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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	resnet18_fair	1.0
16	resnext101_32x16d_wsl	1.0
17	inception_v4	1.0
18	ReAlnet01_ALL	1.0
19	vgg_16	1.0
20	deit_small_imagenet_1_seed-0	.971
21	ReAlnet02_ALL	.971
22	voneresnet-50-robust	.971
23	compact_resnet50_robust_IT	.971
24	BiT-S-R152x2	.971
25	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
26	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.942
27	resnet_50_v1_spiking_l4	.942
28	vit_large_patch14_clip_224:openai_ft_in1k	.942
29	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
30	vit_large_patch14_clip_336:laion2b_ft_in1k	.942
31	cv_18_dagger_408_pretrained	.942
32	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.942
33	vit_base_patch16_dinov3-lvd1689m	.942
34	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
35	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.942
36	vit_base_patch14_dinov2-lvd142m	.942
37	vit_base_patch14_reg4_dinov2-lvd142m	.942
38	vit_large_patch14_clip_224:laion2b_ft_in1k	.942
39	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
40	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
41	resnet50-VITO-8deg-cc	.942
42	nasnet_mobile	.942
43	resnet50-vitoimagevidnet8	.942
44	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.942
45	deit3_base_patch16_224_fb_in1k	.942
46	yudixie_resnet50_imagenet1kpret_0_240908	.883
47	yudixie_resnet50_imagenet1kpret_0_240312	.883
48	deit_small_imagenet_10_seed-0	.883
49	pnasnet_large_halfPrecision	.883
50	nasnet_large	.883
51	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.883
52	convnext_large:fb_in22k_ft_in1k	.883
53	ReAlnet05	.855
54	alexnet_training_seed_06	.855
55	compact_resnet50_IT	.855
56	ReAlnet05_cornet	.855
57	custom_model_cv_18_dagger_408	.855
58	alexnet_fov12	.802
59	resnet_SIN_IN_FT_IN	.802
60	resnet50-vicregl0p9	.802
61	ReAlnet07	.777
62	alexnet_training_seed_08	.777
63	briaai_rmbg_1_4	.777
64	resnet50_imagenet_full	.777
65	ReAlnet07_cornet	.777
66	grcnn	.777
67	yudixie_resnet50_distance_translation_rotation_0_240908	.777
68	deit3_base_patch16_224_fb_in22k_ft_in1k	.777
69	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.777
70	yudixie_resnet50_object_class_0_240908	.728
71	convnext_xlarge:fb_in22k_ft_in1k	.728
72	cvt_cvt-21-224-in1k_4_LucyV4	.728
73	yudixie_resnet18_distance_translation_rotation_0_240719	.728
74	yudixie_resnet50_rotation_reg_0_240908	.728
75	ReAlnet07_ALL	.728
76	resnet50-vicregl0p75	.728
77	focalnet_tiny_lrf_in1k	.728
78	alexnet_ks_torevert	.706
79	cvt_cvt-13-224-in1k_4_LucyV4	.706
80	alexnet	.706
81	ViT_L_32_imagenet1k	.706
82	ReAlnet01_cornet_fov4	.706
83	resnet50-vicreg	.706
84	VOneCORnet-S	.706
85	mobilenet_v2_1_0_224	.706
86	resnet_152_v2	.706
87	ReAlnet09_ALL	.706
88	predictive_coding_pc	.706
89	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.662
90	resnet50_tutorial	.662
91	ReAlnet04_ALL	.662
92	ReAlnet06_ALL	.662
93	resnet18_imagenet21kP	.641
94	fixres_resnext101_32x48d_wsl	.641
95	ReAlnet03	.641
96	resnet50_st_wzc_2	.641
97	yolos_tiny	.641
98	resnet18_imagenet_full	.641
99	ReAlnet03_cornet	.641
100	vit_base_patch16_clip_224:openai_ft_in1k	.641
