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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Maniquet2024-confusion_similarity")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	deepeisnn_ei_imagenet_resnet18	1.0
2	ReAlnet09_ALL	1.0
3	ReAlnet10_ALL	1.0
4	ReAlnet04_ALL	1.0
5	ReAlnet03_ALL	1.0
6	ReAlnet01_ALL	1.0
7	ReAlnet08_ALL	1.0
8	yolos_tiny	1.0
9	ReAlnet07_ALL	1.0
10	ReAlnet06_ALL	1.0
11	ReAlnet02_ALL	.994
12	resnet_50_v1_spiking	.955
13	ReAlnet05_ALL	.946
14	resnet_50_v1_spiking_l3	.944
15	predictive_coding_pc	.928
16	resnet50-sup	.928
17	resnet_50_v1	.928
18	mobilenet_v2_0_75_160	.928
19	shufflenet_v2_x1_0	.917
20	mobilenet_v2_0_5_192	.896
21	mobilenet_v2_0_5_224	.893
22	mobilenet_v2_1_0_128	.892
23	alexnet_fov4	.890
24	mobilenet_v2_0_75_192	.886
25	yudixie_wbh_object_class_scaling_0415_imagenet	.886
26	resnet_50_v1_spiking_l4	.878
27	ReAlnet07_cornet	.873
28	ReAlnet07	.873
29	deepeisnn_ei_cifar10_resnet18	.867
30	resnet_SIN_IN_FT_IN	.858
31	deit_small_imagenet_100_seed-0	.850
32	ReAlnet09_cornet	.832
33	ReAlnet09	.832
34	ReAlnet10_cornet	.832
35	ReAlnet10	.832
36	resnet_101_v1	.831
37	vit_large_patch14_clip_224:openai_ft_in1k	.826
38	vonegrcnn_62e_nobn	.823
39	pnasnet_large_halfPrecision	.822
40	resnet_50_v1_spiking_l4a	.821
41	yudixie_resnet18_random_0_240719	.820
42	ReAlnet01_cornet_fov4	.818
43	ReAlnet08_cornet	.813
44	ReAlnet08	.813
45	ReAlnet01_cornet	.808
46	ReAlnet01	.808
47	VOneCORnet-S	.804
48	vonegrcnn_47e	.803
49	ReAlnet03_cornet	.798
50	ReAlnet03	.798
51	densenet_201_pytorch	.794
52	vonegrcnn_52e_full	.793
53	resnet50-SIN_IN_IN	.785
54	resnet-18_test_m	.763
55	ReAlnet06_cornet	.759
56	ReAlnet06	.759
57	antialias-resnet152	.753
58	ReAlnet02_cornet	.753
59	ReAlnet02	.753
60	ReAlnet05_cornet	.751
61	ReAlnet05	.751
62	yudixie_resnet18_imagenet1kpret_0_240719	.748
63	yudixie_resnet50_imagenet1kpret_0_240908	.743
64	ReAlnet04_cornet	.738
65	ReAlnet04	.738
66	mobilenet_v2_1_0_160	.737
67	resnet50_tutorial	.736
68	vgg_19	.729
69	compact_cornets_V4	.710
70	grcnn	.708
71	effnetb1_272x240	.708
72	compact_cornets_IT	.695
73	resnet50_st_wzc_seed6_2_323	.689
74	resnet50_imagenet_100_seed-0	.679
75	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.678
76	mobilenet_v2_1_0_224	.677
77	deit_small_imagenet_full_seed-0	.669
78	resnet50_primary_visual_cortex	.668
79	compact_resnet50_robust_IT	.665
80	convnext_xlarge:fb_in22k_ft_in1k	.665
81	antialiased-r50	.662
82	resnet50_robust_l2_eps1	.651
83	vgg_16	.645
84	convnext_tiny:in12k_ft_in1k	.640
85	yudixie_wbh_object_class_scaling_0415_run_1	.634
86	mobilenet_v2_1_4_224	.632
87	mobilenet_v2_1-4_224_pytorch	.632
88	Res2Net50_26w_4s	.630
89	artResNet18_1	.626
90	yudixie_wbh_object_class_scaling_0415_run_2	.622
91	convnext_large_in22k_supervised	.607
92	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.607
93	yudixie_resnet50_random_0_240908	.603
94	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.600
95	yudixie_resnet18_category_class_0_240719	.598
96	ReAlnet01_cornet_fov12	.596
97	deit3_base_patch16_224_fb_in22k_ft_in1k	.587
98	yudixie_resnet50_object_class_0_240908	.586
99	resnet50_ewconplus_wzc_seed2_2__323	.583
100	alexnet_training_seed_03	.571
101	alexnet_training_seed_01_fov4	.569
102	ReAlnet01_cornet_fov16	.568
103	deepeisnn_ei_cifar10_vgg19	.565
104	resnet50_st_wzc_seed3_2_323	.564
105	yudixie_resnet50_distance_translation_rotation_0_240908	.562
106	alexnet_training_seed_01_fov16	.562
107	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.562
108	yudixie_wbh_object_class_scaling_0415_run_3	.561
109	resnet18_fair	.560
110	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.559
111	resnet50_hvs_wzc	.556
112	convnext_small_imagenet_10_seed-0	.555
