	resnet50-SIN	.073
196	yudixie_resnet50_distance_translation_0_240908	.073
197	shufflenet_v2_x1_0	.073
198	ReAlnet08_cornet	.073
199	vgg_19	.073
200	ReAlnet08_ALL	.073
201	ReAlnet09_ALL	.073
202	convnext_tiny_imagenet_full_seed-0	.066
203	voneresnet-50	.066
204	yudixie_resnet50_distance_translation_rotation_0_240908	.066
205	yudixie_resnet50_imagenet1kpret_0_240908	.060
206	ReAlnet06	.060
207	yudixie_resnet50_object_class_0_240908	.060
208	VOneCORnet-S	.060
209	ReAlnet06_cornet	.060
210	vgg_16	.060
211	convnext_tiny_sup	.060
212	mobilenet_v2_0_75_224	.055
213	yudixie_resnet50_distance_reg_0_240908	.055
214	convnext_small_imagenet_100_seed-0	.055
215	yudixie_resnet18_imagenet1kpret_0_240719	.055
216	alexnet_training_seed_06	.052
217	mobilenet_v2_1_0_160	.050
218	yudixie_resnet18_category_class_0_240719	.050
219	voneresnet-50-non_stochastic	.050
220	efficientnet_b1_imagenet_full	.041
221	efficientnet_b0_imagenet_full	.041
222	inception_v1	.041
223	mobilenet_v2_1_0_224	.037
224	resnet_50_v1	.034
225	resnet50-sup	.034
226	AdvProp_efficientnet-b4	.028
227	mobilenet_v2_0_75_192	.025
228	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