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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024convergence-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	deepeisnn_ei_imagenet_resnet18	1.0
2	ReAlnet09_cornet	1.0
3	ReAlnet09	1.0
4	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
5	resnet18_ecoset_full	1.0
6	resnet101_imagenet_full	1.0
7	pnasnet_large_pytorch	1.0
8	dinov2_vitl14_reg_lc	1.0
9	alexnet_training_seed_01_fov4	1.0
10	AlexNet_SIN_fov4	1.0
11	compact_resnet50_robust_V4	1.0
12	yudixie_resnet50_random_0_240908	1.0
13	densenet-121	1.0
14	convnext_small_imagenet_10_seed-0	1.0
15	resnet34_ecoset_full	1.0
16	resnet-152_v2_pytorch	1.0
17	AlexNet_SIN_fov16	1.0
18	yudixie_resnet50_distance_translation_rotation_0_240908	1.0
19	alexnet_training_seed_07	1.0
20	artResNet18_1	1.0
21	resnet50_imagenet_100_seed-0	1.0
22	resnet50_imagenet_1_seed-0	1.0
23	deepeisnn_ei_cifar10_vgg19	.985
24	deit3_base_patch16_224_fb_in1k	.985
25	gabor_filter_entropy_4x4_v2	.985
26	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.985
27	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.985
28	resnet152_ecoset_full	.985
29	regnety_032	.925
30	dinov2_vitl14_reg_lc_featread	.925
31	yudixie_wbh_object_class_scaling_0415_run_0	.925
32	resnet50_st_wzc_2	.925
33	alexnet_training_seed_08	.925
34	alexnet_training_seed_02	.925
35	yudixie_resnet18_object_class_0_240719	.894
36	dinov2_vitg14_lc	.839
37	compact_cornets_IT	.839
38	vgg_19	.839
39	yudixie_resnet50_translation_reg_0_240908	.839
40	vit_large_patch14_clip_224:openai_ft_in1k	.839
41	convnext_base:clip_laiona_augreg_ft_in1k_384	.839
42	antialiased-r50	.839
43	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.811
44	ReAlnet04_cornet	.811
45	gabor_filter_entropy_4x4_v2_forcedCosine	.811
46	ReAlnet04	.811
47	resnet_50_v1_spiking_l4	.811
48	mobilenet_v2_1_4_224	.762
49	ReAlnet10_cornet	.762
50	resnet_152_v2	.762
51	ReAlnet10	.762
52	blt_vs	.762
53	mobilenet_v2_1-4_224_pytorch	.762
54	ReAlnet07_ALL	.762
55	resnet_50_v1_spiking_l3	.762
56	yudixie_resnet50_distance_reg_0_240908	.736
57	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.736
58	resnet152_imagenet_full	.736
59	resnet18_fair	.691
60	resnet50_hvs_wzc	.691
61	mobilenet_v2_0_5_192	.691
62	voneresnet-50	.691
63	resnet50-moclr8deg	.691
64	resnet50-SIN	.691
65	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.691
66	convnext_tiny:in12k_ft_in1k	.691
67	yudixie_resnet18_translation_reg_0_240719	.691
68	yudixie_resnet18_distance_translation_0_240719	.691
69	cvt_cvt-21-384-in1k_4_LucyV4	.691
70	resnet50-barlow	.667
71	yudixie_resnet18_distance_rotation_0_240719	.667
72	convnext_tiny_imagenet_full_seed-0	.667
73	deit_small_imagenet_100_seed-0	.667
74	yudixie_wbh_object_class_scaling_0415_imagenet	.627
75	convnext_base_fb_in22k_ft_in1k	.627
76	alexnet_fov16	.627
77	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.627
78	ReAlnet08_cornet	.627
79	ReAlnet03_cornet	.627
80	resnet_50_v2	.627
81	resnet_101_v1	.627
82	gabor_filter_entropy_4x4	.627
83	ReAlnet08	.627
84	ReAlnet03	.627
85	densenet-201	.627
86	vit_large_patch14_clip_224:laion2b_ft_in1k	.627
87	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.627
88	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.627
89	BiT-S-R152x2	.627
90	vonegrcnn_52e_full	.627
91	resnet50_ecoset_full	.627
92	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.627
93	resnext101_32x32d_wsl	.627
