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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	compact_resnet50_robust_V4	1.0
2	ReAlnet10_cornet	1.0
3	ReAlnet10	1.0
4	omnivore_swinB	1.0
5	BiT-S-R101x1	1.0
6	artResNet18_1	1.0
7	deit_small_imagenet_100_seed-0	1.0
8	deit_large_imagenet_full_seed-0	1.0
9	resnet101_ecoset_full	1.0
10	pnasnet_large_pytorch	1.0
11	resnet18_fair	1.0
12	vit_relpos_base_patch16_clsgap_224:sw_in1k	1.0
13	convnext_tiny:in12k_ft_in1k	1.0
14	convnext_femto_ols:d1_in1k	1.0
15	yudixie_resnet18_object_class_0_240719	1.0
16	resnext101_32x16d_wsl	1.0
17	ReAlnet01_ALL	1.0
18	vgg_16	1.0
19	inception_v4	1.0
20	deepeisnn_ei_cifar10_vgg19	.971
21	yudixie_wbh_object_class_scaling_0415_run_1	.971
22	yudixie_wbh_object_class_scaling_0415_run_3	.971
23	compact_resnet50_robust_IT	.971
24	ReAlnet02_ALL	.971
25	voneresnet-50-robust	.971
26	BiT-S-R152x2	.971
27	deit_small_imagenet_1_seed-0	.971
28	dinov2_vitl14_lc	.942
29	vit_huge_clip336_laion	.942
30	vit_large_clip336_openai	.942
31	dinov2_vitl14_reg_lc_featread	.942
32	dinov2_vitl14_lc_res336	.942
33	dinov2_vitl14_reg_lc	.942
34	eva02_large_in22k	.942
35	dinov2_vitg14_reg_lc	.942
36	dinov2_vitg14_lc	.942
37	yudixie_wbh_object_class_scaling_0415_run_2	.942
38	deit3_base_patch16_224_fb_in1k	.942
39	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.942
40	vit_base_patch16_dinov3-lvd1689m	.942
41	vit_base_patch14_reg4_dinov2-lvd142m	.942
42	vit_base_patch14_dinov2-lvd142m	.942
43	cv_18_dagger_408_pretrained	.942
44	resnet_50_v1_spiking_l4	.942
45	nasnet_mobile	.942
46	resnet50-vitoimagevidnet8	.942
47	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.942
48	vit_large_patch14_clip_336:laion2b_ft_in1k	.942
49	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.942
50	vit_large_patch14_clip_224:openai_ft_in1k	.942
51	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.942
52	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.942
53	resnet50-VITO-8deg-cc	.942
54	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.942
55	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.942
56	vit_large_patch14_clip_224:laion2b_ft_in1k	.942
57	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.942
58	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.942
59	seresnext50_32x4d	.883
60	convnext_xxlarge_clip	.883
61	pnasnet_large_halfPrecision	.883
62	yudixie_resnet50_imagenet1kpret_0_240312	.883
63	yudixie_resnet50_imagenet1kpret_0_240908	.883
64	nasnet_large	.883
65	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.883
66	convnext_large:fb_in22k_ft_in1k	.883
67	deit_small_imagenet_10_seed-0	.883
68	compact_resnet50_IT	.855
69	alexnet_training_seed_06	.855
70	ReAlnet05_cornet	.855
71	ReAlnet05	.855
72	custom_model_cv_18_dagger_408	.855
73	alexnet_fov12	.802
74	resnet50-vicregl0p9	.802
75	resnet_SIN_IN_FT_IN	.802
76	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.777
77	deit3_base_patch16_224_fb_in22k_ft_in1k	.777
78	briaai_rmbg_1_4	.777
79	yudixie_resnet50_distance_translation_rotation_0_240908	.777
80	alexnet_training_seed_08	.777
81	ReAlnet07_cornet	.777
82	grcnn	.777
83	ReAlnet07	.777
84	resnet50_imagenet_full	.777
85	convnext_large_in22k_supervised	.728
86	ReAlnet07_ALL	.728
87	yudixie_resnet50_object_class_0_240908	.728
88	yudixie_resnet50_rotation_reg_0_240908	.728
89	convnext_xlarge:fb_in22k_ft_in1k	.728
90	resnet50-vicregl0p75	.728
91	yudixie_resnet18_distance_translation_rotation_0_240719	.728
92	cvt_cvt-21-224-in1k_4_LucyV4	.728
93	focalnet_tiny_lrf_in1k	.728
94	yudixie_wbh_object_class_scaling_0415_run_0	.706
