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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	convnext_large_in22k_supervised	1.0
2	resnet50_robust_l2_eps1	1.0
3	convnext_xxlarge_clip	1.0
4	resnet_101_v2	1.0
5	densenet-121	1.0
6	BiT-S-R50x3	1.0
7	custom_model_cv_18_dagger_408	1.0
8	convnext_large_clip_laion2b_finelayers	.973
9	convnext_base_fb_in22k_ft_in1k	.973
10	mobilenet_v2_1_3_224	.973
11	yudixie_resnet50_translation_rotation_0_240908	.973
12	resnet50-vitoimagevidnet8	.973
13	resnet50-VITO-8deg-cc	.973
14	deepeisnn_ei_cifar10_resnet18	.932
15	resnet_50_v1_spiking	.932
16	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.882
17	cv_18_dagger_408_pretrained	.882
18	nasnet_large	.882
19	resnet50-moclr8deg	.882
20	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.882
21	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.882
22	BiT-S-R152x2	.882
23	deit_small_imagenet_full_seed-0	.882
24	resnet152_imagenet_full	.882
25	alexnet	.882
26	vit_huge_clip336_laion	.845
27	vit_large_clip336_openai	.845
28	dinov2_vitl14_reg_lc_featread	.845
29	dinov2_vitl14_lc_res336	.845
30	dinov2_vitl14_reg_lc	.845
31	eva02_large_in22k	.845
32	dinov2_vitg14_reg_lc	.845
33	dinov2_vitg14_lc	.845
34	dinov2_vitl14_lc	.845
35	deit3_base_patch16_224_fb_in1k	.845
36	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.845
37	vit_base_patch16_dinov3-lvd1689m	.845
38	vit_base_patch14_reg4_dinov2-lvd142m	.845
39	vit_base_patch14_dinov2-lvd142m	.845
40	compact_resnet50_V4	.845
41	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.845
42	vit_large_patch14_clip_336:laion2b_ft_in1k	.845
43	vit_large_patch14_clip_224:openai_ft_in1k	.845
44	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.845
45	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.845
46	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.845
47	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.845
48	vit_relpos_base_patch16_clsgap_224:sw_in1k	.845
49	vit_large_patch14_clip_224:laion2b_ft_in1k	.845
50	vit_base_patch16_clip_224:openai_ft_in1k	.845
51	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.845
52	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.845
53	convnext_large:fb_in22k_ft_in1k	.845
54	BiT-S-R152x4	.845
55	AdvProp_efficientnet-b8	.845
56	AdvProp_efficientnet-b7	.845
57	deit_large_imagenet_full_seed-0	.845
58	deit_base_imagenet_full_seed-0	.845
59	resnet101_ecoset_full	.845
60	cvt_cvt-21-384-in1k_4_LucyV4	.845
61	cvt_cvt-13-384-in1k_4_LucyV4	.845
62	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.845
63	ViT_L_32_imagenet1k	.845
64	compact_vgg19_V4	.800
65	compact_resnet50_IT	.800
66	nasnet_mobile	.800
67	BiT-S-R101x3	.800
68	AlexNet_SIN_fov12	.767
69	vonegrcnn_62e_nobn	.767
70	grcnn	.767
71	densenet-169	.767
72	alexnet_training_seed_01_fov4	.725
73	resnet_152_v1	.725
74	omnivore_swinS	.725
75	convnext_xlarge:fb_in22k_ft_in1k	.725
76	yudixie_resnet18_translation_reg_0_240719	.725
77	mobilenet_v2_1_0_128	.695
78	convnext_tiny:in12k_ft_in1k	.695
79	AdvProp_efficientnet-b6	.695
80	resnet152_ecoset_full	.695
81	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.658
82	yudixie_resnet50_random_0_240908	.630
83	yudixie_resnet18_distance_translation_0_240719	.630
84	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.630
85	resnet-50-robust	.630
