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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	regnety_032	.374
28	convnext_xlarge:fb_in22k_ft_in1k	.354
29	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.354
30	densenet-201	.343
31	densenet_201_pytorch	.343
32	resnet50-SIN	.322
33	grcnn_109	.312
34	convnext_base_imagenet_full_seed-0	.312
35	AT_efficientnet-b2	.302
36	BiT-S-R152x2	.302
37	AdvProp_efficientnet-b2	.302
38	AdvProp_efficientnet-b4	.302
39	AdvProp_efficientnet-b7	.291
40	resnet_101_v1	.281
41	effnetb1_272x240	.281
42	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.270
43	focalnet_tiny_lrf_in1k	.260
44	convnext_base_fb_in22k_ft_in1k	.250
45	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.250
46	regnet_y_400mf	.250
47	AdvProp_efficientnet-b6	.250
48	ReAlnet04	.239
49	convnext_large_imagenet_full_seed-0	.239
50	resnet101_imagenet_full	.239
51	AlexNet_SIN	.239
52	resnet_50_v1_spiking_l4a	.229
53	resnet_50_v1	.229
54	ReAlnet07	.229
55	ReAlnet03	.229
56	ReAlnet01	.229
57	voneresnet-50	.229
58	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.229
59	AdvProp_efficientnet-b8	.229
60	resnet50-sup	.229
61	convnext_small_imagenet_full_seed-0	.229
62	resnet50_tutorial	.229
63	resnet152_imagenet_full	.229
64	resnet50_imagenet_full	.229
65	resnet34_imagenet_full	.229
66	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.229
67	antialias-resnet152	.229
68	resnet50_ewconplus_wzc_seed2_2__323	.218
69	Res2Net50_26w_4s	.218
70	resnet50-SIN_IN_IN	.218
71	resnet_SIN_IN_FT_IN	.218
72	blt_vs	.218
73	ReAlnet09	.208
74	ReAlnet06	.208
75	ReAlnet05	.208
76	ReAlnet02	.208
77	resnet50-SIN_IN	.208
78	resnet50_robust_l2_eps1	.208
79	convnext_small_imagenet_100_seed-0	.208
80	mobilenet_v2_1_0_192	.198
81	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.198
82	AlexNet_SIN_fov4	.187
83	resnet_50_v1_spiking	.187
84	mobilenet_v2_0_75_192	.187
85	ReAlnet10	.187
86	voneresnet-50-robust	.187
87	BiT-S-R152x4	.187
88	BiT-S-R50x3	.187
89	antialiased-r50	.187
90	alexnet_ks_torevert	.187
91	alexnet	.187
92	deit3_base_patch16_224_fb_in22k_ft_in1k	.177
93	resnet50_st_wzc_seed3_2_323	.177
94	mobilenet_v2_1_0_128	.177
95	nasnet_mobile	.177
96	ReAlnet08	.177
97	resnet50-moclr8deg	.177
98	densenet-121	.177
99	efficientnet_b0_imagenet_full	.177
100	resnet18_imagenet_full	.177
101	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.177
102	seresnext50_32x4d	.166
103	pnasnet_large_halfPrecision	.166
104	pnasnet_large	.166
105	resnet_152_v1	.166
106	vit_base_patch16_clip_224:openai_ft_in1k	.166
107	cvt_cvt-13-224-in1k_4_LucyV4	.166
108	resnet50_eMMCR_Vanilla	.166
109	convnext_base_fb_in1k_gram_aligned_dinov3	.156
110	alexnet_fov4	.156
111	resnet50_hvs_wzc	.156
112	inception_v4	.156
113	inception_v3	.156
114	resnet50-vitoimagevidnet8	.156
115	resnet50-VITO-8deg-cc	.156
116	convnext_tiny:in12k_ft_in1k	.156
117	densenet-169	.156
118	BiT-S-R101x3	.156
119	voneresnet-50-non_stochastic	.156
120	resnet50_ewconplus_wzc_seed5_2__323	.146
121	AlexNet_SIN_fov16	.146
