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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_SIN	.636
2	alexnet_training_seed_01	.630
3	alexnet_training_seed_10	.626
4	alexnet_training_seed_07	.578
5	alexnet_training_seed_03	.571
6	alexnet_training_seed_06	.570
7	alexnet_training_seed_04	.492
8	AlexNet_SIN_fov16	.485
9	AlexNet_SIN_fov12	.458
10	alexnet_training_seed_05	.455
11	convnext_small_imagenet_10_seed-0	.438
12	alexnet_training_seed_01_fov12	.415
13	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.407
14	convnext_tiny_imagenet_full_seed-0	.383
15	alexnet_training_seed_01_fov16	.372
16	alexnet_training_seed_09	.369
17	alexnet	.335
18	alexnet_workflow_trigger	.335
19	alexnet_ks_torevert	.335
20	grcnn	.327
21	alexnet_training_seed_08	.313
22	alexnet_training_seed_02	.311
23	alexnet_fov16	.285
24	deit_small_imagenet_full_seed-0	.278
25	alexnet_fov12	.273
26	omnivore_swinS	.269
27	vit_relpos_base_patch16_clsgap_224:sw_in1k	.268
28	vit_large_patch14_clip_336:laion2b_ft_in1k	.262
29	yudixie_resnet18_random_0_240719	.255
30	vit_base_patch16_clip_224:openai_ft_in1k	.254
31	densenet-121	.247
32	convnext_small_imagenet_full_seed-0	.231
33	convnext_large:fb_in22k_ft_in1k	.227
34	deit_large_imagenet_full_seed-0	.220
35	alexnet_training_seed_01_fov4	.220
36	deit_base_imagenet_full_seed-0	.216
37	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.212
38	resnext101_32x8d_wsl	.206
39	nasnet_mobile	.206
40	convnext_small_imagenet_1_seed-0	.203
41	deit_small_imagenet_10_seed-0	.201
42	grcnn_109	.200
43	resnet101_ecoset_full	.199
44	convnext_xlarge:fb_in22k_ft_in1k	.194
45	resnet50-vicreg	.189
46	resnet50-vicregl0p75	.181
47	AdvProp_efficientnet-b2	.178
48	AT_efficientnet-b2	.178
49	mobilenet_v2_1_0_128	.175
50	BiT-S-R50x1	.172
51	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.170
52	s3d	.167
53	resnet50_imagenet_100_seed-0	.167
54	resnet50_ecoset_full	.164
55	resnet50-barlow	.163
56	yolos_tiny	.163
57	resnet50_primary_visual_cortex	.161
58	resnet50-SIN_IN_IN	.159
59	resnet_SIN_IN_FT_IN	.159
60	antialias-resnet152	.155
61	vit_large_patch14_clip_224:laion2b_ft_in1k	.149
62	resnet_152_v1	.148
63	convnext_large_imagenet_full_seed-0	.147
64	artResNet18_1	.143
65	deit_small_imagenet_1_seed-0	.142
66	resnet50_robust_l2_eps3	.141
67	convnext_tiny:in12k_ft_in1k	.138
68	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.137
69	resnet_101_v1	.135
70	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.133
71	resnext101_32x16d_wsl	.133
72	resnet50-SIN	.133
73	resnet_101_v2	.131
74	resnet-18_test_m	.130
75	shufflenet_v2_x1_0	.121
76	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.121
77	resnet50-VITO-8deg-cc	.121
78	resnet50-vitoimagevidnet8	.121
79	resnet50_robust_l2_eps1	.121
80	efficientnet_b0	.119
81	antialiased-rnext101_32x8d	.118
82	resnet_50_v2	.118
83	imagenet_l2_3_0	.117
84	omnivore_swinB	.116
85	compact_resnet50_IT	.114
86	resnet18_imagenet_full	.112
87	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.111
88	mc3_18	.111
89	convnext_base:clip_laiona_augreg_ft_in1k_384	.107
