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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	dinov2_vitg14_reg_lc	.863
4	convnext_xxlarge_clip	.822
5	vit_large_patch14_clip_224:openai_ft_in1k	.801
6	resnext101_32x48d_wsl	.770
7	vit_large_patch14_clip_224:laion2b_ft_in1k	.759
8	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.697
9	convnext_large_clip_laion2b_finelayers	.666
10	vit_large_patch14_clip_336:laion2b_ft_in1k	.655
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.655
12	resnext101_32x8d_wsl	.655
13	dinov2_vitl14_reg_lc	.624
14	dinov2_vitg14_lc	.614
15	vit_huge_clip336_laion	.603
16	eva02_large_in22k	.603
17	resnext101_32x16d_wsl	.603
18	convnext_base:clip_laiona_augreg_ft_in1k_384	.582
19	dinov2_vitl14_lc_res336	.551
20	vit_large_clip336_openai	.510
21	resnext101_32x32d_wsl	.510
22	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.478
23	convnext_large:fb_in22k_ft_in1k	.447
24	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.437
25	dinov2_vitl14_lc	.426
26	convnext_large_in22k_supervised	.385
27	convnext_xlarge:fb_in22k_ft_in1k	.354
28	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.354
29	densenet-201	.343
30	densenet_201_pytorch	.343
31	resnet50-SIN	.322
32	grcnn_109	.312
33	convnext_base_imagenet_full_seed-0	.312
34	AT_efficientnet-b2	.302
35	BiT-S-R152x2	.302
36	AdvProp_efficientnet-b2	.302
37	AdvProp_efficientnet-b4	.302
38	AdvProp_efficientnet-b7	.291
39	resnet_101_v1	.281
40	effnetb1_272x240	.281
41	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.270
42	focalnet_tiny_lrf_in1k	.260
43	convnext_base_fb_in22k_ft_in1k	.250
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.250
45	regnet_y_400mf	.250
46	AdvProp_efficientnet-b6	.250
47	ReAlnet04	.239
48	convnext_large_imagenet_full_seed-0	.239
49	resnet101_imagenet_full	.239
50	AlexNet_SIN	.239
51	resnet_50_v1_spiking_l4a	.229
52	resnet_50_v1	.229
53	ReAlnet07	.229
54	ReAlnet03	.229
55	ReAlnet01	.229
56	voneresnet-50	.229
57	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.229
58	AdvProp_efficientnet-b8	.229
59	resnet50-sup	.229
60	convnext_small_imagenet_full_seed-0	.229
61	resnet50_tutorial	.229
62	resnet152_imagenet_full	.229
63	resnet50_imagenet_full	.229
64	resnet34_imagenet_full	.229
65	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.229
66	antialias-resnet152	.229
67	resnet50_ewconplus_wzc_seed2_2__323	.218
68	Res2Net50_26w_4s	.218
69	resnet50-SIN_IN_IN	.218
70	resnet_SIN_IN_FT_IN	.218
71	blt_vs	.218
72	ReAlnet09	.208
73	ReAlnet06	.208
74	ReAlnet05	.208
75	ReAlnet02	.208
76	resnet50-SIN_IN	.208
77	resnet50_robust_l2_eps1	.208
78	convnext_small_imagenet_100_seed-0	.208
79	mobilenet_v2_1_0_192	.198
80	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.198
81	AlexNet_SIN_fov4	.187
82	resnet_50_v1_spiking	.187
83	mobilenet_v2_0_75_192	.187
84	ReAlnet10	.187
85	voneresnet-50-robust	.187
86	BiT-S-R152x4	.187
87	BiT-S-R50x3	.187
88	antialiased-r50	.187
89	alexnet_ks_torevert	.187
90	alexnet	.187
91	deit3_base_patch16_224_fb_in22k_ft_in1k	.177
92	resnet50_st_wzc_seed3_2_323	.177
93	mobilenet_v2_1_0_128	.177
94	nasnet_mobile	.177
95	ReAlnet08	.177
96	resnet50-moclr8deg	.177
97	densenet-121	.177
98	efficientnet_b0_imagenet_full	.177
99	resnet18_imagenet_full	.177
100	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.177
