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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	dinov2_vitg14_lc	.120
36	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.120
37	resnet50-moclr8deg	.115
38	resnet50_primary_visual_cortex	.109
39	mobilenet_v2_1_0_160	.107
40	pnasnet_large	.106
41	AlexNet_SIN_fov16	.106
42	vit_huge_clip336_laion	.106
43	alexnet_training_seed_01_fov12	.105
44	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
45	yolos_tiny	.093
46	shufflenet_v2_x1_0	.090
47	compact_vgg19_V4	.090
48	resnet-152_v2_pytorch	.090
49	alexnet_training_seed_10	.090
50	alexnet_fov16	.089
51	alexnet_training_seed_07	.089
52	densenet-121	.087
53	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.084
54	vit_relpos_base_patch16_clsgap_224:sw_in1k	.081
55	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.081
56	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.080
57	ReAlnet01_cornet_fov12	.080
58	resnet_101_v2	.080
59	mobilenet_v2_1_0_224	.079
60	alexnet_training_seed_06	.079
61	resnext101_32x48d_wsl	.079
62	resnet50-vitoimagevidnet8	.075
63	resnet50-VITO-8deg-cc	.075
64	inception_v1	.075
65	cvt_cvt-21-224-in1k_4_LucyV4	.074
66	alexnet_training_seed_04	.071
67	ReAlnet08	.070
68	alexnet_training_seed_05	.069
69	deit_large_imagenet_full_seed-0	.069
70	ReAlnet07	.069
71	ReAlnet05	.068
72	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.067
73	ReAlnet06	.065
74	ReAlnet10	.065
75	resnet50_st_wzc_2	.064
76	ReAlnet01	.064
77	ViT_L_32_imagenet1k	.064
78	resnet50-vicregl0p9	.062
79	resnext101_32x32d_wsl	.062
80	resnet34_ecoset_full	.062
81	yudixie_wbh_object_class_scaling_0415_run_3	.061
82	resnet_152_v1	.060
83	ReAlnet02	.060
84	resnet50-vicreg	.058
85	compact_vgg19_IT	.057
86	resnet_101_v1	.057
87	ReAlnet09	.056
88	vit_large_clip336_openai	.056
89	convnext_tiny_imagenet_full_seed-0	.056
90	hmax	.053
91	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.053
92	ReAlnet03	.053
93	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.052
94	ReAlnet04	.050
95	efficientnet_b0_imagenet_full	.050
96	BiT-S-R101x1	.049
97	resnet_152_v2	.048
98	AlexNet_SIN_fov4	.047
99	grcnn	.047
100	effnetb1_272x240	.047
101	voneresnet-50	.046
102	alexnet_training_seed_08	.044
103	densenet-201	.044
104	resnet50-barlow	.043
105	resnet50_st_wzc_seed7_2_323	.043
106	AdvProp_efficientnet-b8	.042
107	resnet50_ewconplus_wzc	.041
108	custom_model_cv_18_dagger_408	.040
109	BiT-S-R152x4	.040
110	resnext101_32x8d_wsl	.040
111	inception_v4	.039
112	resnet50-SIN	.039
113	deepeisnn_ei_imagenet_resnet18	.039
114	dinov2_vitl14_lc_res336	.038
115	omnivore_swinB	.038
116	nasnet_mobile	.038
117	resnet50_st_wzc_seed3_2_323	.037
118	VOneCORnet-S	.037
119	mobilenet_v2_0_75_160	.036
120	resnet101_imagenet_full	.036
121	dinov2_vitl14_lc	.036
122	inception_v3	.035
123	resnet101_ecoset_full	.033
124	densenet-169	.033
125	predictive_coding_pc	.032
126	yudixie_wbh_object_class_scaling_0415_run_2	.031
127	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.031
128	deit3_base_patch16_224_fb_in22k_ft_in1k	.031
129	AT_efficientnet-b2	.031
130	AdvProp_efficientnet-b2	.031
