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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	resnet50-sup	.928
15	resnet_50_v1	.928
16	mobilenet_v2_0_75_160	.928
17	shufflenet_v2_x1_0	.917
18	mobilenet_v2_0_5_192	.896
19	mobilenet_v2_0_5_224	.893
20	mobilenet_v2_1_0_128	.892
21	alexnet_fov4	.890
22	mobilenet_v2_0_75_192	.886
23	resnet_50_v1_spiking_l4	.878
24	ReAlnet07	.873
25	ReAlnet07_cornet	.873
26	resnet_SIN_IN_FT_IN	.858
27	deit_small_imagenet_100_seed-0	.850
28	ReAlnet09	.832
29	ReAlnet09_cornet	.832
30	ReAlnet10	.832
31	ReAlnet10_cornet	.832
32	resnet_101_v1	.831
33	vit_large_patch14_clip_224:openai_ft_in1k	.826
34	vonegrcnn_62e_nobn	.823
35	resnet_50_v1_spiking_l4a	.821
36	yudixie_resnet18_random_0_240719	.820
37	ReAlnet01_cornet_fov4	.818
38	ReAlnet08	.813
39	ReAlnet08_cornet	.813
40	ReAlnet01_cornet	.808
41	ReAlnet01	.808
42	VOneCORnet-S	.804
43	vonegrcnn_47e	.803
44	ReAlnet03	.798
45	ReAlnet03_cornet	.798
46	densenet_201_pytorch	.794
47	vonegrcnn_52e_full	.793
48	resnet50-SIN_IN_IN	.785
49	resnet-18_test_m	.763
50	ReAlnet06	.759
51	ReAlnet06_cornet	.759
52	antialias-resnet152	.753
53	ReAlnet02_cornet	.753
54	ReAlnet02	.753
55	ReAlnet05	.751
56	ReAlnet05_cornet	.751
57	yudixie_resnet18_imagenet1kpret_0_240719	.748
58	yudixie_resnet50_imagenet1kpret_0_240908	.743
59	ReAlnet04_cornet	.738
60	ReAlnet04	.738
61	mobilenet_v2_1_0_160	.737
62	resnet50_tutorial	.736
63	vgg_19	.729
64	compact_cornets_V4	.710
65	grcnn	.708
66	effnetb1_272x240	.708
67	compact_cornets_IT	.695
68	resnet50_imagenet_100_seed-0	.679
69	mobilenet_v2_1_0_224	.677
70	deit_small_imagenet_full_seed-0	.669
71	resnet50_primary_visual_cortex	.668
72	compact_resnet50_robust_IT	.665
73	convnext_xlarge:fb_in22k_ft_in1k	.665
74	antialiased-r50	.662
75	resnet50_robust_l2_eps1	.651
76	vgg_16	.645
77	convnext_tiny:in12k_ft_in1k	.640
78	mobilenet_v2_1_4_224	.632
79	mobilenet_v2_1-4_224_pytorch	.632
80	Res2Net50_26w_4s	.630
81	artResNet18_1	.626
82	yudixie_resnet50_random_0_240908	.603
83	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.600
84	yudixie_resnet18_category_class_0_240719	.598
85	ReAlnet01_cornet_fov12	.596
86	yudixie_resnet50_object_class_0_240908	.586
87	alexnet_training_seed_03	.571
88	alexnet_training_seed_01_fov4	.569
89	ReAlnet01_cornet_fov16	.568
90	yudixie_resnet50_distance_translation_rotation_0_240908	.562
91	alexnet_training_seed_01_fov16	.562
92	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.562
93	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.559
94	convnext_small_imagenet_10_seed-0	.555
95	alexnet_training_seed_10	.555
96	convnext_large:fb_in22k_ft_in1k	.553
97	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.553
98	resnet50_imagenet_full	.552
99	resnet18_ecoset_full	.547
100	inception_v4	.546
101	mobilenet_v2_1_0_192	.541
102	vit_base_patch16_clip_224:openai_ft_in1k	.540
103	resnet18_imagenet21kP	.538
104	voneresnet-50	.535
105	regnet_y_400mf	.533
106	deit_small_imagenet_1_seed-0	.533
107	alexnet_training_seed_01	.530
108	voneresnet-50-non_stochastic	.529
109	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.524
110	resnet50-VITO-8deg-cc	.520
111	resnet50-vitoimagevidnet8	.520
112	alexnet_training_seed_07	.516
113	nasnet_large	.513
114	efficientnet_b2_imagenet_full	.508
115	yudixie_resnet18_translation_rotation_0_240719	.508
116	alexnet_training_seed_08	.502
117	resnet_152_v1	.500
118	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.498
119	yudixie_resnet50_category_class_0_240908	.498
120	resnet50_ewconplus_wzc	.497
121	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.496
122	deit_base_imagenet_full_seed-0	.496
123	resnet152_imagenet_full	.495
124	alexnet_training_seed_01_fov12	.494
125	BiT-S-R50x3	.490
126	resnet50_imagenet_10_seed-0	.490
127	alexnet_training_seed_09	.488
128	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.487
129	yudixie_resnet50_distance_rotation_0_240908	.486
130	AlexNet_SIN_fov4	.482
131	compact_vgg19_IT	.482
132	convnext_femto_ols:d1_in1k	.480
133	compact_resnet50_V4	.475
134	resnet50-SIN_IN	.473
135	alexnet_training_seed_06	.473
136	vit_relpos_base_patch16_clsgap_224:sw_in1k	.472
137	alexnet_ks_torevert	.460
138	alexnet	.460
