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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("BMD2024.dotted_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	.991
2	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.765
3	vit_large_patch14_clip_224:openai_ft_in1k	.626
4	vit_large_patch14_clip_224:laion2b_ft_in1k	.615
5	vit_large_patch14_clip_336:laion2b_ft_in1k	.583
6	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.561
7	convnext_base:clip_laiona_augreg_ft_in1k_384	.465
8	resnext101_32x48d_wsl	.454
9	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.379
10	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.369
11	resnext101_32x8d_wsl	.337
12	resnet50-SIN	.337
13	convnext_large:fb_in22k_ft_in1k	.337
14	resnext101_32x16d_wsl	.315
15	convnext_xlarge:fb_in22k_ft_in1k	.294
16	resnext101_32x32d_wsl	.294
17	AdvProp_efficientnet-b7	.272
18	antialias-resnet152	.261
19	mobilenet_v2_1_0_128	.250
20	focalnet_tiny_lrf_in1k	.250
21	regnet_y_400mf	.250
22	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.250
23	ReAlnet05	.239
24	resnet34_imagenet_full	.239
25	ReAlnet10	.239
26	resnet50-SIN_IN_IN	.239
27	resnet_SIN_IN_FT_IN	.239
28	ReAlnet02	.239
29	ReAlnet06	.227
30	effnetb1_272x240	.227
31	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.227
32	convnext_small_imagenet_100_seed-0	.227
33	ReAlnet07	.216
34	ReAlnet08	.216
35	ReAlnet09	.216
36	convnext_large_imagenet_full_seed-0	.216
37	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.216
38	mobilenet_v2_1_0_224	.216
39	voneresnet-50	.216
40	ReAlnet01	.216
41	grcnn_109	.216
42	ReAlnet04	.216
43	resnet50_robust_l2_eps1	.216
44	antialiased-r50	.216
45	BiT-S-R50x3	.216
46	convnext_tiny_sup	.216
47	ReAlnet03	.216
48	convnext_base_imagenet_full_seed-0	.205
49	cvt_cvt-13-224-in1k_4_LucyV4	.205
50	resnet50-SIN_IN	.205
51	vit_relpos_base_patch16_clsgap_224:sw_in1k	.205
52	pnasnet_large	.205
53	vgg_19	.205
54	AdvProp_efficientnet-b2	.205
55	BiT-S-R152x4	.205
56	AT_efficientnet-b2	.205
57	mobilenet_v2_1_0_192	.194
58	AlexNet_SIN	.194
59	convnext_tiny_imagenet_full_seed-0	.194
60	resnet_50_v1_spiking	.182
61	resnet50_tutorial	.182
62	resnet50_imagenet_full	.182
63	convnext_small_imagenet_full_seed-0	.182
64	deit_small_imagenet_full_seed-0	.182
65	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.182
66	grcnn	.182
67	resnet50-moclr8deg	.182
68	voneresnet-50-robust	.182
69	resnet_50_v1	.182
70	resnet50-sup	.182
71	resnet_101_v1	.182
72	densenet-201	.171
73	densenet_201_pytorch	.171
74	resnet152_imagenet_full	.171
75	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.171
76	shufflenet_v2_x1_0	.171
77	nasnet_mobile	.171
78	AdvProp_efficientnet-b4	.171
79	BiT-S-R152x2	.171
80	blt_vs	.160
81	resnet_50_v1_spiking_l4a	.160
82	alexnet_ks_torevert	.160
83	voneresnet-50-non_stochastic	.160
84	efficientnet_b1_imagenet_full	.160
85	BiT-S-R101x3	.160
86	BiT-S-R101x1	.160
87	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.149
88	mobilenet_v2_0_75_160	.149
89	resnet-50-robust	.149
90	pnasnet_large_pytorch	.149
91	resnet18_imagenet_full	.149
92	imagenet_l2_3_0	.149
93	resnet50_robust_l2_eps3	.149
94	AdvProp_efficientnet-b8	.149
95	resnet_50_v2	.137
96	resnet_50_v1_spiking_l4	.137
97	convnext_tiny:in12k_ft_in1k	.137
98	efficientnet_b0_imagenet_full	.137
99	inception_v3_pytorch	.137
100	efficientnet_b2_imagenet_full	.137
101	deit_large_imagenet_full_seed-0	.137
102	cvt_cvt-13-384-in1k_4_LucyV4	.137
103	deit_base_imagenet_full_seed-0	.137
104	inception_v4	.137
105	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.137
106	vgg_16	.137
107	inception_v1	.137
108	convnext_femto_ols:d1_in1k	.137
109	resnet50_imagenet_10_seed-0	.126
110	alexnet_training_seed_09	.126
111	alexnet_training_seed_05	.126
112	resnet50_ewconplus_wzc	.126
113	alexnet_training_seed_06	.126
114	inception_v3	.126
115	cvt_cvt-21-224-in1k_4_LucyV4	.126
116	alexnet_training_seed_02	.126
117	mobilenet_v2_1_0_160	.126
118	mobilenet_v2_0_5_192	.126
119	alexnet_training_seed_04	.126
120	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.126
