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	yolos_tiny	1.0
2	resnet_50_v1	.928
3	mobilenet_v2_0_75_160	.928
4	shufflenet_v2_x1_0	.917
5	mobilenet_v2_1_0_128	.892
6	ReAlnet07	.873
7	ReAlnet07_cornet	.873
8	resnet_SIN_IN_FT_IN	.858
9	deit_small_imagenet_100_seed-0	.850
10	ReAlnet09_cornet	.832
11	ReAlnet09	.832
12	ReAlnet10	.832
13	ReAlnet10_cornet	.832
14	resnet_101_v1	.831
15	vit_large_patch14_clip_224:openai_ft_in1k	.826
16	vonegrcnn_62e_nobn	.823
17	yudixie_resnet18_random_0_240719	.820
18	ReAlnet08_cornet	.813
19	ReAlnet08	.813
20	ReAlnet_incorrect	.808
21	ReAlnet01_cornet	.808
22	ReAlnet01	.808
23	VOneCORnet-S	.804
24	vonegrcnn_47e	.803
25	ReAlnet03_cornet	.798
26	ReAlnet03	.798
27	densenet_201_pytorch	.794
28	vonegrcnn_52e_full	.793
29	resnet50-SIN_IN_IN	.785
30	resnet-18_test_m	.763
31	ReAlnet06_cornet	.759
32	ReAlnet06	.759
33	antialias-resnet152	.753
34	ReAlnet02_cornet	.753
35	ReAlnet02	.753
36	ReAlnet05_cornet	.751
37	ReAlnet05	.751
38	yudixie_resnet18_imagenet1kpret_0_240719	.748
39	yudixie_resnet50_imagenet1kpret_0_240908	.743
40	ReAlnet04	.738
41	ReAlnet04_cornet	.738
42	mobilenet_v2_1_0_160	.737
43	resnet50_tutorial	.736
44	vgg_19	.729
45	grcnn	.708
46	effnetb1_272x240	.708
47	resnet50_imagenet_100_seed-0	.679
48	mobilenet_v2_1_0_224	.677
49	deit_small_imagenet_full_seed-0	.669
50	convnext_xlarge:fb_in22k_ft_in1k	.665
51	antialiased-r50	.662
52	resnet50_robust_l2_eps1	.651
53	vgg_16	.645
54	convnext_tiny:in12k_ft_in1k	.640
55	mobilenet_v2_1_4_224	.632
56	mobilenet_v2_1-4_224_pytorch	.632
57	Res2Net50_26w_4s	.630
58	artResNet18_1	.626
59	yudixie_resnet50_random_0_240908	.603
60	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.600
61	yudixie_resnet18_category_class_0_240719	.598
62	yudixie_resnet50_object_class_0_240908	.586
63	alexnet_training_seed_03	.571
64	yudixie_resnet50_distance_translation_rotation_0_240908	.562
65	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.562
66	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.559
67	convnext_small_imagenet_10_seed-0	.555
68	alexnet_training_seed_10	.555
69	convnext_large:fb_in22k_ft_in1k	.553
70	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.553
71	resnet18_ecoset_full	.547
72	inception_v4	.546
73	vit_base_patch16_clip_224:openai_ft_in1k	.540
74	resnet18_imagenet21kP	.538
75	voneresnet-50	.535
76	regnet_y_400mf	.533
77	deit_small_imagenet_1_seed-0	.533
78	alexnet_training_seed_01	.530
79	voneresnet-50-non_stochastic	.529
80	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.524
81	resnet50-VITO-8deg-cc	.520
82	resnet50-vitoimagevidnet8	.520
83	alexnet_training_seed_07	.516
84	nasnet_large	.513
85	efficientnet_b2_imagenet_full	.508
86	yudixie_resnet18_translation_rotation_0_240719	.508
87	alexnet_training_seed_08	.502
88	resnet_152_v1	.500
89	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.498
90	yudixie_resnet50_category_class_0_240908	.498
91	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.496
92	deit_base_imagenet_full_seed-0	.496
93	resnet152_imagenet_full	.495
94	resnet50_imagenet_10_seed-0	.490
95	alexnet_training_seed_09	.488
96	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.487
97	yudixie_resnet50_distance_rotation_0_240908	.486
98	convnext_femto_ols:d1_in1k	.480
99	resnet50-SIN_IN	.473
100	alexnet_training_seed_06	.473
101	vit_relpos_base_patch16_clsgap_224:sw_in1k	.472
102	alexnet_ks_torevert	.460
103	efficientnet_b0_imagenet_full	.453
104	alexnet_training_seed_04	.450
105	alexnet_training_seed_05	.444
106	nasnet_mobile	.438
107	briaai_rmbg_1_4	.437
108	densenet-201	.436
109	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.436
110	convnext_small_imagenet_100_seed-0	.435
111	deit_large_imagenet_full_seed-0	.428
112	cvt_cvt-21-384-in1k_4_LucyV4	.424
