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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024quarter-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	eva02_large_in22k	1.0
2	mobilenet_v2_0_5_224	1.0
3	yudixie_resnet50_translation_reg_0_240908	1.0
4	vit_large_patch14_clip_224:laion2b_ft_in1k	1.0
5	omnivore_swinB	1.0
6	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	1.0
7	pnasnet_large_halfPrecision	1.0
8	resnet50_primary_visual_cortex	1.0
9	mobilenet_v2_0_75_160	1.0
10	cv_18_dagger_408_pretrained	1.0
11	alexnet_training_seed_04	1.0
12	resnet_50_v1	1.0
13	resnet50-sup	1.0
14	resnet50_ecoset_full	1.0
15	resnext101_32x8d_wsl	1.0
16	resnet_152_v1	.950
17	inception_v4	.950
18	mobilenet_v2_0_75_192	.950
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.950
20	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.950
21	convnext_large:fb_in22k_ft_in1k	.950
22	deit_large_imagenet_full_seed-0	.950
23	resnet34_ecoset_full	.950
24	resnet18_imagenet_full	.950
25	ViT_L_32_imagenet1k	.950
26	effnetb1_272x240	.950
27	convnext_xxlarge_clip	.923
28	BiT-S-R152x2	.923
29	resnet18_ecoset_full	.923
30	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.923
31	resnet_50_v1_spiking_l4a	.923
32	resnext101_32x48d_wsl	.923
33	cvt_cvt-21-224-in1k_4_LucyV4	.860
34	seresnext50_32x4d	.836
35	dinov2_vitl14_reg_lc	.836
36	mobilenet_v2_1_0_224	.836
37	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.836
38	convnext_tiny:in12k_ft_in1k	.836
39	resnet_SIN_IN_FT_IN	.836
40	antialiased-r50	.836
41	resnet50_tutorial	.836
42	ReAlnet01_cornet_fov16	.836
43	vit_large_patch14_clip_336:laion2b_ft_in1k	.779
44	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.779
45	convnext_base_fb_in22k_ft_in1k	.757
46	yudixie_resnet50_category_class_0_240908	.757
47	grcnn	.757
48	efficientnet_b0	.757
49	deit_small_imagenet_1_seed-0	.757
50	resnet50_st_wzc_seed3_2_323	.757
51	ReAlnet01_cornet_fov4	.757
52	resnet50_hvs_wzc	.757
53	dinov2_vitl14_lc_res336	.706
54	alexnet_training_seed_05	.706
55	BiT-S-R152x4	.706
56	yudixie_resnet18_distance_reg_0_240719	.706
57	yudixie_resnet18_imagenet1kpret_0_240719	.706
58	cvt_cvt-21-384-in1k_4_LucyV4	.706
59	resnet_50_v1_spiking	.686
60	yudixie_resnet50_distance_translation_rotation_0_240908	.686
61	resnet_50_v2	.686
62	AT_efficientnet-b2	.686
63	AdvProp_efficientnet-b2	.686
64	convnext_large_imagenet_full_seed-0	.686
65	convnext_base_imagenet_full_seed-0	.686
66	resnet18_fair	.686
67	voneresnet-50-non_stochastic	.686
68	convnext_xlarge:fb_in22k_ft_in1k	.639
69	convnext_large_clip_laion2b_finelayers	.621
70	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.621
71	predictive_coding_pc	.621
72	efficientnet-b7	.621
73	convnext_tiny_sup	.621
74	convnext_small_imagenet_full_seed-0	.621
75	resnet50_ewconplus_wzc_seed5_2__323	.621
76	artResNet18_1	.579
77	alexnet_training_seed_10	.563
78	alexnet_training_seed_06	.563
79	vonegrcnn_62e_nobn	.563
80	resnet50_robust_l2_eps3	.563
81	imagenet_l2_3_0	.563
82	AdvProp_efficientnet-b7	.563
83	efficientnet_b2_imagenet_full	.563
84	resnet50_imagenet_1_seed-0	.563
85	resnet50_st_wzc_2	.563
86	dinov2_vitg14_reg_lc	.524
87	convnext_large_in22k_supervised	.524
88	convnext_base_fb_in1k_gram_aligned_dinov3	.524
89	cvt_cvt-13-224-in1k_4_LucyV4	.524
90	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.524
91	vit_base_patch14_dinov2-lvd142m	.524
