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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	gabor_filter_entropy_4x4_v2	.927
2	gabor_filter_entropy_4x4_v2_forcedCosine	.883
3	gabor_filter_entropy_4x4	.857
4	AlexNet_SIN	.332
5	AlexNet_SIN_fov12	.318
6	alexnet	.301
7	alexnet_ks_torevert	.300
8	convnext_xlarge:fb_in22k_ft_in1k	.277
9	vit_base_patch14_dinov2-lvd142m	.248
10	alexnet_training_seed_01_fov16	.228
11	grcnn_109	.215
12	convnext_small_imagenet_1_seed-0	.215
13	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.206
14	alexnet_training_seed_01_fov4	.206
15	vit_large_patch14_clip_336:laion2b_ft_in1k	.199
16	vit_large_patch14_clip_224:openai_ft_in1k	.197
17	vit_large_patch14_clip_224:laion2b_ft_in1k	.171
18	deit_small_imagenet_10_seed-0	.171
19	yudixie_resnet18_random_0_240719	.169
20	efficientnet_b2_imagenet_full	.164
21	cvt_cvt-21-384-in1k_4_LucyV4	.161
22	alexnet_training_seed_01	.157
23	mobilenet_v2_0_5_224	.155
24	resnet-50-robust	.153
25	pr1_stl10_disentangled_alpha04	.148
26	omnivore_swinS	.145
27	deit_small_imagenet_1_seed-0	.144
28	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.143
29	deit_small_imagenet_full_seed-0	.135
30	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.134
31	BiT-S-R50x1	.134
32	convnext_large:fb_in22k_ft_in1k	.132
33	convnext_small_imagenet_10_seed-0	.131
34	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.126
35	vit_base_patch16_clip_224:openai_ft_in1k	.126
36	alexnet_training_seed_09	.124
37	convnext_large_imagenet_full_seed-0	.123
38	dinov2_vitg14_lc	.120
39	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.120
40	resnet50-moclr8deg	.115
41	resnet50_primary_visual_cortex	.109
42	mobilenet_v2_1_0_160	.107
43	pnasnet_large	.106
44	AlexNet_SIN_fov16	.106
45	vit_huge_clip336_laion	.106
46	alexnet_training_seed_01_fov12	.105
47	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
48	yolos_tiny	.093
49	shufflenet_v2_x1_0	.090
50	compact_vgg19_V4	.090
51	resnet-152_v2_pytorch	.090
52	alexnet_training_seed_10	.090
53	alexnet_fov16	.089
54	alexnet_training_seed_07	.089
55	densenet-121	.087
56	seresnext50_32x4d	.085
57	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.084
58	vit_relpos_base_patch16_clsgap_224:sw_in1k	.081
59	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.081
60	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.080
61	ReAlnet01_cornet_fov12	.080
62	resnet_101_v2	.080
63	mobilenet_v2_1_0_224	.079
64	alexnet_training_seed_06	.079
65	resnext101_32x48d_wsl	.079
66	resnet50-vitoimagevidnet8	.075
67	resnet50-VITO-8deg-cc	.075
68	inception_v1	.075
69	cvt_cvt-21-224-in1k_4_LucyV4	.074
70	alexnet_training_seed_04	.071
71	ReAlnet08	.070
72	alexnet_training_seed_05	.069
73	deit_large_imagenet_full_seed-0	.069
74	ReAlnet07	.069
75	ReAlnet05	.068
76	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.067
77	ReAlnet06	.065
78	ReAlnet10	.065
79	resnet50_st_wzc_2	.064
80	regnety_032	.064
81	ReAlnet01	.064
82	ViT_L_32_imagenet1k	.064
83	resnet50-vicregl0p9	.062
84	resnext101_32x32d_wsl	.062
85	resnet34_ecoset_full	.062
86	yudixie_wbh_object_class_scaling_0415_run_3	.061
87	resnet_152_v1	.060
88	ReAlnet02	.060
89	gabor_filter_mean_abs_4x4_v2	.058
90	resnet50-vicreg	.058
91	compact_vgg19_IT	.057
92	resnet_101_v1	.057
93	ReAlnet09	.056
94	vit_large_clip336_openai	.056
95	convnext_tiny_imagenet_full_seed-0	.056
96	hmax	.053
97	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.053
98	ReAlnet03	.053
99	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.052
100	ReAlnet04	.050
101	efficientnet_b0_imagenet_full	.050
102	BiT-S-R101x1	.049
