Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("tong.Coggan2024_fMRI.V4-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xlarge:fb_in22k_ft_in1k	.277
2	convnext_small_imagenet_1_seed-0	.215
3	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.206
4	vit_large_patch14_clip_336:laion2b_ft_in1k	.199
5	vit_large_patch14_clip_224:openai_ft_in1k	.197
6	vit_large_patch14_clip_224:laion2b_ft_in1k	.171
7	deit_small_imagenet_10_seed-0	.171
8	yudixie_resnet18_random_0_240719	.169
9	efficientnet_b2_imagenet_full	.164
10	cvt_cvt-21-384-in1k_4_LucyV4	.161
11	alexnet_training_seed_01	.157
12	mobilenet_v2_0_5_224	.155
13	resnet-50-robust	.153
14	omnivore_swinS	.145
15	deit_small_imagenet_1_seed-0	.144
16	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.143
17	deit_small_imagenet_full_seed-0	.135
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.134
19	BiT-S-R50x1	.134
20	convnext_large:fb_in22k_ft_in1k	.132
21	convnext_small_imagenet_10_seed-0	.131
22	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.126
23	vit_base_patch16_clip_224:openai_ft_in1k	.126
24	alexnet_training_seed_09	.124
25	convnext_large_imagenet_full_seed-0	.123
26	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.120
27	resnet50-moclr8deg	.115
28	mobilenet_v2_1_0_160	.107
29	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
30	shufflenet_v2_x1_0	.090
31	resnet-152_v2_pytorch	.090
32	alexnet_training_seed_10	.090
33	alexnet_training_seed_07	.089
34	densenet-121	.087
35	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.084
36	vit_relpos_base_patch16_clsgap_224:sw_in1k	.081
37	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.081
38	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.080
39	mobilenet_v2_1_0_224	.079
40	alexnet_training_seed_06	.079
41	resnext101_32x48d_wsl	.079
42	resnet50-VITO-8deg-cc	.075
43	resnet50-vitoimagevidnet8	.075
44	inception_v1	.075
45	cvt_cvt-21-224-in1k_4_LucyV4	.074
46	alexnet_training_seed_04	.071
47	ReAlnet08	.070
48	alexnet_training_seed_05	.069
49	deit_large_imagenet_full_seed-0	.069
50	ReAlnet07	.069
51	ReAlnet05	.068
52	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.067
53	ReAlnet06	.065
54	ReAlnet10	.065
55	ReAlnet01	.064
56	ViT_L_32_imagenet1k	.064
57	resnet50-vicregl0p9	.062
58	resnext101_32x32d_wsl	.062
59	resnet34_ecoset_full	.062
60	ReAlnet02	.060
61	resnet50-vicreg	.058
62	resnet_101_v1	.057
63	ReAlnet09	.056
64	convnext_tiny_imagenet_full_seed-0	.056
65	hmax	.053
66	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.053
67	ReAlnet03	.053
68	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.052
69	ReAlnet04	.050
70	efficientnet_b0_imagenet_full	.050
71	BiT-S-R101x1	.049
72	effnetb1_272x240	.047
73	voneresnet-50	.046
74	alexnet_training_seed_08	.044
75	densenet-201	.044
76	resnet50-barlow	.043
77	AdvProp_efficientnet-b8	.042
78	custom_model_cv_18_dagger_408	.040
79	resnext101_32x8d_wsl	.040
80	resnet50-SIN	.039
81	omnivore_swinB	.038
82	nasnet_mobile	.038
83	VOneCORnet-S	.037
84	mobilenet_v2_0_75_160	.036
85	resnet101_imagenet_full	.036
86	inception_v3	.035
87	resnet101_ecoset_full	.033
88	densenet-169	.033
89	AT_efficientnet-b2	.031
90	AdvProp_efficientnet-b2	.031
91	ReAlnet_incorrect	.030
92	deit_small_imagenet_100_seed-0	.029
93	resnet18_imagenet_full	.029
94	resnet50_robust_l2_eps1	.028
95	cv_18_dagger_408_pretrained	.028
96	focalnet_tiny_lrf_in1k	.028
97	resnet152_imagenet_full	.027
98	convnext_small_imagenet_full_seed-0	.026
99	convnext_base_imagenet_full_seed-0	.026
100	mobilenet_v2_1_4_224	.025
101	mobilenet_v2_1-4_224_pytorch	.025
102	resnet152_ecoset_full	.024