113	yudixie_resnet18_rotation_reg_0_240719	.419
114	yudixie_resnet18_object_class_0_240719	.418
115	alexnet_training_seed_02	.418
116	vit_large_patch14_clip_224:laion2b_ft_in1k	.416
117	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
118	resnet_152_v2	.410
119	resnet34_ecoset_full	.407
120	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.407
121	convnext_small_imagenet_full_seed-0	.395
122	xception	.392
123	pnasnet_large_pytorch	.381
124	convnext_large_imagenet_full_seed-0	.375
125	resnet50-SIN	.371
126	deit_small_imagenet_10_seed-0	.365
127	yudixie_resnet50_distance_translation_0_240908	.365
128	resnet101_imagenet_full	.362
129	resnet50_imagenet_1_seed-0	.358
130	inception_v1	.351
131	AT_efficientnet-b2	.348
132	AdvProp_efficientnet-b2	.348
133	resnext101_32x16d_wsl	.346
134	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.345
135	resnet50_robust_l2_eps3	.341
136	imagenet_l2_3_0	.341
137	yudixie_resnet50_translation_rotation_0_240908	.341
138	cvt_cvt-13-224-in1k_4_LucyV4	.340
139	yudixie_resnet18_distance_rotation_0_240719	.337
140	yudixie_resnet18_translation_reg_0_240719	.326
141	tv_efficientnet-b1	.326
142	yudixie_resnet50_translation_reg_0_240908	.324
143	inception_v3	.324
144	yudixie_resnet18_distance_translation_rotation_0_240719	.323
145	AlexNet_SIN	.322
146	resnet152_ecoset_full	.314
147	resnet-50-robust	.305
148	yudixie_resnet18_distance_translation_0_240719	.302
149	resnet101_ecoset_full	.298
150	efficientnet_b1_imagenet_full	.289
151	resnet50_ecoset_full	.287
152	blt_vs	.284
153	yudixie_resnet50_distance_reg_0_240908	.280
154	efficientnet-b7	.277
155	BiT-S-R101x1	.270
156	inception_v3_pytorch	.262
157	cvt_cvt-13-384-in1k_4_LucyV4	.255
158	AdvProp_efficientnet-b4	.247
159	resnet_101_v2	.239
160	voneresnet-50-robust	.238
161	yudixie_resnet50_rotation_reg_0_240908	.232
162	grcnn_109	.227
163	resnext101_32x32d_wsl	.220
164	resnet-152_v2_pytorch	.212
165	AdvProp_efficientnet-b6	.206
166	efficientnet_b0	.197
167	BiT-S-R50x1	.195
168	resnext101_32x8d_wsl	.195
169	convnext_tiny_imagenet_full_seed-0	.194
170	resnet_50_v2	.189
171	convnext_small_imagenet_1_seed-0	.186
172	yudixie_resnet18_distance_reg_0_240719	.167
173	resnet50_eMMCR_Vanilla	.164
174	convnext_base_imagenet_full_seed-0	.162
175	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.156
176	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.138
177	resnet50-vicregl0p9	.123
178	resnext101_32x48d_wsl	.106
179	cvt_cvt-21-224-in1k_4_LucyV4	.100
180	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.098
181	focalnet_tiny_lrf_in1k	.092
182	convnext_tiny_sup	.062
183	resnet50-vicreg	.019
184	resnet50-vicregl0p75	-0.002
185	resnet50-moclr8deg	-0.009
186	resnet50-barlow	-0.065
187	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	X
188	ViT_L_32_imagenet1k	X
189	convnext_base:clip_laiona_augreg_ft_in1k_384	X
190	BiT-S-R152x4	X
191	omnivore_swinB	X
192	custom_model_cv_18_dagger_408	X
193	pnasnet_large	X
194	hmax	X
195	mobilenet_v2_1_3_224	X
196	mobilenet_v2_0_5_192	X
197	omnivore_swinS	X
198	antialiased-rnext101_32x8d	X
199	mobilenet_v2_0_5_224	X
200	mobilenet_v2_0_75_192	X
201	AdvProp_efficientnet-b7	X
202	resnet50-sup	X
203	mobilenet_v2_1_0_192	X
204	vit_large_patch14_clip_336:openai_ft_in12k_in1k	X
205	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	X
206	BiT-S-R50x3	X
207	convnext_xxlarge:clip_laion2b_soup_ft_in1k	X
208	ConvLSTM	X
209	PredRNN	X
210	AdvProp_efficientnet-b8	X
211	vit_large_patch14_clip_336:laion2b_ft_in1k	X
212	BiT-S-R101x3	X
213	BiT-S-R152x2	X
214	SimVP	X
215	mobilenet_v2_0_75_224	X
216	TAU	X
217	MIM	X
218	densenet-169	X
219	densenet-121	X

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

Not available

Data: Maniquet2024

Metric: confusion_similarity