139	compact_vgg19_V4	.459
140	alexnet_fov16	.456
141	compact_resnet50_robust_V4	.454
142	efficientnet_b0_imagenet_full	.453
143	AlexNet_SIN_fov16	.453
144	mobilenet_v2_1_3_224	.452
145	alexnet_training_seed_04	.450
146	alexnet_fov12	.448
147	alexnet_training_seed_05	.444
148	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.441
149	nasnet_mobile	.438
150	briaai_rmbg_1_4	.437
151	densenet-201	.436
152	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.436
153	convnext_small_imagenet_100_seed-0	.435
154	AlexNet_SIN_fov12	.431
155	deit_large_imagenet_full_seed-0	.428
156	cvt_cvt-21-384-in1k_4_LucyV4	.424
157	yudixie_resnet18_rotation_reg_0_240719	.419
158	yudixie_resnet18_object_class_0_240719	.418
159	alexnet_training_seed_02	.418
160	vit_large_patch14_clip_224:laion2b_ft_in1k	.416
161	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
162	resnet_152_v2	.410
163	densenet-169	.409
164	resnet34_ecoset_full	.407
165	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.407
166	resnet18_imagenet_full	.406
167	densenet-121	.403
168	convnext_small_imagenet_full_seed-0	.395
169	xception	.392
170	pnasnet_large_pytorch	.381
171	convnext_large_imagenet_full_seed-0	.375
172	resnet50-SIN	.371
173	deit_small_imagenet_10_seed-0	.365
174	yudixie_resnet50_distance_translation_0_240908	.365
175	resnet101_imagenet_full	.362
176	resnet50_imagenet_1_seed-0	.358
177	BiT-S-R101x3	.355
178	inception_v1	.351
179	AdvProp_efficientnet-b2	.348
180	AT_efficientnet-b2	.348
181	resnext101_32x16d_wsl	.346
182	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.345
183	resnet50_robust_l2_eps3	.341
184	imagenet_l2_3_0	.341
185	yudixie_resnet50_translation_rotation_0_240908	.341
186	cvt_cvt-13-224-in1k_4_LucyV4	.340
187	yudixie_resnet18_distance_rotation_0_240719	.337
188	yudixie_resnet18_translation_reg_0_240719	.326
189	tv_efficientnet-b1	.326
190	yudixie_resnet50_translation_reg_0_240908	.324
191	inception_v3	.324
192	yudixie_resnet18_distance_translation_rotation_0_240719	.323
193	AlexNet_SIN	.322
194	AdvProp_efficientnet-b8	.315
195	resnet152_ecoset_full	.314
196	compact_resnet50_IT	.312
197	resnet-50-robust	.305
198	yudixie_resnet18_distance_translation_0_240719	.302
199	resnet101_ecoset_full	.298
200	efficientnet_b1_imagenet_full	.289
201	resnet50_ecoset_full	.287
202	blt_vs	.284
203	AdvProp_efficientnet-b7	.281
204	yudixie_resnet50_distance_reg_0_240908	.280
205	efficientnet-b7	.277
206	convnext_base:clip_laiona_augreg_ft_in1k_384	.270
207	BiT-S-R101x1	.270
208	inception_v3_pytorch	.262
209	cvt_cvt-13-384-in1k_4_LucyV4	.255
210	cv_18_dagger_408_pretrained	.249
211	AdvProp_efficientnet-b4	.247
212	resnet_101_v2	.239
213	voneresnet-50-robust	.238
214	yudixie_resnet50_rotation_reg_0_240908	.232
215	grcnn_109	.227
216	resnext101_32x32d_wsl	.220
217	resnet-152_v2_pytorch	.212
218	AdvProp_efficientnet-b6	.206
219	efficientnet_b0	.197
220	BiT-S-R50x1	.195
221	resnext101_32x8d_wsl	.195
222	convnext_tiny_imagenet_full_seed-0	.194
223	resnet34_imagenet_full	.192
224	resnet_50_v2	.189
225	convnext_small_imagenet_1_seed-0	.186
226	vit_large_patch14_clip_336:laion2b_ft_in1k	.183
227	BiT-S-R152x2	.175
228	yudixie_resnet18_distance_reg_0_240719	.167
229	resnet50_eMMCR_Vanilla	.164
230	convnext_base_imagenet_full_seed-0	.162
231	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.156
232	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.138
233	resnet50-vicregl0p9	.123
234	resnext101_32x48d_wsl	.106
235	cvt_cvt-21-224-in1k_4_LucyV4	.100
236	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.098
237	focalnet_tiny_lrf_in1k	.092
238	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.085
239	convnext_tiny_sup	.062
240	resnet50-vicreg	.019
241	resnet50-vicregl0p75	-0.002
242	resnet50-moclr8deg	-0.009
243	resnet50-barlow	-0.065
244	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
245	mobilenet_v2_0_75_224
246	convnext_xxlarge:clip_laion2b_soup_ft_in1k
247	custom_model_cv_18_dagger_408
248	hmax
249	antialiased-rnext101_32x8d
250	ConvLSTM
251	PredRNN
252	SimVP
253	TAU
254	MIM
255	ViT_L_32_imagenet1k
256	pnasnet_large
257	omnivore_swinB
258	omnivore_swinS
259	BiT-S-R152x4

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