Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("BMD2024.texture_2Behavioral-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	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.971
3	vit_large_patch14_clip_224:laion2b_ft_in1k	.952
4	vit_large_patch14_clip_336:laion2b_ft_in1k	.913
5	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.892
6	vit_large_patch14_clip_224:openai_ft_in1k	.882
7	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.850
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.840
9	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.672
10	convnext_base:clip_laiona_augreg_ft_in1k_384	.609
11	resnext101_32x48d_wsl	.577
12	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.504
13	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.462
14	convnext_xlarge:fb_in22k_ft_in1k	.441
15	AdvProp_efficientnet-b6	.430
16	AdvProp_efficientnet-b7	.430
17	resnext101_32x32d_wsl	.430
18	convnext_large:fb_in22k_ft_in1k	.420
19	AdvProp_efficientnet-b4	.409
20	resnext101_32x16d_wsl	.409
21	AdvProp_efficientnet-b8	.399
22	resnext101_32x8d_wsl	.378
23	blt_vs	.367
24	mobilenet_v2_0_5_192	.357
25	vit_base_patch16_clip_224:openai_ft_in1k	.336
26	mobilenet_v2_0_75_192	.325
27	inception_v4	.325
28	inception_v3_pytorch	.315
29	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.315
30	resnet50_robust_l2_eps1	.304
31	convnext_large_imagenet_full_seed-0	.294
32	efficientnet_b0_imagenet_full	.294
33	grcnn_109	.283
34	imagenet_l2_3_0	.283
35	resnet50_robust_l2_eps3	.283
36	resnet-50-robust	.283
37	resnet_101_v1	.283
38	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.283
39	mobilenet_v2_0_75_160	.273
40	BiT-S-R101x1	.273
41	densenet_201_pytorch	.273
42	resnet18_imagenet_full	.273
43	Res2Net50_26w_4s	.273
44	densenet-201	.273
45	mobilenet_v2_1_0_128	.262
46	vonegrcnn_62e_nobn	.252
47	cvt_cvt-21-384-in1k_4_LucyV4	.252
48	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.252
49	voneresnet-50	.241
50	ReAlnet07	.241
51	AdvProp_efficientnet-b2	.241
52	AT_efficientnet-b2	.241
53	vit_relpos_base_patch16_clsgap_224:sw_in1k	.241
54	vonegrcnn_47e	.241
55	deit_large_imagenet_full_seed-0	.241
56	convnext_base_imagenet_full_seed-0	.241
57	ReAlnet08	.241
58	ReAlnet04	.231
59	deit_small_imagenet_full_seed-0	.231
60	vonegrcnn_52e_full	.231
61	voneresnet-50-non_stochastic	.220
62	mobilenet_v2_0_75_224	.220
63	convnext_small_imagenet_full_seed-0	.220
64	mobilenet_v2_1_0_224	.220
65	mobilenet_v2_0_5_224	.220
66	antialiased-r50	.220
67	cvt_cvt-21-224-in1k_4_LucyV4	.220
68	focalnet_tiny_lrf_in1k	.220
69	voneresnet-50-robust	.210
70	ReAlnet_incorrect	.210
71	nasnet_large	.210
72	ReAlnet02	.210
73	inception_v1	.210
74	resnet_101_v2	.210
75	convnext_tiny:in12k_ft_in1k	.210
76	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.210
77	AlexNet_SIN	.210
78	shufflenet_v2_x1_0	.210
79	convnext_tiny_imagenet_full_seed-0	.210
80	ReAlnet01	.210
81	effnetb1_272x240	.210
82	resnet50-SIN_IN	.199
83	inception_v3	.199
84	mobilenet_v2_1_0_160	.199
85	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.199
86	regnet_y_400mf	.199
87	BiT-S-R50x3	.199
88	cvt_cvt-13-384-in1k_4_LucyV4	.199
89	ReAlnet10	.189
90	custom_model_cv_18_dagger_408	.189
91	BiT-S-R152x4	.189
92	ReAlnet03	.189
93	ReAlnet09	.189
94	efficientnet_b2_imagenet_full	.189
95	nasnet_mobile	.189
96	antialias-resnet152	.178
97	convnext_femto_ols:d1_in1k	.178
98	resnet50-sup	.178
99	deit_base_imagenet_full_seed-0	.178
100	resnet50_imagenet_100_seed-0	.178
101	BiT-S-R50x1	.178
102	resnet_50_v1	.178
103	resnet50-vitoimagevidnet8	.178
104	grcnn	.178
105	densenet-121	.178
106	resnet50-barlow	.178
107	mobilenet_v2_1_0_192	.178
108	resnet50_tutorial	.178
109	resnet50-VITO-8deg-cc	.178
110	resnet152_imagenet_full	.178
111	ReAlnet06	.168
112	resnet50-SIN	.168
113	resnet_152_v1	.168
