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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_large_patch14_clip_336:laion2b_ft_in1k	.655
8	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.655
9	resnext101_32x8d_wsl	.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	resnet50_ewconplus_wzc_seed7_2_323	.302
24	AT_efficientnet-b2	.302
25	BiT-S-R152x2	.302
26	AdvProp_efficientnet-b2	.302
27	AdvProp_efficientnet-b4	.302
28	resnet50_st_wzc_noseed_2_323	.291
29	AdvProp_efficientnet-b7	.291
30	resnet_101_v1	.281
31	effnetb1_272x240	.281
32	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.270
33	focalnet_tiny_lrf_in1k	.260
34	convnext_base_fb_in22k_ft_in1k	.250
35	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.250
36	regnet_y_400mf	.250
37	AdvProp_efficientnet-b6	.250
38	resnet50_st_wzc_seed5_2_323	.239
39	ReAlnet04	.239
40	convnext_large_imagenet_full_seed-0	.239
41	resnet101_imagenet_full	.239
42	AlexNet_SIN	.239
43	resnet50_ewconplus_wzc_2_323	.229
44	resnet_50_v1_spiking_l4a	.229
45	resnet_50_v1	.229
46	ReAlnet07	.229
47	ReAlnet03	.229
48	ReAlnet01	.229
49	voneresnet-50	.229
50	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.229
51	AdvProp_efficientnet-b8	.229
52	resnet50-sup	.229
53	convnext_small_imagenet_full_seed-0	.229
54	resnet50_tutorial	.229
55	resnet152_imagenet_full	.229
56	resnet50_imagenet_full	.229
57	resnet34_imagenet_full	.229
58	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.229
59	antialias-resnet152	.229
60	resnet50_ewconplus_wzc_seed2_2__323	.218
61	Res2Net50_26w_4s	.218
62	resnet50-SIN_IN_IN	.218
63	resnet_SIN_IN_FT_IN	.218
64	blt_vs	.218
65	ReAlnet09	.208
66	ReAlnet06	.208
67	ReAlnet05	.208
68	ReAlnet02	.208
69	resnet50-SIN_IN	.208
70	resnet50_robust_l2_eps1	.208
71	convnext_small_imagenet_100_seed-0	.208
72	mobilenet_v2_1_0_192	.198
73	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.198
74	AlexNet_SIN_fov4	.187
75	resnet_50_v1_spiking	.187
76	mobilenet_v2_0_75_192	.187
77	ReAlnet10	.187
78	voneresnet-50-robust	.187
79	BiT-S-R152x4	.187
80	BiT-S-R50x3	.187
81	antialiased-r50	.187
82	alexnet_ks_torevert	.187
83	alexnet	.187
84	deit3_base_patch16_224_fb_in22k_ft_in1k	.177
85	resnet50_st_wzc_seed3_2_323	.177
86	mobilenet_v2_1_0_128	.177
87	nasnet_mobile	.177
88	ReAlnet08	.177
89	resnet50-moclr8deg	.177
90	densenet-121	.177
91	efficientnet_b0_imagenet_full	.177
92	resnet18_imagenet_full	.177
93	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.177
94	pnasnet_large_halfPrecision	.166
95	pnasnet_large	.166
96	resnet_152_v1	.166
97	vit_base_patch16_clip_224:openai_ft_in1k	.166
98	cvt_cvt-13-224-in1k_4_LucyV4	.166
99	resnet50_eMMCR_Vanilla	.166
100	convnext_base_fb_in1k_gram_aligned_dinov3	.156
101	alexnet_fov4	.156
102	inception_v4	.156
103	inception_v3	.156
104	resnet50-vitoimagevidnet8	.156
105	resnet50-VITO-8deg-cc	.156
106	convnext_tiny:in12k_ft_in1k	.156
107	densenet-169	.156
108	BiT-S-R101x3	.156
109	voneresnet-50-non_stochastic	.156
110	resnet50_ewconplus_wzc_seed5_2__323	.146
