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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	yudixie_wbh_object_class_scaling_0415_run_3	.490
9	AlexNet_SIN_fov16	.485
10	AlexNet_SIN_fov12	.458
11	alexnet_training_seed_05	.455
12	convnext_small_imagenet_10_seed-0	.438
13	alexnet_training_seed_01_fov12	.415
14	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.407
15	convnext_tiny_imagenet_full_seed-0	.383
16	alexnet_training_seed_01_fov16	.372
17	alexnet_training_seed_09	.369
18	yudixie_wbh_object_class_scaling_0415_run_1	.361
19	yudixie_wbh_object_class_scaling_0415_run_0	.361
20	alexnet	.335
21	alexnet_ks_torevert	.335
22	grcnn	.327
23	alexnet_training_seed_08	.313
24	alexnet_training_seed_02	.311
25	deepeisnn_ei_cifar10_resnet18	.303
26	alexnet_fov16	.285
27	pr1_stl10_disentangled_alpha04	.281
28	deit_small_imagenet_full_seed-0	.278
29	alexnet_fov12	.273
30	omnivore_swinS	.269
31	vit_relpos_base_patch16_clsgap_224:sw_in1k	.268
32	convnext_base_fb_in1k_gram_aligned_dinov3	.263
33	vit_large_patch14_clip_336:laion2b_ft_in1k	.262
34	yudixie_resnet18_random_0_240719	.255
35	vit_base_patch16_clip_224:openai_ft_in1k	.254
36	deepeisnn_ei_cifar10_vgg19	.252
37	densenet-121	.247
38	convnext_small_imagenet_full_seed-0	.231
39	convnext_large:fb_in22k_ft_in1k	.227
40	deit_large_imagenet_full_seed-0	.220
41	alexnet_training_seed_01_fov4	.220
42	deit_base_imagenet_full_seed-0	.216
43	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.212
44	resnext101_32x8d_wsl	.206
45	nasnet_mobile	.206
46	convnext_small_imagenet_1_seed-0	.203
47	deit_small_imagenet_10_seed-0	.201
48	grcnn_109	.200
49	resnet101_ecoset_full	.199
50	convnext_base_fb_in22k_ft_in1k	.197
51	convnext_xlarge:fb_in22k_ft_in1k	.194
52	resnet50-vicreg	.189
53	resnet50-vicregl0p75	.181
54	AT_efficientnet-b2	.178
55	AdvProp_efficientnet-b2	.178
56	mobilenet_v2_1_0_128	.175
57	BiT-S-R50x1	.172
58	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.170
59	s3d	.167
60	resnet50_imagenet_100_seed-0	.167
61	resnet50_ecoset_full	.164
62	resnet50-barlow	.163
63	yolos_tiny	.163
64	resnet50_primary_visual_cortex	.161
65	resnet50-SIN_IN_IN	.159
66	resnet_SIN_IN_FT_IN	.159
67	antialias-resnet152	.155
68	vit_large_patch14_clip_224:laion2b_ft_in1k	.149
69	resnet_152_v1	.148
70	convnext_large_imagenet_full_seed-0	.147
71	dinov2_vitg14_lc	.145
72	artResNet18_1	.143
73	deit_small_imagenet_1_seed-0	.142
74	resnet50_robust_l2_eps3	.141
75	convnext_tiny:in12k_ft_in1k	.138
76	resnet50_st_wzc_seed3_2_323	.138
77	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.137
78	resnet_101_v1	.135
79	vit_huge_clip336_laion	.134
80	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.133
81	resnext101_32x16d_wsl	.133
82	resnet50-SIN	.133
83	resnet_101_v2	.131
84	resnet-18_test_m	.130
85	shufflenet_v2_x1_0	.121
86	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.121
87	resnet50-vitoimagevidnet8	.121
88	resnet50-VITO-8deg-cc	.121
89	resnet50_robust_l2_eps1	.121
90	resnet50_hvs_wzc	.120
91	efficientnet_b0	.119
92	antialiased-rnext101_32x8d	.118
93	resnet_50_v2	.118
94	imagenet_l2_3_0	.117
95	omnivore_swinB	.116
96	compact_resnet50_IT	.114
97	resnet18_imagenet_full	.112
98	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.111
99	mc3_18	.111
100	convnext_base:clip_laiona_augreg_ft_in1k_384	.107
101	r3d_18	.107
102	voneresnet-50-non_stochastic	.107
103	resnet50_st_wzc_seed7_2_323	.107
104	resnet18_ecoset_full	.106
105	convnext_base_imagenet_full_seed-0	.105
