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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024round_v-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	1.0
2	mobilenet_v2_0_75_192	1.0
3	ReAlnet01_cornet	1.0
4	resnet50_ecoset_full	1.0
5	grcnn	1.0
6	voneresnet-50-robust	1.0
7	ReAlnet01	1.0
8	yudixie_wbh_object_class_scaling_0415_run_0	1.0
9	dinov2_vitl14_lc_res336	1.0
10	vit_large_clip336_openai	1.0
11	vit_huge_clip336_laion	1.0
12	vit_large_patch14_clip_224:openai_ft_in12k_in1k	1.0
13	resnet152_ecoset_full	1.0
14	yudixie_resnet18_distance_translation_0_240719	1.0
15	yudixie_resnet18_object_class_0_240719	1.0
16	ReAlnet08	1.0
17	resnet50-SIN	1.0
18	nasnet_large	1.0
19	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
20	ReAlnet08_cornet	1.0
21	alexnet_training_seed_01_fov4	1.0
22	resnet_152_v2	1.0
23	convnext_femto_ols:d1_in1k	1.0
24	convnext_small_imagenet_10_seed-0	1.0
25	ReAlnet06	1.0
26	yudixie_resnet50_category_class_0_240908	1.0
27	alexnet_training_seed_04	1.0
28	inception_v4	1.0
29	resnet34_ecoset_full	1.0
30	ReAlnet01_cornet_fov4	1.0
31	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
32	ReAlnet04_cornet	1.0
33	Res2Net50_26w_4s	1.0
34	ReAlnet06_cornet	1.0
35	ReAlnet04	1.0
36	convnext_xlarge:fb_in22k_ft_in1k	.924
37	convnext_base_imagenet_full_seed-0	.924
38	resnet101_imagenet_full	.924
39	mobilenet_v2_0_5_224	.924
40	mobilenet_v2_1-4_224_pytorch	.924
41	ReAlnet01_ALL	.924
42	omnivore_swinB	.924
43	mobilenet_v2_1_4_224	.924
44	vit_large_patch14_clip_336:laion2b_ft_in1k	.905
45	convnext_tiny_imagenet_full_seed-0	.905
46	vit_base_patch16_dinov3-lvd1689m	.905
47	efficientnet-b7	.905
48	compact_vgg19_IT	.905
49	voneresnet-50	.905
50	deepeisnn_ei_imagenet_resnet18	.905
51	deit_small_imagenet_100_seed-0	.836
52	deit_large_imagenet_full_seed-0	.836
53	ReAlnet09	.836
54	resnet101_ecoset_full	.836
55	AlexNet_SIN_fov12	.836
56	nasnet_mobile	.836
57	ReAlnet09_cornet	.836
58	inception_v1	.836
59	vit_large_patch14_clip_224:openai_ft_in1k	.820
60	resnet18_imagenet_full	.820
61	resnext101_32x32d_wsl	.820
62	focalnet_tiny_lrf_in1k	.820
63	resnet152_imagenet_full	.820
64	blt_vs	.820
65	yudixie_resnet18_distance_reg_0_240719	.820
66	resnet50-vicreg	.820
67	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.820
68	convnext_large_in22k_supervised	.820
69	resnet34_imagenet_full	.757
70	yudixie_resnet50_distance_reg_0_240908	.757
71	ViT_L_32_imagenet1k	.757
72	alexnet_training_seed_10	.757
73	vgg_16	.757
74	vonegrcnn_62e_nobn	.757
75	ReAlnet05	.742
76	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.742
77	ReAlnet01_cornet_fov12	.742
78	alexnet_training_seed_01	.742
79	ReAlnet05_cornet	.742
80	resnet18_fair	.742
81	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.686
82	shufflenet_v2_x1_0	.686
83	ReAlnet02_ALL	.686
84	ReAlnet05_ALL	.686
85	ReAlnet10_ALL	.686
86	deit3_base_patch16_224_fb_in22k_ft_in1k	.686
87	alexnet_fov4	.672
88	efficientnet_b2_imagenet_full	.672
89	custom_model_cv_18_dagger_408	.672
90	efficientnet_b0	.672
91	resnet_50_v1_spiking_l3	.672
92	AlexNet_SIN_fov4	.672
93	vgg_19	.672
94	AdvProp_efficientnet-b7	.672
95	ReAlnet07_ALL	.672
96	resnet_50_v2	.672
97	resnet50_st_wzc_seed7_2_323	.672
98	yudixie_wbh_object_class_scaling_0415_run_3	.672
99	inception_v3	.621
100	convnext_small_imagenet_full_seed-0	.621
