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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	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.786
14	resnet50_imagenet_100_seed-0	.778
15	resnet50-SIN	.765
16	AlexNet_SIN	.759
17	convnext_small_imagenet_full_seed-0	.744
18	alexnet_training_seed_05	.733
19	BiT-S-R101x1	.705
20	mobilenet_v2_0_5_192	.700
21	vit_large_patch14_clip_224:openai_ft_in1k	.697
22	alexnet_training_seed_02	.689
23	VOneCORnet-S	.677
24	convnext_femto_ols:d1_in1k	.674
25	deit_large_imagenet_full_seed-0	.668
26	alexnet_training_seed_01_fov12	.660
27	resnet50_ewconplus_wzc_seed2_2__323	.656
28	voneresnet-50-robust	.656
29	grcnn	.655
30	convnext_base_fb_in1k_gram_aligned_dinov3	.655
31	pr1_stl10_disentangled_alpha04	.652
32	alexnet_training_seed_03	.651
33	alexnet_training_seed_07	.642
34	resnet50_ewconplus_wzc_seed6_2_323	.642
35	antialiased-r50	.640
36	alexnet_training_seed_04	.639
37	convnext_base_imagenet_full_seed-0	.632
38	resnet50_robust_l2_eps1	.632
39	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.630
40	resnet50_hvs_wzc	.629
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	omnivore_swinS	.607
47	aughost_fair_resnet18	.606
48	vit_base_patch14_reg4_dinov2-lvd142m	.603
49	dinov2_vitl14_lc	.602
50	vit_base_patch14_dinov2-lvd142m	.590
51	antialiased-rnext101_32x8d	.587
52	ReAlnet03_cornet	.586
53	resnet101_ecoset_full	.577
54	alexnet_training_seed_01_fov16	.575
55	mobilenet_v2_0_75_160	.568
56	ReAlnet10_cornet	.567
57	AlexNet_SIN_fov4	.565
58	ReAlnet07_cornet	.565
59	ReAlnet09_cornet	.559
60	ReAlnet02_cornet	.557
61	convnext_base:clip_laiona_augreg_ft_in1k_384	.554
62	convnext_large_in22k_supervised	.550
63	ReAlnet06_cornet	.540
64	alexnet_training_seed_01	.538
65	mobilenet_v2_0_75_224	.533
66	deit_small_imagenet_10_seed-0	.529
67	convnext_small_imagenet_1_seed-0	.527
68	voneresnet-50	.527
69	ReAlnet04_ALL	.524
70	resnet50_ecoset_full	.521
71	ReAlnet08_cornet	.513
72	resnext101_32x32d_wsl	.511
73	resnet50_robust_l2_eps3	.509
74	resnext101_32x8d_wsl	.506
75	yudixie_resnet18_random_0_240719	.505
76	resnet101_imagenet_full	.504
77	ReAlnet06_ALL	.502
78	ReAlnet08_ALL	.499
79	deepeisnn_ei_cifar10_vgg19	.499
80	ReAlnet05_ALL	.499
81	ReAlnet03_ALL	.491
82	ReAlnet07_ALL	.491
83	resnet50_st_wzc_seed6_2_323	.489
84	resnet_152_v1	.483
85	resnext101_32x48d_wsl	.483
86	ReAlnet04_cornet	.481
87	nasnet_large	.481
88	ReAlnet09_ALL	.473
89	ReAlnet05_cornet	.464
90	resnet_101_v1	.463
91	BiT-S-R50x3	.462
92	grcnn_109	.462
93	efficientnet_b2_imagenet_full	.461
94	voneresnet-50-non_stochastic	.459
95	resnet50-vitoimagevidnet8	.456
96	resnet50-VITO-8deg-cc	.456
97	shufflenet_v2_x1_0	.455
98	resnet50-SIN_IN_IN	.454
99	ReAlnet02_ALL	.454
100	ReAlnet01_ALL	.452
101	alexnet	.449
102	alexnet_ks_torevert	.449
103	deepeisnn_ei_cifar10_resnet18	.445
104	ReAlnet10_ALL	.438
105	resnet18_ecoset_full	.437
106	resnet50_st_wzc_seed7_2_323	.434
107	resnet50-SIN_IN	.431
108	resnet152_ecoset_full	.429
109	resnet_SIN_IN_FT_IN	.428
110	AlexNet_SIN_fov12	.424
111	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.424
112	convnext_base_fb_in22k_ft_in1k	.423
113	artResNet18_1	.413
114	mobilenet_v2_0_75_192	.410
115	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.409
116	vit_large_patch14_clip_224:laion2b_ft_in1k	.399
117	inception_v1	.393