106	vonegrcnn_62e_nobn	.105
107	regnet_y_400mf	.104
108	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.101
109	resnet152_ecoset_full	.100
110	Res2Net50_26w_4s	.100
111	hmax	.097
112	BiT-S-R152x4	.097
113	inception_v4	.097
114	resnet50-moclr8deg	.097
115	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.096
116	resnet50-vicregl0p9	.094
117	dinov2_vitl14_lc	.094
118	resnet50_ewconplus_wzc_seed6_2_323	.094
119	cvt_cvt-21-224-in1k_4_LucyV4	.092
120	antialiased-r50	.089
121	AlexNet_SIN_fov4	.089
122	resnet50_ewconplus_wzc	.088
123	resnet101_imagenet_full	.087
124	resnet152_imagenet_full	.086
125	resnet34_ecoset_full	.085
126	voneresnet-50-robust	.084
127	SeLaVi-VGG-Sound	.081
128	compact_resnet50_robust_IT	.080
129	vit_base_patch14_dinov2-lvd142m	.080
130	mobilenet_v2_1_0_160	.080
131	resnet50_st_wzc_2	.079
132	voneresnet-50	.078
133	vit_large_clip336_openai	.077
134	pnasnet_large	.076
135	resnet50_imagenet_full	.074
136	deit_small_imagenet_100_seed-0	.074
137	resnet-152_v2_pytorch	.073
138	yudixie_resnet50_rotation_reg_0_240908	.073
139	BiT-S-R101x1	.072
140	resnet50_ewconplus_wzc_seed5_2__323	.072
141	SeLaVi-AVE	.071
142	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.069
143	inception_v1	.067
144	resnet50_ewconplus_wzc_seed2_2__323	.064
145	resnet50_st_wzc_seed6_2_323	.063
146	convnext_tiny_sup	.063
147	dinov2_vitl14_lc_res336	.062
148	resnet_50_v1_spiking_l4a	.061
149	resnet_50_v1_spiking_l4	.061
150	resnet_50_v1_spiking	.061
151	resnet_50_v1	.061
152	resnet50-sup	.061
153	custom_model_cv_18_dagger_408	.061
154	mobilenet_v2_1_4_224	.059
155	densenet-201	.058
156	cvt_cvt-21-384-in1k_4_LucyV4	.057
157	yudixie_wbh_object_class_scaling_0415_run_2	.057
158	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.057
159	focalnet_tiny_lrf_in1k	.056
160	blt_vs	.056
161	resnet34_imagenet_full	.055
162	resnet_152_v2	.054
163	resnext101_32x32d_wsl	.053
164	mobilenet_v2_1_0_224	.052
165	VOneCORnet-S	.051
166	r2plus1d_18	.050
167	vit_large_patch14_clip_224:openai_ft_in1k	.048
168	convnext_femto_ols:d1_in1k	.047
169	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.047
170	deit3_base_patch16_224_fb_in22k_ft_in1k	.047
171	compact_resnet50_V4	.045
172	alexnet_fov4	.044
173	efficientnet_b2_imagenet_full	.044
174	mobilenet_v2_0_75_160	.043
175	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.043
176	resnext101_32x48d_wsl	.041
177	ReAlnet04	.040
178	eva02_large_in22k	.040
179	ReAlnet07_ALL	.040
180	deepeisnn_ei_imagenet_resnet18	.040
181	ReAlnet10	.040
182	resnet50-SIN_IN	.038
183	ReAlnet08	.038
184	ReAlnet05	.037
185	BiT-S-R50x3	.037
186	ReAlnet01	.037
187	convnext_small_imagenet_100_seed-0	.037
188	ReAlnet01_cornet_fov4	.037
189	effnetb1_272x240	.036
190	ReAlnet03	.036
191	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.036
192	BiT-S-R101x3	.036
193	cvt_cvt-13-224-in1k_4_LucyV4	.035
194	ReAlnet08_ALL	.035
195	ReAlnet02	.034
196	deit3_base_patch16_224_fb_in1k	.033
197	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.033
198	inception_v3	.033
199	ReAlnet04_ALL	.033
200	ReAlnet07	.032
201	vit_base_patch14_reg4_dinov2-lvd142m	.032
202	ReAlnet02_ALL	.032
203	ReAlnet09_ALL	.031
204	ReAlnet05_ALL	.031
205	resnet-50-robust	.031
206	ReAlnet10_cornet	.030
207	ReAlnet06_ALL	.030
208	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.030
209	ReAlnet10_ALL	.030
210	ReAlnet01_ALL	.029
211	cv_18_dagger_408_pretrained	.029
212	ReAlnet03_ALL	.028
213	densenet-169	.028
214	ReAlnet06	.027