86	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.630
87	deit_small_imagenet_100_seed-0	.596
88	cvt_cvt-13-224-in1k_4_LucyV4	.596
89	resnet101_imagenet_full	.596
90	yolos_tiny	.572
91	resnet50_robust_l2_eps3	.572
92	imagenet_l2_3_0	.572
93	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.572
94	alexnet_fov12	.541
95	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.541
96	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.541
97	convnext_base:clip_laiona_augreg_ft_in1k_384	.541
98	pnasnet_large_halfPrecision	.518
99	mobilenet_v2_1_4_224	.518
100	pnasnet_large	.518
101	resnet50-barlow	.518
102	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.518
103	mobilenet_v2_1-4_224_pytorch	.518
104	focalnet_tiny_lrf_in1k	.518
105	resnet-152_v2_pytorch	.518
106	compact_cornets_IT	.490
107	alexnet_training_seed_01	.490
108	resnet50-vicregl0p75	.490
109	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.470
110	resnet_101_v1	.470
111	BiT-S-R50x1	.470
112	BiT-S-R101x1	.470
113	cvt_cvt-21-224-in1k_4_LucyV4	.470
114	resnet50_ecoset_full	.470
115	resnet34_ecoset_full	.470
116	resnet50_imagenet_full	.470
117	effnetb1_272x240	.470
118	compact_vgg19_IT	.445
119	alexnet_fov16	.445
120	alexnet_training_seed_10	.445
121	resnext101_32x8d_wsl	.445
122	AlexNet_SIN_fov16	.426
123	ReAlnet10_cornet	.426
124	ReAlnet09_cornet	.426
125	ReAlnet10	.426
126	ReAlnet09	.426
127	resnet50-vicreg	.426
128	blt_vs	.426
129	yudixie_resnet18_random_0_240719	.426
130	pnasnet_large_pytorch	.426
131	deepeisnn_ei_cifar10_vgg19	.403
132	yudixie_wbh_object_class_scaling_0415_run_2	.403
133	resnext101_32x48d_wsl	.403
134	resnext101_32x32d_wsl	.403
135	pr1_stl10_disentangled_alpha04	.387
136	resnet_50_v1_spiking_l4	.387
137	mobilenet_v2_1_0_192	.387
138	ReAlnet07_ALL	.351
139	resnet_SIN_IN_FT_IN	.351
140	ReAlnet01_cornet_fov16	.332
141	xception	.332
142	alexnet_training_seed_07	.332
143	alexnet_training_seed_09	.332
144	ReAlnet01_cornet_fov12	.318
145	ReAlnet07_cornet	.318
146	inception_v3	.318
147	ReAlnet07	.318
148	voneresnet-50-robust	.318
149	convnext_small_imagenet_1_seed-0	.318
150	deepeisnn_ei_imagenet_resnet18	.301
151	compact_resnet50_robust_V4	.301
152	AlexNet_SIN	.288
153	yudixie_resnet50_distance_rotation_0_240908	.288
154	vonegrcnn_52e_full	.288
155	antialiased-r50	.288
156	artResNet18_1	.288
157	yudixie_resnet18_distance_reg_0_240719	.288
158	yudixie_resnet18_rotation_reg_0_240719	.288
159	ReAlnet01_ALL	.261
160	resnet_50_v1_spiking_l3	.261
161	ReAlnet05_cornet	.261
162	ReAlnet05	.261
163	yudixie_resnet18_distance_translation_rotation_0_240719	.261
164	alexnet_training_seed_02	.247
165	omnivore_swinB	.247
166	yudixie_wbh_object_class_scaling_0415_imagenet	.237
167	ReAlnet10_ALL	.237
168	densenet-201	.237
169	efficientnet_b0	.237
170	convnext_large_imagenet_full_seed-0	.237
171	resnet50_imagenet_100_seed-0	.237
172	resnet50_imagenet_1_seed-0	.237
173	yudixie_resnet50_rotation_reg_0_240908	.215
174	vonegrcnn_47e	.215
175	efficientnet_b2_imagenet_full	.215
176	resnet34_imagenet_full	.215
177	aughost_fair_resnet18	.203
178	alexnet_training_seed_01_fov16	.203
179	resnet50_st_wzc_seed3_2_323	.195
180	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.195
181	ReAlnet02_ALL	.195
182	resnet_50_v2	.195
183	convnext_femto_ols:d1_in1k	.195
184	yudixie_wbh_object_class_scaling_0415_run_0	.184
