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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("tong.Coggan2024_fMRI.V1-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	alexnet_training_seed_01	.752
2	alexnet_training_seed_10	.733
3	alexnet_training_seed_07	.708
4	alexnet_training_seed_06	.689
5	alexnet_training_seed_09	.683
6	alexnet_training_seed_02	.647
7	alexnet_training_seed_05	.621
8	alexnet_training_seed_04	.621
9	alexnet_training_seed_03	.592
10	AlexNet_SIN_fov16	.547
11	alexnet_training_seed_01_fov16	.453
12	alexnet_training_seed_08	.444
13	convnext_small_imagenet_10_seed-0	.383
14	hmax	.307
15	vit_base_patch16_clip_224:openai_ft_in1k	.284
16	vit_relpos_base_patch16_clsgap_224:sw_in1k	.277
17	convnext_base_imagenet_full_seed-0	.277
18	convnext_base_fb_in1k_gram_aligned_dinov3	.265
19	alexnet_training_seed_01_fov4	.264
20	yudixie_resnet18_random_0_240719	.258
21	pr1_stl10_disentangled_alpha04	.257
22	deit_small_imagenet_10_seed-0	.256
23	vit_large_patch14_clip_336:laion2b_ft_in1k	.254
24	resnet_50_v2	.238
25	resnet-50-robust	.230
26	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.224
27	resnet_152_v2	.221
28	convnext_small_imagenet_1_seed-0	.219
29	convnext_base_fb_in1k	.215
30	alexnet_fov16	.207
31	deit_small_imagenet_1_seed-0	.204
32	predictive_coding_pc	.199
33	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.197
34	deit_small_imagenet_full_seed-0	.187
35	efficientnet_b2_imagenet_full	.185
36	compact_resnet50_robust_V4	.184
37	resnet50_primary_visual_cortex	.183
38	resnet50_eMMCR_Vanilla	.178
39	vit_large_patch14_clip_224:openai_ft_in1k	.176
40	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.170
41	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.169
42	convnext_femto_ols:d1_in1k	.166
43	alexnet_training_seed_01_fov12	.164
44	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.157
45	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.150
46	convnext_tiny_imagenet_full_seed-0	.147
47	densenet-121	.146
48	vit_huge_clip336_laion	.140
49	yolos_tiny	.139
50	resnet_101_v2	.139
51	resnet50-SIN_IN	.137
52	resnet50-vitoimagevidnet8	.136
53	resnet50-VITO-8deg-cc	.136
54	resnet50_st_wzc_seed6_2_323	.130
55	ViT_L_32_imagenet1k	.129
56	deit_small_imagenet_100_seed-0	.127
57	convnext_small_imagenet_full_seed-0	.126
58	resnet50_ewconplus_wzc_seed2_2__323	.124
59	inception_v1	.124
60	resnet50_ewconplus_wzc	.123
61	deit_large_imagenet_full_seed-0	.123
62	deit_base_imagenet_full_seed-0	.121
63	vit_base_patch16_dinov3-lvd1689m	.116
64	shufflenet_v2_x1_0	.114
65	r3d_18	.114
66	resnext101_32x32d_wsl	.112
67	resnet50_ewconplus_wzc_seed5_2__323	.110
68	resnet50_st_wzc_seed3_2_323	.110
69	yudixie_wbh_object_class_scaling_0415_run_3	.107
70	dinov2_vitg14_lc	.107
71	vonegrcnn_62e_nobn	.107
72	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.104
73	antialiased-rnext101_32x8d	.103
74	Res2Net50_26w_4s	.101
75	convnext_large:fb_in22k_ft_in1k	.099
76	convnext_small_imagenet_100_seed-0	.096
77	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.093
78	resnet34_imagenet_full	.086
79	omnivore_swinS	.086
80	vit_large_patch14_clip_224:laion2b_ft_in1k	.086
81	resnet18_ecoset_full	.085
82	resnet50_hvs_wzc	.083
83	AlexNet_SIN_fov4	.083
84	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.082
85	compact_resnet50_robust_IT	.082
86	resnet34_ecoset_full	.080
87	resnet-152_v2_pytorch	.080
88	vit_large_clip336_openai	.079
89	resnet50-moclr8deg	.077
90	yudixie_resnet50_rotation_reg_0_240908	.077
91	BiT-S-R101x1	.075
92	resnet50_ewconplus_wzc_seed6_2_323	.075
93	resnet50-vicregl0p9	.074
