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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	ReAlnet09_ALL	1.1
2	ReAlnet10_ALL	1.1
3	ReAlnet04_ALL	1.1
4	ReAlnet03_ALL	1.0
5	ReAlnet01_ALL	1.0
6	ReAlnet08_ALL	1.0
7	yolos_tiny	1.0
8	ReAlnet07_ALL	1.0
9	ReAlnet06_ALL	1.0
10	ReAlnet02_ALL	.994
11	resnet_50_v1_spiking	.955
12	ReAlnet05_ALL	.946
13	resnet_50_v1_spiking_l3	.944
14	resnet_50_v1	.928
15	mobilenet_v2_0_75_160	.928
16	shufflenet_v2_x1_0	.917
17	mobilenet_v2_1_0_128	.892
18	resnet_50_v1_spiking_l4	.878
19	ReAlnet07	.873
20	ReAlnet07_cornet	.873
21	resnet_SIN_IN_FT_IN	.858
22	deit_small_imagenet_100_seed-0	.850
23	ReAlnet09	.832
24	ReAlnet09_cornet	.832
25	ReAlnet10	.832
26	ReAlnet10_cornet	.832
27	resnet_101_v1	.831
28	vit_large_patch14_clip_224:openai_ft_in1k	.826
29	vonegrcnn_62e_nobn	.823
30	resnet_50_v1_spiking_l4a	.821
31	yudixie_resnet18_random_0_240719	.820
32	ReAlnet08	.813
33	ReAlnet08_cornet	.813
34	ReAlnet01_cornet	.808
35	ReAlnet01	.808
36	VOneCORnet-S	.804
37	vonegrcnn_47e	.803
38	ReAlnet03	.798
39	ReAlnet03_cornet	.798
40	densenet_201_pytorch	.794
41	vonegrcnn_52e_full	.793
42	resnet50-SIN_IN_IN	.785
43	resnet-18_test_m	.763
44	ReAlnet06	.759
45	ReAlnet06_cornet	.759
46	antialias-resnet152	.753
47	ReAlnet02_cornet	.753
48	ReAlnet02	.753
49	ReAlnet05	.751
50	ReAlnet05_cornet	.751
51	yudixie_resnet18_imagenet1kpret_0_240719	.748
52	yudixie_resnet50_imagenet1kpret_0_240908	.743
53	ReAlnet04_cornet	.738
54	ReAlnet04	.738
55	mobilenet_v2_1_0_160	.737
56	resnet50_tutorial	.736
57	vgg_19	.729
58	grcnn	.708
59	effnetb1_272x240	.708
60	resnet50_imagenet_100_seed-0	.679
61	mobilenet_v2_1_0_224	.677
62	deit_small_imagenet_full_seed-0	.669
63	resnet50_primary_visual_cortex	.668
64	convnext_xlarge:fb_in22k_ft_in1k	.665
65	antialiased-r50	.662
66	resnet50_robust_l2_eps1	.651
67	vgg_16	.645
68	convnext_tiny:in12k_ft_in1k	.640
69	mobilenet_v2_1_4_224	.632
70	mobilenet_v2_1-4_224_pytorch	.632
71	Res2Net50_26w_4s	.630
72	artResNet18_1	.626
73	yudixie_resnet50_random_0_240908	.603
74	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.600
75	yudixie_resnet18_category_class_0_240719	.598
76	yudixie_resnet50_object_class_0_240908	.586
77	alexnet_training_seed_03	.571
78	yudixie_resnet50_distance_translation_rotation_0_240908	.562
79	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.562
80	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.559
81	convnext_small_imagenet_10_seed-0	.555
82	alexnet_training_seed_10	.555
83	convnext_large:fb_in22k_ft_in1k	.553
84	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.553
85	resnet18_ecoset_full	.547
86	inception_v4	.546
87	vit_base_patch16_clip_224:openai_ft_in1k	.540
88	resnet18_imagenet21kP	.538
89	voneresnet-50	.535
90	regnet_y_400mf	.533
91	deit_small_imagenet_1_seed-0	.533
92	alexnet_training_seed_01	.530
93	voneresnet-50-non_stochastic	.529
94	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.524
95	resnet50-VITO-8deg-cc	.520
96	resnet50-vitoimagevidnet8	.520
97	alexnet_training_seed_07	.516
98	nasnet_large	.513
99	efficientnet_b2_imagenet_full	.508
100	yudixie_resnet18_translation_rotation_0_240719	.508
101	alexnet_training_seed_08	.502
102	resnet_152_v1	.500
103	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.498
104	yudixie_resnet50_category_class_0_240908	.498
105	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.496
106	deit_base_imagenet_full_seed-0	.496
107	resnet152_imagenet_full	.495
108	resnet50_imagenet_10_seed-0	.490
109	alexnet_training_seed_09	.488
110	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.487
111	yudixie_resnet50_distance_rotation_0_240908	.486
112	convnext_femto_ols:d1_in1k	.480
113	resnet50-SIN_IN	.473
114	alexnet_training_seed_06	.473
115	vit_relpos_base_patch16_clsgap_224:sw_in1k	.472
116	alexnet_ks_torevert	.460
117	efficientnet_b0_imagenet_full	.453
118	alexnet_training_seed_04	.450