131	deit_small_imagenet_100_seed-0	.029
132	regnet_y_400mf	.029
133	resnet18_imagenet_full	.029
134	resnet50_robust_l2_eps1	.028
135	cv_18_dagger_408_pretrained	.028
136	focalnet_tiny_lrf_in1k	.028
137	dinov2_vitl14_reg_lc_featread	.027
138	dinov2_vitl14_reg_lc	.027
139	resnet152_imagenet_full	.027
140	nasnet_large	.027
141	convnext_small_imagenet_full_seed-0	.026
142	convnext_base_imagenet_full_seed-0	.026
143	mobilenet_v2_1_4_224	.025
144	mobilenet_v2_1-4_224_pytorch	.025
145	eva02_large_in22k	.025
146	alexnet_fov4	.024
147	resnet152_ecoset_full	.024
148	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.024
149	BiT-S-R50x3	.024
150	alexnet_fov12	.024
151	BiT-S-R101x3	.023
152	resnet18_imagenet21kP	.023
153	dinov2_vitg14_reg_lc	.023
154	efficientnet-b7	.023
155	resnet50_ewconplus_wzc_seed2_2__323	.022
156	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.022
157	ReAlnet07_ALL	.022
158	ReAlnet02_ALL	.022
159	resnet50_imagenet_full	.022
160	convnext_base:clip_laiona_augreg_ft_in1k_384	.021
161	ReAlnet08_ALL	.021
162	imagenet_l2_3_0	.021
163	resnet18_ecoset_full	.020
164	ReAlnet03_ALL	.019
165	ReAlnet05_ALL	.019
166	ReAlnet10_ALL	.019
167	ReAlnet04_ALL	.019
168	convnext_large_in22k_supervised	.018
169	ReAlnet09_ALL	.018
170	ReAlnet06_ALL	.018
171	resnet50_robust_l2_eps3	.018
172	ReAlnet10_cornet	.018
173	resnet50-vicregl0p75	.017
174	ReAlnet08_cornet	.017
175	ReAlnet01_ALL	.017
176	deit3_base_patch16_224_fb_in1k	.016
177	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.016
178	ReAlnet02_cornet	.016
179	resnet50_ecoset_full	.016
180	BiT-S-R152x2	.016
181	AdvProp_efficientnet-b6	.015
182	voneresnet-50-robust	.015
183	alexnet_training_seed_03	.015
184	resnet50_ewconplus_wzc_seed5_2__323	.015
185	cvt_cvt-13-224-in1k_4_LucyV4	.015
186	ReAlnet01_cornet	.015
187	convnext_xxlarge_clip	.015
188	ReAlnet07_cornet	.014
189	yudixie_resnet50_random_0_240908	.014
190	yudixie_wbh_object_class_scaling_0415_run_1	.014
191	ReAlnet03_cornet	.014
192	ReAlnet05_cornet	.014
193	resnet50_st_wzc_seed6_2_323	.014
194	mobilenet_v2_1_3_224	.014
195	vit_base_patch14_reg4_dinov2-lvd142m	.013
196	ReAlnet04_cornet	.013
197	Res2Net50_26w_4s	.013
198	vonegrcnn_47e	.012
199	alexnet_training_seed_02	.012
200	vonegrcnn_52e_full	.012
201	resnet50_ewconplus_wzc_seed6_2_323	.012
202	vonegrcnn_62e_nobn	.011
203	efficientnet_b0	.011
204	resnet34_imagenet_full	.011
205	cvt_cvt-13-384-in1k_4_LucyV4	.010
206	ReAlnet09_cornet	.010
207	resnet50_hvs_wzc	.010
208	ReAlnet06_cornet	.010
209	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.010
210	mobilenet_v2_1_0_192	.010
211	deepeisnn_ei_cifar10_vgg19	.010
212	mobilenet_v2_1_0_128	.009
213	convnext_small_imagenet_100_seed-0	.009
214	yudixie_resnet18_distance_translation_0_240719	.009
215	resnet-18_test_m	.009
216	mobilenet_v2_0_5_192	.008
217	yudixie_wbh_object_class_scaling_0415_run_0	.008
218	yudixie_resnet50_imagenet1kpret_0_240312	.008
219	resnet_50_v1_spiking_l3	.008
220	resnet_50_v1_spiking_l4a	.008
221	resnet_50_v1_spiking_l4	.008
222	resnet_50_v1_spiking	.008
223	yudixie_resnet50_imagenet1kpret_0_240908	.008
224	resnet_50_v1	.008