94	custom_model_cv_18_dagger_408	.073
95	SeLaVi-Kinetics400	.073
96	resnet50_st_wzc_seed7_2_323	.072
97	resnext101_32x16d_wsl	.071
98	resnet-18_test_m	.068
99	resnet50_st_wzc_2	.068
100	efficientnet_b0	.067
101	s3d	.066
102	mobilenet_v2_1_0_224	.066
103	resnet_152_v1	.063
104	ReAlnet10_cornet	.063
105	ReAlnet10	.063
106	ReAlnet07_ALL	.060
107	ReAlnet08_cornet	.060
108	ReAlnet08	.060
109	alexnet_fov12	.060
110	ReAlnet05_cornet	.059
111	ReAlnet05	.059
112	ReAlnet03_ALL	.059
113	ReAlnet10_ALL	.059
114	grcnn_109	.059
115	ReAlnet05_ALL	.058
116	ReAlnet04_ALL	.058
117	ReAlnet02_ALL	.058
118	cvt_cvt-21-224-in1k_4_LucyV4	.058
119	ReAlnet06_ALL	.058
120	ReAlnet08_ALL	.058
121	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.057
122	ReAlnet09_ALL	.057
123	ReAlnet01_ALL	.057
124	ReAlnet07_cornet	.056
125	ReAlnet07	.056
126	ReAlnet03_cornet	.056
127	ReAlnet03	.056
128	ReAlnet04_cornet	.056
129	ReAlnet04	.056
130	ReAlnet01_cornet	.056
131	ReAlnet01	.056
132	BiT-S-R152x4	.056
133	convnext_large_imagenet_full_seed-0	.055
134	ReAlnet06_cornet	.055
135	ReAlnet06	.055
136	dinov2_vitl14_lc	.055
137	r2plus1d_18	.054
138	resnet50_imagenet_1_seed-0	.054
139	AT_efficientnet-b2	.054
140	AdvProp_efficientnet-b2	.054
141	nasnet_large	.053
142	ReAlnet09_cornet	.053
143	ReAlnet09	.053
144	ReAlnet02_cornet	.053
145	ReAlnet02	.053
146	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.050
147	resnet18_imagenet_full	.049
148	AlexNet_SIN_fov12	.049
149	convnext_tiny:in12k_ft_in1k	.049
150	resnet50_imagenet_full	.047
151	cv_18_dagger_408_pretrained	.044
152	grcnn	.044
153	ReAlnet01_cornet_fov4	.044
154	alexnet_fov4	.043
155	inception_v3	.043
156	compact_cornets_IT	.043
157	compact_resnet50_IT	.041
158	eva02_large_in22k	.039
159	mobilenet_v2_0_5_192	.039
160	resnet101_ecoset_full	.039
161	deit3_base_patch16_224_fb_in1k	.037
162	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.037
163	BiT-S-R101x3	.036
164	resnet50_ecoset_full	.035
165	resnet152_ecoset_full	.035
166	efficientnet-b7	.035
167	resnet_SIN_IN_FT_IN	.035
168	inception_v4	.035
169	resnet50-barlow	.035
170	nasnet_mobile	.034
171	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.034
172	deit3_base_patch16_224_fb_in22k_ft_in1k	.034
173	dinov2_vitl14_lc_res336	.034
174	yudixie_wbh_object_class_scaling_0415_run_2	.034
175	regnet_y_400mf	.033
176	imagenet_l2_3_0	.033
177	resnext101_32x48d_wsl	.033
178	resnet152_imagenet_full	.033
179	yudixie_wbh_object_class_scaling_0415_run_1	.032
180	resnet50-vicregl0p75	.031
181	resnet18_imagenet21kP	.031
182	cvt_cvt-13-224-in1k_4_LucyV4	.030
183	vit_base_patch14_dinov2-lvd142m	.030
184	resnet50_robust_l2_eps3	.029
185	convnext_base:clip_laiona_augreg_ft_in1k_384	.027
186	densenet-201	.027
187	yudixie_wbh_object_class_scaling_0415_run_0	.026
188	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.026
189	effnetb1_272x240	.026
190	resnet_101_v1	.025
191	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.025
192	densenet-169	.025
193	compact_vgg19_IT	.023
194	resnet50-vicreg	.022
195	AdvProp_efficientnet-b6	.022
196	convnext_large_in22k_supervised	.022
197	cvt_cvt-13-384-in1k_4_LucyV4	.020
198	vit_base_patch14_reg4_dinov2-lvd142m	.019
199	convnext_xxlarge_clip	.019
200	cvt_cvt-21-384-in1k_4_LucyV4	.019
201	yudixie_resnet18_distance_translation_0_240719	.018
202	alexnet_ks_torevert	.018
203	alexnet	.018
204	resnext101_32x8d_wsl	.018
205	resnet101_imagenet_full	.017
206	compact_cornets_V4	.017
207	yudixie_resnet50_random_0_240908	.017
208	compact_vgg19_V4	.016
209	artResNet18_1	.016
210	resnet50_robust_l2_eps1	.015
211	AdvProp_efficientnet-b4	.014
