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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	swin_small_patch4_window7_224:ms_in22k_ft_in1k	1.0
2	vit_base_patch16_dinov3-lvd1689m	.969
3	resnet50_ewconplus_wzc	.956
4	alexnet_training_seed_08	.932
5	convnext_small_imagenet_10_seed-0	.927
6	alexnet_training_seed_10	.885
7	convnext_tiny_imagenet_full_seed-0	.874
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.871
9	deit_small_imagenet_full_seed-0	.851
10	alexnet_training_seed_06	.834
11	vit_relpos_base_patch16_clsgap_224:sw_in1k	.833
12	alexnet_training_seed_09	.827
13	resnet50_ewconplus_wzc_2_323	.786
14	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.786
15	resnet50_imagenet_100_seed-0	.778
16	resnet50-SIN	.765
17	AlexNet_SIN	.759
18	convnext_small_imagenet_full_seed-0	.744
19	alexnet_training_seed_05	.733
20	BiT-S-R101x1	.705
21	mobilenet_v2_0_5_192	.700
22	vit_large_patch14_clip_224:openai_ft_in1k	.697
23	alexnet_training_seed_02	.689
24	VOneCORnet-S	.677
25	convnext_femto_ols:d1_in1k	.674
26	deit_large_imagenet_full_seed-0	.668
27	alexnet_training_seed_01_fov12	.660
28	resnet50_ewconplus_wzc_seed2_2__323	.656
29	voneresnet-50-robust	.656
30	grcnn	.655
31	convnext_base_fb_in1k_gram_aligned_dinov3	.655
32	pr1_stl10_disentangled_alpha04	.652
33	alexnet_training_seed_03	.651
34	alexnet_training_seed_07	.642
35	resnet50_ewconplus_wzc_seed6_2_323	.642
36	antialiased-r50	.640
37	alexnet_training_seed_04	.639
38	convnext_base_imagenet_full_seed-0	.632
39	resnet50_robust_l2_eps1	.632
40	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.630
41	focalnet_tiny_lrf_in1k	.620
42	ReAlnet01_cornet	.614
43	BiT-S-R50x1	.613
44	deit_base_imagenet_full_seed-0	.612
45	resnet50_ewconplus_wzc_seed5_2__323	.608
46	resnet50_ewconplus_wzc_seed7_2_323	.607
47	omnivore_swinS	.607
48	aughost_fair_resnet18	.606
49	vit_base_patch14_reg4_dinov2-lvd142m	.603
50	resnet50_ewconplus_noseed_2_323	.602
51	vit_base_patch14_dinov2-lvd142m	.590
52	antialiased-rnext101_32x8d	.587
53	ReAlnet03_cornet	.586
54	resnet50_ewconplus_wzc_seed4_2_323	.581
55	resnet101_ecoset_full	.577
56	alexnet_training_seed_01_fov16	.575
57	resnet50_ewconplus_wzc_seed3_2_323	.568
58	mobilenet_v2_0_75_160	.568
59	ReAlnet10_cornet	.567
60	AlexNet_SIN_fov4	.565
61	ReAlnet07_cornet	.565
62	ReAlnet09_cornet	.559
63	ReAlnet02_cornet	.557
64	convnext_base:clip_laiona_augreg_ft_in1k_384	.554
65	ReAlnet06_cornet	.540
66	alexnet_training_seed_01	.538
67	mobilenet_v2_0_75_224	.533
68	deit_small_imagenet_10_seed-0	.529
69	convnext_small_imagenet_1_seed-0	.527
70	voneresnet-50	.527
71	ReAlnet04_ALL	.524
72	resnet50_ecoset_full	.521
73	ReAlnet08_cornet	.513
74	resnext101_32x32d_wsl	.511
75	resnet50_robust_l2_eps3	.509
76	resnext101_32x8d_wsl	.506
77	yudixie_resnet18_random_0_240719	.505
78	resnet101_imagenet_full	.504
79	ReAlnet06_ALL	.502
80	ReAlnet08_ALL	.499
81	deepeisnn_ei_cifar10_vgg19	.499
82	ReAlnet05_ALL	.499
83	ReAlnet03_ALL	.491
84	ReAlnet07_ALL	.491
85	resnet50_st_wzc_seed6_2_323	.489
86	resnet_152_v1	.483
87	resnext101_32x48d_wsl	.483
88	ReAlnet04_cornet	.481
89	nasnet_large	.481
90	ReAlnet09_ALL	.473
91	ReAlnet05_cornet	.464
92	resnet_101_v1	.463
93	BiT-S-R50x3	.462
94	grcnn_109	.462
95	efficientnet_b2_imagenet_full	.461
96	voneresnet-50-non_stochastic	.459
97	resnet50-vitoimagevidnet8	.456
98	resnet50-VITO-8deg-cc	.456
99	shufflenet_v2_x1_0	.455
100	resnet50-SIN_IN_IN	.454
101	ReAlnet02_ALL	.454
102	ReAlnet01_ALL	.452
103	alexnet	.449
104	alexnet_ks_torevert	.449
105	deepeisnn_ei_cifar10_resnet18	.445