94	resnet50-SIN_IN	.606
95	deit_large_imagenet_full_seed-0	.606
96	alexnet_training_seed_01	.569
97	Res2Net50_26w_4s	.569
98	vit_relpos_base_patch16_clsgap_224:sw_in1k	.569
99	resnet50_robust_l2_eps3	.569
100	imagenet_l2_3_0	.569
101	AdvProp_efficientnet-b7	.569
102	AdvProp_efficientnet-b6	.569
103	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.569
104	mobilenet_v2_1_3_224	.549
105	alexnet_training_seed_03	.549
106	resnet50_imagenet_full	.549
107	vit_huge_clip336_laion	.516
108	yudixie_resnet50_object_class_0_240908	.516
109	mobilenet_v2_1_0_160	.516
110	nasnet_mobile	.516
111	gabor_filter_mean_abs_4x4_v2	.516
112	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
113	vit_base_patch16_clip_224:openai_ft_in1k	.516
114	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.516
115	convnext_small_imagenet_1_seed-0	.516
116	resnet-50-robust	.516
117	effnetb1_272x240	.516
118	compact_resnet50_IT	.468
119	ReAlnet01_cornet	.468
120	grcnn	.468
121	ReAlnet01	.468
122	vit_large_patch14_clip_336:laion2b_ft_in1k	.468
123	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.468
124	AdvProp_efficientnet-b8	.468
125	deit_base_imagenet_full_seed-0	.452
126	convnext_xxlarge_clip	.425
127	predictive_coding_pc	.425
128	resnet50_ewconplus_wzc_seed2_2__323	.425
129	pnasnet_large_halfPrecision	.425
130	alexnet_fov12	.425
131	yudixie_resnet50_imagenet1kpret_0_240908	.425
132	alexnet_training_seed_06	.425
133	alexnet_training_seed_05	.425
134	mobilenet_v2_0_75_224	.425
135	grcnn_109	.425
136	deit_small_imagenet_1_seed-0	.425
137	yudixie_resnet18_distance_reg_0_240719	.425
138	resnet50_tutorial	.425
139	resnet50_primary_visual_cortex	.425
140	ReAlnet01_cornet_fov12	.410
141	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.385
142	seresnext50_32x4d	.385
143	vit_large_clip336_openai	.385
144	dinov2_vitl14_lc_res336	.385
145	deepeisnn_ei_cifar10_resnet18	.385
146	yudixie_wbh_object_class_scaling_0415_run_3	.385
147	vit_base_patch14_reg4_dinov2-lvd142m	.385
148	ReAlnet07_cornet	.385
149	ReAlnet05_cornet	.385
150	mobilenet_v2_1_0_128	.385
151	resnet_101_v2	.385
152	ReAlnet07	.385
153	ReAlnet05	.385
154	convnext_xlarge:fb_in22k_ft_in1k	.385
155	convnext_large:fb_in22k_ft_in1k	.385
156	BiT-S-R50x1	.385
157	AdvProp_efficientnet-b4	.385
158	convnext_small_imagenet_full_seed-0	.385
159	deit_small_imagenet_10_seed-0	.385
160	dinov2_vitl14_lc	.349
161	convnext_large_in22k_supervised	.349
162	vit_base_patch16_dinov3-lvd1689m	.349
163	ReAlnet01_cornet_fov4	.349
164	yudixie_resnet50_category_class_0_240908	.349
165	yudixie_resnet18_translation_rotation_0_240719	.349
166	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.349
167	ReAlnet02_ALL	.349
168	custom_model_cv_18_dagger_408	.349
169	deit3_base_patch16_224_fb_in22k_ft_in1k	.317
170	convnext_base_fb_in1k_gram_aligned_dinov3	.317
171	vit_base_patch14_dinov2-lvd142m	.317
172	alexnet_training_seed_10	.317
173	ReAlnet06_cornet	.317
174	mobilenet_v2_0_75_192	.317
175	vonegrcnn_62e_nobn	.317
176	ReAlnet06	.317
177	resnet50-vicreg	.317
178	resnet50_robust_l2_eps1	.317
179	yudixie_resnet18_imagenet1kpret_0_240719	.317
180	resnet50_imagenet_10_seed-0	.317
181	hmax	.317
182	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.317
183	resnext101_32x8d_wsl	.317
184	yudixie_resnet50_translation_rotation_0_240908	.306
185	eva02_large_in22k	.288
186	dinov2_vitg14_reg_lc	.288
187	convnext_large_clip_laion2b_finelayers	.288