101	resnet-18_test_m	.641
102	Res2Net50_26w_4s	.641
103	mobilenet_v2_1_3_224	.641
104	ReAlnet05_ALL	.641
105	omnivore_swinS	.641
106	inception_v1	.641
107	alexnet_training_seed_05	.601
108	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.601
109	yudixie_resnet18_distance_rotation_0_240719	.601
110	resnet18_ecoset_full	.583
111	resnet50_ewconplus_wzc	.583
112	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.583
113	mobilenet_v2_1_0_128	.583
114	cvt_cvt-13-384-in1k_4_LucyV4	.583
115	resnet50_imagenet_1_seed-0	.583
116	yudixie_resnet18_random_0_240719	.583
117	alexnet_training_seed_01_fov4	.583
118	AdvProp_efficientnet-b7	.583
119	AlexNet_SIN	.546
120	pnasnet_large	.546
121	ReAlnet03_ALL	.546
122	resnet_101_v2	.546
123	antialiased-rnext101_32x8d	.529
124	resnet_50_v1_spiking_l4a	.529
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.529
126	compact_vgg19_IT	.529
127	BiT-S-R101x3	.529
128	yudixie_resnet50_distance_reg_0_240908	.496
129	alexnet_training_seed_07	.496
130	deit_base_imagenet_full_seed-0	.481
131	blt_vs	.481
132	ReAlnet09	.481
133	compact_resnet50_V4	.481
134	yudixie_resnet18_translation_reg_0_240719	.481
135	ReAlnet09_cornet	.481
136	vonegrcnn_47e	.481
137	alexnet_training_seed_01	.451
138	ReAlnet08_ALL	.451
139	convnext_tiny_imagenet_full_seed-0	.437
140	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.437
141	densenet-201	.437
142	ReAlnet01_cornet	.437
143	resnext101_32x8d_wsl	.437
144	hmax	.437
145	mobilenet_v2_1-4_224_pytorch	.437
146	AlexNet_SIN_fov4	.437
147	vgg_19	.437
148	mobilenet_v2_1_4_224	.437
149	ReAlnet01	.437
150	resnet_101_v1	.437
151	alexnet_training_seed_03	.437
152	convnext_base:clip_laiona_augreg_ft_in1k_384	.437
153	mobilenet_v2_1_0_192	.410
154	resnet50-SIN_IN_IN	.410
155	xception	.410
156	convnext_small_imagenet_100_seed-0	.397
157	yudixie_resnet50_random_0_240908	.397
158	ReAlnet08	.397
159	alexnet_training_seed_04	.397
160	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.397
161	alexnet_training_seed_10	.397
162	resnet50-SIN	.397
163	compact_vgg19_V4	.397
164	ReAlnet04_cornet	.397
165	ReAlnet08_cornet	.397
166	resnet50-barlow	.397
167	resnet50-moclr8deg	.397
168	ReAlnet04	.397
169	resnet50_primary_visual_cortex	.397
170	resnet50_ewconplus_wzc_seed3_2_323	.397
171	resnet50_st_wzc_noseed_2_323	.397
172	resnet50_imagenet_10_seed-0	.372
173	resnext101_32x32d_wsl	.372
174	deit_small_imagenet_full_seed-0	.361
175	resnet50_st_wzc_seed5_2_323	.361
176	AdvProp_efficientnet-b6	.361
177	antialiased-r50	.361
178	densenet-169	.361
179	yudixie_resnet18_translation_rotation_0_240719	.338
180	compact_cornets_IT	.338
181	efficientnet_b1_imagenet_full	.328
182	vonegrcnn_62e_nobn	.328
183	alexnet_training_seed_01_fov12	.328
184	resnet50_ewconplus_noseed_2_323	.328
185	convnext_base_fb_in22k_ft_in1k	.328
186	convnext_base_fb_in1k_gram_aligned_dinov3	.328
187	convnext_small_imagenet_1_seed-0	.307
188	resnet50_ewconplus_wzc_seed7_2_323	.298
189	resnext101_32x48d_wsl	.298
190	resnet34_ecoset_full	.298
191	resnet50_ecoset_full	.298
192	yudixie_resnet50_distance_translation_0_240908	.298
193	ReAlnet01_cornet_fov16	.298
194	alexnet_training_seed_01_fov16	.298