119	alexnet_training_seed_05	.444
120	nasnet_mobile	.438
121	briaai_rmbg_1_4	.437
122	densenet-201	.436
123	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.436
124	convnext_small_imagenet_100_seed-0	.435
125	deit_large_imagenet_full_seed-0	.428
126	cvt_cvt-21-384-in1k_4_LucyV4	.424
127	yudixie_resnet18_rotation_reg_0_240719	.419
128	yudixie_resnet18_object_class_0_240719	.418
129	alexnet_training_seed_02	.418
130	vit_large_patch14_clip_224:laion2b_ft_in1k	.416
131	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
132	resnet_152_v2	.410
133	resnet34_ecoset_full	.407
134	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.407
135	convnext_small_imagenet_full_seed-0	.395
136	xception	.392
137	pnasnet_large_pytorch	.381
138	convnext_large_imagenet_full_seed-0	.375
139	resnet50-SIN	.371
140	deit_small_imagenet_10_seed-0	.365
141	yudixie_resnet50_distance_translation_0_240908	.365
142	resnet101_imagenet_full	.362
143	resnet50_imagenet_1_seed-0	.358
144	inception_v1	.351
145	AdvProp_efficientnet-b2	.348
146	AT_efficientnet-b2	.348
147	resnext101_32x16d_wsl	.346
148	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.345
149	resnet50_robust_l2_eps3	.341
150	imagenet_l2_3_0	.341
151	yudixie_resnet50_translation_rotation_0_240908	.341
152	cvt_cvt-13-224-in1k_4_LucyV4	.340
153	yudixie_resnet18_distance_rotation_0_240719	.337
154	yudixie_resnet18_translation_reg_0_240719	.326
155	tv_efficientnet-b1	.326
156	yudixie_resnet50_translation_reg_0_240908	.324
157	inception_v3	.324
158	yudixie_resnet18_distance_translation_rotation_0_240719	.323
159	AlexNet_SIN	.322
160	resnet152_ecoset_full	.314
161	resnet-50-robust	.305
162	yudixie_resnet18_distance_translation_0_240719	.302
163	resnet101_ecoset_full	.298
164	efficientnet_b1_imagenet_full	.289
165	resnet50_ecoset_full	.287
166	blt_vs	.284
167	yudixie_resnet50_distance_reg_0_240908	.280
168	efficientnet-b7	.277
169	BiT-S-R101x1	.270
170	inception_v3_pytorch	.262
171	cvt_cvt-13-384-in1k_4_LucyV4	.255
172	AdvProp_efficientnet-b4	.247
173	resnet_101_v2	.239
174	voneresnet-50-robust	.238
175	yudixie_resnet50_rotation_reg_0_240908	.232
176	grcnn_109	.227
177	resnext101_32x32d_wsl	.220
178	resnet-152_v2_pytorch	.212
179	AdvProp_efficientnet-b6	.206
180	efficientnet_b0	.197
181	BiT-S-R50x1	.195
182	resnext101_32x8d_wsl	.195
183	convnext_tiny_imagenet_full_seed-0	.194
184	resnet_50_v2	.189
185	convnext_small_imagenet_1_seed-0	.186
186	yudixie_resnet18_distance_reg_0_240719	.167
187	resnet50_eMMCR_Vanilla	.164
188	convnext_base_imagenet_full_seed-0	.162
189	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.156
190	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.138
191	resnet50-vicregl0p9	.123
192	resnext101_32x48d_wsl	.106
193	cvt_cvt-21-224-in1k_4_LucyV4	.100
194	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.098
195	focalnet_tiny_lrf_in1k	.092
196	convnext_tiny_sup	.062
197	resnet50-vicreg	.019
198	resnet50-vicregl0p75	-0.002
199	resnet50-moclr8deg	-0.009
200	resnet50-barlow	-0.065
201	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
202	mobilenet_v2_0_75_224
203	vit_large_patch14_clip_336:laion2b_ft_in1k
204	convnext_xxlarge:clip_laion2b_soup_ft_in1k
205	vit_large_patch14_clip_336:openai_ft_in12k_in1k
206	mobilenet_v2_1_0_192
207	mobilenet_v2_0_75_192
208	mobilenet_v2_0_5_224
209	mobilenet_v2_0_5_192
210	custom_model_cv_18_dagger_408
211	hmax
212	antialiased-rnext101_32x8d
213	ConvLSTM
214	PredRNN
215	SimVP
216	TAU
217	MIM
218	ViT_L_32_imagenet1k
219	pnasnet_large
220	omnivore_swinB
221	mobilenet_v2_1_3_224
222	omnivore_swinS
223	AdvProp_efficientnet-b7
224	resnet50-sup
225	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384
226	BiT-S-R50x3
227	AdvProp_efficientnet-b8
228	BiT-S-R152x4
229	BiT-S-R101x3
230	BiT-S-R152x2
231	densenet-169
232	densenet-121
233	convnext_base:clip_laiona_augreg_ft_in1k_384

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