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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_xxlarge_clip	.508
8	vit_huge_clip336_laion	.497
9	eva02_large_in22k	.465
10	convnext_base:clip_laiona_augreg_ft_in1k_384	.465
11	resnext101_32x48d_wsl	.454
12	vit_large_clip336_openai	.401
13	convnext_large_clip_laion2b_finelayers	.390
14	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.379
15	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.369
16	convnext_large_in22k_supervised	.347
17	resnet50-SIN	.337
18	convnext_large:fb_in22k_ft_in1k	.337
19	resnext101_32x8d_wsl	.337
20	resnext101_32x16d_wsl	.315
21	dinov2_vitl14_lc_res336	.304
22	convnext_xlarge:fb_in22k_ft_in1k	.294
23	resnext101_32x32d_wsl	.294
24	dinov2_vitl14_reg_lc	.283
25	dinov2_vitg14_reg_lc	.283
26	AdvProp_efficientnet-b7	.272
27	dinov2_vitg14_lc	.261
28	antialias-resnet152	.261
29	dinov2_vitl14_lc	.250
30	mobilenet_v2_1_0_128	.250
31	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.250
32	regnet_y_400mf	.250
33	focalnet_tiny_lrf_in1k	.250
34	resnet50_ewconplus_wzc_seed6_2_323	.239
35	resnet50_st_wzc_seed7_2_323	.239
36	ReAlnet10	.239
37	ReAlnet05	.239
38	ReAlnet02	.239
39	resnet_SIN_IN_FT_IN	.239
40	resnet34_imagenet_full	.239
41	resnet50-SIN_IN_IN	.239
42	ReAlnet06	.227
43	convnext_small_imagenet_100_seed-0	.227
44	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.227
45	effnetb1_272x240	.227
46	mobilenet_v2_1_0_224	.216
47	grcnn_109	.216
48	ReAlnet09	.216
49	ReAlnet08	.216
50	ReAlnet07	.216
51	ReAlnet03	.216
52	ReAlnet04	.216
53	ReAlnet01	.216
54	voneresnet-50	.216
55	resnet50_robust_l2_eps1	.216
56	convnext_tiny_sup	.216
57	BiT-S-R50x3	.216
58	antialiased-r50	.216
59	convnext_large_imagenet_full_seed-0	.216
60	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.216
61	convnext_base_fb_in1k_gram_aligned_dinov3	.205
62	resnet50_ewconplus_wzc_seed2_2__323	.205
63	pnasnet_large_halfPrecision	.205
64	pnasnet_large	.205
65	vgg_19	.205
66	resnet50-SIN_IN	.205
67	vit_relpos_base_patch16_clsgap_224:sw_in1k	.205
68	AT_efficientnet-b2	.205
69	BiT-S-R152x4	.205
70	AdvProp_efficientnet-b2	.205
71	convnext_base_imagenet_full_seed-0	.205
72	cvt_cvt-13-224-in1k_4_LucyV4	.205
73	resnet50_st_wzc_seed6_2_323	.194
74	AlexNet_SIN_fov16	.194
75	mobilenet_v2_1_0_192	.194
76	convnext_tiny_imagenet_full_seed-0	.194
77	AlexNet_SIN	.194
78	resnet_50_v1_spiking	.182
79	resnet_101_v1	.182
80	resnet_50_v1	.182
81	grcnn	.182
82	voneresnet-50-robust	.182
83	resnet50-moclr8deg	.182
84	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.182
85	resnet50-sup	.182
86	convnext_small_imagenet_full_seed-0	.182
87	deit_small_imagenet_full_seed-0	.182
88	resnet50_tutorial	.182
89	resnet50_imagenet_full	.182
90	AlexNet_SIN_fov4	.171
91	nasnet_mobile	.171
92	shufflenet_v2_x1_0	.171
93	densenet-201	.171
94	BiT-S-R152x2	.171
95	AdvProp_efficientnet-b4	.171
96	resnet152_imagenet_full	.171
97	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.171
98	densenet_201_pytorch	.171
99	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.160
100	resnet_50_v1_spiking_l4a	.160
101	BiT-S-R101x1	.160
102	BiT-S-R101x3	.160
103	blt_vs	.160
104	efficientnet_b1_imagenet_full	.160
105	resnet50_hvs_wzc	.160
