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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Ferguson2024gray_hard-value_delta")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	deit_small_imagenet_100_seed-0	1.0
2	deit_large_imagenet_full_seed-0	1.0
3	ReAlnet10	1.0
4	pnasnet_large_pytorch	1.0
5	compact_resnet50_robust_V4	1.0
6	resnet101_ecoset_full	1.0
7	omnivore_swinB	1.0
8	artResNet18_1	1.0
9	ReAlnet10_cornet	1.0
10	BiT-S-R101x1	1.0
11	convnext_tiny:in12k_ft_in1k	1.0
12	yudixie_resnet18_object_class_0_240719	1.0
13	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
14	convnext_femto_ols:d1_in1k	1.0
15	resnext101_32x16d_wsl	1.0
16	inception_v4	1.0
17	ReAlnet01_ALL	1.0
18	vgg_16	1.0
19	deit_small_imagenet_1_seed-0	.971
20	ReAlnet02_ALL	.971
21	voneresnet-50-robust	.971
22	compact_resnet50_robust_IT	.971
23	BiT-S-R152x2	.971
24	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
25	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.942
26	resnet_50_v1_spiking_l4	.942
27	vit_large_patch14_clip_224:openai_ft_in1k	.942
28	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
29	vit_large_patch14_clip_336:laion2b_ft_in1k	.942
30	cv_18_dagger_408_pretrained	.942
31	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.942
32	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
33	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.942
34	vit_large_patch14_clip_224:laion2b_ft_in1k	.942
35	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
36	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
37	resnet50-VITO-8deg-cc	.942
38	nasnet_mobile	.942
39	resnet50-vitoimagevidnet8	.942
40	yudixie_resnet50_imagenet1kpret_0_240908	.883
41	yudixie_resnet50_imagenet1kpret_0_240312	.883
42	deit_small_imagenet_10_seed-0	.883
43	nasnet_large	.883
44	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.883
45	convnext_large:fb_in22k_ft_in1k	.883
46	ReAlnet05	.855
47	alexnet_training_seed_06	.855
48	compact_resnet50_IT	.855
49	ReAlnet05_cornet	.855
50	custom_model_cv_18_dagger_408	.855
51	alexnet_fov12	.802
52	resnet_SIN_IN_FT_IN	.802
53	resnet50-vicregl0p9	.802
54	ReAlnet07	.777
55	alexnet_training_seed_08	.777
56	briaai_rmbg_1_4	.777
57	resnet50_imagenet_full	.777
58	ReAlnet07_cornet	.777
59	grcnn	.777
60	yudixie_resnet50_distance_translation_rotation_0_240908	.777
61	yudixie_resnet50_object_class_0_240908	.728
62	convnext_xlarge:fb_in22k_ft_in1k	.728
63	cvt_cvt-21-224-in1k_4_LucyV4	.728
64	yudixie_resnet18_distance_translation_rotation_0_240719	.728
65	yudixie_resnet50_rotation_reg_0_240908	.728
66	ReAlnet07_ALL	.728
67	resnet50-vicregl0p75	.728
68	focalnet_tiny_lrf_in1k	.728
69	alexnet_ks_torevert	.706
70	cvt_cvt-13-224-in1k_4_LucyV4	.706
71	alexnet	.706
72	ViT_L_32_imagenet1k	.706
73	ReAlnet01_cornet_fov4	.706
74	alexnet_workflow_trigger	.706
75	resnet50-vicreg	.706
76	VOneCORnet-S	.706
77	mobilenet_v2_1_0_224	.706
78	resnet_152_v2	.706
79	ReAlnet09_ALL	.706
80	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.662
81	resnet50_tutorial	.662
82	ReAlnet04_ALL	.662