103	resnet_152_v2	.048
104	AlexNet_SIN_fov4	.047
105	grcnn	.047
106	effnetb1_272x240	.047
107	voneresnet-50	.046
108	alexnet_training_seed_08	.044
109	densenet-201	.044
110	resnet50-barlow	.043
111	resnet50_st_wzc_seed7_2_323	.043
112	AdvProp_efficientnet-b8	.042
113	resnet50_ewconplus_wzc	.041
114	custom_model_cv_18_dagger_408	.040
115	BiT-S-R152x4	.040
116	resnext101_32x8d_wsl	.040
117	inception_v4	.039
118	resnet50-SIN	.039
119	deepeisnn_ei_imagenet_resnet18	.039
120	dinov2_vitl14_lc_res336	.038
121	omnivore_swinB	.038
122	nasnet_mobile	.038
123	resnet50_st_wzc_seed3_2_323	.037
124	VOneCORnet-S	.037
125	mobilenet_v2_0_75_160	.036
126	resnet101_imagenet_full	.036
127	dinov2_vitl14_lc	.036
128	inception_v3	.035
129	resnet101_ecoset_full	.033
130	densenet-169	.033
131	predictive_coding_pc	.032
132	yudixie_wbh_object_class_scaling_0415_run_2	.031
133	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.031
134	deit3_base_patch16_224_fb_in22k_ft_in1k	.031
135	AT_efficientnet-b2	.031
136	AdvProp_efficientnet-b2	.031
137	deit_small_imagenet_100_seed-0	.029
138	regnet_y_400mf	.029
139	resnet18_imagenet_full	.029
140	resnet50_robust_l2_eps1	.028
141	cv_18_dagger_408_pretrained	.028
142	focalnet_tiny_lrf_in1k	.028
143	dinov2_vitl14_reg_lc_featread	.027
144	dinov2_vitl14_reg_lc	.027
145	resnet152_imagenet_full	.027
146	nasnet_large	.027
147	convnext_small_imagenet_full_seed-0	.026
148	convnext_base_imagenet_full_seed-0	.026
149	mobilenet_v2_1_4_224	.025
150	mobilenet_v2_1-4_224_pytorch	.025
151	eva02_large_in22k	.025
152	alexnet_fov4	.024
153	resnet152_ecoset_full	.024
154	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.024
155	BiT-S-R50x3	.024
156	alexnet_fov12	.024
157	BiT-S-R101x3	.023
158	resnet18_imagenet21kP	.023
159	dinov2_vitg14_reg_lc	.023
160	efficientnet-b7	.023
161	resnet50_ewconplus_wzc_seed2_2__323	.022
162	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.022
163	ReAlnet07_ALL	.022
164	ReAlnet02_ALL	.022
165	resnet50_imagenet_full	.022
166	convnext_base:clip_laiona_augreg_ft_in1k_384	.021
167	ReAlnet08_ALL	.021
168	imagenet_l2_3_0	.021
169	resnet18_ecoset_full	.020
170	ReAlnet03_ALL	.019
171	ReAlnet05_ALL	.019
172	ReAlnet10_ALL	.019
173	ReAlnet04_ALL	.019
174	convnext_large_in22k_supervised	.018
175	ReAlnet09_ALL	.018
176	ReAlnet06_ALL	.018
177	resnet50_robust_l2_eps3	.018
178	ReAlnet10_cornet	.018
179	resnet50-vicregl0p75	.017
180	ReAlnet08_cornet	.017
181	ReAlnet01_ALL	.017
182	deit3_base_patch16_224_fb_in1k	.016
183	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.016
184	ReAlnet02_cornet	.016
185	resnet50_ecoset_full	.016
186	BiT-S-R152x2	.016
187	AdvProp_efficientnet-b6	.015
188	voneresnet-50-robust	.015
189	alexnet_training_seed_03	.015
190	resnet50_ewconplus_wzc_seed5_2__323	.015
191	cvt_cvt-13-224-in1k_4_LucyV4	.015
192	ReAlnet01_cornet	.015
193	convnext_xxlarge_clip	.015
194	ReAlnet07_cornet	.014
195	yudixie_resnet50_random_0_240908	.014
196	yudixie_wbh_object_class_scaling_0415_run_1	.014
197	ReAlnet03_cornet	.014
198	ReAlnet05_cornet	.014
199	resnet50_st_wzc_seed6_2_323	.014
200	mobilenet_v2_1_3_224	.014
201	vit_base_patch14_reg4_dinov2-lvd142m	.013
202	ReAlnet04_cornet	.013
203	Res2Net50_26w_4s	.013
204	eicircuit_resnet18_bn_20260919	.012
205	vonegrcnn_47e	.012
206	alexnet_training_seed_02	.012
207	vonegrcnn_52e_full	.012
208	resnet50_ewconplus_wzc_seed6_2_323	.012
209	vonegrcnn_62e_nobn	.011
210	efficientnet_b0	.011
211	resnet34_imagenet_full	.011
212	cvt_cvt-13-384-in1k_4_LucyV4	.010
213	ReAlnet09_cornet	.010
214	resnet50_hvs_wzc	.010
215	ReAlnet06_cornet	.010