90	r3d_18	.107
91	voneresnet-50-non_stochastic	.107
92	resnet18_ecoset_full	.106
93	convnext_base_imagenet_full_seed-0	.105
94	vonegrcnn_62e_nobn	.105
95	regnet_y_400mf	.104
96	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.101
97	resnet152_ecoset_full	.100
98	Res2Net50_26w_4s	.100
99	hmax	.097
100	BiT-S-R152x4	.097
101	inception_v4	.097
102	resnet50-moclr8deg	.097
103	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.096
104	resnet50-vicregl0p9	.094
105	cvt_cvt-21-224-in1k_4_LucyV4	.092
106	antialiased-r50	.089
107	AlexNet_SIN_fov4	.089
108	resnet50_ewconplus_wzc	.088
109	resnet101_imagenet_full	.087
110	resnet152_imagenet_full	.086
111	resnet34_ecoset_full	.085
112	voneresnet-50-robust	.084
113	SeLaVi-VGG-Sound	.081
114	compact_resnet50_robust_IT	.080
115	vit_base_patch14_dinov2:lvd142m	.080
116	mobilenet_v2_1_0_160	.080
117	voneresnet-50	.078
118	pnasnet_large	.076
119	resnet50_imagenet_full	.074
120	deit_small_imagenet_100_seed-0	.074
121	resnet-152_v2_pytorch	.073
122	yudixie_resnet50_rotation_reg_0_240908	.073
123	BiT-S-R101x1	.072
124	SeLaVi-AVE	.071
125	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.069
126	inception_v1	.067
127	convnext_tiny_sup	.063
128	resnet_50_v1_spiking	.061
129	resnet_50_v1_spiking_l4	.061
130	resnet_50_v1_spiking_l4a	.061
131	resnet_50_v1	.061
132	resnet50-sup	.061
133	custom_model_cv_18_dagger_408	.061
134	mobilenet_v2_1_4_224	.059
135	densenet-201	.058
136	cvt_cvt-21-384-in1k_4_LucyV4	.057
137	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.057
138	focalnet_tiny_lrf_in1k	.056
139	blt_vs	.056
140	resnet34_imagenet_full	.055
141	resnet_152_v2	.054
142	resnext101_32x32d_wsl	.053
143	mobilenet_v2_1_0_224	.052
144	VOneCORnet-S	.051
145	r2plus1d_18	.050
146	vit_large_patch14_clip_224:openai_ft_in1k	.048
147	convnext_femto_ols:d1_in1k	.047
148	compact_resnet50_V4	.045
149	alexnet_fov4	.044
150	efficientnet_b2_imagenet_full	.044
151	mobilenet_v2_0_75_160	.043
152	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.043
153	resnext101_32x48d_wsl	.041
154	ReAlnet04	.040
155	ReAlnet07_ALL	.040
156	ReAlnet10	.040
157	resnet50-SIN_IN	.038
158	ReAlnet08	.038
159	ReAlnet05	.037
160	BiT-S-R50x3	.037
161	ReAlnet01	.037
162	convnext_small_imagenet_100_seed-0	.037
163	ReAlnet01_cornet_fov4	.037
164	effnetb1_272x240	.036
165	ReAlnet03	.036
166	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.036
167	BiT-S-R101x3	.036
168	cvt_cvt-13-224-in1k_4_LucyV4	.035
169	ReAlnet08_ALL	.035
170	ReAlnet02	.034
171	inception_v3	.033
172	ReAlnet04_ALL	.033
173	ReAlnet07	.032
174	vit_base_patch14_reg4_dinov2:lvd142m	.032
175	ReAlnet02_ALL	.032
176	ReAlnet09_ALL	.031
177	ReAlnet05_ALL	.031
178	resnet-50-robust	.031
179	ReAlnet10_cornet	.030
180	ReAlnet06_ALL	.030
181	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.030
182	ReAlnet10_ALL	.030
183	ReAlnet01_ALL	.029
184	cv_18_dagger_408_pretrained	.029
185	ReAlnet03_ALL	.028
186	densenet-169	.028
187	ReAlnet06	.027
188	ReAlnet09	.026
189	cvt_cvt-13-384-in1k_4_LucyV4	.026
