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