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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024gray_hard-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	deit_small_imagenet_100_seed-0	1.0
2	deit_large_imagenet_full_seed-0	1.0
3	ReAlnet10	1.0
4	pnasnet_large_pytorch	1.0
5	compact_resnet50_robust_V4	1.0
6	resnet101_ecoset_full	1.0
7	omnivore_swinB	1.0
8	artResNet18_1	1.0
9	ReAlnet10_cornet	1.0
10	BiT-S-R101x1	1.0
11	convnext_tiny:in12k_ft_in1k	1.0
12	yudixie_resnet18_object_class_0_240719	1.0
13	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
14	convnext_femto_ols:d1_in1k	1.0
15	inception_v4	1.0
16	ReAlnet01_ALL	1.0
17	vgg_16	1.0
18	deit_small_imagenet_1_seed-0	.971
19	ReAlnet02_ALL	.971
20	voneresnet-50-robust	.971
21	compact_resnet50_robust_IT	.971
22	BiT-S-R152x2	.971
23	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
24	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.942
25	resnet_50_v1_spiking_l4	.942
26	vit_large_patch14_clip_224:openai_ft_in1k	.942
27	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
28	vit_large_patch14_clip_336:laion2b_ft_in1k	.942
29	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.942
30	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
31	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.942
32	vit_large_patch14_clip_224:laion2b_ft_in1k	.942
33	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
34	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
35	resnet50-VITO-8deg-cc	.942
36	nasnet_mobile	.942
37	resnet50-vitoimagevidnet8	.942
38	yudixie_resnet50_imagenet1kpret_0_240908	.883
39	deit_small_imagenet_10_seed-0	.883
40	nasnet_large	.883
41	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.883
42	convnext_large:fb_in22k_ft_in1k	.883
43	ReAlnet05	.855
44	alexnet_training_seed_06	.855
45	compact_resnet50_IT	.855
46	ReAlnet05_cornet	.855
47	custom_model_cv_18_dagger_408	.855
48	resnet_SIN_IN_FT_IN	.802
49	resnet50-vicregl0p9	.802
50	ReAlnet07	.777
51	alexnet_training_seed_08	.777
52	briaai_rmbg_1_4	.777
53	resnet50_imagenet_full	.777
54	ReAlnet07_cornet	.777
55	grcnn	.777
56	yudixie_resnet50_distance_translation_rotation_0_240908	.777
57	yudixie_resnet50_object_class_0_240908	.728
58	convnext_xlarge:fb_in22k_ft_in1k	.728
59	cvt_cvt-21-224-in1k_4_LucyV4	.728
60	yudixie_resnet18_distance_translation_rotation_0_240719	.728
61	yudixie_resnet50_rotation_reg_0_240908	.728
62	ReAlnet07_ALL	.728
63	resnet50-vicregl0p75	.728
64	focalnet_tiny_lrf_in1k	.728
65	cvt_cvt-13-224-in1k_4_LucyV4	.706
66	ViT_L_32_imagenet1k	.706
67	resnet50-vicreg	.706
68	VOneCORnet-S	.706
69	mobilenet_v2_1_0_224	.706
70	resnet_152_v2	.706
71	ReAlnet09_ALL	.706
72	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.662
73	resnet50_tutorial	.662
74	ReAlnet04_ALL	.662
75	ReAlnet06_ALL	.662
76	ReAlnet03	.641
77	yolos_tiny	.641
78	resnet18_imagenet_full	.641
79	ReAlnet03_cornet	.641
80	vit_base_patch16_clip_224:openai_ft_in1k	.641
81	resnet-18_test_m	.641
82	Res2Net50_26w_4s	.641
83	mobilenet_v2_1_3_224	.641
84	ReAlnet05_ALL	.641
85	omnivore_swinS	.641
86	inception_v1	.641
87	alexnet_training_seed_05	.601
88	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.601
89	yudixie_resnet18_distance_rotation_0_240719	.601
90	resnet18_ecoset_full	.583
91	resnet50_ewconplus_wzc	.583
92	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.583
93	mobilenet_v2_1_0_128	.583
94	cvt_cvt-13-384-in1k_4_LucyV4	.583
95	resnet50_imagenet_1_seed-0	.583
96	yudixie_resnet18_random_0_240719	.583
97	AdvProp_efficientnet-b7	.583
98	pnasnet_large	.546
99	ReAlnet03_ALL	.546
100	resnet_101_v2	.546
101	resnet_50_v1_spiking_l4a	.529
102	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.529
103	compact_vgg19_IT	.529
104	BiT-S-R101x3	.529
105	yudixie_resnet50_distance_reg_0_240908	.496
106	alexnet_training_seed_07	.496
107	deit_base_imagenet_full_seed-0	.481
108	blt_vs	.481
109	ReAlnet09	.481
110	compact_resnet50_V4	.481
111	yudixie_resnet18_translation_reg_0_240719	.481
112	ReAlnet09_cornet	.481
