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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("tong.Coggan2024_fMRI.V4-rdm")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	AlexNet_SIN	.332
2	AlexNet_SIN_fov12	.318
3	alexnet	.301
4	alexnet_ks_torevert	.300
5	convnext_xlarge:fb_in22k_ft_in1k	.277
6	vit_base_patch14_dinov2-lvd142m	.248
7	alexnet_training_seed_01_fov16	.228
8	grcnn_109	.215
9	convnext_small_imagenet_1_seed-0	.215
10	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.206
11	alexnet_training_seed_01_fov4	.206
12	vit_large_patch14_clip_336:laion2b_ft_in1k	.199
13	vit_large_patch14_clip_224:openai_ft_in1k	.197
14	vit_large_patch14_clip_224:laion2b_ft_in1k	.171
15	deit_small_imagenet_10_seed-0	.171
16	yudixie_resnet18_random_0_240719	.169
17	efficientnet_b2_imagenet_full	.164
18	cvt_cvt-21-384-in1k_4_LucyV4	.161
19	alexnet_training_seed_01	.157
20	mobilenet_v2_0_5_224	.155
21	resnet-50-robust	.153
22	pr1_stl10_disentangled_alpha04	.148
23	omnivore_swinS	.145
24	deit_small_imagenet_1_seed-0	.144
25	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.143
26	deit_small_imagenet_full_seed-0	.135
27	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.134
28	BiT-S-R50x1	.134
29	convnext_large:fb_in22k_ft_in1k	.132
30	convnext_small_imagenet_10_seed-0	.131
31	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.126
32	vit_base_patch16_clip_224:openai_ft_in1k	.126
33	alexnet_training_seed_09	.124
34	convnext_large_imagenet_full_seed-0	.123
35	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.120
36	resnet50-moclr8deg	.115
37	resnet50_primary_visual_cortex	.109
38	mobilenet_v2_1_0_160	.107
39	pnasnet_large	.106
40	AlexNet_SIN_fov16	.106
41	alexnet_training_seed_01_fov12	.105
42	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
43	yolos_tiny	.093
44	shufflenet_v2_x1_0	.090
45	compact_vgg19_V4	.090
46	resnet-152_v2_pytorch	.090
47	alexnet_training_seed_10	.090
48	alexnet_fov16	.089
49	alexnet_training_seed_07	.089
50	densenet-121	.087
51	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.084
52	vit_relpos_base_patch16_clsgap_224:sw_in1k	.081
53	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.081
54	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.080
55	ReAlnet01_cornet_fov12	.080
56	resnet_101_v2	.080
57	mobilenet_v2_1_0_224	.079
58	alexnet_training_seed_06	.079
59	resnext101_32x48d_wsl	.079
60	resnet50-VITO-8deg-cc	.075
61	resnet50-vitoimagevidnet8	.075
62	inception_v1	.075
63	cvt_cvt-21-224-in1k_4_LucyV4	.074
64	alexnet_training_seed_04	.071
65	ReAlnet08	.070
66	alexnet_training_seed_05	.069
67	deit_large_imagenet_full_seed-0	.069
68	ReAlnet07	.069
69	ReAlnet05	.068
70	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.067
71	ReAlnet06	.065
72	ReAlnet10	.065
73	resnet50_st_wzc_2	.064
74	ReAlnet01	.064
75	ViT_L_32_imagenet1k	.064
76	resnet50-vicregl0p9	.062
77	resnext101_32x32d_wsl	.062
78	resnet34_ecoset_full	.062
79	yudixie_wbh_object_class_scaling_0415_run_3	.061
80	resnet_152_v1	.060
81	ReAlnet02	.060
82	resnet50-vicreg	.058
83	compact_vgg19_IT	.057
84	resnet_101_v1	.057
85	ReAlnet09	.056
86	convnext_tiny_imagenet_full_seed-0	.056
87	hmax	.053
88	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.053
89	ReAlnet03	.053
90	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.052
91	ReAlnet04	.050
92	efficientnet_b0_imagenet_full	.050
93	BiT-S-R101x1	.049
94	resnet_152_v2	.048
95	AlexNet_SIN_fov4	.047
96	grcnn	.047
97	effnetb1_272x240	.047
98	voneresnet-50	.046
99	alexnet_training_seed_08	.044
100	densenet-201	.044
101	resnet50-barlow	.043