92	gabor_filter_mean_abs_4x4_v2	.510
93	vit_huge_clip336_laion	.510
94	vit_large_clip336_openai	.510
95	dinov2_vitl14_reg_lc_featread	.510
96	yudixie_resnet50_imagenet1kpret_0_240908	.510
97	mobilenet_v2_1_0_128	.510
98	vgg_16	.510
99	resnet50-SIN_IN_IN	.510
100	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.510
101	densenet-201	.510
102	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.510
103	resnet50_robust_l2_eps1	.510
104	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.510
105	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.510
106	densenet-121	.510
107	resnet50-vicregl0p75	.510
108	BiT-S-R50x3	.510
109	deit_base_imagenet_full_seed-0	.510
110	resnet50_imagenet_100_seed-0	.510
111	resnet101_imagenet_full	.510
112	vit_base_patch16_dinov3-lvd1689m	.510
113	ReAlnet01_cornet_fov12	.510
114	resnet50-vicreg	.475
115	BiT-S-R50x1	.475
116	deit_small_imagenet_full_seed-0	.475
117	custom_model_cv_18_dagger_408	.475
118	resnet_50_v1_spiking_l3	.475
119	resnext101_32x32d_wsl	.475
120	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.475
121	yudixie_wbh_object_class_scaling_0415_run_3	.461
122	inception_v3	.461
123	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.461
124	densenet-169	.461
125	vonegrcnn_47e	.461
126	resnet-152_v2_pytorch	.461
127	AlexNet_SIN	.461
128	mobilenet_v2_1_0_160	.430
129	yudixie_resnet50_rotation_reg_0_240908	.430
130	vit_relpos_base_patch16_clsgap_224:sw_in1k	.430
131	yudixie_resnet18_random_0_240719	.430
132	gabor_filter_entropy_4x4_v2_forcedCosine	.418
133	yudixie_wbh_object_class_scaling_0415_run_0	.418
134	deit3_base_patch16_224_fb_in22k_ft_in1k	.418
135	mobilenet_v2_0_5_192	.418
136	grcnn_109	.418
137	Res2Net50_26w_4s	.418
138	yudixie_resnet18_distance_rotation_0_240719	.418
139	vit_base_patch16_clip_224:openai_ft_in1k	.390
140	yudixie_resnet18_distance_translation_0_240719	.390
141	vit_large_patch14_clip_224:openai_ft_in1k	.379
142	convnext_base:clip_laiona_augreg_ft_in1k_384	.379
143	blt_vs	.379
144	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.379
145	resnet50_imagenet_10_seed-0	.379
146	focalnet_tiny_lrf_in1k	.379
147	alexnet_training_seed_01_fov4	.379
148	dinov2_vitg14_lc	.353
149	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.353
150	pr1_stl10_disentangled_alpha04	.343
151	xception	.343
152	yudixie_resnet18_distance_translation_rotation_0_240719	.343
153	dinov2_vitl14_lc	.320
154	resnet-18_test_m	.320
155	briaai_rmbg_1_4	.311
156	alexnet_training_seed_02	.311
157	ReAlnet05_cornet	.311
158	yudixie_resnet50_distance_rotation_0_240908	.311
159	ReAlnet05	.311
160	compact_resnet50_IT	.311
161	nasnet_mobile	.289
162	resnet152_ecoset_full	.289
163	deepeisnn_ei_imagenet_resnet18	.281
164	yudixie_resnet50_distance_translation_0_240908	.281
165	deit_small_imagenet_100_seed-0	.281
166	deit_small_imagenet_10_seed-0	.281
167	efficientnet_b1_imagenet_full	.281
168	alexnet_fov16	.281
169	alexnet_fov12	.281
170	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.262
171	cvt_cvt-13-384-in1k_4_LucyV4	.262
172	eicircuit_resnet18_bn_20260919	.255
173	gabor_filter_entropy_4x4_v2	.255
174	ReAlnet07_cornet	.255
175	vgg_19	.255
176	ReAlnet07	.255
177	resnet50-SIN	.255
178	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.255
179	resnet152_imagenet_full	.255
180	alexnet	.255
181	resnet50_ewconplus_wzc_seed6_2_323	.255
182	alexnet_training_seed_01_fov12	.255
183	compact_cornets_IT	.255
