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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_workflow_trigger	.461
138	alexnet_ks_torevert	.460
139	alexnet	.460
140	compact_vgg19_V4	.459
141	alexnet_fov16	.456
142	compact_resnet50_robust_V4	.454
143	efficientnet_b0_imagenet_full	.453
144	AlexNet_SIN_fov16	.453
145	mobilenet_v2_1_3_224	.452
146	alexnet_training_seed_04	.450
147	alexnet_fov12	.448
148	alexnet_training_seed_05	.444
149	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.441
150	nasnet_mobile	.438
151	briaai_rmbg_1_4	.437
152	densenet-201	.436
153	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.436
154	convnext_small_imagenet_100_seed-0	.435
155	AlexNet_SIN_fov12	.431
156	deit_large_imagenet_full_seed-0	.428
157	cvt_cvt-21-384-in1k_4_LucyV4	.424
158	yudixie_resnet18_rotation_reg_0_240719	.419
159	yudixie_resnet18_object_class_0_240719	.418
160	alexnet_training_seed_02	.418
161	vit_large_patch14_clip_224:laion2b_ft_in1k	.416
162	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
163	resnet_152_v2	.410
164	densenet-169	.409
165	resnet34_ecoset_full	.407
166	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.407
167	resnet18_imagenet_full	.406
168	densenet-121	.403
169	convnext_small_imagenet_full_seed-0	.395
170	xception	.392
171	pnasnet_large_pytorch	.381
172	convnext_large_imagenet_full_seed-0	.375
173	resnet50-SIN	.371
174	deit_small_imagenet_10_seed-0	.365
175	yudixie_resnet50_distance_translation_0_240908	.365
176	resnet101_imagenet_full	.362
177	resnet50_imagenet_1_seed-0	.358
178	BiT-S-R101x3	.355
179	inception_v1	.351
180	AdvProp_efficientnet-b2	.348
181	AT_efficientnet-b2	.348
182	resnext101_32x16d_wsl	.346
183	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.345
184	resnet50_robust_l2_eps3	.341
185	imagenet_l2_3_0	.341
186	yudixie_resnet50_translation_rotation_0_240908	.341
187	cvt_cvt-13-224-in1k_4_LucyV4	.340
188	yudixie_resnet18_distance_rotation_0_240719	.337
189	yudixie_resnet18_translation_reg_0_240719	.326
190	tv_efficientnet-b1	.326
191	yudixie_resnet50_translation_reg_0_240908	.324
192	inception_v3	.324
193	yudixie_resnet18_distance_translation_rotation_0_240719	.323
194	AlexNet_SIN	.322
195	AdvProp_efficientnet-b8	.315
196	resnet152_ecoset_full	.314
197	compact_resnet50_IT	.312
198	resnet-50-robust	.305
199	yudixie_resnet18_distance_translation_0_240719	.302
200	resnet101_ecoset_full	.298
201	efficientnet_b1_imagenet_full	.289
202	resnet50_ecoset_full	.287
203	blt_vs	.284
204	AdvProp_efficientnet-b7	.281
205	yudixie_resnet50_distance_reg_0_240908	.280
206	efficientnet-b7	.277
207	convnext_base:clip_laiona_augreg_ft_in1k_384	.270
208	BiT-S-R101x1	.270
209	inception_v3_pytorch	.262
210	cvt_cvt-13-384-in1k_4_LucyV4	.255
211	cv_18_dagger_408_pretrained	.249
212	AdvProp_efficientnet-b4	.247
213	resnet_101_v2	.239
214	voneresnet-50-robust	.238
215	yudixie_resnet50_rotation_reg_0_240908	.232
216	vit_base_patch16_dinov3:lvd1689m	.231
217	grcnn_109	.227
218	resnext101_32x32d_wsl	.220
219	resnet-152_v2_pytorch	.212
220	AdvProp_efficientnet-b6	.206
221	efficientnet_b0	.197
222	BiT-S-R50x1	.195
223	resnext101_32x8d_wsl	.195
224	convnext_tiny_imagenet_full_seed-0	.194
225	resnet34_imagenet_full	.192
226	resnet_50_v2	.189
227	convnext_small_imagenet_1_seed-0	.186
228	vit_large_patch14_clip_336:laion2b_ft_in1k	.183
229	BiT-S-R152x2	.175
230	yudixie_resnet18_distance_reg_0_240719	.167
231	resnet50_eMMCR_Vanilla	.164
232	convnext_base_imagenet_full_seed-0	.162
233	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.156
234	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.138
235	resnet50-vicregl0p9	.123
236	resnext101_32x48d_wsl	.106
237	cvt_cvt-21-224-in1k_4_LucyV4	.100
238	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.098
239	focalnet_tiny_lrf_in1k	.092
240	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.085
241	convnext_tiny_sup	.062
242	resnet50-vicreg	.019
243	resnet50-vicregl0p75	-0.002
244	resnet50-moclr8deg	-0.009
245	resnet50-barlow	-0.065
246	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
247	mobilenet_v2_0_75_224
248	convnext_xxlarge:clip_laion2b_soup_ft_in1k
249	custom_model_cv_18_dagger_408
250	hmax
251	antialiased-rnext101_32x8d
252	ConvLSTM
253	PredRNN
254	SimVP
255	TAU
256	MIM
257	ViT_L_32_imagenet1k
258	vit_base_patch14_reg4_dinov2:lvd142m
259	vit_base_patch14_dinov2:lvd142m
260	pnasnet_large
261	omnivore_swinB
262	omnivore_swinS
263	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