Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("tong.Coggan2024_fMRI.V2-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	.630
2	alexnet_training_seed_10	.626
3	alexnet_training_seed_07	.578
4	alexnet_training_seed_03	.571
5	alexnet_training_seed_06	.570
6	alexnet_training_seed_04	.492
7	alexnet_training_seed_05	.455
8	convnext_small_imagenet_10_seed-0	.438
9	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.407
10	convnext_tiny_imagenet_full_seed-0	.383
11	alexnet_training_seed_09	.369
12	alexnet_training_seed_08	.313
13	alexnet_training_seed_02	.311
14	deit_small_imagenet_full_seed-0	.278
15	omnivore_swinS	.269
16	vit_relpos_base_patch16_clsgap_224:sw_in1k	.268
17	vit_large_patch14_clip_336:laion2b_ft_in1k	.262
18	yudixie_resnet18_random_0_240719	.255
19	vit_base_patch16_clip_224:openai_ft_in1k	.254
20	densenet-121	.247
21	convnext_small_imagenet_full_seed-0	.231
22	convnext_large:fb_in22k_ft_in1k	.227
23	deit_large_imagenet_full_seed-0	.220
24	deit_base_imagenet_full_seed-0	.216
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.212
26	resnext101_32x8d_wsl	.206
27	nasnet_mobile	.206
28	convnext_small_imagenet_1_seed-0	.203
29	deit_small_imagenet_10_seed-0	.201
30	resnet101_ecoset_full	.199
31	convnext_xlarge:fb_in22k_ft_in1k	.194
32	resnet50-vicreg	.189
33	resnet50-vicregl0p75	.181
34	AT_efficientnet-b2	.178
35	AdvProp_efficientnet-b2	.178
36	mobilenet_v2_1_0_128	.175
37	BiT-S-R50x1	.172
38	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.170
39	resnet50_imagenet_100_seed-0	.167
40	resnet50_ecoset_full	.164
41	resnet50-barlow	.163
42	resnet50-SIN_IN_IN	.159
43	vit_large_patch14_clip_224:laion2b_ft_in1k	.149
44	convnext_large_imagenet_full_seed-0	.147
45	artResNet18_1	.143
46	deit_small_imagenet_1_seed-0	.142
47	resnet50_robust_l2_eps3	.141
48	convnext_tiny:in12k_ft_in1k	.138
49	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.137
50	resnet_101_v1	.135
51	resnet50-SIN	.133
52	resnet-18_test_m	.130
53	shufflenet_v2_x1_0	.121
54	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.121
55	resnet50-VITO-8deg-cc	.121
56	resnet50-vitoimagevidnet8	.121
57	resnet50_robust_l2_eps1	.121
58	efficientnet_b0	.119
59	resnet_50_v2	.118
60	imagenet_l2_3_0	.117
61	omnivore_swinB	.116
62	resnet18_imagenet_full	.112
63	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.111
64	convnext_base:clip_laiona_augreg_ft_in1k_384	.107
65	voneresnet-50-non_stochastic	.107
66	resnet18_ecoset_full	.106
67	convnext_base_imagenet_full_seed-0	.105
68	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.101
69	resnet152_ecoset_full	.100
70	Res2Net50_26w_4s	.100
71	hmax	.097
72	resnet50-moclr8deg	.097
73	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.096
74	resnet50-vicregl0p9	.094
75	cvt_cvt-21-224-in1k_4_LucyV4	.092
76	antialiased-r50	.089
77	resnet101_imagenet_full	.087
78	resnet152_imagenet_full	.086
79	resnet34_ecoset_full	.085
80	voneresnet-50-robust	.084
81	mobilenet_v2_1_0_160	.080
82	voneresnet-50	.078
83	ReAlnet_incorrect	.075
84	resnet50_imagenet_full	.074
85	deit_small_imagenet_100_seed-0	.074
86	resnet-152_v2_pytorch	.073
87	yudixie_resnet50_rotation_reg_0_240908	.073
88	BiT-S-R101x1	.072
89	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.069
90	inception_v1	.067
91	convnext_tiny_sup	.063
92	resnet50-sup	.061
93	resnet_50_v1	.061
94	custom_model_cv_18_dagger_408	.061
95	mobilenet_v2_1_4_224	.059
96	densenet-201	.058
97	cvt_cvt-21-384-in1k_4_LucyV4	.057
98	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.057
99	focalnet_tiny_lrf_in1k	.056
100	blt_vs	.056
101	resnet34_imagenet_full	.055