106	voneresnet-50-non_stochastic	.160
107	alexnet_ks_torevert	.160
108	alexnet	.160
109	mobilenet_v2_0_75_160	.149
110	AdvProp_efficientnet-b8	.149
111	resnet50_robust_l2_eps3	.149
112	imagenet_l2_3_0	.149
113	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.149
114	resnet18_imagenet_full	.149
115	pnasnet_large_pytorch	.149
116	resnet-50-robust	.149
117	convnext_base_fb_in1k	.137
118	resnet_50_v1_spiking_l4	.137
119	resnet_50_v2	.137
120	vgg_16	.137
121	inception_v4	.137
122	inception_v1	.137
123	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.137
124	convnext_tiny:in12k_ft_in1k	.137
125	convnext_femto_ols:d1_in1k	.137
126	deit_large_imagenet_full_seed-0	.137
127	deit_base_imagenet_full_seed-0	.137
128	efficientnet_b2_imagenet_full	.137
129	efficientnet_b0_imagenet_full	.137
130	cvt_cvt-13-384-in1k_4_LucyV4	.137
131	inception_v3_pytorch	.137
132	convnext_base_fb_in22k_ft_in1k	.126
133	resnet50_st_wzc_seed3_2_323	.126
134	alexnet_training_seed_01_fov16	.126
135	alexnet_fov16	.126
136	alexnet_fov4	.126
137	cv_18_dagger_408_pretrained	.126
138	resnet_50_v1_spiking_l3	.126
139	alexnet_training_seed_07	.126
140	alexnet_training_seed_10	.126
141	alexnet_training_seed_08	.126
142	alexnet_training_seed_04	.126
143	alexnet_training_seed_02	.126
144	alexnet_training_seed_06	.126
145	alexnet_training_seed_05	.126
146	alexnet_training_seed_09	.126
147	mobilenet_v2_1_0_160	.126
148	resnet_152_v1	.126
149	resnet_152_v2	.126
150	resnet_101_v2	.126
151	mobilenet_v2_0_75_192	.126
152	mobilenet_v2_0_5_224	.126
153	mobilenet_v2_0_5_192	.126
154	inception_v3	.126
155	vonegrcnn_62e_nobn	.126
156	resnet50-vitoimagevidnet8	.126
157	Res2Net50_26w_4s	.126
158	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.126
159	resnet50-VITO-8deg-cc	.126
160	efficientnet_b0	.126
161	vit_base_patch16_clip_224:openai_ft_in1k	.126
162	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.126
163	densenet-121	.126
164	densenet-169	.126
165	resnet50-vicregl0p9	.126
166	resnet50-vicregl0p75	.126
167	vonegrcnn_52e_full	.126
168	vonegrcnn_47e	.126
169	artResNet18_1	.126
170	convnext_small_imagenet_10_seed-0	.126
171	convnext_small_imagenet_1_seed-0	.126
172	deit_small_imagenet_1_seed-0	.126
173	deit_small_imagenet_10_seed-0	.126
174	resnet50_imagenet_100_seed-0	.126
175	resnet50_imagenet_10_seed-0	.126
176	resnet50_imagenet_1_seed-0	.126
177	cvt_cvt-21-384-in1k_4_LucyV4	.126
178	cvt_cvt-21-224-in1k_4_LucyV4	.126
179	resnet50_st_wzc_2	.126
180	resnet50_ewconplus_wzc	.126
181	resnet-152_v2_pytorch	.126
182	resnet50_eMMCR_Vanilla	.126
183	tv_efficientnet-b1	.126
184	bp_resnet50_julios	.126
185	AdvProp_efficientnet-b6	.126
186	custom_model_cv_18_dagger_408	.126
187	alexnet_training_seed_01_fov4	.115
188	alexnet_fov12	.115
189	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.115
190	BiT-S-R50x1	.115
191	resnet101_imagenet_full	.115
192	ViT_L_32_imagenet1k	.115
193	deit3_base_patch16_224_fb_in22k_ft_in1k	.104
194	alexnet_training_seed_01_fov12	.104
195	deit_small_imagenet_100_seed-0	.104
196	deit3_base_patch16_224_fb_in1k	.093
197	mobilenet_v2_1_3_224	.093
198	alexnet_training_seed_01	.093
199	nasnet_large	.093
200	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.093
201	AlexNet_SIN_fov12	.081
