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