102	resnet50_st_wzc_seed7_2_323	.043
103	AdvProp_efficientnet-b8	.042
104	resnet50_ewconplus_wzc	.041
105	custom_model_cv_18_dagger_408	.040
106	BiT-S-R152x4	.040
107	resnext101_32x8d_wsl	.040
108	resnet50_st_wzc_2_323	.039
109	inception_v4	.039
110	resnet50-SIN	.039
111	deepeisnn_ei_imagenet_resnet18	.039
112	omnivore_swinB	.038
113	nasnet_mobile	.038
114	resnet50_st_wzc_seed3_2_323	.037
115	VOneCORnet-S	.037
116	mobilenet_v2_0_75_160	.036
117	resnet101_imagenet_full	.036
118	inception_v3	.035
119	resnet101_ecoset_full	.033
120	densenet-169	.033
121	predictive_coding_pc	.032
122	yudixie_wbh_object_class_scaling_0415_run_2	.031
123	deit3_base_patch16_224_fb_in22k_ft_in1k	.031
124	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.031
125	resnet50_ewconplus_wzc_seed4_2_323	.031
126	AdvProp_efficientnet-b2	.031
127	AT_efficientnet-b2	.031
128	deit_small_imagenet_100_seed-0	.029
129	resnet50_ewconplus_noseed_2_323	.029
130	regnet_y_400mf	.029
131	resnet18_imagenet_full	.029
132	resnet50_robust_l2_eps1	.028
133	cv_18_dagger_408_pretrained	.028
134	focalnet_tiny_lrf_in1k	.028
135	resnet152_imagenet_full	.027
136	nasnet_large	.027
137	resnet50_ewconplus_wzc_2_323	.026
138	convnext_small_imagenet_full_seed-0	.026
139	convnext_base_imagenet_full_seed-0	.026
140	mobilenet_v2_1_4_224	.025
141	mobilenet_v2_1-4_224_pytorch	.025
142	alexnet_fov4	.024
143	resnet152_ecoset_full	.024
144	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.024
145	BiT-S-R50x3	.024
146	alexnet_fov12	.024
147	BiT-S-R101x3	.023
148	resnet18_imagenet21kP	.023
149	efficientnet-b7	.023
150	resnet50_ewconplus_wzc_seed2_2__323	.022
151	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.022
152	ReAlnet07_ALL	.022
153	ReAlnet02_ALL	.022
154	resnet50_imagenet_full	.022
155	convnext_base:clip_laiona_augreg_ft_in1k_384	.021
156	ReAlnet08_ALL	.021
157	imagenet_l2_3_0	.021
158	resnet50_st_wzc_seed5_2_323	.020
159	resnet18_ecoset_full	.020
160	ReAlnet03_ALL	.019
161	ReAlnet05_ALL	.019
162	ReAlnet10_ALL	.019
163	ReAlnet04_ALL	.019
164	ReAlnet09_ALL	.018
165	ReAlnet06_ALL	.018
166	resnet50_robust_l2_eps3	.018
167	ReAlnet10_cornet	.018
168	resnet50-vicregl0p75	.017
169	ReAlnet08_cornet	.017
170	ReAlnet01_ALL	.017
171	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.016
172	deit3_base_patch16_224_fb_in1k	.016
173	ReAlnet02_cornet	.016
174	resnet50_ecoset_full	.016
175	BiT-S-R152x2	.016
176	AdvProp_efficientnet-b6	.015
177	voneresnet-50-robust	.015
178	alexnet_training_seed_03	.015
179	resnet50_ewconplus_wzc_seed5_2__323	.015
180	resnet50_ewconplus_wzc_seed7_2_323	.015
181	cvt_cvt-13-224-in1k_4_LucyV4	.015
182	ReAlnet01_cornet	.015
183	ReAlnet07_cornet	.014
184	yudixie_resnet50_random_0_240908	.014
185	yudixie_wbh_object_class_scaling_0415_run_1	.014
186	ReAlnet03_cornet	.014
187	ReAlnet05_cornet	.014
188	resnet50_st_wzc_seed6_2_323	.014
189	mobilenet_v2_1_3_224	.014
190	vit_base_patch14_reg4_dinov2-lvd142m	.013
191	ReAlnet04_cornet	.013
192	Res2Net50_26w_4s	.013
193	vonegrcnn_47e	.012
194	alexnet_training_seed_02	.012
195	vonegrcnn_52e_full	.012
196	resnet50_st_wzc_seed4_2_323	.012
197	resnet50_ewconplus_wzc_seed6_2_323	.012
198	vonegrcnn_62e_nobn	.011
199	efficientnet_b0	.011
200	resnet34_imagenet_full	.011
201	cvt_cvt-13-384-in1k_4_LucyV4	.010
202	ReAlnet09_cornet	.010
203	ReAlnet06_cornet	.010
204	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.010
205	mobilenet_v2_1_0_192	.010
206	deepeisnn_ei_cifar10_vgg19	.010
207	resnet50_st_wzc_noseed_2_323	.010
208	mobilenet_v2_1_0_128	.009
209	convnext_small_imagenet_100_seed-0	.009