114	densenet-169	.168
115	deit_small_imagenet_100_seed-0	.168
116	alexnet_ks_torevert	.168
117	BiT-S-R101x3	.168
118	resnet50_eMMCR_Vanilla	.157
119	vgg_16	.157
120	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.147
121	efficientnet_b1_imagenet_full	.147
122	ReAlnet05	.147
123	cvt_cvt-13-224-in1k_4_LucyV4	.147
124	resnet50_imagenet_1_seed-0	.147
125	tv_efficientnet-b1	.147
126	resnet50_imagenet_full	.147
127	convnext_small_imagenet_100_seed-0	.136
128	bp_resnet50_julios	.136
129	alexnet_training_seed_03	.126
130	resnet101_imagenet_full	.126
131	mobilenet_v2_1_3_224	.126
132	convnext_tiny_sup	.126
133	pnasnet_large	.126
134	artResNet18_1	.126
135	alexnet_training_seed_04	.115
136	resnet34_imagenet_full	.115
137	resnet50-SIN_IN_IN	.115
138	xception	.115
139	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.115
140	efficientnet_b0	.115
141	resnet_SIN_IN_FT_IN	.115
142	BiT-S-R152x2	.115
143	resnet_50_v2	.115
144	deit_small_imagenet_1_seed-0	.105
145	resnet-152_v2_pytorch	.105
146	alexnet_training_seed_02	.105
147	alexnet_training_seed_06	.105
148	resnet_152_v2	.105
149	resnet50-moclr8deg	.105
150	alexnet_training_seed_09	.105
151	deit_small_imagenet_10_seed-0	.105
152	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.105
153	convnext_small_imagenet_10_seed-0	.105
154	ViT_L_32_imagenet1k	.105
155	alexnet_training_seed_05	.105
156	alexnet_training_seed_10	.105
157	resnet50-vicregl0p75	.105
158	resnet50-vicregl0p9	.105
159	pnasnet_large_pytorch	.105
160	alexnet_training_seed_08	.105
161	convnext_small_imagenet_1_seed-0	.105
162	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.105
163	vgg_19	.084
164	alexnet_training_seed_07	.073
165	mobilenet_v2_1-4_224_pytorch	.073
166	alexnet_training_seed_01	.073
167	resnet50_imagenet_10_seed-0	.073
168	mobilenet_v2_1_4_224	.073
169	resnet34_ecoset_full	X
170	omnivore_swinS	X
171	resnet18_ecoset_full	X
172	yudixie_resnet50_distance_rotation_0_240908	X
173	ReAlnet10_cornet	X
174	ReAlnet02_cornet	X
175	ReAlnet03_cornet	X
176	yudixie_resnet50_imagenet1kpret_0_240908	X
177	yudixie_resnet50_random_0_240908	X
178	yudixie_resnet50_distance_translation_rotation_0_240908	X
179	yudixie_resnet18_distance_translation_rotation_0_240719	X
180	SimVP	X
181	MIM	X
182	TAU	X
183	PredRNN	X
184	ConvLSTM	X
185	resnet18_imagenet21kP	X
186	antialiased-rnext101_32x8d	X
187	ReAlnet01_cornet	X
188	hmax	X
189	yolos_tiny	X
190	briaai_rmbg_1_4	X
191	yudixie_resnet50_object_class_0_240908	X
192	yudixie_resnet50_distance_reg_0_240908	X
193	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
194	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
195	ReAlnet06_cornet	X
196	yudixie_resnet18_random_0_240719	X
197	yudixie_resnet18_object_class_0_240719	X
198	yudixie_resnet18_rotation_reg_0_240719	X
199	resnet-18_test_m	X
200	yudixie_resnet18_imagenet1kpret_0_240719	X
201	yudixie_resnet18_translation_reg_0_240719	X
202	yudixie_resnet18_category_class_0_240719	X
203	yudixie_resnet18_distance_reg_0_240719	X
204	yudixie_resnet50_distance_translation_0_240908	X
205	efficientnet-b7	X
206	yudixie_resnet18_distance_rotation_0_240719	X
207	ReAlnet07_cornet	X
208	yudixie_resnet18_translation_rotation_0_240719	X
209	yudixie_resnet50_rotation_reg_0_240908	X
210	yudixie_resnet50_translation_rotation_0_240908	X
211	yudixie_resnet50_category_class_0_240908	X
212	ReAlnet08_cornet	X
213	ReAlnet05_cornet	X
214	VOneCORnet-S	X
215	omnivore_swinB	X
216	resnet50-vicreg	X
217	yudixie_resnet18_distance_translation_0_240719	X
218	resnet152_ecoset_full	X
219	yudixie_resnet50_translation_reg_0_240908	X
220	ReAlnet09_cornet	X
221	resnet101_ecoset_full	X
222	resnet50_ecoset_full	X
223	ReAlnet04_cornet	X

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: BMD2024.texture_2Behavioral

Metric: accuracy_distance