118	pnasnet_large	.392
119	alexnet_training_seed_01_fov4	.385
120	deit_small_imagenet_1_seed-0	.384
121	resnet50_imagenet_full	.380
122	cvt_cvt-21-224-in1k_4_LucyV4	.380
123	AlexNet_SIN_fov16	.377
124	resnet50_st_wzc_2	.376
125	ReAlnet03	.375
126	ReAlnet01	.369
127	resnet_50_v1_spiking	.366
128	resnet_50_v1	.366
129	resnet50-sup	.366
130	ReAlnet01_cornet_fov12	.366
131	blt_vs	.365
132	resnet50_primary_visual_cortex	.365
133	dinov2_vitl14_lc_res336	.360
134	resnet34_imagenet_full	.359
135	mobilenet_v2_0_5_224	.358
136	resnet50_st_wzc_seed3_2_323	.357
137	convnext_base_fb_in1k	.356
138	resnet50-vicregl0p9	.355
139	convnext_large_imagenet_full_seed-0	.354
140	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.349
141	ReAlnet09	.345
142	ReAlnet10	.336
143	ReAlnet06	.331
144	imagenet_l2_3_0	.330
145	ReAlnet02	.329
146	effnetb1_272x240	.328
147	mobilenet_v2_1_0_160	.327
148	alexnet_fov16	.326
149	xception	.324
150	ReAlnet05	.323
151	resnet-18_test_m	.322
152	ReAlnet07	.318
153	nasnet_mobile	.310
154	ReAlnet08	.309
155	ReAlnet04	.308
156	BiT-S-R101x3	.300
157	convnext_xxlarge_clip	.294
158	deepeisnn_ei_imagenet_resnet18	.289
159	ReAlnet01_cornet_fov16	.288
160	resnet50-moclr8deg	.285
161	dinov2_vitg14_lc	.283
162	alexnet_fov4	.282
163	resnet18_imagenet_full	.271
164	dinov2_vitg14_reg_lc	.262
165	resnet18_imagenet21kP	.261
166	yudixie_wbh_object_class_scaling_0415_imagenet	.256
167	yudixie_resnet18_imagenet1kpret_0_240719	.256
168	hmax	.254
169	resnet50-vicregl0p75	.252
170	vit_huge_clip336_laion	.250
171	resnet50-barlow	.245
172	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.245
173	resnet50-vicreg	.244
174	convnext_xlarge:fb_in22k_ft_in1k	.243
175	efficientnet_b0	.242
176	vonegrcnn_52e_full	.241
177	omnivore_swinB	.240
178	resnet18_fair	.238
179	resnet50_imagenet_10_seed-0	.233
180	dinov2_vitl14_reg_lc_featread	.233
181	dinov2_vitl14_reg_lc	.233
182	resnet_101_v2	.230
183	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.230
184	vit_base_patch16_clip_224:openai_ft_in1k	.229
185	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.228
186	yudixie_wbh_object_class_scaling_0415_run_3	.221
187	regnet_y_400mf	.218
188	resnet152_imagenet_full	.215
189	yudixie_resnet50_imagenet1kpret_0_240312	.213
190	resnet_50_v1_spiking_l4	.213
191	yudixie_resnet50_imagenet1kpret_0_240908	.213
192	resnet_50_v1_spiking_l4a	.213
193	resnet50_tutorial	.213
194	Res2Net50_26w_4s	.211
195	alexnet_fov12	.210
196	vonegrcnn_47e	.207
197	resnet34_ecoset_full	.203
198	inception_v3	.201
199	deit3_base_patch16_224_fb_in1k	.196
200	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.196
201	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.188
202	tv_efficientnet-b1	.186
203	cvt_cvt-21-384-in1k_4_LucyV4	.184
204	resnet-152_v2_pytorch	.180
205	predictive_coding_pc	.178
206	vonegrcnn_62e_nobn	.177
207	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.173
208	convnext_tiny_sup	.173
209	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.166
210	eva02_large_in22k	.166
211	resnet_152_v2	.164
212	compact_vgg19_IT	.160
213	yolos_tiny	.159
214	convnext_large:fb_in22k_ft_in1k	.158
215	deit_small_imagenet_100_seed-0	.158
216	resnet_50_v2	.158
217	antialias-resnet152	.149
218	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.149
219	vit_large_clip336_openai	.145
220	AdvProp_efficientnet-b4	.144