202	xception	.081
203	alexnet_training_seed_03	.081
204	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.081
205	mobilenet_v2_1_4_224	.036
206	resnet50-barlow	.036
207	mobilenet_v2_1-4_224_pytorch	.036
208	mobilenet_v2_0_75_224	.014
209	dinov2_vitl14_reg_lc_featread
210	deepeisnn_ei_imagenet_resnet18
211	deepeisnn_ei_cifar10_resnet18
212	deepeisnn_ei_cifar10_vgg19
213	yudixie_wbh_object_class_scaling_0415_run_1
214	yudixie_wbh_object_class_scaling_0415_run_2
215	yudixie_wbh_object_class_scaling_0415_run_3
216	yudixie_wbh_object_class_scaling_0415_imagenet
217	yudixie_wbh_object_class_scaling_0415_run_0
218	predictive_coding_pc
219	pr1_stl10_disentangled_alpha04
220	resnet18_fair
221	vit_base_patch16_dinov3-lvd1689m
222	vit_base_patch14_reg4_dinov2-lvd142m
223	vit_base_patch14_dinov2-lvd142m
224	aughost_fair_resnet18
225	ReAlnet01_cornet_fov16
226	ReAlnet01_cornet_fov12
227	ReAlnet01_cornet_fov4
228	yudixie_resnet50_category_class_0_240908
229	yudixie_resnet18_imagenet1kpret_0_240719
230	resnet152_ecoset_full
231	ReAlnet10_cornet
232	ReAlnet03_ALL
233	ReAlnet05_cornet
234	compact_resnet50_robust_IT
235	compact_vgg19_IT
236	compact_cornets_IT
237	compact_cornets_V4
238	compact_resnet50_robust_V4
239	compact_vgg19_V4
240	compact_resnet50_IT
241	compact_resnet50_V4
242	ReAlnet10_ALL
243	ReAlnet09_ALL
244	ReAlnet08_ALL
245	ReAlnet07_ALL
246	ReAlnet06_ALL
247	ReAlnet05_ALL
248	ReAlnet04_ALL
249	ReAlnet02_ALL
250	ReAlnet01_ALL
251	resnet50_primary_visual_cortex
252	yolos_tiny
253	briaai_rmbg_1_4
254	yudixie_resnet50_distance_reg_0_240908
255	yudixie_resnet50_object_class_0_240908
256	yudixie_resnet50_cat_obj_class_all_latents_0_240908
257	yudixie_resnet50_imagenet1kpret_0_240908
258	yudixie_resnet50_random_0_240908
259	yudixie_resnet50_distance_translation_rotation_0_240908
260	ReAlnet09_cornet
261	ReAlnet08_cornet
262	ReAlnet07_cornet
263	ReAlnet06_cornet
264	ReAlnet04_cornet
265	ReAlnet02_cornet
266	ReAlnet03_cornet
267	ReAlnet01_cornet
268	yudixie_resnet50_translation_rotation_0_240908
269	yudixie_resnet50_rotation_reg_0_240908
270	yudixie_resnet50_distance_translation_0_240908
271	yudixie_resnet50_translation_reg_0_240908
272	yudixie_resnet50_distance_rotation_0_240908
273	resnet50-vicreg
274	efficientnet-b7
275	resnet-18_test_m
276	omnivore_swinS
277	omnivore_swinB
278	VOneCORnet-S
279	yudixie_resnet18_distance_reg_0_240719
280	yudixie_resnet18_translation_reg_0_240719
281	yudixie_resnet18_rotation_reg_0_240719
282	yudixie_resnet18_object_class_0_240719
283	yudixie_resnet18_distance_translation_rotation_0_240719
284	yudixie_resnet18_cat_obj_class_all_latents_0_240719
285	yudixie_resnet18_random_0_240719
286	yudixie_resnet18_category_class_0_240719
287	yudixie_resnet18_distance_rotation_0_240719
288	yudixie_resnet18_translation_rotation_0_240719
289	yudixie_resnet18_distance_translation_0_240719
290	resnet101_ecoset_full
291	resnet50_ecoset_full
292	resnet34_ecoset_full
293	resnet18_ecoset_full
294	MIM
295	TAU
296	SimVP
297	PredRNN
298	ConvLSTM
299	resnet18_imagenet21kP
300	antialiased-rnext101_32x8d
301	hmax

Benchmark bibtex

None

Ceiling

0.93.

Note that scores are relative to this ceiling.

Data: BMD2024.dotted_2Behavioral

Metric: accuracy_distance