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

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

Not available

Data: tong.Coggan2024_fMRI.V1

Metric: rdm