83	ReAlnet06_ALL	.662
84	resnet18_imagenet21kP	.641
85	fixres_resnext101_32x48d_wsl	.641
86	ReAlnet03	.641
87	yolos_tiny	.641
88	resnet18_imagenet_full	.641
89	ReAlnet03_cornet	.641
90	vit_base_patch16_clip_224:openai_ft_in1k	.641
91	resnet-18_test_m	.641
92	Res2Net50_26w_4s	.641
93	mobilenet_v2_1_3_224	.641
94	ReAlnet05_ALL	.641
95	omnivore_swinS	.641
96	inception_v1	.641
97	alexnet_training_seed_05	.601
98	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.601
99	yudixie_resnet18_distance_rotation_0_240719	.601
100	resnet18_ecoset_full	.583
101	resnet50_ewconplus_wzc	.583
102	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.583
103	mobilenet_v2_1_0_128	.583
104	cvt_cvt-13-384-in1k_4_LucyV4	.583
105	resnet50_imagenet_1_seed-0	.583
106	yudixie_resnet18_random_0_240719	.583
107	alexnet_training_seed_01_fov4	.583
108	AdvProp_efficientnet-b7	.583
109	AlexNet_SIN	.546
110	pnasnet_large	.546
111	ReAlnet03_ALL	.546
112	resnet_101_v2	.546
113	antialiased-rnext101_32x8d	.529
114	resnet_50_v1_spiking_l4a	.529
115	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.529
116	compact_vgg19_IT	.529
117	BiT-S-R101x3	.529
118	yudixie_resnet50_distance_reg_0_240908	.496
119	alexnet_training_seed_07	.496
120	deit_base_imagenet_full_seed-0	.481
121	blt_vs	.481
122	ReAlnet09	.481
123	compact_resnet50_V4	.481
124	yudixie_resnet18_translation_reg_0_240719	.481
125	ReAlnet09_cornet	.481
126	vonegrcnn_47e	.481
127	alexnet_training_seed_01	.451
128	ReAlnet08_ALL	.451
129	convnext_tiny_imagenet_full_seed-0	.437
130	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.437
131	densenet-201	.437
132	ReAlnet01_cornet	.437
133	resnext101_32x8d_wsl	.437
134	hmax	.437
135	mobilenet_v2_1-4_224_pytorch	.437
136	AlexNet_SIN_fov4	.437
137	vgg_19	.437
138	mobilenet_v2_1_4_224	.437
139	ReAlnet01	.437
140	resnet_101_v1	.437
141	alexnet_training_seed_03	.437
142	convnext_base:clip_laiona_augreg_ft_in1k_384	.437
143	mobilenet_v2_1_0_192	.410
144	resnet50-SIN_IN_IN	.410
145	xception	.410
146	convnext_small_imagenet_100_seed-0	.397
147	yudixie_resnet50_random_0_240908	.397
148	ReAlnet08	.397
149	alexnet_training_seed_04	.397
150	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.397
151	alexnet_training_seed_10	.397
152	resnet50-SIN	.397
153	compact_vgg19_V4	.397
154	ReAlnet04_cornet	.397
155	ReAlnet08_cornet	.397
156	resnet50-barlow	.397
157	resnet50-moclr8deg	.397
158	ReAlnet04	.397
159	resnet50_primary_visual_cortex	.397
160	resnet50_imagenet_10_seed-0	.372
161	resnext101_32x32d_wsl	.372
162	deit_small_imagenet_full_seed-0	.361
163	AdvProp_efficientnet-b6	.361
164	antialiased-r50	.361
165	densenet-169	.361
166	yudixie_resnet18_translation_rotation_0_240719	.338
167	compact_cornets_IT	.338
168	efficientnet_b1_imagenet_full	.328
169	vonegrcnn_62e_nobn	.328
170	alexnet_training_seed_01_fov12	.328
171	convnext_small_imagenet_1_seed-0	.307
172	resnext101_32x48d_wsl	.298
173	resnet34_ecoset_full	.298
174	resnet50_ecoset_full	.298
175	yudixie_resnet50_distance_translation_0_240908	.298
176	ReAlnet01_cornet_fov16	.298