212	omnivore_swinB	.014
213	convnext_tiny_sup	.013
214	voneresnet-50-robust	.013
215	yudixie_resnet50_imagenet1kpret_0_240312	.013
216	resnet_50_v1_spiking_l3	.013
217	resnet_50_v1_spiking_l4a	.013
218	resnet_50_v1_spiking	.013
219	resnet_50_v1_spiking_l4	.013
220	yudixie_resnet50_imagenet1kpret_0_240908	.013
221	resnet_50_v1	.013
222	resnet50-sup	.013
223	resnet50_tutorial	.013
224	tv_efficientnet-b1	.012
225	mobilenet_v2_1_4_224	.011
226	mobilenet_v2_1-4_224_pytorch	.011
227	yudixie_resnet18_category_class_0_240719	.011
228	AlexNet_SIN	.011
229	BiT-S-R50x1	.011
230	deepeisnn_ei_cifar10_vgg19	.010
231	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.009
232	antialiased-r50	.009
233	convnext_large_clip_laion2b_finelayers	.009
234	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.009
235	resnet50-SIN_IN_IN	.008
236	mobilenet_v2_1_3_224	.008
237	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.008
238	mobilenet_v2_0_75_224	.007
239	yudixie_resnet18_translation_rotation_0_240719	.007
240	mobilenet_v2_1_0_192	.007
241	convnext_xlarge:fb_in22k_ft_in1k	.007
242	dinov2_vitg14_reg_lc	.007
243	dinov2_vitl14_reg_lc_featread	.006
244	dinov2_vitl14_reg_lc	.006
245	xception	.006
246	BiT-S-R50x3	.006
247	resnet50_imagenet_100_seed-0	.006
248	vonegrcnn_52e_full	.005
249	vonegrcnn_47e	.005
250	antialias-resnet152	.005
251	mc3_18	.005
252	yudixie_wbh_object_class_scaling_0415_imagenet	.004
253	yudixie_resnet18_imagenet1kpret_0_240719	.004
254	yudixie_resnet50_translation_reg_0_240908	.004
255	mobilenet_v2_1_0_160	.004
256	pnasnet_large	.004
257	compact_resnet50_V4	.004
258	yudixie_resnet50_object_class_0_240908	.004
259	yudixie_resnet18_translation_reg_0_240719	.004
260	resnet50_imagenet_10_seed-0	.003
261	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.003
262	resnet50-SIN	.002
263	mobilenet_v2_0_5_224	.002
264	voneresnet-50-non_stochastic	.002
265	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.001
266	convnext_base_fb_in22k_ft_in1k	.001
267	yudixie_resnet18_rotation_reg_0_240719	.001
268	yudixie_resnet18_distance_rotation_0_240719	.001
269	yudixie_resnet18_distance_reg_0_240719	.001
270	deepeisnn_ei_cifar10_resnet18	.001
271	yudixie_resnet50_distance_reg_0_240908	.001
272	yudixie_resnet50_distance_translation_rotation_0_240908	.001
273	voneresnet-50	.001
274	yudixie_resnet50_category_class_0_240908	.001
275	VOneCORnet-S	.001
276	AdvProp_efficientnet-b8	.001
277	blt_vs	.001
278	yudixie_resnet50_distance_translation_0_240908	.000
279	vgg_16	.000
280	yudixie_resnet50_translation_rotation_0_240908	.000
281	AdvProp_efficientnet-b7	.000
282	yudixie_resnet18_distance_translation_rotation_0_240719	.000
283	yudixie_resnet50_distance_rotation_0_240908	.000
284	resnet18_fair	.000
285	aughost_fair_resnet18	.000
286	briaai_rmbg_1_4	.000
287	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.000
288	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.000
289	efficientnet_b1_imagenet_full	.000
290	mobilenet_v2_0_75_160	.000
291	deepeisnn_ei_imagenet_resnet18	.000
292	BiT-S-R152x2	.001
293	vgg_19	.002
294	focalnet_tiny_lrf_in1k	.002
295	yudixie_resnet18_object_class_0_240719	.003
296	mobilenet_v2_0_75_192	.004
297	pnasnet_large_halfPrecision
298	ReAlnet01_cornet_fov16	1.0
299	ReAlnet01_cornet_fov12	1.0
300	SeLaVi-Kinetics-Sound
301	mvit_v2_s
302	SeLaVi-VGG-Sound
303	mvit_v1_b
304	fixres_resnext101_32x48d_wsl
305	SeLaVi-AVE
306	mobilenet_v2_1_0_128
307	efficientnet_b0_imagenet_full
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

0.45.

Note that scores are relative to this ceiling.

Data: tong.Coggan2024_fMRI.V1

Metric: rdm