111	AlexNet_SIN_fov16	.146
112	resnet50_ewconplus_wzc	.146
113	mobilenet_v2_1_3_224	.146
114	mobilenet_v2_1_0_224	.146
115	nasnet_large	.146
116	mobilenet_v2_0_75_160	.146
117	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.146
118	deit_small_imagenet_full_seed-0	.146
119	cvt_cvt-13-384-in1k_4_LucyV4	.146
120	pnasnet_large_pytorch	.146
121	resnet50_st_wzc_2	.135
122	mobilenet_v2_0_5_192	.135
123	grcnn	.135
124	convnext_tiny_sup	.135
125	convnext_tiny_imagenet_full_seed-0	.135
126	tv_efficientnet-b1	.135
127	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.125
128	convnext_base_fb_in1k	.125
129	alexnet_training_seed_03	.125
130	inception_v1	.125
131	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.125
132	vit_relpos_base_patch16_clsgap_224:sw_in1k	.125
133	BiT-S-R101x1	.125
134	cvt_cvt-21-384-in1k_4_LucyV4	.125
135	inception_v3_pytorch	.125
136	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
137	ViT_L_32_imagenet1k	.125
138	resnet_50_v1_spiking_l4	.114
139	resnet_50_v2	.114
140	mobilenet_v2_0_5_224	.114
141	shufflenet_v2_x1_0	.114
142	resnet50-vicregl0p75	.114
143	BiT-S-R50x1	.114
144	deit_small_imagenet_100_seed-0	.114
145	deit_large_imagenet_full_seed-0	.114
146	resnet50_imagenet_100_seed-0	.114
147	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.104
148	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.104
149	resnet50_ewconplus_noseed_2_323	.104
150	alexnet_training_seed_01_fov16	.104
151	alexnet_fov16	.104
152	cv_18_dagger_408_pretrained	.104
153	resnet_50_v1_spiking_l3	.104
154	alexnet_training_seed_08	.104
155	alexnet_training_seed_10	.104
156	alexnet_training_seed_04	.104
157	alexnet_training_seed_02	.104
158	alexnet_training_seed_06	.104
159	alexnet_training_seed_05	.104
160	alexnet_training_seed_09	.104
161	resnet_152_v2	.104
162	resnet_101_v2	.104
163	vgg_16	.104
164	vonegrcnn_62e_nobn	.104
165	efficientnet_b0	.104
166	resnet50_robust_l2_eps3	.104
167	imagenet_l2_3_0	.104
168	vonegrcnn_52e_full	.104
169	vonegrcnn_47e	.104
170	convnext_small_imagenet_10_seed-0	.104
171	convnext_small_imagenet_1_seed-0	.104
172	deit_small_imagenet_1_seed-0	.104
173	deit_small_imagenet_10_seed-0	.104
174	deit_base_imagenet_full_seed-0	.104
175	efficientnet_b2_imagenet_full	.104
176	resnet50_imagenet_10_seed-0	.104
177	resnet50_imagenet_1_seed-0	.104
178	cvt_cvt-21-224-in1k_4_LucyV4	.104
179	resnet50-barlow	.104
180	resnet-152_v2_pytorch	.104
181	bp_resnet50_julios	.104
182	resnet-50-robust	.104
183	custom_model_cv_18_dagger_408	.104
184	alexnet_fov12	.094
185	mobilenet_v2_1_0_160	.094
186	mobilenet_v2_0_75_224	.094
187	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.094
188	artResNet18_1	.094
189	alexnet_training_seed_01_fov4	.083
190	mobilenet_v2_1_4_224	.083
191	alexnet_training_seed_07	.083
192	vgg_19	.083
193	convnext_femto_ols:d1_in1k	.083
194	resnet50-vicregl0p9	.083
195	efficientnet_b1_imagenet_full	.083
196	mobilenet_v2_1-4_224_pytorch	.083
197	AlexNet_SIN_fov12	.073
