Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("BMD2024.dotted_1Behavioral-accuracy_distance")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	1.0
2	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.983
3	vit_large_patch14_clip_224:openai_ft_in1k	.801
4	resnext101_32x48d_wsl	.770
5	vit_large_patch14_clip_224:laion2b_ft_in1k	.759
6	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.697
7	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.655
8	resnext101_32x8d_wsl	.655
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.655
10	resnext101_32x16d_wsl	.603
11	convnext_base:clip_laiona_augreg_ft_in1k_384	.582
12	resnext101_32x32d_wsl	.510
13	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.478
14	convnext_large:fb_in22k_ft_in1k	.447
15	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.437
16	convnext_xlarge:fb_in22k_ft_in1k	.354
17	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.354
18	densenet-201	.343
19	densenet_201_pytorch	.343
20	resnet50-SIN	.322
21	grcnn_109	.312
22	convnext_base_imagenet_full_seed-0	.312
23	AdvProp_efficientnet-b4	.302
24	AdvProp_efficientnet-b2	.302
25	BiT-S-R152x2	.302
26	AT_efficientnet-b2	.302
27	AdvProp_efficientnet-b7	.291
28	resnet_101_v1	.281
29	effnetb1_272x240	.281
30	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.270
31	focalnet_tiny_lrf_in1k	.260
32	regnet_y_400mf	.250
33	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.250
34	AdvProp_efficientnet-b6	.250
35	ReAlnet04	.239
36	convnext_large_imagenet_full_seed-0	.239
37	resnet101_imagenet_full	.239
38	AlexNet_SIN	.239
39	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.229
40	resnet152_imagenet_full	.229
41	resnet_50_v1	.229
42	convnext_small_imagenet_full_seed-0	.229
43	AdvProp_efficientnet-b8	.229
44	voneresnet-50	.229
45	ReAlnet_incorrect	.229
46	ReAlnet01	.229
47	resnet50-sup	.229
48	resnet50_tutorial	.229
49	resnet50_imagenet_full	.229
50	ReAlnet07	.229
51	resnet34_imagenet_full	.229
52	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.229
53	antialias-resnet152	.229
54	ReAlnet03	.229
55	blt_vs	.218
56	Res2Net50_26w_4s	.218
57	resnet50-SIN_IN_IN	.218
58	resnet_SIN_IN_FT_IN	.218
59	convnext_small_imagenet_100_seed-0	.208
60	ReAlnet06	.208
61	ReAlnet09	.208
62	resnet50-SIN_IN	.208
63	resnet50_robust_l2_eps1	.208
64	ReAlnet02	.208
65	ReAlnet05	.208
66	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.198
67	mobilenet_v2_1_0_192	.198
68	alexnet_ks_torevert	.187
69	mobilenet_v2_0_75_192	.187
70	BiT-S-R152x4	.187
71	BiT-S-R50x3	.187
72	antialiased-r50	.187
73	voneresnet-50-robust	.187
74	ReAlnet10	.187
75	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.177
76	resnet50-moclr8deg	.177
77	resnet18_imagenet_full	.177
78	densenet-121	.177
79	efficientnet_b0_imagenet_full	.177
80	nasnet_mobile	.177
81	mobilenet_v2_1_0_128	.177
82	ReAlnet08	.177
83	cvt_cvt-13-224-in1k_4_LucyV4	.166
84	pnasnet_large	.166
85	resnet_152_v1	.166
86	resnet50_eMMCR_Vanilla	.166
87	vit_base_patch16_clip_224:openai_ft_in1k	.166
88	convnext_tiny:in12k_ft_in1k	.156
89	inception_v3	.156
90	inception_v4	.156
91	resnet50-vitoimagevidnet8	.156
92	voneresnet-50-non_stochastic	.156
93	resnet50-VITO-8deg-cc	.156
94	densenet-169	.156
95	BiT-S-R101x3	.156
96	nasnet_large	.146
97	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.146
98	mobilenet_v2_1_0_224	.146
99	mobilenet_v2_1_3_224	.146
100	mobilenet_v2_0_75_160	.146
101	deit_small_imagenet_full_seed-0	.146
102	cvt_cvt-13-384-in1k_4_LucyV4	.146
103	pnasnet_large_pytorch	.146
104	convnext_tiny_imagenet_full_seed-0	.135
105	tv_efficientnet-b1	.135
106	grcnn	.135
107	mobilenet_v2_0_5_192	.135
108	convnext_tiny_sup	.135
109	ViT_L_32_imagenet1k	.125
110	alexnet_training_seed_03	.125
111	BiT-S-R101x1	.125
112	vit_relpos_base_patch16_clsgap_224:sw_in1k	.125