106	ReAlnet10_ALL	.438
107	resnet18_ecoset_full	.437
108	resnet50_st_wzc_seed7_2_323	.434
109	resnet50-SIN_IN	.431
110	resnet152_ecoset_full	.429
111	resnet_SIN_IN_FT_IN	.428
112	AlexNet_SIN_fov12	.424
113	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.424
114	convnext_base_fb_in22k_ft_in1k	.423
115	resnet50_st_wzc_noseed_2_323	.414
116	artResNet18_1	.413
117	mobilenet_v2_0_75_192	.410
118	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.409
119	vit_large_patch14_clip_224:laion2b_ft_in1k	.399
120	inception_v1	.393
121	pnasnet_large	.392
122	alexnet_training_seed_01_fov4	.385
123	deit_small_imagenet_1_seed-0	.384
124	resnet50_imagenet_full	.380
125	cvt_cvt-21-224-in1k_4_LucyV4	.380
126	AlexNet_SIN_fov16	.377
127	resnet50_st_wzc_2	.376
128	ReAlnet03	.375
129	ReAlnet01	.369
130	resnet_50_v1_spiking	.366
131	resnet_50_v1	.366
132	resnet50-sup	.366
133	ReAlnet01_cornet_fov12	.366
134	blt_vs	.365
135	resnet50_primary_visual_cortex	.365
136	resnet34_imagenet_full	.359
137	mobilenet_v2_0_5_224	.358
138	resnet50_st_wzc_seed3_2_323	.357
139	convnext_base_fb_in1k	.356
140	resnet50-vicregl0p9	.355
141	convnext_large_imagenet_full_seed-0	.354
142	resnet50_st_wzc_seed4_2_323	.353
143	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.349
144	ReAlnet09	.345
145	resnet50_st_wzc_2_323	.339
146	ReAlnet10	.336
147	ReAlnet06	.331
148	imagenet_l2_3_0	.330
149	ReAlnet02	.329
150	effnetb1_272x240	.328
151	mobilenet_v2_1_0_160	.327
152	alexnet_fov16	.326
153	xception	.324
154	ReAlnet05	.323
155	resnet-18_test_m	.322
156	ReAlnet07	.318
157	resnet50_st_wzc_seed5_2_323	.314
158	nasnet_mobile	.310
159	ReAlnet08	.309
160	ReAlnet04	.308
161	BiT-S-R101x3	.300
162	deepeisnn_ei_imagenet_resnet18	.289
163	ReAlnet01_cornet_fov16	.288
164	resnet50-moclr8deg	.285
165	alexnet_fov4	.282
166	resnet18_imagenet_full	.271
167	resnet18_imagenet21kP	.261
168	yudixie_wbh_object_class_scaling_0415_imagenet	.256
169	yudixie_resnet18_imagenet1kpret_0_240719	.256
170	hmax	.254
171	resnet50-vicregl0p75	.252
172	resnet50-barlow	.245
173	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.245
174	resnet50-vicreg	.244
175	convnext_xlarge:fb_in22k_ft_in1k	.243
176	efficientnet_b0	.242
177	vonegrcnn_52e_full	.241
178	omnivore_swinB	.240
179	resnet18_fair	.238
180	resnet50_imagenet_10_seed-0	.233
181	resnet_101_v2	.230
182	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.230
183	vit_base_patch16_clip_224:openai_ft_in1k	.229
184	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.228
185	yudixie_wbh_object_class_scaling_0415_run_3	.221
186	regnet_y_400mf	.218
187	resnet152_imagenet_full	.215
188	yudixie_resnet50_imagenet1kpret_0_240312	.213
189	resnet_50_v1_spiking_l4	.213
190	yudixie_resnet50_imagenet1kpret_0_240908	.213
191	resnet_50_v1_spiking_l4a	.213
192	resnet50_tutorial	.213
193	Res2Net50_26w_4s	.211
194	alexnet_fov12	.210
195	vonegrcnn_47e	.207
196	resnet34_ecoset_full	.203
197	inception_v3	.201
198	deit3_base_patch16_224_fb_in1k	.196
199	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.196
200	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.188
201	tv_efficientnet-b1	.186
202	cvt_cvt-21-384-in1k_4_LucyV4	.184
203	resnet-152_v2_pytorch	.180
204	predictive_coding_pc	.178
205	vonegrcnn_62e_nobn	.177
206	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.173
207	convnext_tiny_sup	.173
208	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.166
209	resnet_152_v2	.164
210	compact_vgg19_IT	.160
211	yolos_tiny	.159
212	convnext_large:fb_in22k_ft_in1k	.158
