Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("tong.Coggan2024_fMRI.V1-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	alexnet_training_seed_01	.752
2	alexnet_training_seed_10	.733
3	alexnet_training_seed_07	.708
4	alexnet_training_seed_06	.689
5	alexnet_training_seed_09	.683
6	alexnet_training_seed_02	.647
7	alexnet_training_seed_05	.621
8	alexnet_training_seed_04	.621
9	alexnet_training_seed_03	.592
10	alexnet_training_seed_08	.444
11	convnext_small_imagenet_10_seed-0	.383
12	hmax	.307
13	vit_base_patch16_clip_224:openai_ft_in1k	.284
14	vit_relpos_base_patch16_clsgap_224:sw_in1k	.277
15	convnext_base_imagenet_full_seed-0	.277
16	yudixie_resnet18_random_0_240719	.258
17	deit_small_imagenet_10_seed-0	.256
18	vit_large_patch14_clip_336:laion2b_ft_in1k	.254
19	resnet_50_v2	.238
20	resnet-50-robust	.230
21	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.224
22	convnext_small_imagenet_1_seed-0	.219
23	deit_small_imagenet_1_seed-0	.204
24	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.197
25	deit_small_imagenet_full_seed-0	.187
26	efficientnet_b2_imagenet_full	.185
27	vit_large_patch14_clip_224:openai_ft_in1k	.176
28	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.170
29	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.169
30	convnext_femto_ols:d1_in1k	.166
31	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.157
32	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.150
33	convnext_tiny_imagenet_full_seed-0	.147
34	densenet-121	.146
35	yolos_tiny	.139
36	resnet_101_v2	.139
37	resnet50-SIN_IN	.137
38	resnet50-vitoimagevidnet8	.136
39	resnet50-VITO-8deg-cc	.136
40	ViT_L_32_imagenet1k	.129
41	deit_small_imagenet_100_seed-0	.127
42	convnext_small_imagenet_full_seed-0	.126
43	inception_v1	.124
44	deit_large_imagenet_full_seed-0	.123
45	deit_base_imagenet_full_seed-0	.121
46	shufflenet_v2_x1_0	.114
47	resnext101_32x32d_wsl	.112
48	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.104
49	Res2Net50_26w_4s	.101
50	convnext_large:fb_in22k_ft_in1k	.099
51	convnext_small_imagenet_100_seed-0	.096
52	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.093
53	resnet34_imagenet_full	.086
54	omnivore_swinS	.086
55	vit_large_patch14_clip_224:laion2b_ft_in1k	.086
56	resnet18_ecoset_full	.085
57	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.082
58	resnet34_ecoset_full	.080
59	resnet-152_v2_pytorch	.080
60	resnet50-moclr8deg	.077
61	yudixie_resnet50_rotation_reg_0_240908	.077
62	BiT-S-R101x1	.075
63	resnet50-vicregl0p9	.074
64	custom_model_cv_18_dagger_408	.073
65	resnet-18_test_m	.068
66	efficientnet_b0	.067
67	mobilenet_v2_1_0_224	.066
68	ReAlnet10	.063
69	ReAlnet10_cornet	.063
70	ReAlnet08_cornet	.060
71	ReAlnet08	.060
72	ReAlnet05	.059
73	ReAlnet05_cornet	.059
74	cvt_cvt-21-224-in1k_4_LucyV4	.058
75	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.057
76	ReAlnet07_cornet	.056
77	ReAlnet07	.056
78	ReAlnet03_cornet	.056
79	ReAlnet03	.056
80	ReAlnet04_cornet	.056
81	ReAlnet04	.056
82	ReAlnet_incorrect	.056
83	ReAlnet01_cornet	.056
84	ReAlnet01	.056
85	convnext_large_imagenet_full_seed-0	.055
86	ReAlnet06_cornet	.055
87	ReAlnet06	.055
88	resnet50_imagenet_1_seed-0	.054
89	AdvProp_efficientnet-b2	.054
90	AT_efficientnet-b2	.054
91	ReAlnet09	.053
92	ReAlnet09_cornet	.053
93	ReAlnet02_cornet	.053
94	ReAlnet02	.053
95	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.050
96	resnet18_imagenet_full	.049
97	convnext_tiny:in12k_ft_in1k	.049
98	resnet50_imagenet_full	.047
99	cv_18_dagger_408_pretrained	.044
100	inception_v3	.043
101	mobilenet_v2_0_5_192	.039
102	resnet101_ecoset_full	.039
103	BiT-S-R101x3	.036
104	resnet50_ecoset_full	.035
105	resnet152_ecoset_full	.035
106	efficientnet-b7	.035
107	resnet50-barlow	.035
108	nasnet_mobile	.034
109	imagenet_l2_3_0	.033