215	predictive_coding_pc	.026
216	ReAlnet09	.026
217	cvt_cvt-13-384-in1k_4_LucyV4	.026
218	convnext_large_in22k_supervised	.025
219	ReAlnet05_cornet	.025
220	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.025
221	ReAlnet08_cornet	.025
222	resnet18_imagenet21kP	.024
223	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.024
224	ReAlnet07_cornet	.024
225	ReAlnet03_cornet	.023
226	yudixie_resnet50_random_0_240908	.023
227	BiT-S-R152x2	.023
228	nasnet_large	.022
229	ReAlnet04_cornet	.022
230	resnet50_imagenet_10_seed-0	.021
231	ReAlnet06_cornet	.021
232	compact_vgg19_V4	.020
233	compact_cornets_IT	.020
234	ReAlnet01_cornet	.020
235	ReAlnet02_cornet	.018
236	compact_vgg19_IT	.018
237	briaai_rmbg_1_4	.016
238	dinov2_vitg14_reg_lc	.016
239	convnext_xxlarge_clip	.016
240	ReAlnet09_cornet	.016
241	dinov2_vitl14_reg_lc_featread	.015
242	dinov2_vitl14_reg_lc	.015
243	yudixie_resnet50_imagenet1kpret_0_240312	.014
244	resnet_50_v1_spiking_l3	.014
245	yudixie_resnet50_imagenet1kpret_0_240908	.014
246	resnet50_tutorial	.014
247	efficientnet-b7	.014
248	AdvProp_efficientnet-b6	.013
249	xception	.012
250	mobilenet_v2_1-4_224_pytorch	.011
251	convnext_large_clip_laion2b_finelayers	.011
252	efficientnet_b0_imagenet_full	.010
253	AdvProp_efficientnet-b8	.010
254	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.010
255	AdvProp_efficientnet-b4	.009
256	yudixie_wbh_object_class_scaling_0415_imagenet	.008
257	yudixie_resnet18_imagenet1kpret_0_240719	.008
258	vit_base_patch16_dinov3-lvd1689m	.006
259	mobilenet_v2_1_3_224	.005
260	SeLaVi-Kinetics400	.005
261	vonegrcnn_47e	.005
262	vonegrcnn_52e_full	.004
263	tv_efficientnet-b1	.004
264	efficientnet_b1_imagenet_full	.004
265	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.003
266	yudixie_resnet18_distance_reg_0_240719	.003
267	yudixie_resnet50_distance_reg_0_240908	.003
268	yudixie_resnet18_rotation_reg_0_240719	.003
269	yudixie_resnet50_distance_translation_rotation_0_240908	.003
270	yudixie_resnet18_distance_rotation_0_240719	.002
271	ViT_L_32_imagenet1k	.002
272	convnext_base_fb_in1k	.002
273	mobilenet_v2_1_0_192	.001
274	yudixie_resnet50_translation_rotation_0_240908	.001
275	yudixie_resnet50_distance_rotation_0_240908	.001
276	resnet50_imagenet_1_seed-0	.001
277	vgg_16	.001
278	AdvProp_efficientnet-b7	.000
279	yudixie_resnet18_translation_reg_0_240719	.000
280	mobilenet_v2_0_75_224	.000
281	resnet18_fair	.000
282	yudixie_resnet18_category_class_0_240719	.000
283	yudixie_resnet50_object_class_0_240908	.000
284	mobilenet_v2_0_5_224	.000
285	vgg_19	.000
286	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.001
287	yudixie_resnet18_object_class_0_240719	.001
288	yudixie_resnet50_category_class_0_240908	.001
289	yudixie_resnet50_translation_reg_0_240908	.001
290	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.002
291	yudixie_resnet18_distance_translation_rotation_0_240719	.002
292	yudixie_resnet18_translation_rotation_0_240719	.003
293	yudixie_resnet50_distance_translation_0_240908	.003
294	yudixie_resnet18_distance_translation_0_240719	.005
295	pnasnet_large_halfPrecision
296	ReAlnet01_cornet_fov16	1.0
297	ReAlnet01_cornet_fov12	1.0
298	compact_cornets_V4	1.0
299	compact_resnet50_robust_V4	1.0
300	mvit_v2_s
301	SeLaVi-Kinetics-Sound
302	resnet50_eMMCR_Vanilla
303	mvit_v1_b
304	fixres_resnext101_32x48d_wsl
305	mobilenet_v2_0_75_192
306	mobilenet_v2_0_5_192
307	pnasnet_large_pytorch

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