216	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.010
217	mobilenet_v2_1_0_192	.010
218	deepeisnn_ei_cifar10_vgg19	.010
219	mobilenet_v2_1_0_128	.009
220	convnext_small_imagenet_100_seed-0	.009
221	yudixie_resnet18_distance_translation_0_240719	.009
222	resnet-18_test_m	.009
223	mobilenet_v2_0_5_192	.008
224	yudixie_wbh_object_class_scaling_0415_run_0	.008
225	yudixie_resnet50_imagenet1kpret_0_240312	.008
226	resnet_50_v1_spiking_l3	.008
227	resnet_50_v1_spiking_l4a	.008
228	resnet_50_v1_spiking_l4	.008
229	resnet_50_v1_spiking	.008
230	yudixie_resnet50_imagenet1kpret_0_240908	.008
231	resnet_50_v1	.008
232	resnet50-sup	.008
233	resnet50_tutorial	.008
234	convnext_large_clip_laion2b_finelayers	.008
235	antialiased-rnext101_32x8d	.008
236	AdvProp_efficientnet-b4	.007
237	briaai_rmbg_1_4	.007
238	voneresnet-50-non_stochastic	.006
239	convnext_tiny_sup	.006
240	compact_resnet50_robust_IT	.005
241	resnet_50_v2	.005
242	resnet50-SIN_IN_IN	.005
243	resnet_SIN_IN_FT_IN	.005
244	deit_base_imagenet_full_seed-0	.005
245	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.005
246	ReAlnet01_cornet_fov4	.005
247	xception	.005
248	tv_efficientnet-b1	.004
249	yudixie_wbh_object_class_scaling_0415_imagenet	.004
250	yudixie_resnet18_imagenet1kpret_0_240719	.004
251	AdvProp_efficientnet-b7	.004
252	antialias-resnet152	.004
253	resnet50-SIN_IN	.004
254	yudixie_resnet18_translation_rotation_0_240719	.003
255	compact_resnet50_IT	.003
256	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.003
257	convnext_base_fb_in1k_gram_aligned_dinov3	.002
258	convnext_base_fb_in1k	.002
259	deepeisnn_ei_cifar10_resnet18	.002
260	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.002
261	convnext_base_fb_in22k_ft_in1k	.002
262	resnet50_imagenet_100_seed-0	.002
263	artResNet18_1	.002
264	antialiased-r50	.002
265	yudixie_resnet18_translation_reg_0_240719	.001
266	resnet50_imagenet_10_seed-0	.001
267	resnet50_imagenet_1_seed-0	.001
268	yudixie_resnet18_rotation_reg_0_240719	.000
269	convnext_femto_ols:d1_in1k	.000
270	blt_vs	.000
271	yudixie_resnet18_distance_rotation_0_240719	.000
272	yudixie_resnet18_distance_reg_0_240719	.000
273	efficientnet_b1_imagenet_full	.000
274	yudixie_resnet18_distance_translation_rotation_0_240719	.000
275	vgg_16	.000
276	vit_base_patch16_dinov3-lvd1689m	.001
277	mobilenet_v2_0_75_224	.001
278	convnext_tiny:in12k_ft_in1k	.002
279	yudixie_resnet50_rotation_reg_0_240908	.003
280	vgg_19	.003
281	yudixie_resnet50_object_class_0_240908	.003
282	yudixie_resnet50_translation_rotation_0_240908	.003
283	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.003
284	resnet18_fair	.003
285	yudixie_resnet18_object_class_0_240719	.004
286	yudixie_resnet50_category_class_0_240908	.004
287	yudixie_resnet50_translation_reg_0_240908	.004
288	yudixie_resnet50_distance_translation_rotation_0_240908	.005
289	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.006
290	yudixie_resnet50_distance_translation_0_240908	.007
291	yudixie_resnet18_category_class_0_240719	.007
292	compact_cornets_IT	.007
293	yudixie_resnet50_distance_rotation_0_240908	.007
294	yudixie_resnet50_distance_reg_0_240908	.008
295	pnasnet_large_halfPrecision
296	ReAlnet01_cornet_fov16	1.0
297	compact_cornets_V4	1.0
298	compact_resnet50_robust_V4	1.0
299	compact_resnet50_V4
300	mvit_v2_s
301	r2plus1d_18
302	SeLaVi-Kinetics-Sound
303	SeLaVi-VGG-Sound
304	SeLaVi-Kinetics400
305	resnet50_eMMCR_Vanilla
306	mvit_v1_b
307	s3d
308	mc3_18
309	fixres_resnext101_32x48d_wsl
310	resnext101_32x16d_wsl
311	r3d_18
312	SeLaVi-AVE
313	mobilenet_v2_0_75_192
314	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