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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	resnet50_simclr_linhead_01norm	1.0
4	resnet50_barlowtwins_linhead	1.0
5	ReAlnet09	1.0
6	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	1.0
7	resnet18_ecoset_full	1.0
8	resnet101_imagenet_full	1.0
9	pnasnet_large_pytorch	1.0
10	dinov2_vitl14_reg_lc	1.0
11	alexnet_training_seed_01_fov4	1.0
12	AlexNet_SIN_fov4	1.0
13	compact_resnet50_robust_V4	1.0
14	yudixie_resnet50_random_0_240908	1.0
15	resnext101_32x16d_wsl	1.0
16	densenet-121	1.0
17	convnext_small_imagenet_10_seed-0	1.0
18	resnet34_ecoset_full	1.0
19	resnet-152_v2_pytorch	1.0
20	mehrer2020_alexnet_training_seed_01	1.0
21	AlexNet_SIN_fov16	1.0
22	yudixie_resnet50_distance_translation_rotation_0_240908	1.0
23	alexnet_training_seed_07	1.0
24	artResNet18_1	1.0
25	resnet50_imagenet_100_seed-0	1.0
26	resnet50_imagenet_1_seed-0	1.0
27	deepeisnn_ei_cifar10_vgg19	.985
28	deit3_base_patch16_224_fb_in1k	.985
29	gabor_filter_entropy_4x4_v2	.985
30	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.985
31	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.985
32	resnet152_ecoset_full	.985
33	regnety_032	.925
34	dinov2_vitl14_reg_lc_featread	.925
35	yudixie_wbh_object_class_scaling_0415_run_0	.925
36	resnet50_st_wzc_2	.925
37	alexnet_training_seed_08	.925
38	alexnet_training_seed_02	.925
39	mehrer2020_alexnet_training_seed_01_fov16	.894
40	yudixie_resnet18_object_class_0_240719	.894
41	dinov2_vitg14_lc	.839
42	compact_cornets_IT	.839
43	vgg_19	.839
44	yudixie_resnet50_translation_reg_0_240908	.839
45	vit_large_patch14_clip_224:openai_ft_in1k	.839
46	convnext_base:clip_laiona_augreg_ft_in1k_384	.839
47	antialiased-r50	.839
48	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.811
49	ReAlnet04_cornet	.811
50	gabor_filter_entropy_4x4_v2_forcedCosine	.811
51	ReAlnet04	.811
52	resnet_50_v1_spiking_l4	.811
53	mobilenet_v2_1_4_224	.762
54	ReAlnet10_cornet	.762
55	resnet_152_v2	.762
56	ReAlnet10	.762
57	blt_vs	.762
58	mobilenet_v2_1-4_224_pytorch	.762
59	ReAlnet07_ALL	.762
60	resnet_50_v1_spiking_l3	.762
61	yudixie_resnet50_distance_reg_0_240908	.736
62	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.736
63	resnet152_imagenet_full	.736
64	resnet18_fair	.691
65	resnet50_hvs_wzc	.691
66	mobilenet_v2_0_5_192	.691
67	voneresnet-50	.691
68	resnet50-moclr8deg	.691
69	resnet50-SIN	.691
70	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.691
71	convnext_tiny:in12k_ft_in1k	.691
72	yudixie_resnet18_translation_reg_0_240719	.691
73	yudixie_resnet18_distance_translation_0_240719	.691
74	cvt_cvt-21-384-in1k_4_LucyV4	.691
75	resnet50-barlow	.667
76	yudixie_resnet18_distance_rotation_0_240719	.667
77	convnext_tiny_imagenet_full_seed-0	.667
78	deit_small_imagenet_100_seed-0	.667
79	yudixie_wbh_object_class_scaling_0415_imagenet	.627
80	convnext_base_fb_in22k_ft_in1k	.627
81	alexnet_fov16	.627
82	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.627
83	ReAlnet08_cornet	.627
84	ReAlnet03_cornet	.627
85	resnet_50_v2	.627
86	resnet_101_v1	.627
87	gabor_filter_entropy_4x4	.627
88	ReAlnet08	.627
89	ReAlnet03	.627
90	densenet-201	.627
91	vit_large_patch14_clip_224:laion2b_ft_in1k	.627
92	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.627
93	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.627
94	BiT-S-R152x2	.627
95	vonegrcnn_52e_full	.627
96	resnet50_ecoset_full	.627
97	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.627
98	resnext101_32x32d_wsl	.627
99	mehrer2020_alexnet_training_seed_01_fov12	.606
100	resnet50-SIN_IN	.606
101	deit_large_imagenet_full_seed-0	.606
102	alexnet_training_seed_01	.569
103	Res2Net50_26w_4s	.569