95	predictive_coding_pc	.706
96	alexnet_ks_torevert	.706
97	ReAlnet01_cornet_fov4	.706
98	ReAlnet09_ALL	.706
99	mobilenet_v2_1_0_224	.706
100	resnet_152_v2	.706
101	resnet50-vicreg	.706
102	VOneCORnet-S	.706
103	cvt_cvt-13-224-in1k_4_LucyV4	.706
104	ViT_L_32_imagenet1k	.706
105	alexnet	.706
106	ReAlnet06_ALL	.662
107	ReAlnet04_ALL	.662
108	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.662
109	resnet50_tutorial	.662
110	fixres_resnext101_32x48d_wsl	.641
111	resnet18_imagenet21kP	.641
112	resnet50_st_wzc_2	.641
113	ReAlnet05_ALL	.641
114	yolos_tiny	.641
115	mobilenet_v2_1_3_224	.641
116	ReAlnet03_cornet	.641
117	inception_v1	.641
118	ReAlnet03	.641
119	Res2Net50_26w_4s	.641
120	resnet-18_test_m	.641
121	omnivore_swinS	.641
122	vit_base_patch16_clip_224:openai_ft_in1k	.641
123	resnet18_imagenet_full	.641
124	alexnet_training_seed_05	.601
125	yudixie_resnet18_distance_rotation_0_240719	.601
126	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.601
127	alexnet_training_seed_01_fov4	.583
128	resnet50_ewconplus_wzc	.583
129	mobilenet_v2_1_0_128	.583
130	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.583
131	AdvProp_efficientnet-b7	.583
132	yudixie_resnet18_random_0_240719	.583
133	resnet50_imagenet_1_seed-0	.583
134	cvt_cvt-13-384-in1k_4_LucyV4	.583
135	resnet18_ecoset_full	.583
136	AlexNet_SIN	.546
137	ReAlnet03_ALL	.546
138	pnasnet_large	.546
139	resnet_101_v2	.546
140	antialiased-rnext101_32x8d	.529
141	compact_vgg19_IT	.529
142	resnet_50_v1_spiking_l4a	.529
143	BiT-S-R101x3	.529
144	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.529
145	yudixie_resnet50_distance_reg_0_240908	.496
146	alexnet_training_seed_07	.496
147	compact_resnet50_V4	.481
148	ReAlnet09_cornet	.481
149	ReAlnet09	.481
150	vonegrcnn_47e	.481
151	blt_vs	.481
152	yudixie_resnet18_translation_reg_0_240719	.481
153	deit_base_imagenet_full_seed-0	.481
154	regnety_032	.451
155	ReAlnet08_ALL	.451
156	alexnet_training_seed_01	.451
157	deepeisnn_ei_imagenet_resnet18	.437
158	yudixie_wbh_object_class_scaling_0415_imagenet	.437
159	AlexNet_SIN_fov4	.437
160	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.437
161	mobilenet_v2_1_4_224	.437
162	alexnet_training_seed_03	.437
163	ReAlnet01_cornet	.437
164	resnet_101_v1	.437
165	vgg_19	.437
166	ReAlnet01	.437
167	densenet-201	.437
168	convnext_base:clip_laiona_augreg_ft_in1k_384	.437
169	convnext_tiny_imagenet_full_seed-0	.437
170	mobilenet_v2_1-4_224_pytorch	.437
171	hmax	.437
172	resnext101_32x8d_wsl	.437
173	gabor_filter_entropy_4x4	.410
174	xception	.410
175	mobilenet_v2_1_0_192	.410
176	resnet50-SIN_IN_IN	.410
177	convnext_large_clip_laion2b_finelayers	.397
178	compact_vgg19_V4	.397
179	resnet50_primary_visual_cortex	.397
180	yudixie_resnet50_random_0_240908	.397
181	alexnet_training_seed_10	.397
182	alexnet_training_seed_04	.397
183	ReAlnet08_cornet	.397
184	ReAlnet04_cornet	.397
185	ReAlnet08	.397
186	ReAlnet04	.397
187	resnet50-moclr8deg	.397
188	resnet50-barlow	.397
189	resnet50-SIN	.397
190	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.397
191	convnext_small_imagenet_100_seed-0	.397
192	resnet50_imagenet_10_seed-0	.372
193	resnext101_32x32d_wsl	.372
194	densenet-169	.361
195	antialiased-r50	.361
196	AdvProp_efficientnet-b6	.361
197	deit_small_imagenet_full_seed-0	.361
198	compact_cornets_IT	.338
199	yudixie_resnet18_translation_rotation_0_240719	.338
200	gabor_filter_entropy_4x4_v2	.328
201	convnext_base_fb_in1k_gram_aligned_dinov3	.328
202	convnext_base_fb_in22k_ft_in1k	.328
203	alexnet_training_seed_01_fov12	.328
204	vonegrcnn_62e_nobn	.328