195	convnext_small_imagenet_10_seed-0	.279
196	ReAlnet10_ALL	.279
197	ReAlnet06	.270
198	resnet50_eMMCR_Vanilla	.270
199	alexnet_training_seed_02	.270
200	mobilenet_v2_0_75_160	.270
201	mobilenet_v2_0_5_224	.270
202	resnet50_imagenet_100_seed-0	.270
203	resnet34_imagenet_full	.270
204	ReAlnet01_cornet_fov12	.270
205	aughost_fair_resnet18	.270
206	yudixie_resnet50_translation_reg_0_240908	.270
207	ReAlnet06_cornet	.270
208	voneresnet-50	.270
209	convnext_tiny_sup	.270
210	yudixie_resnet18_rotation_reg_0_240719	.253
211	AlexNet_SIN_fov16	.253
212	resnet152_ecoset_full	.246
213	voneresnet-50-non_stochastic	.246
214	alexnet_fov16	.246
215	ReAlnet02_cornet	.246
216	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.246
217	ReAlnet02	.246
218	yudixie_resnet50_category_class_0_240908	.230
219	resnet101_imagenet_full	.223
220	convnext_small_imagenet_full_seed-0	.223
221	resnet_50_v1_spiking_l3	.223
222	AdvProp_efficientnet-b8	.223
223	BiT-S-R50x1	.223
224	densenet-121	.223
225	resnet50_st_wzc_seed7_2_323	.223
226	resnet_50_v1	.203
227	resnet50-sup	.203
228	AdvProp_efficientnet-b4	.203
229	resnet50_ewconplus_wzc_2_323	.203
230	BiT-S-R152x4	.190
231	inception_v3	.184
232	alexnet_fov4	.184
233	resnet152_imagenet_full	.184
234	efficientnet_b0_imagenet_full	.184
235	yudixie_resnet18_category_class_0_240719	.184
236	antialias-resnet152	.173
237	AlexNet_SIN_fov12	.173
238	alexnet_training_seed_09	.167
239	yudixie_resnet50_translation_rotation_0_240908	.167
240	grcnn_109	.167
241	vonegrcnn_52e_full	.167
242	BiT-S-R50x3	.167
243	cvt_cvt-21-384-in1k_4_LucyV4	.157
244	tv_efficientnet-b1	.152
245	effnetb1_272x240	.152
246	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.152
247	yudixie_resnet18_distance_translation_0_240719	.152
248	shufflenet_v2_x1_0	.152
249	resnet_50_v2	.152
250	resnet50_ewconplus_wzc_seed5_2__323	.152
251	mobilenet_v2_1_0_160	.138
252	resnet_152_v1	.138
253	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.126
254	yudixie_resnet50_distance_rotation_0_240908	.126
255	yudixie_resnet18_imagenet1kpret_0_240719	.126
256	convnext_large_imagenet_full_seed-0	.126
257	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.114
258	mobilenet_v2_0_75_224	.114
259	resnet50_ewconplus_wzc_seed4_2_323	.114
260	yudixie_resnet18_distance_reg_0_240719	.104
261	efficientnet-b7	.104
262	pr1_stl10_disentangled_alpha04	.094
263	efficientnet_b0	.086
264	resnet-152_v2_pytorch	.078
265	convnext_base_imagenet_full_seed-0	.078
266	resnet50_robust_l2_eps1	.073
267	regnet_y_400mf	.071
268	resnet50-SIN_IN	.071
269	resnet50_ewconplus_wzc_seed2_2__323	.071
270	resnet-50-robust	.064
271	imagenet_l2_3_0	.064
272	resnet50_robust_l2_eps3	.064
273	compact_cornets_V4	.053
274	mobilenet_v2_0_5_192	.048
275	mobilenet_v2_0_75_192	.048
276	resnet_50_v1_spiking	.044
277	AdvProp_efficientnet-b2	.033
278	AT_efficientnet-b2	.033
279	efficientnet_b2_imagenet_full	.020
280	SeLaVi-AVE
281	SeLaVi-Kinetics400
282	r3d_18
283	mc3_18
284	s3d
285	mvit_v2_s
286	mvit_v1_b
287	SeLaVi-Kinetics-Sound
288	SeLaVi-VGG-Sound
289	r2plus1d_18
290	convnext_base_fb_in1k
291	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3

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