104	vit_relpos_base_patch16_clsgap_224:sw_in1k	.569
105	resnet50_robust_l2_eps3	.569
106	imagenet_l2_3_0	.569
107	AdvProp_efficientnet-b7	.569
108	AdvProp_efficientnet-b6	.569
109	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.569
110	mobilenet_v2_1_3_224	.549
111	alexnet_training_seed_03	.549
112	resnet50_imagenet_full	.549
113	vit_huge_clip336_laion	.516
114	yudixie_resnet50_object_class_0_240908	.516
115	mobilenet_v2_1_0_160	.516
116	nasnet_mobile	.516
117	resnet50_byol_linhead	.516
118	gabor_filter_mean_abs_4x4_v2	.516
119	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.516
120	vit_base_patch16_clip_224:openai_ft_in1k	.516
121	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.516
122	convnext_small_imagenet_1_seed-0	.516
123	resnet-50-robust	.516
124	effnetb1_272x240	.516
125	compact_resnet50_IT	.468
126	ReAlnet01_cornet	.468
127	grcnn	.468
128	ReAlnet01	.468
129	vit_large_patch14_clip_336:laion2b_ft_in1k	.468
130	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.468
131	AdvProp_efficientnet-b8	.468
132	deit_base_imagenet_full_seed-0	.452
133	convnext_xxlarge_clip	.425
134	predictive_coding_pc	.425
135	resnet50_ewconplus_wzc_seed2_2__323	.425
136	pnasnet_large_halfPrecision	.425
137	alexnet_fov12	.425
138	yudixie_resnet50_imagenet1kpret_0_240908	.425
139	alexnet_training_seed_06	.425
140	alexnet_training_seed_05	.425
141	mobilenet_v2_0_75_224	.425
142	mehrer2020_alexnet_training_seed_09	.425
143	mehrer2020_alexnet_training_seed_08	.425
144	mehrer2020_alexnet_training_seed_02	.425
145	grcnn_109	.425
146	deit_small_imagenet_1_seed-0	.425
147	yudixie_resnet18_distance_reg_0_240719	.425
148	resnet50_tutorial	.425
149	resnet50_primary_visual_cortex	.425
150	ReAlnet01_cornet_fov12	.410
151	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.385
152	seresnext50_32x4d	.385
153	vit_large_clip336_openai	.385
154	dinov2_vitl14_lc_res336	.385
155	deepeisnn_ei_cifar10_resnet18	.385
156	yudixie_wbh_object_class_scaling_0415_run_3	.385
157	vit_base_patch14_reg4_dinov2-lvd142m	.385
158	ReAlnet07_cornet	.385
159	ReAlnet05_cornet	.385
160	mobilenet_v2_1_0_128	.385
161	resnet_101_v2	.385
162	mehrer2020_alexnet_training_seed_04	.385
163	eicircuit_resnet18_bn_20260919	.385
164	ReAlnet07	.385
165	ReAlnet05	.385
166	convnext_xlarge:fb_in22k_ft_in1k	.385
167	convnext_large:fb_in22k_ft_in1k	.385
168	BiT-S-R50x1	.385
169	AdvProp_efficientnet-b4	.385
170	convnext_small_imagenet_full_seed-0	.385
171	deit_small_imagenet_10_seed-0	.385
172	dinov2_vitl14_lc	.349
173	convnext_large_in22k_supervised	.349
174	vit_base_patch16_dinov3-lvd1689m	.349
175	ReAlnet01_cornet_fov4	.349
176	yudixie_resnet50_category_class_0_240908	.349
177	yudixie_resnet18_translation_rotation_0_240719	.349
178	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.349
179	ReAlnet02_ALL	.349
180	custom_model_cv_18_dagger_408	.349
181	deit3_base_patch16_224_fb_in22k_ft_in1k	.317
182	convnext_base_fb_in1k_gram_aligned_dinov3	.317
183	vit_base_patch14_dinov2-lvd142m	.317
184	alexnet_training_seed_10	.317
185	ReAlnet06_cornet	.317
186	mobilenet_v2_0_75_192	.317
187	vonegrcnn_62e_nobn	.317
188	ReAlnet06	.317
189	resnet50-vicreg	.317
190	resnet50_robust_l2_eps1	.317
191	yudixie_resnet18_imagenet1kpret_0_240719	.317
192	resnet50_imagenet_10_seed-0	.317
193	hmax	.317
194	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.317
195	resnext101_32x8d_wsl	.317
196	yudixie_resnet50_translation_rotation_0_240908	.306
197	eva02_large_in22k	.288
198	dinov2_vitg14_reg_lc	.288
199	convnext_large_clip_laion2b_finelayers	.288
200	alexnet_training_seed_01_fov16	.288
201	mobilenet_v2_0_5_224	.288
202	resnet50_vicreg_linhead	.288
