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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	resnet50-SIN	.337
12	convnext_large:fb_in22k_ft_in1k	.337
13	resnext101_32x8d_wsl	.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	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.250
21	regnet_y_400mf	.250
22	focalnet_tiny_lrf_in1k	.250
23	resnet50_ewconplus_wzc_seed6_2_323	.239
24	resnet50_st_wzc_seed7_2_323	.239
25	ReAlnet10	.239
26	ReAlnet05	.239
27	ReAlnet02	.239
28	resnet_SIN_IN_FT_IN	.239
29	resnet34_imagenet_full	.239
30	resnet50-SIN_IN_IN	.239
31	resnet50_st_wzc_noseed_2_323	.227
32	resnet50_ewconplus_wzc_seed7_2_323	.227
33	ReAlnet06	.227
34	convnext_small_imagenet_100_seed-0	.227
35	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.227
36	effnetb1_272x240	.227
37	resnet50_ewconplus_wzc_seed3_2_323	.216
38	mobilenet_v2_1_0_224	.216
39	grcnn_109	.216
40	ReAlnet09	.216
41	ReAlnet08	.216
42	ReAlnet07	.216
43	ReAlnet03	.216
44	ReAlnet04	.216
45	ReAlnet01	.216
46	voneresnet-50	.216
47	resnet50_robust_l2_eps1	.216
48	convnext_tiny_sup	.216
49	BiT-S-R50x3	.216
50	antialiased-r50	.216
51	convnext_large_imagenet_full_seed-0	.216
52	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.216
53	convnext_base_fb_in1k_gram_aligned_dinov3	.205
54	resnet50_st_wzc_2_323	.205
55	resnet50_ewconplus_wzc_seed2_2__323	.205
56	pnasnet_large_halfPrecision	.205
57	pnasnet_large	.205
58	vgg_19	.205
59	resnet50-SIN_IN	.205
60	vit_relpos_base_patch16_clsgap_224:sw_in1k	.205
61	AT_efficientnet-b2	.205
62	BiT-S-R152x4	.205
63	AdvProp_efficientnet-b2	.205
64	convnext_base_imagenet_full_seed-0	.205
65	cvt_cvt-13-224-in1k_4_LucyV4	.205
66	resnet50_st_wzc_seed5_2_323	.194
67	resnet50_st_wzc_seed6_2_323	.194
68	AlexNet_SIN_fov16	.194
69	mobilenet_v2_1_0_192	.194
70	convnext_tiny_imagenet_full_seed-0	.194
71	AlexNet_SIN	.194
72	resnet50_ewconplus_wzc_2_323	.182
73	resnet_50_v1_spiking	.182
74	resnet_101_v1	.182
75	resnet_50_v1	.182
76	grcnn	.182
77	voneresnet-50-robust	.182
78	resnet50-moclr8deg	.182
79	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.182
80	resnet50-sup	.182
81	convnext_small_imagenet_full_seed-0	.182
82	deit_small_imagenet_full_seed-0	.182
83	resnet50_tutorial	.182
84	resnet50_imagenet_full	.182
85	resnet50_st_wzc_seed4_2_323	.171
86	AlexNet_SIN_fov4	.171
87	nasnet_mobile	.171
88	shufflenet_v2_x1_0	.171
89	densenet-201	.171
90	BiT-S-R152x2	.171
91	AdvProp_efficientnet-b4	.171
92	resnet152_imagenet_full	.171
93	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.171
94	densenet_201_pytorch	.171
95	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.160
96	resnet_50_v1_spiking_l4a	.160
97	BiT-S-R101x1	.160
98	BiT-S-R101x3	.160
99	blt_vs	.160
100	efficientnet_b1_imagenet_full	.160
101	voneresnet-50-non_stochastic	.160
102	alexnet_ks_torevert	.160
103	alexnet	.160
104	mobilenet_v2_0_75_160	.149
105	AdvProp_efficientnet-b8	.149
106	resnet50_robust_l2_eps3	.149
107	imagenet_l2_3_0	.149
108	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.149
109	resnet18_imagenet_full	.149
110	pnasnet_large_pytorch	.149