190	ReAlnet05_cornet	.025
191	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.025
192	ReAlnet08_cornet	.025
193	resnet18_imagenet21kP	.024
194	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.024
195	ReAlnet07_cornet	.024
196	ReAlnet03_cornet	.023
197	yudixie_resnet50_random_0_240908	.023
198	BiT-S-R152x2	.023
199	nasnet_large	.022
200	ReAlnet04_cornet	.022
201	resnet50_imagenet_10_seed-0	.021
202	ReAlnet06_cornet	.021
203	compact_vgg19_V4	.020
204	compact_cornets_IT	.020
205	ReAlnet01_cornet	.020
206	ReAlnet02_cornet	.018
207	compact_vgg19_IT	.018
208	briaai_rmbg_1_4	.016
209	ReAlnet09_cornet	.016
210	yudixie_resnet50_imagenet1kpret_0_240908	.014
211	resnet50_tutorial	.014
212	resnet_50_v1_spiking_l3	.014
213	yudixie_resnet50_imagenet1kpret_0_240312	.014
214	efficientnet-b7	.014
215	AdvProp_efficientnet-b6	.013
216	xception	.012
217	mobilenet_v2_1-4_224_pytorch	.011
218	efficientnet_b0_imagenet_full	.010
219	AdvProp_efficientnet-b8	.010
220	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.010
221	AdvProp_efficientnet-b4	.009
222	yudixie_resnet18_imagenet1kpret_0_240719	.008
223	vit_base_patch16_dinov3:lvd1689m	.006
224	mobilenet_v2_1_3_224	.005
225	SeLaVi-Kinetics400	.005
226	vonegrcnn_47e	.005
227	vonegrcnn_52e_full	.004
228	tv_efficientnet-b1	.004
229	efficientnet_b1_imagenet_full	.004
230	yudixie_resnet18_distance_reg_0_240719	.003
231	yudixie_resnet50_distance_reg_0_240908	.003
232	yudixie_resnet18_rotation_reg_0_240719	.003
233	yudixie_resnet50_distance_translation_rotation_0_240908	.003
234	yudixie_resnet18_distance_rotation_0_240719	.002
235	ViT_L_32_imagenet1k	.002
236	mobilenet_v2_1_0_192	.001
237	yudixie_resnet50_translation_rotation_0_240908	.001
238	yudixie_resnet50_distance_rotation_0_240908	.001
239	resnet50_imagenet_1_seed-0	.001
240	vgg_16	.001
241	AdvProp_efficientnet-b7	.000
242	yudixie_resnet18_translation_reg_0_240719	.000
243	mobilenet_v2_0_75_224	.000
244	yudixie_resnet18_category_class_0_240719	.000
245	yudixie_resnet50_object_class_0_240908	.000
246	mobilenet_v2_0_5_224	.000
247	vgg_19	.000
248	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.001
249	yudixie_resnet18_object_class_0_240719	.001
250	yudixie_resnet50_category_class_0_240908	.001
251	yudixie_resnet50_translation_reg_0_240908	.001
252	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.002
253	yudixie_resnet18_distance_translation_rotation_0_240719	.002
254	yudixie_resnet18_translation_rotation_0_240719	.003
255	yudixie_resnet50_distance_translation_0_240908	.003
256	yudixie_resnet18_distance_translation_0_240719	.005
257	pnasnet_large_pytorch
258	mobilenet_v2_0_5_192
259	mobilenet_v2_0_75_192
260	ReAlnet01_cornet_fov12	nan
261	fixres_resnext101_32x48d_wsl
262	compact_resnet50_robust_V4	nan
263	ReAlnet01_cornet_fov16	nan
264	mvit_v1_b
265	resnet50_eMMCR_Vanilla
266	compact_cornets_V4	nan
267	SeLaVi-Kinetics-Sound
268	mvit_v2_s

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

0.45.

Note that scores are relative to this ceiling.

Data: tong.Coggan2024_fMRI.V2

Metric: rdm