101	deit_small_imagenet_10_seed-0	.621
102	deit_base_imagenet_full_seed-0	.621
103	resnet18_ecoset_full	.621
104	ReAlnet03_ALL	.621
105	omnivore_swinS	.621
106	AdvProp_efficientnet-b4	.621
107	ReAlnet08_ALL	.621
108	densenet-169	.621
109	resnext101_32x48d_wsl	.621
110	yudixie_resnet50_distance_translation_rotation_0_240908	.609
111	ReAlnet03	.609
112	ReAlnet07	.609
113	voneresnet-50-non_stochastic	.609
114	ReAlnet03_cornet	.609
115	ReAlnet07_cornet	.609
116	compact_cornets_V4	.609
117	compact_cornets_IT	.609
118	grcnn_109	.609
119	vonegrcnn_47e	.609
120	yudixie_wbh_object_class_scaling_0415_run_1	.609
121	convnext_xxlarge_clip	.609
122	alexnet_fov12	.562
123	yudixie_resnet18_imagenet1kpret_0_240719	.562
124	AdvProp_efficientnet-b8	.562
125	convnext_tiny_sup	.562
126	alexnet_training_seed_05	.551
127	resnet-50-robust	.551
128	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.551
129	ReAlnet02_cornet	.551
130	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.551
131	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.551
132	yudixie_resnet18_translation_reg_0_240719	.551
133	resnet_SIN_IN_FT_IN	.551
134	ReAlnet02	.551
135	alexnet_training_seed_03	.509
136	cvt_cvt-21-384-in1k_4_LucyV4	.509
137	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.509
138	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
139	aughost_fair_resnet18	.509
140	resnet50_hvs_wzc	.509
141	convnext_large_clip_laion2b_finelayers	.509
142	hmax	.509
143	mobilenet_v2_0_75_224	.499
144	resnet50_st_wzc_seed3_2_323	.499
145	resnet_50_v1_spiking_l4a	.499
146	convnext_small_imagenet_1_seed-0	.499
147	compact_resnet50_robust_IT	.499
148	BiT-S-R50x1	.499
149	dinov2_vitg14_reg_lc	.499
150	convnext_large_imagenet_full_seed-0	.461
151	cv_18_dagger_408_pretrained	.461
152	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.461
153	resnet50_imagenet_100_seed-0	.461
154	vit_base_patch16_clip_224:openai_ft_in1k	.461
155	ReAlnet06_ALL	.461
156	dinov2_vitl14_reg_lc	.452
157	convnext_base:clip_laiona_augreg_ft_in1k_384	.417
158	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.417
159	cvt_cvt-13-224-in1k_4_LucyV4	.417
160	resnet50-SIN_IN	.417
161	resnet-18_test_m	.417
162	resnet50_primary_visual_cortex	.417
163	pr1_stl10_disentangled_alpha04	.417
164	mobilenet_v2_0_75_160	.409
165	vit_base_patch14_dinov2-lvd142m	.409
166	yudixie_resnet18_distance_rotation_0_240719	.409
167	vit_relpos_base_patch16_clsgap_224:sw_in1k	.409
168	resnet50-vitoimagevidnet8	.409
169	mobilenet_v2_1_3_224	.409
170	resnet50-VITO-8deg-cc	.409
171	resnet_50_v1	.409
172	resnet_101_v2	.409
173	resnet50-sup	.409
174	resnet_152_v1	.409
175	dinov2_vitg14_lc	.409
176	effnetb1_272x240	.378
177	VOneCORnet-S	.378
178	imagenet_l2_3_0	.378
179	resnet50_robust_l2_eps3	.378
180	BiT-S-R50x3	.378
181	ReAlnet09_ALL	.378
182	yudixie_wbh_object_class_scaling_0415_imagenet	.378
183	resnet50_imagenet_full	.370
184	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.370
185	ReAlnet10	.370
186	briaai_rmbg_1_4	.370
187	resnet50-SIN_IN_IN	.370
188	xception	.370
189	AlexNet_SIN_fov16	.370
190	ReAlnet10_cornet	.370
191	convnext_large:fb_in22k_ft_in1k	.370
192	cvt_cvt-13-384-in1k_4_LucyV4	.342
193	artResNet18_1	.342
194	resnet50-vicregl0p9	.342
195	yudixie_resnet50_random_0_240908	.335
196	yudixie_resnet50_object_class_0_240908	.335