111	resnet-50-robust	.149
112	convnext_base_fb_in1k	.137
113	resnet_50_v1_spiking_l4	.137
114	resnet_50_v2	.137
115	vgg_16	.137
116	inception_v4	.137
117	inception_v1	.137
118	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.137
119	convnext_tiny:in12k_ft_in1k	.137
120	convnext_femto_ols:d1_in1k	.137
121	deit_large_imagenet_full_seed-0	.137
122	deit_base_imagenet_full_seed-0	.137
123	efficientnet_b2_imagenet_full	.137
124	efficientnet_b0_imagenet_full	.137
125	cvt_cvt-13-384-in1k_4_LucyV4	.137
126	inception_v3_pytorch	.137
127	convnext_base_fb_in22k_ft_in1k	.126
128	resnet50_st_wzc_seed3_2_323	.126
129	resnet50_ewconplus_noseed_2_323	.126
130	alexnet_training_seed_01_fov16	.126
131	alexnet_fov16	.126
132	alexnet_fov4	.126
133	cv_18_dagger_408_pretrained	.126
134	resnet_50_v1_spiking_l3	.126
135	alexnet_training_seed_07	.126
136	alexnet_training_seed_10	.126
137	alexnet_training_seed_08	.126
138	alexnet_training_seed_04	.126
139	alexnet_training_seed_02	.126
140	alexnet_training_seed_06	.126
141	alexnet_training_seed_05	.126
142	alexnet_training_seed_09	.126
143	mobilenet_v2_1_0_160	.126
144	resnet_152_v1	.126
145	resnet_152_v2	.126
146	resnet_101_v2	.126
147	mobilenet_v2_0_75_192	.126
148	mobilenet_v2_0_5_224	.126
149	mobilenet_v2_0_5_192	.126
150	inception_v3	.126
151	vonegrcnn_62e_nobn	.126
152	resnet50-vitoimagevidnet8	.126
153	Res2Net50_26w_4s	.126
154	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.126
155	resnet50-VITO-8deg-cc	.126
156	efficientnet_b0	.126
157	vit_base_patch16_clip_224:openai_ft_in1k	.126
158	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.126
159	densenet-121	.126
160	densenet-169	.126
161	resnet50-vicregl0p9	.126
162	resnet50-vicregl0p75	.126
163	vonegrcnn_52e_full	.126
164	vonegrcnn_47e	.126
165	artResNet18_1	.126
166	convnext_small_imagenet_10_seed-0	.126
167	convnext_small_imagenet_1_seed-0	.126
168	deit_small_imagenet_1_seed-0	.126
169	deit_small_imagenet_10_seed-0	.126
170	resnet50_imagenet_100_seed-0	.126
171	resnet50_imagenet_10_seed-0	.126
172	resnet50_imagenet_1_seed-0	.126
173	cvt_cvt-21-384-in1k_4_LucyV4	.126
174	cvt_cvt-21-224-in1k_4_LucyV4	.126
175	resnet50_st_wzc_2	.126
176	resnet50_ewconplus_wzc	.126
177	resnet-152_v2_pytorch	.126
178	resnet50_eMMCR_Vanilla	.126
179	tv_efficientnet-b1	.126
180	bp_resnet50_julios	.126
181	AdvProp_efficientnet-b6	.126
182	custom_model_cv_18_dagger_408	.126
183	alexnet_training_seed_01_fov4	.115
184	alexnet_fov12	.115
185	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.115
186	BiT-S-R50x1	.115
187	resnet101_imagenet_full	.115
188	ViT_L_32_imagenet1k	.115
189	deit3_base_patch16_224_fb_in22k_ft_in1k	.104
190	alexnet_training_seed_01_fov12	.104
191	deit_small_imagenet_100_seed-0	.104
192	deit3_base_patch16_224_fb_in1k	.093
193	mobilenet_v2_1_3_224	.093
194	alexnet_training_seed_01	.093
195	nasnet_large	.093
196	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.093
197	AlexNet_SIN_fov12	.081
198	xception	.081
199	alexnet_training_seed_03	.081
200	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.081