221	briaai_rmbg_1_4	.142
222	custom_model_cv_18_dagger_408	.139
223	compact_vgg19_V4	.136
224	efficientnet-b7	.131
225	convnext_small_imagenet_100_seed-0	.120
226	vit_large_patch14_clip_336:laion2b_ft_in1k	.119
227	BiT-S-R152x4	.115
228	mobilenet_v2_1_0_224	.114
229	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.107
230	densenet-201	.104
231	mobilenet_v2_1-4_224_pytorch	.103
232	mobilenet_v2_1_4_224	.103
233	inception_v4	.102
234	AdvProp_efficientnet-b6	.101
235	cv_18_dagger_408_pretrained	.099
236	efficientnet_b0_imagenet_full	.099
237	convnext_tiny:in12k_ft_in1k	.097
238	AdvProp_efficientnet-b8	.093
239	mobilenet_v2_1_0_128	.092
240	resnet-50-robust	.084
241	AT_efficientnet-b2	.082
242	AdvProp_efficientnet-b2	.082
243	mobilenet_v2_1_0_192	.078
244	AdvProp_efficientnet-b7	.075
245	ReAlnet01_cornet_fov4	.074
246	yudixie_wbh_object_class_scaling_0415_run_1	.071
247	yudixie_wbh_object_class_scaling_0415_run_2	.067
248	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.066
249	mobilenet_v2_1_3_224	.063
250	yudixie_wbh_object_class_scaling_0415_run_0	.062
251	yudixie_resnet18_distance_reg_0_240719	.060
252	densenet-169	.057
253	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.057
254	convnext_large_clip_laion2b_finelayers	.055
255	efficientnet_b1_imagenet_full	.052
256	BiT-S-R152x2	.048
257	yudixie_resnet50_random_0_240908	.035
258	densenet-121	.035
259	yudixie_resnet18_distance_rotation_0_240719	.033
260	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.030
261	resnet_50_v1_spiking_l3	.025
262	yudixie_resnet18_translation_reg_0_240719	.023
263	vgg_16	.022
264	yudixie_resnet18_distance_translation_rotation_0_240719	.019
265	ViT_L_32_imagenet1k	.015
266	cvt_cvt-13-384-in1k_4_LucyV4	.011
267	yudixie_resnet18_translation_rotation_0_240719	.009
268	vgg_19	.008
269	compact_cornets_IT	.002
270	yudixie_resnet50_distance_rotation_0_240908	.002
271	yudixie_resnet18_distance_translation_0_240719	.001
272	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.000
273	yudixie_resnet18_rotation_reg_0_240719	.000
274	yudixie_resnet50_distance_translation_rotation_0_240908	.000
275	resnet50_imagenet_1_seed-0	.000
276	cvt_cvt-13-224-in1k_4_LucyV4	.000
277	yudixie_resnet50_translation_rotation_0_240908	.000
278	yudixie_resnet50_object_class_0_240908	.000
279	compact_resnet50_robust_IT	.000
280	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.001
281	deit3_base_patch16_224_fb_in22k_ft_in1k	.001
282	yudixie_resnet18_object_class_0_240719	.001
283	yudixie_resnet50_distance_translation_0_240908	.001
284	yudixie_resnet50_rotation_reg_0_240908	.001
285	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.001
286	yudixie_resnet50_category_class_0_240908	.002
287	yudixie_resnet50_translation_reg_0_240908	.002
288	compact_resnet50_IT	.003
289	yudixie_resnet18_category_class_0_240719	.005
290	yudixie_resnet50_distance_reg_0_240908	.016
291	pnasnet_large_halfPrecision
292	compact_cornets_V4	1.0
293	compact_resnet50_robust_V4	1.0
294	compact_resnet50_V4
295	s3d
296	mvit_v2_s
297	r2plus1d_18
298	SeLaVi-Kinetics-Sound
299	SeLaVi-VGG-Sound
300	SeLaVi-Kinetics400
301	resnet50_eMMCR_Vanilla
302	mvit_v1_b
303	mc3_18
304	resnext101_32x16d_wsl
305	fixres_resnext101_32x48d_wsl
306	r3d_18
307	SeLaVi-AVE
308	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