205	efficientnet_b1_imagenet_full	.328
206	convnext_small_imagenet_1_seed-0	.307
207	gabor_filter_mean_abs_4x4_v2	.298
208	alexnet_training_seed_01_fov16	.298
209	ReAlnet01_cornet_fov16	.298
210	yudixie_resnet50_distance_translation_0_240908	.298
211	resnet50_ecoset_full	.298
212	resnet34_ecoset_full	.298
213	resnext101_32x48d_wsl	.298
214	ReAlnet10_ALL	.279
215	convnext_small_imagenet_10_seed-0	.279
216	deepeisnn_ei_cifar10_resnet18	.270
217	resnet50_eMMCR_Vanilla	.270
218	ReAlnet01_cornet_fov12	.270
219	alexnet_training_seed_02	.270
220	ReAlnet06_cornet	.270
221	mobilenet_v2_0_5_224	.270
222	mobilenet_v2_0_75_160	.270
223	yudixie_resnet50_translation_reg_0_240908	.270
224	ReAlnet06	.270
225	voneresnet-50	.270
226	convnext_tiny_sup	.270
227	resnet50_imagenet_100_seed-0	.270
228	resnet34_imagenet_full	.270
229	AlexNet_SIN_fov16	.253
230	yudixie_resnet18_rotation_reg_0_240719	.253
231	alexnet_fov16	.246
232	ReAlnet02_cornet	.246
233	ReAlnet02	.246
234	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.246
235	resnet152_ecoset_full	.246
236	voneresnet-50-non_stochastic	.246
237	yudixie_resnet50_category_class_0_240908	.230
238	resnet50_st_wzc_seed7_2_323	.223
239	resnet_50_v1_spiking_l3	.223
240	densenet-121	.223
241	BiT-S-R50x1	.223
242	AdvProp_efficientnet-b8	.223
243	convnext_small_imagenet_full_seed-0	.223
244	resnet101_imagenet_full	.223
245	resnet_50_v1	.203
246	AdvProp_efficientnet-b4	.203
247	resnet50-sup	.203
248	BiT-S-R152x4	.190
249	alexnet_fov4	.184
250	inception_v3	.184
251	yudixie_resnet18_category_class_0_240719	.184
252	efficientnet_b0_imagenet_full	.184
253	resnet152_imagenet_full	.184
254	antialias-resnet152	.173
255	AlexNet_SIN_fov12	.173
256	resnet50_hvs_wzc	.167
257	alexnet_training_seed_09	.167
258	grcnn_109	.167
259	yudixie_resnet50_translation_rotation_0_240908	.167
260	BiT-S-R50x3	.167
261	vonegrcnn_52e_full	.167
262	cvt_cvt-21-384-in1k_4_LucyV4	.157
263	resnet50_ewconplus_wzc_seed5_2__323	.152
264	tv_efficientnet-b1	.152
265	resnet_50_v2	.152
266	shufflenet_v2_x1_0	.152
267	yudixie_resnet18_distance_translation_0_240719	.152
268	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.152
269	effnetb1_272x240	.152
270	gabor_filter_entropy_4x4_v2_forcedCosine	.138
271	mobilenet_v2_1_0_160	.138
272	resnet_152_v1	.138
273	yudixie_resnet50_distance_rotation_0_240908	.126
274	convnext_large_imagenet_full_seed-0	.126
275	yudixie_resnet18_imagenet1kpret_0_240719	.126
276	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.126
277	mobilenet_v2_0_75_224	.114
278	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.114
279	efficientnet-b7	.104
280	yudixie_resnet18_distance_reg_0_240719	.104
281	pr1_stl10_disentangled_alpha04	.094
282	efficientnet_b0	.086
283	convnext_base_imagenet_full_seed-0	.078
284	resnet-152_v2_pytorch	.078
285	resnet50_robust_l2_eps1	.073
286	resnet50_ewconplus_wzc_seed2_2__323	.071
287	regnet_y_400mf	.071
288	resnet50-SIN_IN	.071
289	resnet50_robust_l2_eps3	.064
290	imagenet_l2_3_0	.064
291	resnet-50-robust	.064
292	compact_cornets_V4	.053
293	mobilenet_v2_0_75_192	.048
294	mobilenet_v2_0_5_192	.048
295	resnet_50_v1_spiking	.044
296	AT_efficientnet-b2	.033
297	AdvProp_efficientnet-b2	.033
298	eicircuit_resnet18_bn_20260919	.022
299	efficientnet_b2_imagenet_full	.020
300	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3
301	convnext_base_fb_in1k
302	r2plus1d_18
303	SeLaVi-VGG-Sound
304	SeLaVi-Kinetics-Sound
305	mvit_v1_b
306	mvit_v2_s
307	s3d
308	mc3_18
309	r3d_18
310	SeLaVi-Kinetics400
311	SeLaVi-AVE

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