203	mobilenet_v2_0_75_160	.288
204	omnivore_swinB	.288
205	convnext_large_imagenet_full_seed-0	.288
206	convnext_base_imagenet_full_seed-0	.288
207	yudixie_resnet18_random_0_240719	.288
208	compact_cornets_V4	.261
209	nasnet_large	.261
210	efficientnet_b2_imagenet_full	.261
211	cvt_cvt-13-224-in1k_4_LucyV4	.261
212	ViT_L_32_imagenet1k	.261
213	efficientnet_b0_imagenet_full	.252
214	ReAlnet01_cornet_fov16	.237
215	mobilenet_v2_1_0_224	.237
216	alexnet_training_seed_04	.237
217	alexnet_training_seed_09	.237
218	vgg_16	.237
219	yudixie_resnet50_distance_translation_0_240908	.237
220	ReAlnet01_ALL	.237
221	voneresnet-50-non_stochastic	.237
222	yudixie_wbh_object_class_scaling_0415_run_2	.215
223	convnext_base_fb_in1k	.215
224	ReAlnet02_cornet	.215
225	resnet_50_v1	.215
226	ReAlnet02	.215
227	voneresnet-50-robust	.215
228	resnet50-sup	.215
229	yudixie_resnet18_category_class_0_240719	.215
230	efficientnet_b1_imagenet_full	.215
231	resnet50_ewconplus_wzc	.195
232	compact_vgg19_IT	.195
233	resnet50_simsiam_linhead	.195
234	yudixie_resnet50_rotation_reg_0_240908	.195
235	shufflenet_v2_x1_0	.195
236	resnet50-SIN_IN_IN	.195
237	omnivore_swinS	.195
238	AT_efficientnet-b2	.195
239	convnext_tiny_sup	.195
240	AdvProp_efficientnet-b2	.195
241	cvt_cvt-21-224-in1k_4_LucyV4	.195
242	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.195
243	compact_resnet50_V4	.188
244	resnet_152_v1	.177
245	ijepa_vith14_linhead	.177
246	mehrer2020_alexnet_training_seed_06	.177
247	efficientnet-b7	.177
248	VOneCORnet-S	.177
249	efficientnet_b0	.177
250	resnet50-vicregl0p9	.177
251	yudixie_resnet18_distance_translation_rotation_0_240719	.177
252	ReAlnet06_ALL	.177
253	ReAlnet03_ALL	.177
254	resnet_50_v1_spiking	.177
255	resnet18_imagenet_full	.171
256	cv_18_dagger_408_pretrained	.160
257	AlexNet_SIN	.160
258	compact_vgg19_V4	.160
259	mehrer2020_alexnet_training_seed_10	.160
260	mehrer2020_alexnet_training_seed_07	.160
261	BiT-S-R101x3	.160
262	ReAlnet09_ALL	.160
263	resnet34_imagenet_full	.155
264	convnext_femto_ols:d1_in1k	.145
265	convnext_small_imagenet_100_seed-0	.145
266	ReAlnet05_ALL	.145
267	deit_small_imagenet_full_seed-0	.141
268	pr1_stl10_disentangled_alpha04	.132
269	resnet50_st_wzc_seed3_2_323	.132
270	mobilenet_v2_1_0_192	.132
271	resnet50-vitoimagevidnet8	.132
272	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.132
273	resnet50-VITO-8deg-cc	.132
274	resnext101_32x48d_wsl	.132
275	mehrer2020_alexnet_training_seed_03	.127
276	cvt_cvt-13-384-in1k_4_LucyV4	.120
277	ReAlnet04_ALL	.120
278	mehrer2020_alexnet_training_seed_01_fov4	.109
279	resnet-18_test_m	.109
280	resnet_50_v1_spiking_l4a	.109
281	focalnet_tiny_lrf_in1k	.109
282	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.099
283	BiT-S-R152x4	.099
284	inception_v3	.089
285	yudixie_wbh_object_class_scaling_0415_run_1	.081
286	resnet50-vicregl0p75	.081
287	ReAlnet10_ALL	.081
288	yolos_tiny	.074
289	yudixie_resnet50_distance_rotation_0_240908	.074
290	resnet101_ecoset_full	.071
291	resnet50_ewconplus_wzc_seed5_2__323	.067
292	AlexNet_SIN_fov12	.067
293	compact_resnet50_robust_IT	.067
294	densenet-169	.067
295	briaai_rmbg_1_4	.061
296	xception	.055
297	mehrer2020_alexnet_training_seed_05	.055
298	alexnet_fov4	.050
299	resnet_SIN_IN_FT_IN	.050
300	vonegrcnn_47e	.050
301	alexnet_training_seed_01_fov12	.044
302	inception_v1	.041
303	BiT-S-R50x3	.037
304	yudixie_resnet18_rotation_reg_0_240719	.037
305	ReAlnet08_ALL	.034
306	inception_v4	.028
307	alexnet	.028
308	BiT-S-R101x1	.013
309	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3
310	resnet50_st_wzc_seed7_2_323
311	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