185	compact_resnet50_robust_IT	.184
186	alexnet_training_seed_05	.184
187	ReAlnet03_ALL	.177
188	AlexNet_SIN_fov4	.177
189	mobilenet_v2_0_75_160	.177
190	mobilenet_v2_0_5_224	.177
191	efficientnet-b7	.177
192	resnet50_primary_visual_cortex	.160
193	ReAlnet01_cornet_fov4	.160
194	alexnet_fov4	.160
195	ReAlnet01_cornet	.160
196	resnet_152_v2	.160
197	grcnn_109	.160
198	ReAlnet01	.160
199	convnext_small_imagenet_full_seed-0	.160
200	resnet18_imagenet_full	.160
201	compact_cornets_V4	.152
202	alexnet_training_seed_03	.152
203	deit_small_imagenet_10_seed-0	.152
204	resnet18_fair	.145
205	ReAlnet02_cornet	.145
206	ReAlnet02	.145
207	resnet50-vicregl0p9	.145
208	yudixie_wbh_object_class_scaling_0415_run_1	.138
209	hmax	.138
210	ReAlnet04_cornet	.132
211	ReAlnet04	.132
212	alexnet_training_seed_08	.125
213	ReAlnet04_ALL	.120
214	resnet_50_v1_spiking_l4a	.120
215	yudixie_resnet50_category_class_0_240908	.120
216	deit_small_imagenet_1_seed-0	.120
217	yudixie_resnet18_distance_rotation_0_240719	.120
218	yudixie_wbh_object_class_scaling_0415_run_3	.113
219	alexnet_training_seed_01_fov12	.113
220	resnet50_st_wzc_seed6_2_323	.108
221	ReAlnet05_ALL	.108
222	Res2Net50_26w_4s	.108
223	resnet50-SIN_IN_IN	.108
224	convnext_small_imagenet_10_seed-0	.108
225	convnext_base_imagenet_full_seed-0	.108
226	briaai_rmbg_1_4	.103
227	ReAlnet06_ALL	.098
228	ReAlnet03_cornet	.098
229	inception_v4	.098
230	ReAlnet03	.098
231	yudixie_resnet18_translation_rotation_0_240719	.098
232	resnet50_hvs_wzc	.089
233	mobilenet_v2_0_5_192	.089
234	yudixie_resnet50_translation_reg_0_240908	.089
235	AT_efficientnet-b2	.089
236	AdvProp_efficientnet-b2	.089
237	yudixie_resnet18_object_class_0_240719	.089
238	resnet50_imagenet_10_seed-0	.089
239	resnet-18_test_m	.081
240	resnet18_ecoset_full	.081
241	resnet50_tutorial	.081
242	alexnet_training_seed_04	.076
243	resnet50_ewconplus_wzc_seed5_2__323	.073
244	resnet50_st_wzc_seed7_2_323	.073
245	ReAlnet09_ALL	.073
246	ReAlnet08_ALL	.073
247	resnet50_ewconplus_wzc	.073
248	ReAlnet08_cornet	.073
249	vgg_19	.073
250	yudixie_resnet50_distance_translation_0_240908	.073
251	ReAlnet08	.073
252	shufflenet_v2_x1_0	.073
253	resnet50-SIN	.073
254	resnet50-SIN_IN	.073
255	convnext_base_fb_in1k	.066
256	yudixie_resnet50_distance_translation_rotation_0_240908	.066
257	voneresnet-50	.066
258	convnext_tiny_imagenet_full_seed-0	.066
259	convnext_base_fb_in1k_gram_aligned_dinov3	.060
260	resnet50_st_wzc_2	.060
261	yudixie_resnet50_object_class_0_240908	.060
262	yudixie_resnet50_imagenet1kpret_0_240908	.060
263	ReAlnet06_cornet	.060
264	vgg_16	.060
265	ReAlnet06	.060
266	VOneCORnet-S	.060
267	convnext_tiny_sup	.060
268	predictive_coding_pc	.055
269	yudixie_resnet50_distance_reg_0_240908	.055
270	mobilenet_v2_0_75_224	.055
271	convnext_small_imagenet_100_seed-0	.055
272	yudixie_resnet18_imagenet1kpret_0_240719	.055
273	alexnet_training_seed_06	.052
274	mobilenet_v2_1_0_160	.050
275	yudixie_resnet18_category_class_0_240719	.050
276	voneresnet-50-non_stochastic	.050
277	inception_v1	.041
278	efficientnet_b1_imagenet_full	.041
279	efficientnet_b0_imagenet_full	.041
280	resnet50_ewconplus_wzc_seed6_2_323	.037
281	mobilenet_v2_1_0_224	.037
282	resnet_50_v1	.034
283	resnet50-sup	.034
284	AdvProp_efficientnet-b4	.028
285	mobilenet_v2_0_75_192	.025
286	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