113	alexnet_training_seed_10	.555
114	convnext_large:fb_in22k_ft_in1k	.553
115	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.553
116	resnet50_imagenet_full	.552
117	resnet18_ecoset_full	.547
118	inception_v4	.546
119	yudixie_wbh_object_class_scaling_0415_run_0	.541
120	mobilenet_v2_1_0_192	.541
121	vit_base_patch16_clip_224:openai_ft_in1k	.540
122	resnet18_imagenet21kP	.538
123	voneresnet-50	.535
124	regnet_y_400mf	.533
125	deit_small_imagenet_1_seed-0	.533
126	alexnet_training_seed_01	.530
127	voneresnet-50-non_stochastic	.529
128	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.524
129	resnet50-vitoimagevidnet8	.520
130	resnet50-VITO-8deg-cc	.520
131	alexnet_training_seed_07	.516
132	nasnet_large	.513
133	efficientnet_b2_imagenet_full	.508
134	yudixie_resnet18_translation_rotation_0_240719	.508
135	resnet50_ewconplus_wzc_seed6_2_323	.502
136	resnet50_st_wzc_2	.502
137	alexnet_training_seed_08	.502
138	resnet_152_v1	.500
139	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.498
140	yudixie_resnet50_category_class_0_240908	.498
141	resnet50_ewconplus_wzc	.497
142	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.496
143	deit_base_imagenet_full_seed-0	.496
144	resnet152_imagenet_full	.495
145	alexnet_training_seed_01_fov12	.494
146	BiT-S-R50x3	.490
147	resnet50_imagenet_10_seed-0	.490
148	alexnet_training_seed_09	.488
149	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.487
150	resnet50_st_wzc_seed7_2_323	.486
151	yudixie_resnet50_distance_rotation_0_240908	.486
152	AlexNet_SIN_fov4	.482
153	compact_vgg19_IT	.482
154	convnext_femto_ols:d1_in1k	.480
155	compact_resnet50_V4	.475
156	resnet50-SIN_IN	.473
157	alexnet_training_seed_06	.473
158	vit_relpos_base_patch16_clsgap_224:sw_in1k	.472
159	persistence_memory	.465
160	alexnet_ks_torevert	.460
161	alexnet	.460
162	compact_vgg19_V4	.459
163	alexnet_fov16	.456
164	compact_resnet50_robust_V4	.454
165	persistence_mem	.453
166	efficientnet_b0_imagenet_full	.453
167	AlexNet_SIN_fov16	.453
168	mobilenet_v2_1_3_224	.452
169	alexnet_training_seed_04	.450
170	alexnet_fov12	.448
171	alexnet_training_seed_05	.444
172	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.441
173	vonealexnet	.440
174	nasnet_mobile	.438
175	briaai_rmbg_1_4	.437
176	densenet-201	.436
177	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.436
178	convnext_small_imagenet_100_seed-0	.435
179	AlexNet_SIN_fov12	.431
180	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.430
181	deit_large_imagenet_full_seed-0	.428
182	cvt_cvt-21-384-in1k_4_LucyV4	.424
183	yudixie_resnet18_rotation_reg_0_240719	.419
184	yudixie_resnet18_object_class_0_240719	.418
185	alexnet_training_seed_02	.418
186	vit_large_patch14_clip_224:laion2b_ft_in1k	.416
187	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
188	resnet_152_v2	.410
189	densenet-169	.409
190	resnet34_ecoset_full	.407
191	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.407
192	resnet18_imagenet_full	.406
193	convnext_base_fb_in22k_ft_in1k	.406
194	densenet-121	.403
195	convnext_small_imagenet_full_seed-0	.395
196	xception	.392
197	pr1_stl10_disentangled_alpha04	.387
198	pnasnet_large_pytorch	.381
199	convnext_large_imagenet_full_seed-0	.375
200	resnet50-SIN	.371
201	resnet50_ewconplus_wzc_seed5_2__323	.367
202	deit_small_imagenet_10_seed-0	.365
203	yudixie_resnet50_distance_translation_0_240908	.365
204	resnet101_imagenet_full	.362
205	resnet50_imagenet_1_seed-0	.358
206	BiT-S-R101x3	.355
207	inception_v1	.351
208	AT_efficientnet-b2	.348
209	AdvProp_efficientnet-b2	.348
210	resnext101_32x16d_wsl	.346
211	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.345
212	imagenet_l2_3_0	.341
213	resnet50_robust_l2_eps3	.341