122	resnet50_ewconplus_wzc	.146
123	mobilenet_v2_1_3_224	.146
124	mobilenet_v2_1_0_224	.146
125	nasnet_large	.146
126	mobilenet_v2_0_75_160	.146
127	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.146
128	deit_small_imagenet_full_seed-0	.146
129	cvt_cvt-13-384-in1k_4_LucyV4	.146
130	pnasnet_large_pytorch	.146
131	resnet50_st_wzc_2	.135
132	mobilenet_v2_0_5_192	.135
133	grcnn	.135
134	convnext_tiny_sup	.135
135	convnext_tiny_imagenet_full_seed-0	.135
136	tv_efficientnet-b1	.135
137	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.125
138	convnext_base_fb_in1k	.125
139	alexnet_training_seed_03	.125
140	inception_v1	.125
141	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.125
142	vit_relpos_base_patch16_clsgap_224:sw_in1k	.125
143	BiT-S-R101x1	.125
144	cvt_cvt-21-384-in1k_4_LucyV4	.125
145	inception_v3_pytorch	.125
146	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
147	ViT_L_32_imagenet1k	.125
148	resnet_50_v1_spiking_l4	.114
149	resnet_50_v2	.114
150	mobilenet_v2_0_5_224	.114
151	shufflenet_v2_x1_0	.114
152	resnet50-vicregl0p75	.114
153	BiT-S-R50x1	.114
154	deit_small_imagenet_100_seed-0	.114
155	deit_large_imagenet_full_seed-0	.114
156	resnet50_imagenet_100_seed-0	.114
157	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.104
158	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.104
159	alexnet_training_seed_01_fov16	.104
160	alexnet_fov16	.104
161	cv_18_dagger_408_pretrained	.104
162	resnet_50_v1_spiking_l3	.104
163	alexnet_training_seed_08	.104
164	alexnet_training_seed_10	.104
165	alexnet_training_seed_04	.104
166	alexnet_training_seed_02	.104
167	alexnet_training_seed_06	.104
168	alexnet_training_seed_05	.104
169	alexnet_training_seed_09	.104
170	resnet_152_v2	.104
171	resnet_101_v2	.104
172	vgg_16	.104
173	vonegrcnn_62e_nobn	.104
174	efficientnet_b0	.104
175	resnet50_robust_l2_eps3	.104
176	imagenet_l2_3_0	.104
177	vonegrcnn_52e_full	.104
178	vonegrcnn_47e	.104
179	convnext_small_imagenet_10_seed-0	.104
180	convnext_small_imagenet_1_seed-0	.104
181	deit_small_imagenet_1_seed-0	.104
182	deit_small_imagenet_10_seed-0	.104
183	deit_base_imagenet_full_seed-0	.104
184	efficientnet_b2_imagenet_full	.104
185	resnet50_imagenet_10_seed-0	.104
186	resnet50_imagenet_1_seed-0	.104
187	cvt_cvt-21-224-in1k_4_LucyV4	.104
188	resnet50-barlow	.104
189	resnet-152_v2_pytorch	.104
190	bp_resnet50_julios	.104
191	resnet-50-robust	.104
192	custom_model_cv_18_dagger_408	.104
193	alexnet_fov12	.094
194	mobilenet_v2_1_0_160	.094
195	mobilenet_v2_0_75_224	.094
196	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.094
197	artResNet18_1	.094
198	alexnet_training_seed_01_fov4	.083
199	mobilenet_v2_1_4_224	.083
200	alexnet_training_seed_07	.083
201	vgg_19	.083
202	convnext_femto_ols:d1_in1k	.083
203	resnet50-vicregl0p9	.083
204	efficientnet_b1_imagenet_full	.083
205	mobilenet_v2_1-4_224_pytorch	.083
206	AlexNet_SIN_fov12	.073
207	alexnet_training_seed_01	.073
208	alexnet_training_seed_01_fov12	.062
209	xception	.042
210	gabor_filter_mean_abs_4x4_v2