101	pnasnet_large_halfPrecision	.166
102	pnasnet_large	.166
103	resnet_152_v1	.166
104	vit_base_patch16_clip_224:openai_ft_in1k	.166
105	cvt_cvt-13-224-in1k_4_LucyV4	.166
106	resnet50_eMMCR_Vanilla	.166
107	convnext_base_fb_in1k_gram_aligned_dinov3	.156
108	alexnet_fov4	.156
109	resnet50_hvs_wzc	.156
110	inception_v4	.156
111	inception_v3	.156
112	resnet50-vitoimagevidnet8	.156
113	resnet50-VITO-8deg-cc	.156
114	convnext_tiny:in12k_ft_in1k	.156
115	densenet-169	.156
116	BiT-S-R101x3	.156
117	voneresnet-50-non_stochastic	.156
118	resnet50_ewconplus_wzc_seed5_2__323	.146
119	AlexNet_SIN_fov16	.146
120	resnet50_ewconplus_wzc	.146
121	mobilenet_v2_1_3_224	.146
122	mobilenet_v2_1_0_224	.146
123	nasnet_large	.146
124	mobilenet_v2_0_75_160	.146
125	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.146
126	deit_small_imagenet_full_seed-0	.146
127	cvt_cvt-13-384-in1k_4_LucyV4	.146
128	pnasnet_large_pytorch	.146
129	resnet50_st_wzc_2	.135
130	mobilenet_v2_0_5_192	.135
131	grcnn	.135
132	convnext_tiny_sup	.135
133	convnext_tiny_imagenet_full_seed-0	.135
134	tv_efficientnet-b1	.135
135	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.125
136	convnext_base_fb_in1k	.125
137	alexnet_training_seed_03	.125
138	inception_v1	.125
139	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.125
140	vit_relpos_base_patch16_clsgap_224:sw_in1k	.125
141	BiT-S-R101x1	.125
142	cvt_cvt-21-384-in1k_4_LucyV4	.125
143	inception_v3_pytorch	.125
144	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
145	ViT_L_32_imagenet1k	.125
146	resnet_50_v1_spiking_l4	.114
147	resnet_50_v2	.114
148	mobilenet_v2_0_5_224	.114
149	shufflenet_v2_x1_0	.114
150	resnet50-vicregl0p75	.114
151	BiT-S-R50x1	.114
152	deit_small_imagenet_100_seed-0	.114
153	deit_large_imagenet_full_seed-0	.114
154	resnet50_imagenet_100_seed-0	.114
155	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.104
156	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.104
157	alexnet_training_seed_01_fov16	.104
158	alexnet_fov16	.104
159	cv_18_dagger_408_pretrained	.104
160	resnet_50_v1_spiking_l3	.104
161	alexnet_training_seed_08	.104
162	alexnet_training_seed_10	.104
163	alexnet_training_seed_04	.104
164	alexnet_training_seed_02	.104
165	alexnet_training_seed_06	.104
166	alexnet_training_seed_05	.104
167	alexnet_training_seed_09	.104
168	resnet_152_v2	.104
169	resnet_101_v2	.104
170	vgg_16	.104
171	vonegrcnn_62e_nobn	.104
172	efficientnet_b0	.104
173	resnet50_robust_l2_eps3	.104
174	imagenet_l2_3_0	.104
175	vonegrcnn_52e_full	.104
176	vonegrcnn_47e	.104
177	convnext_small_imagenet_10_seed-0	.104
178	convnext_small_imagenet_1_seed-0	.104
179	deit_small_imagenet_1_seed-0	.104
180	deit_small_imagenet_10_seed-0	.104
181	deit_base_imagenet_full_seed-0	.104
182	efficientnet_b2_imagenet_full	.104
183	resnet50_imagenet_10_seed-0	.104
184	resnet50_imagenet_1_seed-0	.104
185	cvt_cvt-21-224-in1k_4_LucyV4	.104
186	resnet50-barlow	.104
187	resnet-152_v2_pytorch	.104
188	bp_resnet50_julios	.104
189	resnet-50-robust	.104
190	custom_model_cv_18_dagger_408	.104
191	alexnet_fov12	.094
192	mobilenet_v2_1_0_160	.094
193	mobilenet_v2_0_75_224	.094
194	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.094
195	artResNet18_1	.094
196	alexnet_training_seed_01_fov4	.083