188	alexnet_training_seed_01_fov16	.288
189	mobilenet_v2_0_5_224	.288
190	mobilenet_v2_0_75_160	.288
191	omnivore_swinB	.288
192	convnext_large_imagenet_full_seed-0	.288
193	convnext_base_imagenet_full_seed-0	.288
194	yudixie_resnet18_random_0_240719	.288
195	compact_cornets_V4	.261
196	nasnet_large	.261
197	efficientnet_b2_imagenet_full	.261
198	cvt_cvt-13-224-in1k_4_LucyV4	.261
199	ViT_L_32_imagenet1k	.261
200	efficientnet_b0_imagenet_full	.252
201	ReAlnet01_cornet_fov16	.237
202	mobilenet_v2_1_0_224	.237
203	alexnet_training_seed_04	.237
204	alexnet_training_seed_09	.237
205	vgg_16	.237
206	yudixie_resnet50_distance_translation_0_240908	.237
207	ReAlnet01_ALL	.237
208	voneresnet-50-non_stochastic	.237
209	yudixie_wbh_object_class_scaling_0415_run_2	.215
210	convnext_base_fb_in1k	.215
211	ReAlnet02_cornet	.215
212	resnet_50_v1	.215
213	ReAlnet02	.215
214	voneresnet-50-robust	.215
215	resnet50-sup	.215
216	yudixie_resnet18_category_class_0_240719	.215
217	efficientnet_b1_imagenet_full	.215
218	resnet50_ewconplus_wzc	.195
219	compact_vgg19_IT	.195
220	yudixie_resnet50_rotation_reg_0_240908	.195
221	shufflenet_v2_x1_0	.195
222	resnet50-SIN_IN_IN	.195
223	omnivore_swinS	.195
224	AT_efficientnet-b2	.195
225	convnext_tiny_sup	.195
226	AdvProp_efficientnet-b2	.195
227	cvt_cvt-21-224-in1k_4_LucyV4	.195
228	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.195
229	compact_resnet50_V4	.188
230	resnet_152_v1	.177
231	efficientnet-b7	.177
232	VOneCORnet-S	.177
233	efficientnet_b0	.177
234	resnet50-vicregl0p9	.177
235	yudixie_resnet18_distance_translation_rotation_0_240719	.177
236	ReAlnet06_ALL	.177
237	ReAlnet03_ALL	.177
238	resnet_50_v1_spiking	.177
239	resnet18_imagenet_full	.171
240	cv_18_dagger_408_pretrained	.160
241	AlexNet_SIN	.160
242	compact_vgg19_V4	.160
243	BiT-S-R101x3	.160
244	ReAlnet09_ALL	.160
245	resnet34_imagenet_full	.155
246	convnext_femto_ols:d1_in1k	.145
247	convnext_small_imagenet_100_seed-0	.145
248	ReAlnet05_ALL	.145
249	deit_small_imagenet_full_seed-0	.141
250	pr1_stl10_disentangled_alpha04	.132
251	resnet50_st_wzc_seed3_2_323	.132
252	mobilenet_v2_1_0_192	.132
253	resnet50-vitoimagevidnet8	.132
254	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.132
255	resnet50-VITO-8deg-cc	.132
256	resnext101_32x48d_wsl	.132
257	cvt_cvt-13-384-in1k_4_LucyV4	.120
258	ReAlnet04_ALL	.120
259	resnet-18_test_m	.109
260	resnet_50_v1_spiking_l4a	.109
261	focalnet_tiny_lrf_in1k	.109
262	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.099
263	BiT-S-R152x4	.099
264	inception_v3	.089
265	yudixie_wbh_object_class_scaling_0415_run_1	.081
266	resnet50-vicregl0p75	.081
267	ReAlnet10_ALL	.081
268	yolos_tiny	.074
269	yudixie_resnet50_distance_rotation_0_240908	.074
270	resnet101_ecoset_full	.071
271	resnet50_ewconplus_wzc_seed5_2__323	.067
272	AlexNet_SIN_fov12	.067
273	compact_resnet50_robust_IT	.067
274	densenet-169	.067
275	briaai_rmbg_1_4	.061
276	xception	.055
277	alexnet_fov4	.050
278	resnet_SIN_IN_FT_IN	.050
279	vonegrcnn_47e	.050
280	alexnet_training_seed_01_fov12	.044
281	inception_v1	.041
282	BiT-S-R50x3	.037
283	yudixie_resnet18_rotation_reg_0_240719	.037
284	ReAlnet08_ALL	.034
285	inception_v4	.028
286	alexnet	.028
287	BiT-S-R101x1	.013
288	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3
289	resnet50_st_wzc_seed7_2_323
290	pnasnet_large

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: Ferguson2024convergence

Metric: value_delta