211	gabor_filter_entropy_4x4_v2_forcedCosine
212	gabor_filter_entropy_4x4_v2
213	gabor_filter_entropy_4x4
214	dinov2_vitl14_reg_lc_featread
215	deepeisnn_ei_imagenet_resnet18
216	deepeisnn_ei_cifar10_resnet18
217	deepeisnn_ei_cifar10_vgg19
218	yudixie_wbh_object_class_scaling_0415_run_1
219	yudixie_wbh_object_class_scaling_0415_run_2
220	yudixie_wbh_object_class_scaling_0415_imagenet
221	yudixie_wbh_object_class_scaling_0415_run_3
222	yudixie_wbh_object_class_scaling_0415_run_0
223	predictive_coding_pc
224	resnet18_fair
225	vit_base_patch14_reg4_dinov2-lvd142m
226	vit_base_patch16_dinov3-lvd1689m
227	vit_base_patch14_dinov2-lvd142m
228	ReAlnet01_cornet_fov16
229	ReAlnet01_cornet_fov12
230	ReAlnet01_cornet_fov4
231	yudixie_resnet18_category_class_0_240719
232	yolos_tiny
233	resnet50_ecoset_full
234	ReAlnet07_ALL
235	ReAlnet03_ALL
236	resnet101_ecoset_full
237	compact_resnet50_robust_IT
238	compact_cornets_IT
239	compact_cornets_V4
240	compact_vgg19_IT
241	compact_vgg19_V4
242	compact_resnet50_robust_V4
243	compact_resnet50_IT
244	compact_resnet50_V4
245	ReAlnet10_ALL
246	ReAlnet09_ALL
247	ReAlnet08_ALL
248	ReAlnet06_ALL
249	ReAlnet05_ALL
250	ReAlnet04_ALL
251	ReAlnet02_ALL
252	ReAlnet01_ALL
253	resnet50_primary_visual_cortex
254	briaai_rmbg_1_4
255	yudixie_resnet50_distance_reg_0_240908
256	yudixie_resnet50_category_class_0_240908
257	yudixie_resnet50_object_class_0_240908
258	yudixie_resnet50_distance_translation_rotation_0_240908
259	yudixie_resnet50_random_0_240908
260	yudixie_resnet50_imagenet1kpret_0_240908
261	yudixie_resnet50_cat_obj_class_all_latents_0_240908
262	ReAlnet10_cornet
263	ReAlnet09_cornet
264	ReAlnet08_cornet
265	ReAlnet07_cornet
266	ReAlnet06_cornet
267	ReAlnet05_cornet
268	ReAlnet04_cornet
269	ReAlnet03_cornet
270	ReAlnet02_cornet
271	ReAlnet01_cornet
272	yudixie_resnet50_translation_rotation_0_240908
273	yudixie_resnet50_distance_translation_0_240908
274	yudixie_resnet50_translation_reg_0_240908
275	yudixie_resnet50_rotation_reg_0_240908
276	yudixie_resnet50_distance_rotation_0_240908
277	efficientnet-b7
278	resnet-18_test_m
279	omnivore_swinS
280	omnivore_swinB
281	VOneCORnet-S
282	yudixie_resnet18_distance_reg_0_240719
283	yudixie_resnet18_translation_reg_0_240719
284	yudixie_resnet18_imagenet1kpret_0_240719
285	yudixie_resnet18_rotation_reg_0_240719
286	yudixie_resnet18_object_class_0_240719
287	yudixie_resnet18_distance_translation_rotation_0_240719
288	yudixie_resnet18_cat_obj_class_all_latents_0_240719
289	yudixie_resnet18_random_0_240719
290	yudixie_resnet18_distance_rotation_0_240719
291	yudixie_resnet18_translation_rotation_0_240719
292	yudixie_resnet18_distance_translation_0_240719
293	resnet152_ecoset_full
294	resnet34_ecoset_full
295	resnet18_ecoset_full
296	resnet50-vicreg
297	antialiased-rnext101_32x8d
298	MIM
299	TAU
300	SimVP
301	PredRNN
302	ConvLSTM
303	resnet18_imagenet21kP
304	hmax

Benchmark bibtex

None

Ceiling

0.98.

Note that scores are relative to this ceiling.

Data: BMD2024.dotted_1Behavioral

Metric: accuracy_distance