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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	regnety_032	1.0
2	convnext_large_in22k_supervised	1.0
3	resnet50_robust_l2_eps1	1.0
4	convnext_xxlarge_clip	1.0
5	resnet_101_v2	1.0
6	densenet-121	1.0
7	BiT-S-R50x3	1.0
8	custom_model_cv_18_dagger_408	1.0
9	convnext_large_clip_laion2b_finelayers	.973
10	convnext_base_fb_in22k_ft_in1k	.973
11	mobilenet_v2_1_3_224	.973
12	yudixie_resnet50_translation_rotation_0_240908	.973
13	resnet50-vitoimagevidnet8	.973
14	resnet50-VITO-8deg-cc	.973
15	deepeisnn_ei_cifar10_resnet18	.932
16	resnet_50_v1_spiking	.932
17	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.882
18	cv_18_dagger_408_pretrained	.882
19	nasnet_large	.882
20	resnet50-moclr8deg	.882
21	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.882
22	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
23	BiT-S-R152x2	.882
24	deit_small_imagenet_full_seed-0	.882
25	resnet152_imagenet_full	.882
26	alexnet	.882
27	vit_huge_clip336_laion	.845
28	vit_large_clip336_openai	.845
29	dinov2_vitl14_reg_lc_featread	.845
30	dinov2_vitl14_lc_res336	.845
31	dinov2_vitl14_reg_lc	.845
32	eva02_large_in22k	.845
33	dinov2_vitg14_reg_lc	.845
34	dinov2_vitg14_lc	.845
35	dinov2_vitl14_lc	.845
36	deit3_base_patch16_224_fb_in1k	.845
37	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.845
38	vit_base_patch16_dinov3-lvd1689m	.845
39	vit_base_patch14_reg4_dinov2-lvd142m	.845
40	vit_base_patch14_dinov2-lvd142m	.845
41	compact_resnet50_V4	.845
42	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.845
43	vit_large_patch14_clip_336:laion2b_ft_in1k	.845
44	vit_large_patch14_clip_224:openai_ft_in1k	.845
45	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.845
46	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.845
47	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.845
48	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.845
49	vit_relpos_base_patch16_clsgap_224:sw_in1k	.845
50	vit_large_patch14_clip_224:laion2b_ft_in1k	.845
51	vit_base_patch16_clip_224:openai_ft_in1k	.845
52	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.845
53	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.845
54	convnext_large:fb_in22k_ft_in1k	.845
55	BiT-S-R152x4	.845
56	AdvProp_efficientnet-b8	.845
57	AdvProp_efficientnet-b7	.845
58	deit_large_imagenet_full_seed-0	.845
59	deit_base_imagenet_full_seed-0	.845
60	resnet101_ecoset_full	.845
61	cvt_cvt-21-384-in1k_4_LucyV4	.845
62	cvt_cvt-13-384-in1k_4_LucyV4	.845
63	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.845
64	ViT_L_32_imagenet1k	.845
65	compact_vgg19_V4	.800
66	compact_resnet50_IT	.800
67	nasnet_mobile	.800
68	BiT-S-R101x3	.800
69	gabor_filter_mean_abs_4x4_v2	.767
70	AlexNet_SIN_fov12	.767
71	vonegrcnn_62e_nobn	.767
72	grcnn	.767
73	densenet-169	.767
74	alexnet_training_seed_01_fov4	.725
75	resnet_152_v1	.725
76	omnivore_swinS	.725
77	convnext_xlarge:fb_in22k_ft_in1k	.725
78	yudixie_resnet18_translation_reg_0_240719	.725
79	mobilenet_v2_1_0_128	.695
80	convnext_tiny:in12k_ft_in1k	.695
81	AdvProp_efficientnet-b6	.695
82	resnet152_ecoset_full	.695
83	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.658
84	yudixie_resnet50_random_0_240908	.630
85	yudixie_resnet18_distance_translation_0_240719	.630
86	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.630
87	resnet-50-robust	.630
88	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.630