201	mobilenet_v2_1_4_224	.036
202	resnet50-barlow	.036
203	mobilenet_v2_1-4_224_pytorch	.036
204	mobilenet_v2_0_75_224	.014
205	deepeisnn_ei_imagenet_resnet18
206	deepeisnn_ei_cifar10_resnet18
207	deepeisnn_ei_cifar10_vgg19
208	yudixie_wbh_object_class_scaling_0415_run_1
209	yudixie_wbh_object_class_scaling_0415_run_2
210	yudixie_wbh_object_class_scaling_0415_run_3
211	yudixie_wbh_object_class_scaling_0415_imagenet
212	yudixie_wbh_object_class_scaling_0415_run_0
213	predictive_coding_pc
214	pr1_stl10_disentangled_alpha04
215	resnet18_fair
216	vit_base_patch16_dinov3-lvd1689m
217	vit_base_patch14_reg4_dinov2-lvd142m
218	vit_base_patch14_dinov2-lvd142m
219	aughost_fair_resnet18
220	ReAlnet01_cornet_fov16
221	ReAlnet01_cornet_fov12
222	ReAlnet01_cornet_fov4
223	yudixie_resnet50_category_class_0_240908
224	yudixie_resnet18_imagenet1kpret_0_240719
225	resnet152_ecoset_full
226	ReAlnet10_cornet
227	ReAlnet03_ALL
228	ReAlnet05_cornet
229	compact_resnet50_robust_IT
230	compact_vgg19_IT
231	compact_cornets_IT
232	compact_cornets_V4
233	compact_resnet50_robust_V4
234	compact_vgg19_V4
235	compact_resnet50_IT
236	compact_resnet50_V4
237	ReAlnet10_ALL
238	ReAlnet09_ALL
239	ReAlnet08_ALL
240	ReAlnet07_ALL
241	ReAlnet06_ALL
242	ReAlnet05_ALL
243	ReAlnet04_ALL
244	ReAlnet02_ALL
245	ReAlnet01_ALL
246	resnet50_primary_visual_cortex
247	yolos_tiny
248	briaai_rmbg_1_4
249	yudixie_resnet50_distance_reg_0_240908
250	yudixie_resnet50_object_class_0_240908
251	yudixie_resnet50_cat_obj_class_all_latents_0_240908
252	yudixie_resnet50_imagenet1kpret_0_240908
253	yudixie_resnet50_random_0_240908
254	yudixie_resnet50_distance_translation_rotation_0_240908
255	ReAlnet09_cornet
256	ReAlnet08_cornet
257	ReAlnet07_cornet
258	ReAlnet06_cornet
259	ReAlnet04_cornet
260	ReAlnet02_cornet
261	ReAlnet03_cornet
262	ReAlnet01_cornet
263	yudixie_resnet50_translation_rotation_0_240908
264	yudixie_resnet50_rotation_reg_0_240908
265	yudixie_resnet50_distance_translation_0_240908
266	yudixie_resnet50_translation_reg_0_240908
267	yudixie_resnet50_distance_rotation_0_240908
268	resnet50-vicreg
269	efficientnet-b7
270	resnet-18_test_m
271	omnivore_swinS
272	omnivore_swinB
273	VOneCORnet-S
274	yudixie_resnet18_distance_reg_0_240719
275	yudixie_resnet18_translation_reg_0_240719
276	yudixie_resnet18_rotation_reg_0_240719
277	yudixie_resnet18_object_class_0_240719
278	yudixie_resnet18_distance_translation_rotation_0_240719
279	yudixie_resnet18_cat_obj_class_all_latents_0_240719
280	yudixie_resnet18_random_0_240719
281	yudixie_resnet18_category_class_0_240719
282	yudixie_resnet18_distance_rotation_0_240719
283	yudixie_resnet18_translation_rotation_0_240719
284	yudixie_resnet18_distance_translation_0_240719
285	resnet101_ecoset_full
286	resnet50_ecoset_full
287	resnet34_ecoset_full
288	resnet18_ecoset_full
289	MIM
290	TAU
291	SimVP
292	PredRNN
293	ConvLSTM
294	resnet18_imagenet21kP
295	antialiased-rnext101_32x8d
296	hmax

Benchmark bibtex

None

Ceiling

0.93.

Note that scores are relative to this ceiling.

Data: BMD2024.dotted_2Behavioral

Metric: accuracy_distance