110	resnext101_32x48d_wsl	.033
111	resnet152_imagenet_full	.033
112	resnet50-vicregl0p75	.031
113	cvt_cvt-13-224-in1k_4_LucyV4	.030
114	resnet50_robust_l2_eps3	.029
115	convnext_base:clip_laiona_augreg_ft_in1k_384	.027
116	densenet-201	.027
117	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.026
118	effnetb1_272x240	.026
119	resnet_101_v1	.025
120	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.025
121	densenet-169	.025
122	resnet50-vicreg	.022
123	AdvProp_efficientnet-b6	.022
124	cvt_cvt-13-384-in1k_4_LucyV4	.020
125	cvt_cvt-21-384-in1k_4_LucyV4	.019
126	yudixie_resnet18_distance_translation_0_240719	.018
127	resnext101_32x8d_wsl	.018
128	resnet101_imagenet_full	.017
129	yudixie_resnet50_random_0_240908	.017
130	artResNet18_1	.016
131	resnet50_robust_l2_eps1	.015
132	AdvProp_efficientnet-b4	.014
133	omnivore_swinB	.014
134	convnext_tiny_sup	.013
135	voneresnet-50-robust	.013
136	resnet50_tutorial	.013
137	resnet50-sup	.013
138	yudixie_resnet50_imagenet1kpret_0_240908	.013
139	resnet_50_v1	.013
140	mobilenet_v2_1_4_224	.011
141	mobilenet_v2_1-4_224_pytorch	.011
142	yudixie_resnet18_category_class_0_240719	.011
143	BiT-S-R50x1	.011
144	antialiased-r50	.009
145	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.009
146	resnet50-SIN_IN_IN	.008
147	mobilenet_v2_1_3_224	.008
148	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.008
149	mobilenet_v2_0_75_224	.007
150	yudixie_resnet18_translation_rotation_0_240719	.007
151	mobilenet_v2_1_0_192	.007
152	convnext_xlarge:fb_in22k_ft_in1k	.007
153	BiT-S-R50x3	.006
154	resnet50_imagenet_100_seed-0	.006
155	vonegrcnn_52e_full	.005
156	vonegrcnn_47e	.005
157	yudixie_resnet18_imagenet1kpret_0_240719	.004
158	yudixie_resnet50_translation_reg_0_240908	.004
159	mobilenet_v2_1_0_160	.004
160	yudixie_resnet50_object_class_0_240908	.004
161	yudixie_resnet18_translation_reg_0_240719	.004
162	resnet50_imagenet_10_seed-0	.003
163	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.003
164	resnet50-SIN	.002
165	mobilenet_v2_0_5_224	.002
166	voneresnet-50-non_stochastic	.002
167	yudixie_resnet18_rotation_reg_0_240719	.001
168	yudixie_resnet18_distance_rotation_0_240719	.001
169	yudixie_resnet18_distance_reg_0_240719	.001
170	yudixie_resnet50_distance_reg_0_240908	.001
171	yudixie_resnet50_distance_translation_rotation_0_240908	.001
172	voneresnet-50	.001
173	yudixie_resnet50_category_class_0_240908	.001
174	VOneCORnet-S	.001
175	AdvProp_efficientnet-b8	.001
176	blt_vs	.001
177	yudixie_resnet50_distance_translation_0_240908	.000
178	vgg_16	.000
179	yudixie_resnet50_translation_rotation_0_240908	.000
180	AdvProp_efficientnet-b7	.000
181	yudixie_resnet18_distance_translation_rotation_0_240719	.000
182	yudixie_resnet50_distance_rotation_0_240908	.000
183	briaai_rmbg_1_4	.000
184	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
185	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.000
186	efficientnet_b1_imagenet_full	.000
187	mobilenet_v2_0_75_160	.000
188	BiT-S-R152x2	.001
189	vgg_19	.002
190	focalnet_tiny_lrf_in1k	.002
191	yudixie_resnet18_object_class_0_240719	.003
192	mobilenet_v2_0_75_192	.004
193	inception_v4	X
194	pnasnet_large	X
195	vonegrcnn_62e_nobn	X
196	grcnn	X
197	resnet_152_v1	X
198	pnasnet_large_pytorch	X
199	xception	X
200	mobilenet_v2_1_0_128	X
201	efficientnet_b0_imagenet_full	X
202	resnet_SIN_IN_FT_IN	X
203	nasnet_large	X
204	resnet_152_v2	X
205	BiT-S-R152x4	X
206	grcnn_109	X

Benchmark bibtex

@inproceedings{santurkar2019computer,
    title={Computer Vision with a Single (Robust) Classifier},
    author={Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Andrew Ilyas and Logan Engstrom and Aleksander Madry},
    booktitle={ArXiv preprint arXiv:1906.09453},
    year={2019}
}

Ceiling

Not available

Data: tong.Coggan2024_fMRI.V1

Metric: rdm