89	deit_small_imagenet_100_seed-0	.596
90	cvt_cvt-13-224-in1k_4_LucyV4	.596
91	resnet101_imagenet_full	.596
92	yolos_tiny	.572
93	resnet50_robust_l2_eps3	.572
94	imagenet_l2_3_0	.572
95	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.572
96	alexnet_fov12	.541
97	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.541
98	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.541
99	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
100	pnasnet_large_halfPrecision	.518
101	mobilenet_v2_1_4_224	.518
102	pnasnet_large	.518
103	resnet50-barlow	.518
104	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.518
105	mobilenet_v2_1-4_224_pytorch	.518
106	focalnet_tiny_lrf_in1k	.518
107	resnet-152_v2_pytorch	.518
108	compact_cornets_IT	.490
109	alexnet_training_seed_01	.490
110	resnet50-vicregl0p75	.490
111	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.470
112	resnet_101_v1	.470
113	BiT-S-R50x1	.470
114	BiT-S-R101x1	.470
115	cvt_cvt-21-224-in1k_4_LucyV4	.470
116	resnet50_ecoset_full	.470
117	resnet34_ecoset_full	.470
118	resnet50_imagenet_full	.470
119	effnetb1_272x240	.470
120	compact_vgg19_IT	.445
121	alexnet_fov16	.445
122	alexnet_training_seed_10	.445
123	resnext101_32x8d_wsl	.445
124	AlexNet_SIN_fov16	.426
125	ReAlnet10_cornet	.426
126	ReAlnet09_cornet	.426
127	ReAlnet10	.426
128	ReAlnet09	.426
129	resnet50-vicreg	.426
130	blt_vs	.426
131	yudixie_resnet18_random_0_240719	.426
132	pnasnet_large_pytorch	.426
133	deepeisnn_ei_cifar10_vgg19	.403
134	yudixie_wbh_object_class_scaling_0415_run_2	.403
135	resnext101_32x48d_wsl	.403
136	resnext101_32x32d_wsl	.403
137	pr1_stl10_disentangled_alpha04	.387
138	resnet_50_v1_spiking_l4	.387
139	mobilenet_v2_1_0_192	.387
140	ReAlnet07_ALL	.351
141	resnet_SIN_IN_FT_IN	.351
142	gabor_filter_entropy_4x4	.332
143	ReAlnet01_cornet_fov16	.332
144	xception	.332
145	alexnet_training_seed_07	.332
146	alexnet_training_seed_09	.332
147	ReAlnet01_cornet_fov12	.318
148	ReAlnet07_cornet	.318
149	inception_v3	.318
150	ReAlnet07	.318
151	voneresnet-50-robust	.318
152	convnext_small_imagenet_1_seed-0	.318
153	deepeisnn_ei_imagenet_resnet18	.301
154	compact_resnet50_robust_V4	.301
155	AlexNet_SIN	.288
156	yudixie_resnet50_distance_rotation_0_240908	.288
157	vonegrcnn_52e_full	.288
158	antialiased-r50	.288
159	artResNet18_1	.288
160	yudixie_resnet18_distance_reg_0_240719	.288
161	yudixie_resnet18_rotation_reg_0_240719	.288
162	ReAlnet01_ALL	.261
163	resnet_50_v1_spiking_l3	.261
164	ReAlnet05_cornet	.261
165	ReAlnet05	.261
166	yudixie_resnet18_distance_translation_rotation_0_240719	.261
167	alexnet_training_seed_02	.247
168	omnivore_swinB	.247
169	yudixie_wbh_object_class_scaling_0415_imagenet	.237
170	ReAlnet10_ALL	.237
171	densenet-201	.237
172	efficientnet_b0	.237
173	convnext_large_imagenet_full_seed-0	.237
174	resnet50_imagenet_100_seed-0	.237
175	resnet50_imagenet_1_seed-0	.237
176	gabor_filter_entropy_4x4_v2	.224
177	eicircuit_resnet18_bn_20260919	.215
178	seresnext50_32x4d	.215
179	yudixie_resnet50_rotation_reg_0_240908	.215
180	vonegrcnn_47e	.215
181	efficientnet_b2_imagenet_full	.215
182	resnet34_imagenet_full	.215
183	alexnet_training_seed_01_fov16	.203
184	resnet50_st_wzc_seed3_2_323	.195
185	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.195
186	ReAlnet02_ALL	.195
187	resnet_50_v2	.195
188	convnext_femto_ols:d1_in1k	.195
189	yudixie_wbh_object_class_scaling_0415_run_0	.184