197	mobilenet_v2_1_4_224	.083
198	alexnet_training_seed_07	.083
199	vgg_19	.083
200	convnext_femto_ols:d1_in1k	.083
201	resnet50-vicregl0p9	.083
202	efficientnet_b1_imagenet_full	.083
203	mobilenet_v2_1-4_224_pytorch	.083
204	AlexNet_SIN_fov12	.073
205	alexnet_training_seed_01	.073
206	alexnet_training_seed_01_fov12	.062
207	xception	.042
208	dinov2_vitl14_reg_lc_featread
209	deepeisnn_ei_imagenet_resnet18
210	deepeisnn_ei_cifar10_resnet18
211	deepeisnn_ei_cifar10_vgg19
212	yudixie_wbh_object_class_scaling_0415_run_1
213	yudixie_wbh_object_class_scaling_0415_run_2
214	yudixie_wbh_object_class_scaling_0415_imagenet
215	yudixie_wbh_object_class_scaling_0415_run_3
216	yudixie_wbh_object_class_scaling_0415_run_0
217	predictive_coding_pc
218	resnet18_fair
219	vit_base_patch14_reg4_dinov2-lvd142m
220	vit_base_patch16_dinov3-lvd1689m
221	vit_base_patch14_dinov2-lvd142m
222	aughost_fair_resnet18
223	ReAlnet01_cornet_fov16
224	ReAlnet01_cornet_fov12
225	ReAlnet01_cornet_fov4
226	yudixie_resnet18_category_class_0_240719
227	yolos_tiny
228	resnet50_ecoset_full
229	ReAlnet07_ALL
230	ReAlnet03_ALL
231	resnet101_ecoset_full
232	compact_resnet50_robust_IT
233	compact_cornets_IT
234	compact_cornets_V4
235	compact_vgg19_IT
236	compact_vgg19_V4
237	compact_resnet50_robust_V4
238	compact_resnet50_IT
239	compact_resnet50_V4
240	ReAlnet10_ALL
241	ReAlnet09_ALL
242	ReAlnet08_ALL
243	ReAlnet06_ALL
244	ReAlnet05_ALL
245	ReAlnet04_ALL
246	ReAlnet02_ALL
247	ReAlnet01_ALL
248	resnet50_primary_visual_cortex
249	briaai_rmbg_1_4
250	yudixie_resnet50_distance_reg_0_240908
251	yudixie_resnet50_category_class_0_240908
252	yudixie_resnet50_object_class_0_240908
253	yudixie_resnet50_distance_translation_rotation_0_240908
254	yudixie_resnet50_random_0_240908
255	yudixie_resnet50_imagenet1kpret_0_240908
256	yudixie_resnet50_cat_obj_class_all_latents_0_240908
257	ReAlnet10_cornet
258	ReAlnet09_cornet
259	ReAlnet08_cornet
260	ReAlnet07_cornet
261	ReAlnet06_cornet
262	ReAlnet05_cornet
263	ReAlnet04_cornet
264	ReAlnet03_cornet
265	ReAlnet02_cornet
266	ReAlnet01_cornet
267	yudixie_resnet50_translation_rotation_0_240908
268	yudixie_resnet50_distance_translation_0_240908
269	yudixie_resnet50_translation_reg_0_240908
270	yudixie_resnet50_rotation_reg_0_240908
271	yudixie_resnet50_distance_rotation_0_240908
272	efficientnet-b7
273	resnet-18_test_m
274	omnivore_swinS
275	omnivore_swinB
276	VOneCORnet-S
277	yudixie_resnet18_distance_reg_0_240719
278	yudixie_resnet18_translation_reg_0_240719
279	yudixie_resnet18_imagenet1kpret_0_240719
280	yudixie_resnet18_rotation_reg_0_240719
281	yudixie_resnet18_object_class_0_240719
282	yudixie_resnet18_distance_translation_rotation_0_240719
283	yudixie_resnet18_cat_obj_class_all_latents_0_240719
284	yudixie_resnet18_random_0_240719
285	yudixie_resnet18_distance_rotation_0_240719
286	yudixie_resnet18_translation_rotation_0_240719
287	yudixie_resnet18_distance_translation_0_240719
288	resnet152_ecoset_full
289	resnet34_ecoset_full
290	resnet18_ecoset_full
291	resnet50-vicreg
292	antialiased-rnext101_32x8d
293	MIM
294	TAU
295	SimVP
296	PredRNN
297	ConvLSTM
298	resnet18_imagenet21kP
299	hmax

Benchmark bibtex

None

Ceiling

0.98.

Note that scores are relative to this ceiling.

Data: BMD2024.dotted_1Behavioral

Metric: accuracy_distance