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	yolos_tiny	.093
31	shufflenet_v2_x1_0	.090
32	resnet-152_v2_pytorch	.090
33	alexnet_training_seed_10	.090
34	alexnet_training_seed_07	.089
35	densenet-121	.087
36	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.084
37	vit_relpos_base_patch16_clsgap_224:sw_in1k	.081
38	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.081
39	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.080
40	mobilenet_v2_1_0_224	.079
41	alexnet_training_seed_06	.079
42	resnext101_32x48d_wsl	.079
43	resnet50-vitoimagevidnet8	.075
44	resnet50-VITO-8deg-cc	.075
45	inception_v1	.075
46	cvt_cvt-21-224-in1k_4_LucyV4	.074
47	alexnet_training_seed_04	.071
48	ReAlnet08	.070
49	alexnet_training_seed_05	.069
50	deit_large_imagenet_full_seed-0	.069
51	ReAlnet07	.069
52	ReAlnet05	.068
53	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.067
54	ReAlnet06	.065
55	ReAlnet10	.065
56	ReAlnet01	.064
57	ViT_L_32_imagenet1k	.064
58	resnet50-vicregl0p9	.062
59	resnext101_32x32d_wsl	.062
60	resnet34_ecoset_full	.062
61	ReAlnet02	.060
62	resnet50-vicreg	.058
63	resnet_101_v1	.057
64	ReAlnet09	.056
65	convnext_tiny_imagenet_full_seed-0	.056
66	hmax	.053
67	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.053
68	ReAlnet03	.053
69	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.052
70	ReAlnet04	.050
71	efficientnet_b0_imagenet_full	.050
72	BiT-S-R101x1	.049
73	effnetb1_272x240	.047
74	voneresnet-50	.046
75	alexnet_training_seed_08	.044
76	densenet-201	.044
77	resnet50-barlow	.043
78	AdvProp_efficientnet-b8	.042
79	custom_model_cv_18_dagger_408	.040
80	resnext101_32x8d_wsl	.040
81	resnet50-SIN	.039
82	omnivore_swinB	.038
83	nasnet_mobile	.038
84	VOneCORnet-S	.037
85	mobilenet_v2_0_75_160	.036
86	resnet101_imagenet_full	.036
87	inception_v3	.035
88	resnet101_ecoset_full	.033
89	densenet-169	.033
90	AT_efficientnet-b2	.031
91	AdvProp_efficientnet-b2	.031
92	ReAlnet_incorrect	.030
93	deit_small_imagenet_100_seed-0	.029
94	resnet18_imagenet_full	.029
95	resnet50_robust_l2_eps1	.028
96	cv_18_dagger_408_pretrained	.028
97	focalnet_tiny_lrf_in1k	.028
98	resnet152_imagenet_full	.027
99	convnext_small_imagenet_full_seed-0	.026
100	convnext_base_imagenet_full_seed-0	.026
101	mobilenet_v2_1_4_224	.025
102	mobilenet_v2_1-4_224_pytorch	.025
103	resnet152_ecoset_full	.024
104	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.024
105	BiT-S-R50x3	.024
106	BiT-S-R101x3	.023
107	efficientnet-b7	.023
108	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.022
109	resnet50_imagenet_full	.022
110	convnext_base:clip_laiona_augreg_ft_in1k_384	.021
111	imagenet_l2_3_0	.021
112	resnet18_ecoset_full	.020
113	resnet50_robust_l2_eps3	.018
114	ReAlnet10_cornet	.018
115	resnet50-vicregl0p75	.017
116	ReAlnet08_cornet	.017
117	ReAlnet02_cornet	.016
118	resnet50_ecoset_full	.016
119	BiT-S-R152x2	.016
120	AdvProp_efficientnet-b6	.015
121	voneresnet-50-robust	.015
122	alexnet_training_seed_03	.015