190	compact_resnet50_robust_IT	.184
191	alexnet_training_seed_05	.184
192	ReAlnet03_ALL	.177
193	AlexNet_SIN_fov4	.177
194	mobilenet_v2_0_75_160	.177
195	mobilenet_v2_0_5_224	.177
196	efficientnet-b7	.177
197	resnet50_primary_visual_cortex	.160
198	ReAlnet01_cornet_fov4	.160
199	alexnet_fov4	.160
200	ReAlnet01_cornet	.160
201	resnet_152_v2	.160
202	grcnn_109	.160
203	ReAlnet01	.160
204	convnext_small_imagenet_full_seed-0	.160
205	resnet18_imagenet_full	.160
206	compact_cornets_V4	.152
207	alexnet_training_seed_03	.152
208	deit_small_imagenet_10_seed-0	.152
209	resnet18_fair	.145
210	ReAlnet02_cornet	.145
211	ReAlnet02	.145
212	resnet50-vicregl0p9	.145
213	gabor_filter_entropy_4x4_v2_forcedCosine	.138
214	yudixie_wbh_object_class_scaling_0415_run_1	.138
215	hmax	.138
216	ReAlnet04_cornet	.132
217	ReAlnet04	.132
218	alexnet_training_seed_08	.125
219	ReAlnet04_ALL	.120
220	resnet_50_v1_spiking_l4a	.120
221	yudixie_resnet50_category_class_0_240908	.120
222	deit_small_imagenet_1_seed-0	.120
223	yudixie_resnet18_distance_rotation_0_240719	.120
224	yudixie_wbh_object_class_scaling_0415_run_3	.113
225	alexnet_training_seed_01_fov12	.113
226	resnet50_st_wzc_seed6_2_323	.108
227	ReAlnet05_ALL	.108
228	Res2Net50_26w_4s	.108
229	resnet50-SIN_IN_IN	.108
230	convnext_small_imagenet_10_seed-0	.108
231	convnext_base_imagenet_full_seed-0	.108
232	briaai_rmbg_1_4	.103
233	ReAlnet06_ALL	.098
234	ReAlnet03_cornet	.098
235	inception_v4	.098
236	ReAlnet03	.098
237	yudixie_resnet18_translation_rotation_0_240719	.098
238	resnet50_hvs_wzc	.089
239	mobilenet_v2_0_5_192	.089
240	yudixie_resnet50_translation_reg_0_240908	.089
241	AT_efficientnet-b2	.089
242	AdvProp_efficientnet-b2	.089
243	yudixie_resnet18_object_class_0_240719	.089
244	resnet50_imagenet_10_seed-0	.089
245	resnet-18_test_m	.081
246	resnet18_ecoset_full	.081
247	resnet50_tutorial	.081
248	alexnet_training_seed_04	.076
249	resnet50_ewconplus_wzc_seed5_2__323	.073
250	resnet50_st_wzc_seed7_2_323	.073
251	ReAlnet09_ALL	.073
252	ReAlnet08_ALL	.073
253	resnet50_ewconplus_wzc	.073
254	ReAlnet08_cornet	.073
255	vgg_19	.073
256	yudixie_resnet50_distance_translation_0_240908	.073
257	ReAlnet08	.073
258	shufflenet_v2_x1_0	.073
259	resnet50-SIN	.073
260	resnet50-SIN_IN	.073
261	convnext_base_fb_in1k	.066
262	yudixie_resnet50_distance_translation_rotation_0_240908	.066
263	voneresnet-50	.066
264	convnext_tiny_imagenet_full_seed-0	.066
265	convnext_base_fb_in1k_gram_aligned_dinov3	.060
266	resnet50_st_wzc_2	.060
267	yudixie_resnet50_object_class_0_240908	.060
268	yudixie_resnet50_imagenet1kpret_0_240908	.060
269	ReAlnet06_cornet	.060
270	vgg_16	.060
271	ReAlnet06	.060
272	VOneCORnet-S	.060
273	convnext_tiny_sup	.060
274	predictive_coding_pc	.055
275	yudixie_resnet50_distance_reg_0_240908	.055
276	mobilenet_v2_0_75_224	.055
277	convnext_small_imagenet_100_seed-0	.055
278	yudixie_resnet18_imagenet1kpret_0_240719	.055
279	alexnet_training_seed_06	.052
280	mobilenet_v2_1_0_160	.050
281	yudixie_resnet18_category_class_0_240719	.050
282	voneresnet-50-non_stochastic	.050
283	inception_v1	.041
284	efficientnet_b1_imagenet_full	.041
285	efficientnet_b0_imagenet_full	.041
286	resnet50_ewconplus_wzc_seed6_2_323	.037
287	mobilenet_v2_1_0_224	.037
288	resnet_50_v1	.034
289	resnet50-sup	.034
290	AdvProp_efficientnet-b4	.028
291	mobilenet_v2_0_75_192	.025
292	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