213	deit_small_imagenet_100_seed-0	.158
214	resnet_50_v2	.158
215	antialias-resnet152	.149
216	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.149
217	AdvProp_efficientnet-b4	.144
218	briaai_rmbg_1_4	.142
219	custom_model_cv_18_dagger_408	.139
220	compact_vgg19_V4	.136
221	efficientnet-b7	.131
222	convnext_small_imagenet_100_seed-0	.120
223	vit_large_patch14_clip_336:laion2b_ft_in1k	.119
224	BiT-S-R152x4	.115
225	mobilenet_v2_1_0_224	.114
226	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.107
227	densenet-201	.104
228	mobilenet_v2_1-4_224_pytorch	.103
229	mobilenet_v2_1_4_224	.103
230	inception_v4	.102
231	AdvProp_efficientnet-b6	.101
232	cv_18_dagger_408_pretrained	.099
233	efficientnet_b0_imagenet_full	.099
234	convnext_tiny:in12k_ft_in1k	.097
235	AdvProp_efficientnet-b8	.093
236	mobilenet_v2_1_0_128	.092
237	resnet-50-robust	.084
238	AT_efficientnet-b2	.082
239	AdvProp_efficientnet-b2	.082
240	mobilenet_v2_1_0_192	.078
241	AdvProp_efficientnet-b7	.075
242	ReAlnet01_cornet_fov4	.074
243	yudixie_wbh_object_class_scaling_0415_run_1	.071
244	yudixie_wbh_object_class_scaling_0415_run_2	.067
245	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.066
246	mobilenet_v2_1_3_224	.063
247	yudixie_wbh_object_class_scaling_0415_run_0	.062
248	yudixie_resnet18_distance_reg_0_240719	.060
249	densenet-169	.057
250	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.057
251	efficientnet_b1_imagenet_full	.052
252	BiT-S-R152x2	.048
253	yudixie_resnet50_random_0_240908	.035
254	densenet-121	.035
255	yudixie_resnet18_distance_rotation_0_240719	.033
256	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.030
257	resnet_50_v1_spiking_l3	.025
258	yudixie_resnet18_translation_reg_0_240719	.023
259	vgg_16	.022
260	yudixie_resnet18_distance_translation_rotation_0_240719	.019
261	ViT_L_32_imagenet1k	.015
262	cvt_cvt-13-384-in1k_4_LucyV4	.011
263	yudixie_resnet18_translation_rotation_0_240719	.009
264	vgg_19	.008
265	compact_cornets_IT	.002
266	yudixie_resnet50_distance_rotation_0_240908	.002
267	yudixie_resnet18_distance_translation_0_240719	.001
268	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.000
269	yudixie_resnet18_rotation_reg_0_240719	.000
270	yudixie_resnet50_distance_translation_rotation_0_240908	.000
271	resnet50_imagenet_1_seed-0	.000
272	cvt_cvt-13-224-in1k_4_LucyV4	.000
273	yudixie_resnet50_translation_rotation_0_240908	.000
274	yudixie_resnet50_object_class_0_240908	.000
275	compact_resnet50_robust_IT	.000
276	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.001
277	deit3_base_patch16_224_fb_in22k_ft_in1k	.001
278	yudixie_resnet18_object_class_0_240719	.001
279	yudixie_resnet50_distance_translation_0_240908	.001
280	yudixie_resnet50_rotation_reg_0_240908	.001
281	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.001
282	yudixie_resnet50_category_class_0_240908	.002
283	yudixie_resnet50_translation_reg_0_240908	.002
284	compact_resnet50_IT	.003
285	yudixie_resnet18_category_class_0_240719	.005
286	yudixie_resnet50_distance_reg_0_240908	.016
287	pnasnet_large_halfPrecision
288	compact_cornets_V4	1.0
289	compact_resnet50_robust_V4	1.0
290	compact_resnet50_V4
291	s3d
292	mvit_v2_s
293	r2plus1d_18
294	SeLaVi-Kinetics-Sound
295	SeLaVi-VGG-Sound
296	SeLaVi-Kinetics400
297	resnet50_eMMCR_Vanilla
298	mvit_v1_b
299	mc3_18
300	resnext101_32x16d_wsl
301	fixres_resnext101_32x48d_wsl
302	r3d_18
303	SeLaVi-AVE
304	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

0.24.

Note that scores are relative to this ceiling.

Data: tong.Coggan2024_fMRI.IT

Metric: rdm