210	yudixie_resnet18_distance_translation_0_240719	.009
211	resnet-18_test_m	.009
212	mobilenet_v2_0_5_192	.008
213	yudixie_wbh_object_class_scaling_0415_run_0	.008
214	resnet_50_v1_spiking	.008
215	resnet50_tutorial	.008
216	yudixie_resnet50_imagenet1kpret_0_240908	.008
217	resnet_50_v1_spiking_l4	.008
218	resnet_50_v1_spiking_l4a	.008
219	resnet_50_v1_spiking_l3	.008
220	resnet_50_v1	.008
221	resnet50-sup	.008
222	yudixie_resnet50_imagenet1kpret_0_240312	.008
223	antialiased-rnext101_32x8d	.008
224	resnet50_ewconplus_wzc_seed3_2_323	.007
225	AdvProp_efficientnet-b4	.007
226	briaai_rmbg_1_4	.007
227	voneresnet-50-non_stochastic	.006
228	convnext_tiny_sup	.006
229	compact_resnet50_robust_IT	.005
230	resnet_50_v2	.005
231	resnet50-SIN_IN_IN	.005
232	resnet_SIN_IN_FT_IN	.005
233	deit_base_imagenet_full_seed-0	.005
234	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.005
235	ReAlnet01_cornet_fov4	.005
236	xception	.005
237	tv_efficientnet-b1	.004
238	yudixie_resnet18_imagenet1kpret_0_240719	.004
239	yudixie_wbh_object_class_scaling_0415_imagenet	.004
240	AdvProp_efficientnet-b7	.004
241	antialias-resnet152	.004
242	resnet50-SIN_IN	.004
243	yudixie_resnet18_translation_rotation_0_240719	.003
244	compact_resnet50_IT	.003
245	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.003
246	convnext_base_fb_in1k	.002
247	convnext_base_fb_in1k_gram_aligned_dinov3	.002
248	deepeisnn_ei_cifar10_resnet18	.002
249	convnext_base_fb_in22k_ft_in1k	.002
250	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.002
251	resnet50_imagenet_100_seed-0	.002
252	artResNet18_1	.002
253	antialiased-r50	.002
254	yudixie_resnet18_translation_reg_0_240719	.001
255	resnet50_imagenet_10_seed-0	.001
256	resnet50_imagenet_1_seed-0	.001
257	yudixie_resnet18_rotation_reg_0_240719	.000
258	convnext_femto_ols:d1_in1k	.000
259	blt_vs	.000
260	yudixie_resnet18_distance_rotation_0_240719	.000
261	yudixie_resnet18_distance_reg_0_240719	.000
262	efficientnet_b1_imagenet_full	.000
263	yudixie_resnet18_distance_translation_rotation_0_240719	.000
264	vgg_16	.000
265	aughost_fair_resnet18	.000
266	vit_base_patch16_dinov3-lvd1689m	.001
267	mobilenet_v2_0_75_224	.001
268	convnext_tiny:in12k_ft_in1k	.002
269	yudixie_resnet50_rotation_reg_0_240908	.003
270	vgg_19	.003
271	yudixie_resnet50_object_class_0_240908	.003
272	yudixie_resnet50_translation_rotation_0_240908	.003
273	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.003
274	resnet18_fair	.003
275	yudixie_resnet18_object_class_0_240719	.004
276	yudixie_resnet50_category_class_0_240908	.004
277	yudixie_resnet50_translation_reg_0_240908	.004
278	yudixie_resnet50_distance_translation_rotation_0_240908	.005
279	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.006
280	yudixie_resnet50_distance_translation_0_240908	.007
281	yudixie_resnet18_category_class_0_240719	.007
282	compact_cornets_IT	.007
283	yudixie_resnet50_distance_rotation_0_240908	.007
284	yudixie_resnet50_distance_reg_0_240908	.008
285	pnasnet_large_halfPrecision
286	pnasnet_large_pytorch
287	mobilenet_v2_0_75_192
288	compact_resnet50_V4
289	SeLaVi-AVE
290	ReAlnet01_cornet_fov16	1.0
291	r3d_18
292	resnext101_32x16d_wsl
293	fixres_resnext101_32x48d_wsl
294	compact_resnet50_robust_V4	1.0
295	mc3_18
296	s3d
297	mvit_v1_b
298	resnet50_eMMCR_Vanilla
299	compact_cornets_V4	1.0
300	SeLaVi-Kinetics400
301	SeLaVi-VGG-Sound
302	SeLaVi-Kinetics-Sound
303	r2plus1d_18
304	mvit_v2_s

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

0.33.

Note that scores are relative to this ceiling.

Data: tong.Coggan2024_fMRI.V4

Metric: rdm