102	resnext101_32x32d_wsl	.053
103	mobilenet_v2_1_0_224	.052
104	VOneCORnet-S	.051
105	vit_large_patch14_clip_224:openai_ft_in1k	.048
106	convnext_femto_ols:d1_in1k	.047
107	efficientnet_b2_imagenet_full	.044
108	mobilenet_v2_0_75_160	.043
109	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.043
110	resnext101_32x48d_wsl	.041
111	ReAlnet04	.040
112	ReAlnet10	.040
113	resnet50-SIN_IN	.038
114	ReAlnet08	.038
115	ReAlnet05	.037
116	BiT-S-R50x3	.037
117	ReAlnet01	.037
118	convnext_small_imagenet_100_seed-0	.037
119	effnetb1_272x240	.036
120	ReAlnet03	.036
121	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.036
122	BiT-S-R101x3	.036
123	cvt_cvt-13-224-in1k_4_LucyV4	.035
124	ReAlnet02	.034
125	inception_v3	.033
126	ReAlnet07	.032
127	resnet-50-robust	.031
128	ReAlnet10_cornet	.030
129	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.030
130	cv_18_dagger_408_pretrained	.029
131	densenet-169	.028
132	ReAlnet06	.027
133	ReAlnet09	.026
134	cvt_cvt-13-384-in1k_4_LucyV4	.026
135	ReAlnet05_cornet	.025
136	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.025
137	ReAlnet08_cornet	.025
138	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.024
139	ReAlnet07_cornet	.024
140	ReAlnet03_cornet	.023
141	yudixie_resnet50_random_0_240908	.023
142	BiT-S-R152x2	.023
143	ReAlnet04_cornet	.022
144	resnet50_imagenet_10_seed-0	.021
145	ReAlnet06_cornet	.021
146	ReAlnet01_cornet	.020
147	ReAlnet02_cornet	.018
148	ReAlnet09_cornet	.016
149	yudixie_resnet50_imagenet1kpret_0_240908	.014
150	resnet50_tutorial	.014
151	efficientnet-b7	.014
152	AdvProp_efficientnet-b6	.013
153	mobilenet_v2_1-4_224_pytorch	.011
154	efficientnet_b0_imagenet_full	.010
155	AdvProp_efficientnet-b8	.010
156	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.010
157	AdvProp_efficientnet-b4	.009
158	yudixie_resnet18_imagenet1kpret_0_240719	.008
159	mobilenet_v2_1_3_224	.005
160	efficientnet_b1_imagenet_full	.004
161	yudixie_resnet18_distance_reg_0_240719	.003
162	yudixie_resnet50_distance_reg_0_240908	.003
163	yudixie_resnet18_rotation_reg_0_240719	.003
164	yudixie_resnet50_distance_translation_rotation_0_240908	.003
165	yudixie_resnet18_distance_rotation_0_240719	.002
166	ViT_L_32_imagenet1k	.002
167	mobilenet_v2_1_0_192	.001
168	yudixie_resnet50_translation_rotation_0_240908	.001
169	yudixie_resnet50_distance_rotation_0_240908	.001
170	resnet50_imagenet_1_seed-0	.001
171	vgg_16	.001
172	AdvProp_efficientnet-b7	.000
173	yudixie_resnet18_translation_reg_0_240719	.000
174	mobilenet_v2_0_75_224	.000
175	yudixie_resnet18_category_class_0_240719	.000
176	yudixie_resnet50_object_class_0_240908	.000
177	mobilenet_v2_0_5_224	.000
178	vgg_19	.000
179	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.001
180	yudixie_resnet18_object_class_0_240719	.001
181	yudixie_resnet50_category_class_0_240908	.001
182	yudixie_resnet50_translation_reg_0_240908	.001
183	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.002
184	yudixie_resnet18_distance_translation_rotation_0_240719	.002
185	yudixie_resnet18_translation_rotation_0_240719	.003
186	yudixie_resnet50_distance_translation_0_240908	.003
187	yudixie_resnet18_distance_translation_0_240719	.005
188	resnet_SIN_IN_FT_IN	X
189	grcnn	X
190	BiT-S-R152x4	X
191	nasnet_large	X
192	vonegrcnn_52e_full	X
193	vonegrcnn_62e_nobn	X
194	resnet_101_v2	X
195	vonegrcnn_47e	X
196	pnasnet_large	X
197	resnet_152_v1	X
198	grcnn_109	X
199	inception_v4	X
200	pnasnet_large_pytorch	X
201	resnet_152_v2	X
202	mobilenet_v2_0_5_192	X
203	xception	X
204	mobilenet_v2_0_75_192	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.V2

Metric: rdm