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	yolos_tiny	.163
43	resnet50-SIN_IN_IN	.159
44	vit_large_patch14_clip_224:laion2b_ft_in1k	.149
45	convnext_large_imagenet_full_seed-0	.147
46	artResNet18_1	.143
47	deit_small_imagenet_1_seed-0	.142
48	resnet50_robust_l2_eps3	.141
49	convnext_tiny:in12k_ft_in1k	.138
50	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.137
51	resnet_101_v1	.135
52	resnet50-SIN	.133
53	resnet-18_test_m	.130
54	shufflenet_v2_x1_0	.121
55	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.121
56	resnet50-VITO-8deg-cc	.121
57	resnet50-vitoimagevidnet8	.121
58	resnet50_robust_l2_eps1	.121
59	efficientnet_b0	.119
60	resnet_50_v2	.118
61	imagenet_l2_3_0	.117
62	omnivore_swinB	.116
63	resnet18_imagenet_full	.112
64	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.111
65	convnext_base:clip_laiona_augreg_ft_in1k_384	.107
66	voneresnet-50-non_stochastic	.107
67	resnet18_ecoset_full	.106
68	convnext_base_imagenet_full_seed-0	.105
69	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.101
70	resnet152_ecoset_full	.100
71	Res2Net50_26w_4s	.100
72	hmax	.097
73	resnet50-moclr8deg	.097
74	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.096
75	resnet50-vicregl0p9	.094
76	cvt_cvt-21-224-in1k_4_LucyV4	.092
77	antialiased-r50	.089
78	resnet101_imagenet_full	.087
79	resnet152_imagenet_full	.086
80	resnet34_ecoset_full	.085
81	voneresnet-50-robust	.084
82	mobilenet_v2_1_0_160	.080
83	voneresnet-50	.078
84	ReAlnet_incorrect	.075
85	resnet50_imagenet_full	.074
86	deit_small_imagenet_100_seed-0	.074
87	resnet-152_v2_pytorch	.073
88	yudixie_resnet50_rotation_reg_0_240908	.073
89	BiT-S-R101x1	.072
90	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.069
91	inception_v1	.067
92	convnext_tiny_sup	.063
93	resnet50-sup	.061
94	resnet_50_v1	.061
95	custom_model_cv_18_dagger_408	.061
96	mobilenet_v2_1_4_224	.059
97	densenet-201	.058
98	cvt_cvt-21-384-in1k_4_LucyV4	.057
99	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.057
100	focalnet_tiny_lrf_in1k	.056
101	blt_vs	.056
102	resnet34_imagenet_full	.055
103	resnext101_32x32d_wsl	.053
104	mobilenet_v2_1_0_224	.052
105	VOneCORnet-S	.051
106	vit_large_patch14_clip_224:openai_ft_in1k	.048
107	convnext_femto_ols:d1_in1k	.047
108	efficientnet_b2_imagenet_full	.044
109	mobilenet_v2_0_75_160	.043
110	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.043
111	resnext101_32x48d_wsl	.041
112	ReAlnet04	.040
113	ReAlnet10	.040
114	resnet50-SIN_IN	.038
115	ReAlnet08	.038
116	ReAlnet05	.037
117	BiT-S-R50x3	.037
118	ReAlnet01	.037
119	convnext_small_imagenet_100_seed-0	.037
120	effnetb1_272x240	.036
121	ReAlnet03	.036
122	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.036
123	BiT-S-R101x3	.036
124	cvt_cvt-13-224-in1k_4_LucyV4	.035
125	ReAlnet02	.034
126	inception_v3	.033
127	ReAlnet07	.032
128	resnet-50-robust	.031
129	ReAlnet10_cornet	.030
130	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.030
131	cv_18_dagger_408_pretrained	.029
132	densenet-169	.028
133	ReAlnet06	.027
134	ReAlnet09	.026
135	cvt_cvt-13-384-in1k_4_LucyV4	.026
136	ReAlnet05_cornet	.025
137	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.025
138	ReAlnet08_cornet	.025
139	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.024
140	ReAlnet07_cornet	.024
141	ReAlnet03_cornet	.023
142	yudixie_resnet50_random_0_240908	.023
143	BiT-S-R152x2	.023
144	ReAlnet04_cornet	.022
145	resnet50_imagenet_10_seed-0	.021
146	ReAlnet06_cornet	.021
147	ReAlnet01_cornet	.020
148	ReAlnet02_cornet	.018
149	briaai_rmbg_1_4	.016
150	ReAlnet09_cornet	.016
151	resnet50_tutorial	.014
152	yudixie_resnet50_imagenet1kpret_0_240908	.014
153	efficientnet-b7	.014
154	AdvProp_efficientnet-b6	.013
155	mobilenet_v2_1-4_224_pytorch	.011
156	efficientnet_b0_imagenet_full	.010
157	AdvProp_efficientnet-b8	.010
158	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.010
159	AdvProp_efficientnet-b4	.009
160	yudixie_resnet18_imagenet1kpret_0_240719	.008
161	mobilenet_v2_1_3_224	.005
162	efficientnet_b1_imagenet_full	.004
163	yudixie_resnet18_distance_reg_0_240719	.003
164	yudixie_resnet50_distance_reg_0_240908	.003
165	yudixie_resnet18_rotation_reg_0_240719	.003
166	yudixie_resnet50_distance_translation_rotation_0_240908	.003
167	yudixie_resnet18_distance_rotation_0_240719	.002
168	ViT_L_32_imagenet1k	.002
169	mobilenet_v2_1_0_192	.001
170	yudixie_resnet50_translation_rotation_0_240908	.001
171	yudixie_resnet50_distance_rotation_0_240908	.001
172	resnet50_imagenet_1_seed-0	.001
173	vgg_16	.001
174	AdvProp_efficientnet-b7	.000
175	yudixie_resnet18_translation_reg_0_240719	.000
176	mobilenet_v2_0_75_224	.000
177	yudixie_resnet18_category_class_0_240719	.000
178	yudixie_resnet50_object_class_0_240908	.000
179	mobilenet_v2_0_5_224	.000
180	vgg_19	.000
181	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.001
182	yudixie_resnet18_object_class_0_240719	.001
183	yudixie_resnet50_category_class_0_240908	.001
184	yudixie_resnet50_translation_reg_0_240908	.001
185	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.002
186	yudixie_resnet18_distance_translation_rotation_0_240719	.002
187	yudixie_resnet18_translation_rotation_0_240719	.003
188	yudixie_resnet50_distance_translation_0_240908	.003
189	yudixie_resnet18_distance_translation_0_240719	.005
190	resnet_152_v1	X
191	vonegrcnn_52e_full	X
192	resnet_SIN_IN_FT_IN	X
193	resnet_152_v2	X
194	xception	X
195	vonegrcnn_47e	X
196	BiT-S-R152x4	X
197	resnet_101_v2	X
198	inception_v4	X
199	mobilenet_v2_0_75_192	X
200	mobilenet_v2_0_5_192	X
201	nasnet_large	X
202	pnasnet_large	X
203	pnasnet_large_pytorch	X
204	grcnn	X
205	grcnn_109	X
206	vonegrcnn_62e_nobn	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