197	resnet50_ewconplus_wzc	.335
198	AlexNet_SIN	.335
199	deit3_base_patch16_224_fb_in1k	.335
200	yudixie_resnet50_distance_translation_0_240908	.310
201	convnext_base_fb_in1k	.310
202	compact_resnet50_IT	.304
203	yudixie_resnet50_distance_rotation_0_240908	.304
204	resnet50-moclr8deg	.304
205	alexnet_training_seed_01_fov16	.304
206	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.281
207	efficientnet_b0_imagenet_full	.281
208	eva02_large_in22k	.281
209	yudixie_resnet50_imagenet1kpret_0_240908	.275
210	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.275
211	resnet50_imagenet_10_seed-0	.275
212	ReAlnet01_cornet_fov16	.275
213	mobilenet_v2_1_0_224	.275
214	densenet-121	.275
215	deit_small_imagenet_full_seed-0	.254
216	densenet-201	.254
217	AdvProp_efficientnet-b6	.254
218	vit_base_patch14_reg4_dinov2-lvd142m	.254
219	alexnet	.249
220	deit_small_imagenet_1_seed-0	.249
221	resnet50_robust_l2_eps1	.249
222	alexnet_training_seed_01_fov12	.249
223	BiT-S-R152x2	.249
224	predictive_coding_pc	.249
225	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.230
226	resnet50-barlow	.230
227	ReAlnet04_ALL	.230
228	vonegrcnn_52e_full	.230
229	AdvProp_efficientnet-b2	.230
230	BiT-S-R152x4	.230
231	AT_efficientnet-b2	.230
232	yudixie_wbh_object_class_scaling_0415_run_2	.230
233	alexnet_training_seed_09	.225
234	resnet_50_v1_spiking_l4	.225
235	alexnet_training_seed_02	.225
236	alexnet_training_seed_08	.225
237	yudixie_resnet18_translation_rotation_0_240719	.225
238	yudixie_resnet18_distance_translation_rotation_0_240719	.225
239	yudixie_resnet50_translation_rotation_0_240908	.225
240	resnet_101_v1	.225
241	dinov2_vitl14_reg_lc_featread	.225
242	mobilenet_v2_1_0_128	.208
243	resnet-152_v2_pytorch	.208
244	convnext_base_fb_in22k_ft_in1k	.208
245	convnext_base_fb_in1k_gram_aligned_dinov3	.208
246	mobilenet_v2_1_0_160	.204
247	mobilenet_v2_1_0_192	.204
248	compact_vgg19_V4	.204
249	antialiased-r50	.204
250	mobilenet_v2_0_5_192	.189
251	convnext_tiny:in12k_ft_in1k	.185
252	alexnet_training_seed_06	.185
253	yudixie_resnet50_rotation_reg_0_240908	.185
254	cvt_cvt-21-224-in1k_4_LucyV4	.171
255	alexnet_fov16	.167
256	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.155
257	resnet50-vicregl0p75	.155
258	efficientnet_b1_imagenet_full	.140
259	resnet50_st_wzc_2	.140
260	resnet50_tutorial	.137
261	alexnet_training_seed_07	.137
262	pnasnet_large_pytorch	.124
263	deepeisnn_ei_cifar10_vgg19	.124
264	resnext101_32x8d_wsl	.102
265	deepeisnn_ei_cifar10_resnet18	.102
266	pnasnet_large_halfPrecision	.083
267	BiT-S-R101x3	.083
268	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.076
269	convnext_small_imagenet_100_seed-0	.070
270	resnet50_imagenet_1_seed-0	.068
271	yudixie_resnet18_category_class_0_240719	.068
272	yudixie_resnet18_rotation_reg_0_240719	.068
273	pnasnet_large	.062
274	compact_resnet50_V4	.051
275	compact_resnet50_robust_V4	.051
276	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.035
277	yudixie_resnet50_translation_reg_0_240908	.034
278	resnet_50_v1_spiking	.025
279	yolos_tiny	.025
280	BiT-S-R101x1	.025
281	yudixie_resnet18_random_0_240719	.023
282	resnet50_ewconplus_wzc_seed5_2__323
283	dinov2_vitl14_lc

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

0.85.

Note that scores are relative to this ceiling.

Data: Ferguson2024round_v

Metric: value_delta