225	resnet50-sup	.008
226	resnet50_tutorial	.008
227	convnext_large_clip_laion2b_finelayers	.008
228	antialiased-rnext101_32x8d	.008
229	AdvProp_efficientnet-b4	.007
230	briaai_rmbg_1_4	.007
231	voneresnet-50-non_stochastic	.006
232	convnext_tiny_sup	.006
233	compact_resnet50_robust_IT	.005
234	resnet_50_v2	.005
235	resnet50-SIN_IN_IN	.005
236	resnet_SIN_IN_FT_IN	.005
237	deit_base_imagenet_full_seed-0	.005
238	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.005
239	ReAlnet01_cornet_fov4	.005
240	xception	.005
241	tv_efficientnet-b1	.004
242	yudixie_wbh_object_class_scaling_0415_imagenet	.004
243	yudixie_resnet18_imagenet1kpret_0_240719	.004
244	AdvProp_efficientnet-b7	.004
245	antialias-resnet152	.004
246	resnet50-SIN_IN	.004
247	yudixie_resnet18_translation_rotation_0_240719	.003
248	compact_resnet50_IT	.003
249	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.003
250	convnext_base_fb_in1k_gram_aligned_dinov3	.002
251	convnext_base_fb_in1k	.002
252	deepeisnn_ei_cifar10_resnet18	.002
253	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.002
254	convnext_base_fb_in22k_ft_in1k	.002
255	resnet50_imagenet_100_seed-0	.002
256	artResNet18_1	.002
257	antialiased-r50	.002
258	yudixie_resnet18_translation_reg_0_240719	.001
259	resnet50_imagenet_10_seed-0	.001
260	resnet50_imagenet_1_seed-0	.001
261	yudixie_resnet18_rotation_reg_0_240719	.000
262	convnext_femto_ols:d1_in1k	.000
263	blt_vs	.000
264	yudixie_resnet18_distance_rotation_0_240719	.000
265	yudixie_resnet18_distance_reg_0_240719	.000
266	efficientnet_b1_imagenet_full	.000
267	yudixie_resnet18_distance_translation_rotation_0_240719	.000
268	vgg_16	.000
269	aughost_fair_resnet18	.000
270	vit_base_patch16_dinov3-lvd1689m	.001
271	mobilenet_v2_0_75_224	.001
272	convnext_tiny:in12k_ft_in1k	.002
273	yudixie_resnet50_rotation_reg_0_240908	.003
274	vgg_19	.003
275	yudixie_resnet50_object_class_0_240908	.003
276	yudixie_resnet50_translation_rotation_0_240908	.003
277	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.003
278	resnet18_fair	.003
279	yudixie_resnet18_object_class_0_240719	.004
280	yudixie_resnet50_category_class_0_240908	.004
281	yudixie_resnet50_translation_reg_0_240908	.004
282	yudixie_resnet50_distance_translation_rotation_0_240908	.005
283	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.006
284	yudixie_resnet50_distance_translation_0_240908	.007
285	yudixie_resnet18_category_class_0_240719	.007
286	compact_cornets_IT	.007
287	yudixie_resnet50_distance_rotation_0_240908	.007
288	yudixie_resnet50_distance_reg_0_240908	.008
289	pnasnet_large_halfPrecision
290	ReAlnet01_cornet_fov16	1.0
291	compact_cornets_V4	1.0
292	compact_resnet50_robust_V4	1.0
293	compact_resnet50_V4
294	mvit_v2_s
295	r2plus1d_18
296	SeLaVi-Kinetics-Sound
297	SeLaVi-VGG-Sound
298	SeLaVi-Kinetics400
299	resnet50_eMMCR_Vanilla
300	mvit_v1_b
301	s3d
302	mc3_18
303	fixres_resnext101_32x48d_wsl
304	resnext101_32x16d_wsl
305	r3d_18
306	SeLaVi-AVE
307	mobilenet_v2_0_75_192
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

0.33.

Note that scores are relative to this ceiling.

Data: tong.Coggan2024_fMRI.V4

Metric: rdm