214	yudixie_resnet50_translation_rotation_0_240908	.341
215	cvt_cvt-13-224-in1k_4_LucyV4	.340
216	yudixie_resnet18_distance_rotation_0_240719	.337
217	yudixie_resnet18_translation_reg_0_240719	.326
218	tv_efficientnet-b1	.326
219	yudixie_resnet50_translation_reg_0_240908	.324
220	inception_v3	.324
221	yudixie_resnet18_distance_translation_rotation_0_240719	.323
222	AlexNet_SIN	.322
223	convnext_large_clip_laion2b_finelayers	.317
224	AdvProp_efficientnet-b8	.315
225	resnet152_ecoset_full	.314
226	compact_resnet50_IT	.312
227	resnet-50-robust	.305
228	yudixie_resnet18_distance_translation_0_240719	.302
229	resnet101_ecoset_full	.298
230	convnext_base_fb_in1k	.293
231	efficientnet_b1_imagenet_full	.289
232	resnet50_ecoset_full	.287
233	blt_vs	.284
234	AdvProp_efficientnet-b7	.281
235	dinov2_vitl14_reg_lc	.280
236	yudixie_resnet50_distance_reg_0_240908	.280
237	efficientnet-b7	.277
238	convnext_base:clip_laiona_augreg_ft_in1k_384	.270
239	BiT-S-R101x1	.270
240	inception_v3_pytorch	.262
241	convnext_base_fb_in1k_gram_aligned_dinov3	.258
242	dinov2_vitl14_lc	.257
243	cvt_cvt-13-384-in1k_4_LucyV4	.255
244	cv_18_dagger_408_pretrained	.249
245	AdvProp_efficientnet-b4	.247
246	resnet_101_v2	.239
247	voneresnet-50-robust	.238
248	yudixie_resnet50_rotation_reg_0_240908	.232
249	grcnn_109	.227
250	resnext101_32x32d_wsl	.220
251	resnet-152_v2_pytorch	.212
252	dinov2_vitg14_lc	.209
253	AdvProp_efficientnet-b6	.206
254	efficientnet_b0	.197
255	BiT-S-R50x1	.195
256	resnext101_32x8d_wsl	.195
257	convnext_tiny_imagenet_full_seed-0	.194
258	resnet34_imagenet_full	.192
259	resnet_50_v2	.189
260	convnext_small_imagenet_1_seed-0	.186
261	vit_large_patch14_clip_336:laion2b_ft_in1k	.183
262	BiT-S-R152x2	.175
263	yudixie_resnet18_distance_reg_0_240719	.167
264	resnet50_eMMCR_Vanilla	.164
265	vit_base_patch16_dinov3-lvd1689m	.163
266	convnext_base_imagenet_full_seed-0	.162
267	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.156
268	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.138
269	resnet50-vicregl0p9	.123
270	resnext101_32x48d_wsl	.106
271	cvt_cvt-21-224-in1k_4_LucyV4	.100
272	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.098
273	focalnet_tiny_lrf_in1k	.092
274	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.085
275	deit3_base_patch16_224_fb_in1k	.065
276	convnext_tiny_sup	.062
277	resnet50-vicreg	.019
278	resnet50-vicregl0p75	.000
279	resnet50-moclr8deg	.000
280	resnet50-barlow	.000
281	dinov2_vitl14_reg_lc_featread
282	dinov2_vitl14_lc_res336
283	eva02_large_in22k
284	dinov2_vitg14_reg_lc
285	vit_base_patch14_reg4_dinov2-lvd142m
286	vit_base_patch14_dinov2-lvd142m
287	pnasnet_large
288	mobilenet_v2_0_75_224
289	vit_huge_clip336_laion
290	convnext_xxlarge_clip
291	vit_large_clip336_openai
292	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
293	omnivore_swinS
294	omnivore_swinB
295	convnext_xxlarge:clip_laion2b_soup_ft_in1k
296	BiT-S-R152x4
297	ViT_L_32_imagenet1k
298	MIM
299	TAU
300	SimVP
301	PredRNN
302	ConvLSTM
303	antialiased-rnext101_32x8d
304	hmax
305	custom_model_cv_18_dagger_408

Benchmark bibtex

@article {Maniquet2024.04.02.587669,
	author = {Maniquet, Tim and de Beeck, Hans Op and Costantino, Andrea Ivan},
	title = {Recurrent issues with deep neural network models of visual recognition},
	elocation-id = {2024.04.02.587669},
	year = {2024},
	doi = {10.1101/2024.04.02.587669},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/04/10/2024.04.02.587669},
	eprint = {https://www.biorxiv.org/content/early/2024/04/10/2024.04.02.587669.full.pdf},
	journal = {bioRxiv}
}

Ceiling

0.54.

Note that scores are relative to this ceiling.

Data: Maniquet2024

Metric: confusion_similarity