113	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.125
114	inception_v1	.125
115	cvt_cvt-21-384-in1k_4_LucyV4	.125
116	inception_v3_pytorch	.125
117	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
118	BiT-S-R50x1	.114
119	deit_large_imagenet_full_seed-0	.114
120	mobilenet_v2_0_5_224	.114
121	resnet50_imagenet_100_seed-0	.114
122	deit_small_imagenet_100_seed-0	.114
123	resnet50-vicregl0p75	.114
124	resnet_50_v2	.114
125	shufflenet_v2_x1_0	.114
126	imagenet_l2_3_0	.104
127	resnet50_robust_l2_eps3	.104
128	resnet-152_v2_pytorch	.104
129	bp_resnet50_julios	.104
130	vgg_16	.104
131	alexnet_training_seed_06	.104
132	alexnet_training_seed_05	.104
133	convnext_small_imagenet_10_seed-0	.104
134	efficientnet_b2_imagenet_full	.104
135	vonegrcnn_62e_nobn	.104
136	alexnet_training_seed_10	.104
137	deit_small_imagenet_1_seed-0	.104
138	deit_small_imagenet_10_seed-0	.104
139	resnet_152_v2	.104
140	alexnet_training_seed_04	.104
141	convnext_small_imagenet_1_seed-0	.104
142	cvt_cvt-21-224-in1k_4_LucyV4	.104
143	resnet50_imagenet_1_seed-0	.104
144	vonegrcnn_47e	.104
145	alexnet_training_seed_02	.104
146	resnet-50-robust	.104
147	vonegrcnn_52e_full	.104
148	alexnet_training_seed_09	.104
149	resnet_101_v2	.104
150	deit_base_imagenet_full_seed-0	.104
151	efficientnet_b0	.104
152	resnet50_imagenet_10_seed-0	.104
153	resnet50-barlow	.104
154	alexnet_training_seed_08	.104
155	custom_model_cv_18_dagger_408	.104
156	artResNet18_1	.094
157	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.094
158	mobilenet_v2_0_75_224	.094
159	mobilenet_v2_1_0_160	.094
160	resnet50-vicregl0p9	.083
161	efficientnet_b1_imagenet_full	.083
162	alexnet_training_seed_07	.083
163	mobilenet_v2_1-4_224_pytorch	.083
164	mobilenet_v2_1_4_224	.083
165	convnext_femto_ols:d1_in1k	.083
166	vgg_19	.083
167	alexnet_training_seed_01	.073
168	xception	.042
169	ReAlnet10_cornet	X
170	ReAlnet03_cornet	X
171	ReAlnet04_cornet	X
172	resnet34_ecoset_full	X
173	resnet50_ecoset_full	X
174	yudixie_resnet50_rotation_reg_0_240908	X
175	resnet101_ecoset_full	X
176	yudixie_resnet50_translation_reg_0_240908	X
177	resnet152_ecoset_full	X
178	yudixie_resnet18_distance_translation_0_240719	X
179	yudixie_resnet50_distance_reg_0_240908	X
180	ReAlnet05_cornet	X
181	yudixie_resnet18_translation_rotation_0_240719	X
182	yudixie_resnet18_distance_rotation_0_240719	X
183	briaai_rmbg_1_4	X
184	yudixie_resnet18_category_class_0_240719	X
185	resnet-18_test_m	X
186	yudixie_resnet50_distance_translation_0_240908	X
187	yudixie_resnet18_random_0_240719	X
188	ReAlnet02_cornet	X
189	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
190	yudixie_resnet18_distance_translation_rotation_0_240719	X
191	ReAlnet06_cornet	X
192	yudixie_resnet18_object_class_0_240719	X
193	yudixie_resnet18_rotation_reg_0_240719	X
194	yudixie_resnet18_imagenet1kpret_0_240719	X
195	yudixie_resnet18_translation_reg_0_240719	X
196	yudixie_resnet18_distance_reg_0_240719	X
197	efficientnet-b7	X
198	ReAlnet07_cornet	X
199	ReAlnet01_cornet	X
200	yudixie_resnet50_translation_rotation_0_240908	X
201	ReAlnet08_cornet	X
202	VOneCORnet-S	X
203	omnivore_swinB	X
204	antialiased-rnext101_32x8d	X
205	resnet50-vicreg	X
206	ReAlnet09_cornet	X
207	yolos_tiny	X
208	hmax	X
209	omnivore_swinS	X
210	resnet18_ecoset_full	X
211	yudixie_resnet50_category_class_0_240908	X
212	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
213	yudixie_resnet50_imagenet1kpret_0_240908	X
214	yudixie_resnet50_random_0_240908	X
215	yudixie_resnet50_distance_translation_rotation_0_240908	X
216	yudixie_resnet50_object_class_0_240908	X
217	MIM	X
218	TAU	X
219	SimVP	X
220	PredRNN	X
221	ConvLSTM	X
222	resnet18_imagenet21kP	X
223	yudixie_resnet50_distance_rotation_0_240908	X

Benchmark bibtex

None

Ceiling

0.98.

Note that scores are relative to this ceiling.

Data: BMD2024.dotted_1Behavioral

Metric: accuracy_distance