103	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.024
104	BiT-S-R50x3	.024
105	BiT-S-R101x3	.023
106	efficientnet-b7	.023
107	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.022
108	resnet50_imagenet_full	.022
109	convnext_base:clip_laiona_augreg_ft_in1k_384	.021
110	imagenet_l2_3_0	.021
111	resnet18_ecoset_full	.020
112	resnet50_robust_l2_eps3	.018
113	ReAlnet10_cornet	.018
114	resnet50-vicregl0p75	.017
115	ReAlnet08_cornet	.017
116	ReAlnet02_cornet	.016
117	resnet50_ecoset_full	.016
118	BiT-S-R152x2	.016
119	AdvProp_efficientnet-b6	.015
120	voneresnet-50-robust	.015
121	alexnet_training_seed_03	.015
122	cvt_cvt-13-224-in1k_4_LucyV4	.015
123	ReAlnet01_cornet	.015
124	ReAlnet07_cornet	.014
125	yudixie_resnet50_random_0_240908	.014
126	ReAlnet03_cornet	.014
127	ReAlnet05_cornet	.014
128	mobilenet_v2_1_3_224	.014
129	ReAlnet04_cornet	.013
130	Res2Net50_26w_4s	.013
131	alexnet_training_seed_02	.012
132	efficientnet_b0	.011
133	resnet34_imagenet_full	.011
134	cvt_cvt-13-384-in1k_4_LucyV4	.010
135	ReAlnet09_cornet	.010
136	ReAlnet06_cornet	.010
137	mobilenet_v2_1_0_192	.010
138	mobilenet_v2_1_0_128	.009
139	convnext_small_imagenet_100_seed-0	.009
140	yudixie_resnet18_distance_translation_0_240719	.009
141	resnet-18_test_m	.009
142	mobilenet_v2_0_5_192	.008
143	resnet50_tutorial	.008
144	resnet_50_v1	.008
145	resnet50-sup	.008
146	yudixie_resnet50_imagenet1kpret_0_240908	.008
147	AdvProp_efficientnet-b4	.007
148	voneresnet-50-non_stochastic	.006
149	convnext_tiny_sup	.006
150	resnet_50_v2	.005
151	resnet50-SIN_IN_IN	.005
152	deit_base_imagenet_full_seed-0	.005
153	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.005
154	yudixie_resnet18_imagenet1kpret_0_240719	.004
155	AdvProp_efficientnet-b7	.004
156	resnet50-SIN_IN	.004
157	yudixie_resnet18_translation_rotation_0_240719	.003
158	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.003
159	resnet50_imagenet_100_seed-0	.002
160	artResNet18_1	.002
161	antialiased-r50	.002
162	yudixie_resnet18_translation_reg_0_240719	.001
163	resnet50_imagenet_10_seed-0	.001
164	resnet50_imagenet_1_seed-0	.001
165	yudixie_resnet18_rotation_reg_0_240719	.000
166	convnext_femto_ols:d1_in1k	.000
167	blt_vs	.000
168	yudixie_resnet18_distance_rotation_0_240719	.000
169	yudixie_resnet18_distance_reg_0_240719	.000
170	efficientnet_b1_imagenet_full	.000
171	yudixie_resnet18_distance_translation_rotation_0_240719	.000
172	vgg_16	.000
173	mobilenet_v2_0_75_224	.001
174	convnext_tiny:in12k_ft_in1k	.002
175	yudixie_resnet50_rotation_reg_0_240908	.003
176	vgg_19	.003
177	yudixie_resnet50_object_class_0_240908	.003
178	yudixie_resnet50_translation_rotation_0_240908	.003
179	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.003
180	yudixie_resnet18_object_class_0_240719	.004
181	yudixie_resnet50_category_class_0_240908	.004
182	yudixie_resnet50_translation_reg_0_240908	.004
183	yudixie_resnet50_distance_translation_rotation_0_240908	.005
184	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.006
185	yudixie_resnet50_distance_translation_0_240908	.007
186	yudixie_resnet18_category_class_0_240719	.007
187	yudixie_resnet50_distance_rotation_0_240908	.007
188	yudixie_resnet50_distance_reg_0_240908	.008
189	resnet_SIN_IN_FT_IN	X
190	xception	X
191	mobilenet_v2_0_75_192	X
192	resnet_152_v1	X
193	pnasnet_large	X
194	vonegrcnn_47e	X
195	nasnet_large	X
196	pnasnet_large_pytorch	X
197	vonegrcnn_62e_nobn	X
198	resnet_101_v2	X
199	resnet_152_v2	X
200	grcnn_109	X
201	inception_v4	X
202	grcnn	X
203	BiT-S-R152x4	X
204	vonegrcnn_52e_full	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.V4

Metric: rdm