177	alexnet_training_seed_01_fov16	.298
178	convnext_small_imagenet_10_seed-0	.279
179	ReAlnet10_ALL	.279
180	ReAlnet06	.270
181	resnet50_eMMCR_Vanilla	.270
182	alexnet_training_seed_02	.270
183	mobilenet_v2_0_75_160	.270
184	mobilenet_v2_0_5_224	.270
185	resnet50_imagenet_100_seed-0	.270
186	resnet34_imagenet_full	.270
187	ReAlnet01_cornet_fov12	.270
188	yudixie_resnet50_translation_reg_0_240908	.270
189	ReAlnet06_cornet	.270
190	voneresnet-50	.270
191	convnext_tiny_sup	.270
192	yudixie_resnet18_rotation_reg_0_240719	.253
193	AlexNet_SIN_fov16	.253
194	resnet152_ecoset_full	.246
195	voneresnet-50-non_stochastic	.246
196	alexnet_fov16	.246
197	ReAlnet02_cornet	.246
198	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.246
199	ReAlnet02	.246
200	yudixie_resnet50_category_class_0_240908	.230
201	resnet101_imagenet_full	.223
202	convnext_small_imagenet_full_seed-0	.223
203	resnet_50_v1_spiking_l3	.223
204	AdvProp_efficientnet-b8	.223
205	BiT-S-R50x1	.223
206	densenet-121	.223
207	resnet_50_v1	.203
208	resnet50-sup	.203
209	AdvProp_efficientnet-b4	.203
210	BiT-S-R152x4	.190
211	inception_v3	.184
212	alexnet_fov4	.184
213	resnet152_imagenet_full	.184
214	efficientnet_b0_imagenet_full	.184
215	yudixie_resnet18_category_class_0_240719	.184
216	antialias-resnet152	.173
217	AlexNet_SIN_fov12	.173
218	alexnet_training_seed_09	.167
219	yudixie_resnet50_translation_rotation_0_240908	.167
220	grcnn_109	.167
221	vonegrcnn_52e_full	.167
222	BiT-S-R50x3	.167
223	cvt_cvt-21-384-in1k_4_LucyV4	.157
224	tv_efficientnet-b1	.152
225	effnetb1_272x240	.152
226	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.152
227	yudixie_resnet18_distance_translation_0_240719	.152
228	shufflenet_v2_x1_0	.152
229	resnet_50_v2	.152
230	mobilenet_v2_1_0_160	.138
231	resnet_152_v1	.138
232	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.126
233	yudixie_resnet50_distance_rotation_0_240908	.126
234	yudixie_resnet18_imagenet1kpret_0_240719	.126
235	convnext_large_imagenet_full_seed-0	.126
236	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.114
237	mobilenet_v2_0_75_224	.114
238	yudixie_resnet18_distance_reg_0_240719	.104
239	efficientnet-b7	.104
240	efficientnet_b0	.086
241	resnet-152_v2_pytorch	.078
242	convnext_base_imagenet_full_seed-0	.078
243	resnet50_robust_l2_eps1	.073
244	regnet_y_400mf	.071
245	resnet50-SIN_IN	.071
246	resnet-50-robust	.064
247	imagenet_l2_3_0	.064
248	resnet50_robust_l2_eps3	.064
249	vit_base_patch16_dinov3:lvd1689m	.058
250	compact_cornets_V4	.053
251	vit_base_patch14_reg4_dinov2:lvd142m	.053
252	mobilenet_v2_0_5_192	.048
253	mobilenet_v2_0_75_192	.048
254	vit_base_patch14_dinov2:lvd142m	.048
255	resnet_50_v1_spiking	.044
256	AdvProp_efficientnet-b2	.033
257	AT_efficientnet-b2	.033
258	efficientnet_b2_imagenet_full	.020
259	SeLaVi-AVE
260	SeLaVi-Kinetics400
261	r3d_18
262	mc3_18
263	s3d
264	mvit_v2_s
265	mvit_v1_b
266	SeLaVi-Kinetics-Sound
267	SeLaVi-VGG-Sound
268	r2plus1d_18

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: Ferguson2024gray_hard

Metric: value_delta