121	alexnet_training_seed_08	.126
122	mobilenet_v2_0_5_224	.126
123	convnext_small_imagenet_1_seed-0	.126
124	alexnet_training_seed_10	.126
125	resnet50_imagenet_100_seed-0	.126
126	resnet50_imagenet_1_seed-0	.126
127	custom_model_cv_18_dagger_408	.126
128	AdvProp_efficientnet-b6	.126
129	bp_resnet50_julios	.126
130	tv_efficientnet-b1	.126
131	resnet50_eMMCR_Vanilla	.126
132	mobilenet_v2_0_75_192	.126
133	resnet-152_v2_pytorch	.126
134	convnext_small_imagenet_10_seed-0	.126
135	deit_small_imagenet_10_seed-0	.126
136	alexnet_training_seed_07	.126
137	vit_base_patch16_clip_224:openai_ft_in1k	.126
138	cvt_cvt-21-384-in1k_4_LucyV4	.126
139	efficientnet_b0	.126
140	Res2Net50_26w_4s	.126
141	resnet50-VITO-8deg-cc	.126
142	deit_small_imagenet_1_seed-0	.126
143	resnet_50_v1_spiking_l3	.126
144	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.126
145	resnet_101_v2	.126
146	resnet50-vitoimagevidnet8	.126
147	vonegrcnn_62e_nobn	.126
148	artResNet18_1	.126
149	resnet_152_v2	.126
150	vonegrcnn_47e	.126
151	vonegrcnn_52e_full	.126
152	resnet50-vicregl0p75	.126
153	resnet50-vicregl0p9	.126
154	densenet-169	.126
155	densenet-121	.126
156	resnet_152_v1	.126
157	resnet101_imagenet_full	.115
158	ViT_L_32_imagenet1k	.115
159	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.115
160	BiT-S-R50x1	.115
161	deit_small_imagenet_100_seed-0	.104
162	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.093
163	alexnet_training_seed_01	.093
164	nasnet_large	.093
165	mobilenet_v2_1_3_224	.093
166	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.081
167	xception	.081
168	alexnet_training_seed_03	.081
169	mobilenet_v2_1-4_224_pytorch	.036
170	resnet50-barlow	.036
171	mobilenet_v2_1_4_224	.036
172	mobilenet_v2_0_75_224	.014
173	yudixie_resnet50_cat_obj_class_all_latents_0_240908
174	yudixie_resnet50_object_class_0_240908
175	yudixie_resnet50_category_class_0_240908
176	yudixie_resnet50_distance_reg_0_240908
177	briaai_rmbg_1_4
178	ReAlnet01_cornet
179	hmax
180	antialiased-rnext101_32x8d
181	resnet18_imagenet21kP
182	ConvLSTM
183	PredRNN
184	SimVP
185	TAU
186	yolos_tiny
187	MIM
188	compact_resnet50_V4
189	ReAlnet03_cornet
190	ReAlnet02_cornet
191	yudixie_resnet50_distance_rotation_0_240908
192	resnet18_ecoset_full
193	compact_resnet50_IT
194	resnet34_ecoset_full
195	resnet50_ecoset_full
196	resnet101_ecoset_full
197	ReAlnet04_cornet
198	resnet152_ecoset_full
199	compact_vgg19_V4
200	yudixie_resnet50_translation_reg_0_240908
201	yudixie_resnet18_distance_translation_0_240719
202	yudixie_resnet18_translation_rotation_0_240719
203	yudixie_resnet18_distance_rotation_0_240719
204	yudixie_resnet50_distance_translation_0_240908
205	yudixie_resnet50_rotation_reg_0_240908
206	yudixie_resnet18_category_class_0_240719
207	resnet-18_test_m
208	ReAlnet05_cornet
209	yudixie_resnet18_random_0_240719
210	yudixie_resnet18_cat_obj_class_all_latents_0_240719
211	yudixie_resnet18_distance_translation_rotation_0_240719
212	yudixie_resnet18_object_class_0_240719
213	yudixie_resnet18_rotation_reg_0_240719
214	yudixie_resnet18_imagenet1kpret_0_240719
215	yudixie_resnet18_translation_reg_0_240719
216	yudixie_resnet18_distance_reg_0_240719
217	efficientnet-b7
218	compact_resnet50_robust_V4
219	ReAlnet06_cornet
220	ReAlnet01_ALL
221	yudixie_resnet50_translation_rotation_0_240908
222	ReAlnet07_cornet
223	ReAlnet02_ALL
224	resnet50-vicreg
225	ReAlnet08_cornet
226	VOneCORnet-S
227	omnivore_swinB
228	compact_cornets_V4
229	ReAlnet04_ALL
230	ReAlnet03_ALL
231	ReAlnet09_cornet
232	compact_cornets_IT
233	omnivore_swinS
234	ReAlnet05_ALL
235	compact_vgg19_IT
236	ReAlnet06_ALL
237	ReAlnet10_cornet
238	yudixie_resnet50_distance_translation_rotation_0_240908
239	compact_resnet50_robust_IT
240	ReAlnet07_ALL
241	yudixie_resnet50_random_0_240908
242	ReAlnet08_ALL
243	ReAlnet09_ALL
244	yudixie_resnet50_imagenet1kpret_0_240908
245	resnet50_primary_visual_cortex
246	ReAlnet10_ALL

Benchmark bibtex

None

Ceiling

0.93.

Note that scores are relative to this ceiling.

Data: BMD2024.dotted_2Behavioral

Metric: accuracy_distance