123	cvt_cvt-13-224-in1k_4_LucyV4	.015
124	ReAlnet01_cornet	.015
125	ReAlnet07_cornet	.014
126	yudixie_resnet50_random_0_240908	.014
127	ReAlnet03_cornet	.014
128	ReAlnet05_cornet	.014
129	mobilenet_v2_1_3_224	.014
130	ReAlnet04_cornet	.013
131	Res2Net50_26w_4s	.013
132	alexnet_training_seed_02	.012
133	efficientnet_b0	.011
134	resnet34_imagenet_full	.011
135	cvt_cvt-13-384-in1k_4_LucyV4	.010
136	ReAlnet09_cornet	.010
137	ReAlnet06_cornet	.010
138	mobilenet_v2_1_0_192	.010
139	mobilenet_v2_1_0_128	.009
140	convnext_small_imagenet_100_seed-0	.009
141	yudixie_resnet18_distance_translation_0_240719	.009
142	resnet-18_test_m	.009
143	mobilenet_v2_0_5_192	.008
144	resnet50_tutorial	.008
145	yudixie_resnet50_imagenet1kpret_0_240908	.008
146	resnet50-sup	.008
147	resnet_50_v1	.008
148	AdvProp_efficientnet-b4	.007
149	briaai_rmbg_1_4	.007
150	voneresnet-50-non_stochastic	.006
151	convnext_tiny_sup	.006
152	resnet_50_v2	.005
153	resnet50-SIN_IN_IN	.005
154	deit_base_imagenet_full_seed-0	.005
155	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.005
156	yudixie_resnet18_imagenet1kpret_0_240719	.004
157	AdvProp_efficientnet-b7	.004
158	resnet50-SIN_IN	.004
159	yudixie_resnet18_translation_rotation_0_240719	.003
160	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.003
161	resnet50_imagenet_100_seed-0	.002
162	artResNet18_1	.002
163	antialiased-r50	.002
164	yudixie_resnet18_translation_reg_0_240719	.001
165	resnet50_imagenet_10_seed-0	.001
166	resnet50_imagenet_1_seed-0	.001
167	yudixie_resnet18_rotation_reg_0_240719	.000
168	convnext_femto_ols:d1_in1k	.000
169	blt_vs	.000
170	yudixie_resnet18_distance_rotation_0_240719	.000
171	yudixie_resnet18_distance_reg_0_240719	.000
172	efficientnet_b1_imagenet_full	.000
173	yudixie_resnet18_distance_translation_rotation_0_240719	.000
174	vgg_16	.000
175	mobilenet_v2_0_75_224	.001
176	convnext_tiny:in12k_ft_in1k	.002
177	yudixie_resnet50_rotation_reg_0_240908	.003
178	vgg_19	.003
179	yudixie_resnet50_object_class_0_240908	.003
180	yudixie_resnet50_translation_rotation_0_240908	.003
181	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.003
182	yudixie_resnet18_object_class_0_240719	.004
183	yudixie_resnet50_category_class_0_240908	.004
184	yudixie_resnet50_translation_reg_0_240908	.004
185	yudixie_resnet50_distance_translation_rotation_0_240908	.005
186	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.006
187	yudixie_resnet50_distance_translation_0_240908	.007
188	yudixie_resnet18_category_class_0_240719	.007
189	yudixie_resnet50_distance_rotation_0_240908	.007
190	yudixie_resnet50_distance_reg_0_240908	.008
191	resnet_152_v1	X
192	mobilenet_v2_0_75_192	X
193	resnet_152_v2	X
194	pnasnet_large_pytorch	X
195	vonegrcnn_52e_full	X
196	nasnet_large	X
197	resnet_SIN_IN_FT_IN	X
198	vonegrcnn_47e	X
199	BiT-S-R152x4	X
200	grcnn	X
201	grcnn_109	X
202	resnet_101_v2	X
203	vonegrcnn_62e_nobn	X
204	inception_v4	X
205	pnasnet_large	X
206	xception	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