184	compact_cornets_V4	.255
185	deepeisnn_ei_cifar10_resnet18	.231
186	mobilenet_v2_1_4_224	.231
187	resnet50-barlow	.231
188	BiT-S-R101x3	.231
189	yudixie_resnet18_translation_rotation_0_240719	.231
190	resnet34_imagenet_full	.231
191	mobilenet_v2_1-4_224_pytorch	.231
192	resnet-50-robust	.231
193	AlexNet_SIN_fov12	.231
194	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.215
195	vit_base_patch14_reg4_dinov2-lvd142m	.215
196	regnety_032	.209
197	yudixie_resnet50_distance_reg_0_240908	.209
198	ReAlnet10_cornet	.209
199	ReAlnet01_cornet	.209
200	ReAlnet10	.209
201	ReAlnet01	.209
202	VOneCORnet-S	.209
203	efficientnet_b0_imagenet_full	.209
204	alexnet_training_seed_01_fov16	.209
205	yudixie_wbh_object_class_scaling_0415_imagenet	.195
206	BiT-S-R101x1	.189
207	convnext_small_imagenet_1_seed-0	.189
208	resnet50_imagenet_full	.189
209	alexnet_fov4	.189
210	alexnet_training_seed_07	.171
211	ReAlnet09_cornet	.171
212	ReAlnet03_cornet	.171
213	resnet_101_v1	.171
214	inception_v1	.171
215	yudixie_resnet50_translation_rotation_0_240908	.171
216	ReAlnet09	.171
217	ReAlnet03	.171
218	shufflenet_v2_x1_0	.171
219	omnivore_swinS	.171
220	resnet101_ecoset_full	.171
221	resnet50_ewconplus_wzc	.171
222	ReAlnet04_cornet	.155
223	ReAlnet02_cornet	.155
224	resnet_152_v2	.155
225	ReAlnet02	.155
226	ReAlnet04	.155
227	voneresnet-50	.155
228	resnet50-vicregl0p9	.155
229	convnext_small_imagenet_10_seed-0	.155
230	yudixie_wbh_object_class_scaling_0415_run_1	.141
231	yudixie_resnet50_object_class_0_240908	.141
232	alexnet_training_seed_01	.141
233	ReAlnet06_cornet	.141
234	mobilenet_v2_1_0_192	.141
235	ReAlnet06	.141
236	resnet50-vitoimagevidnet8	.141
237	resnet50-VITO-8deg-cc	.141
238	convnext_femto_ols:d1_in1k	.141
239	AdvProp_efficientnet-b8	.141
240	convnext_small_imagenet_100_seed-0	.141
241	AlexNet_SIN_fov16	.141
242	resnet_50_v1_spiking_l4	.127
243	yudixie_resnet50_random_0_240908	.127
244	ReAlnet08_cornet	.127
245	ReAlnet08	.127
246	compact_resnet50_robust_V4	.127
247	ReAlnet09_ALL	.127
248	pnasnet_large_pytorch	.127
249	resnet_101_v2	.119
250	yolos_tiny	.115
251	mobilenet_v2_1_3_224	.115
252	alexnet_training_seed_08	.115
253	compact_vgg19_IT	.115
254	AdvProp_efficientnet-b4	.115
255	deepeisnn_ei_cifar10_vgg19	.104
256	yudixie_resnet18_object_class_0_240719	.104
257	ReAlnet07_ALL	.104
258	alexnet_training_seed_03	.095
259	vonegrcnn_52e_full	.095
260	yudixie_resnet18_translation_reg_0_240719	.095
261	compact_resnet50_robust_IT	.095
262	ReAlnet08_ALL	.095
263	yudixie_resnet18_rotation_reg_0_240719	.086
264	alexnet_training_seed_09	.078
265	voneresnet-50-robust	.078
266	resnet50-SIN_IN	.078
267	compact_resnet50_V4	.078
268	ReAlnet03_ALL	.078
269	convnext_tiny_imagenet_full_seed-0	.070
270	ReAlnet04_ALL	.070
271	resnet50-moclr8deg	.064
272	yudixie_resnet18_category_class_0_240719	.064
273	ReAlnet05_ALL	.064
274	ReAlnet01_ALL	.064
275	ReAlnet10_ALL	.058
276	gabor_filter_entropy_4x4	.052
277	yudixie_wbh_object_class_scaling_0415_run_2	.052
278	ReAlnet06_ALL	.052
279	nasnet_large	.047
280	AdvProp_efficientnet-b6	.043
281	compact_vgg19_V4	.043
282	AlexNet_SIN_fov4	.021
283	ReAlnet02_ALL	.019
284	pnasnet_large
285	mobilenet_v2_0_75_224
286	hmax
287	convnext_base_fb_in1k

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

0.88.

Note that scores are relative to this ceiling.

Data: Ferguson2024quarter

Metric: value_delta