113	vonegrcnn_47e	.481
114	alexnet_training_seed_01	.451
115	ReAlnet08_ALL	.451
116	convnext_tiny_imagenet_full_seed-0	.437
117	densenet-201	.437
118	ReAlnet01_cornet	.437
119	resnext101_32x8d_wsl	.437
120	hmax	.437
121	mobilenet_v2_1-4_224_pytorch	.437
122	vgg_19	.437
123	mobilenet_v2_1_4_224	.437
124	ReAlnet01	.437
125	resnet_101_v1	.437
126	alexnet_training_seed_03	.437
127	convnext_base:clip_laiona_augreg_ft_in1k_384	.437
128	mobilenet_v2_1_0_192	.410
129	resnet50-SIN_IN_IN	.410
130	xception	.410
131	convnext_small_imagenet_100_seed-0	.397
132	yudixie_resnet50_random_0_240908	.397
133	ReAlnet08	.397
134	alexnet_training_seed_04	.397
135	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.397
136	alexnet_training_seed_10	.397
137	resnet50-SIN	.397
138	compact_vgg19_V4	.397
139	ReAlnet04_cornet	.397
140	ReAlnet08_cornet	.397
141	resnet50-barlow	.397
142	resnet50-moclr8deg	.397
143	ReAlnet04	.397
144	resnet50_primary_visual_cortex	.397
145	resnet50_imagenet_10_seed-0	.372
146	resnext101_32x32d_wsl	.372
147	deit_small_imagenet_full_seed-0	.361
148	AdvProp_efficientnet-b6	.361
149	antialiased-r50	.361
150	densenet-169	.361
151	yudixie_resnet18_translation_rotation_0_240719	.338
152	compact_cornets_IT	.338
153	efficientnet_b1_imagenet_full	.328
154	vonegrcnn_62e_nobn	.328
155	convnext_small_imagenet_1_seed-0	.307
156	resnext101_32x48d_wsl	.298
157	resnet34_ecoset_full	.298
158	resnet50_ecoset_full	.298
159	yudixie_resnet50_distance_translation_0_240908	.298
160	convnext_small_imagenet_10_seed-0	.279
161	ReAlnet10_ALL	.279
162	ReAlnet06	.270
163	alexnet_training_seed_02	.270
164	mobilenet_v2_0_75_160	.270
165	mobilenet_v2_0_5_224	.270
166	resnet50_imagenet_100_seed-0	.270
167	resnet34_imagenet_full	.270
168	yudixie_resnet50_translation_reg_0_240908	.270
169	ReAlnet06_cornet	.270
170	voneresnet-50	.270
171	convnext_tiny_sup	.270
172	yudixie_resnet18_rotation_reg_0_240719	.253
173	resnet152_ecoset_full	.246
174	voneresnet-50-non_stochastic	.246
175	ReAlnet02_cornet	.246
176	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.246
177	ReAlnet02	.246
178	yudixie_resnet50_category_class_0_240908	.230
179	resnet101_imagenet_full	.223
180	convnext_small_imagenet_full_seed-0	.223
181	resnet_50_v1_spiking_l3	.223
182	AdvProp_efficientnet-b8	.223
183	BiT-S-R50x1	.223
184	densenet-121	.223
185	resnet_50_v1	.203
186	resnet50-sup	.203
187	AdvProp_efficientnet-b4	.203
188	BiT-S-R152x4	.190
189	inception_v3	.184
190	resnet152_imagenet_full	.184
191	efficientnet_b0_imagenet_full	.184
192	yudixie_resnet18_category_class_0_240719	.184
193	alexnet_training_seed_09	.167
194	yudixie_resnet50_translation_rotation_0_240908	.167
195	grcnn_109	.167
196	vonegrcnn_52e_full	.167
197	BiT-S-R50x3	.167
198	cvt_cvt-21-384-in1k_4_LucyV4	.157
199	effnetb1_272x240	.152
200	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.152
201	yudixie_resnet18_distance_translation_0_240719	.152
202	shufflenet_v2_x1_0	.152
203	resnet_50_v2	.152
204	mobilenet_v2_1_0_160	.138
205	resnet_152_v1	.138
206	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.126
207	yudixie_resnet50_distance_rotation_0_240908	.126
208	yudixie_resnet18_imagenet1kpret_0_240719	.126
209	convnext_large_imagenet_full_seed-0	.126
210	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.114
211	mobilenet_v2_0_75_224	.114
212	yudixie_resnet18_distance_reg_0_240719	.104
213	efficientnet-b7	.104
214	efficientnet_b0	.086
215	resnet-152_v2_pytorch	.078
216	convnext_base_imagenet_full_seed-0	.078
217	resnet50_robust_l2_eps1	.073
218	resnet50-SIN_IN	.071
219	resnet-50-robust	.064
220	imagenet_l2_3_0	.064
221	resnet50_robust_l2_eps3	.064
222	compact_cornets_V4	.053
223	mobilenet_v2_0_5_192	.048
224	mobilenet_v2_0_75_192	.048
225	resnet_50_v1_spiking	.044
226	AdvProp_efficientnet-b2	.033
227	AT_efficientnet-b2	.033
228	efficientnet_b2_imagenet_full	.020

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

0.86.

Note that scores are relative to this ceiling.

Data: Ferguson2024gray_hard

Metric: value_delta