198	alexnet_training_seed_01	.073
199	alexnet_training_seed_01_fov12	.062
200	xception	.042
201	deepeisnn_ei_imagenet_resnet18
202	deepeisnn_ei_cifar10_resnet18
203	deepeisnn_ei_cifar10_vgg19
204	yudixie_wbh_object_class_scaling_0415_run_1
205	yudixie_wbh_object_class_scaling_0415_run_2
206	yudixie_wbh_object_class_scaling_0415_imagenet
207	yudixie_wbh_object_class_scaling_0415_run_3
208	yudixie_wbh_object_class_scaling_0415_run_0
209	predictive_coding_pc
210	resnet18_fair
211	vit_base_patch14_reg4_dinov2-lvd142m
212	vit_base_patch16_dinov3-lvd1689m
213	vit_base_patch14_dinov2-lvd142m
214	aughost_fair_resnet18
215	ReAlnet01_cornet_fov16
216	ReAlnet01_cornet_fov12
217	ReAlnet01_cornet_fov4
218	yudixie_resnet18_category_class_0_240719
219	yolos_tiny
220	resnet50_ecoset_full
221	ReAlnet07_ALL
222	ReAlnet03_ALL
223	resnet101_ecoset_full
224	compact_resnet50_robust_IT
225	compact_cornets_IT
226	compact_cornets_V4
227	compact_vgg19_IT
228	compact_vgg19_V4
229	compact_resnet50_robust_V4
230	compact_resnet50_IT
231	compact_resnet50_V4
232	ReAlnet10_ALL
233	ReAlnet09_ALL
234	ReAlnet08_ALL
235	ReAlnet06_ALL
236	ReAlnet05_ALL
237	ReAlnet04_ALL
238	ReAlnet02_ALL
239	ReAlnet01_ALL
240	resnet50_primary_visual_cortex
241	briaai_rmbg_1_4
242	yudixie_resnet50_distance_reg_0_240908
243	yudixie_resnet50_category_class_0_240908
244	yudixie_resnet50_object_class_0_240908
245	yudixie_resnet50_distance_translation_rotation_0_240908
246	yudixie_resnet50_random_0_240908
247	yudixie_resnet50_imagenet1kpret_0_240908
248	yudixie_resnet50_cat_obj_class_all_latents_0_240908
249	ReAlnet10_cornet
250	ReAlnet09_cornet
251	ReAlnet08_cornet
252	ReAlnet07_cornet
253	ReAlnet06_cornet
254	ReAlnet05_cornet
255	ReAlnet04_cornet
256	ReAlnet03_cornet
257	ReAlnet02_cornet
258	ReAlnet01_cornet
259	yudixie_resnet50_translation_rotation_0_240908
260	yudixie_resnet50_distance_translation_0_240908
261	yudixie_resnet50_translation_reg_0_240908
262	yudixie_resnet50_rotation_reg_0_240908
263	yudixie_resnet50_distance_rotation_0_240908
264	efficientnet-b7
265	resnet-18_test_m
266	omnivore_swinS
267	omnivore_swinB
268	VOneCORnet-S
269	yudixie_resnet18_distance_reg_0_240719
270	yudixie_resnet18_translation_reg_0_240719
271	yudixie_resnet18_imagenet1kpret_0_240719
272	yudixie_resnet18_rotation_reg_0_240719
273	yudixie_resnet18_object_class_0_240719
274	yudixie_resnet18_distance_translation_rotation_0_240719
275	yudixie_resnet18_cat_obj_class_all_latents_0_240719
276	yudixie_resnet18_random_0_240719
277	yudixie_resnet18_distance_rotation_0_240719
278	yudixie_resnet18_translation_rotation_0_240719
279	yudixie_resnet18_distance_translation_0_240719
280	resnet152_ecoset_full
281	resnet34_ecoset_full
282	resnet18_ecoset_full
283	resnet50-vicreg
284	antialiased-rnext101_32x8d
285	MIM
286	TAU
287	SimVP
288	PredRNN
289	ConvLSTM
290	resnet18_imagenet21kP
291	hmax

Benchmark bibtex

None

Ceiling

0.98.

Note that scores are relative to this ceiling.

Data: BMD2024.dotted_1Behavioral

Metric: accuracy_distance