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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	eicircuit_resnet18_bn_20260919	.950
14	ReAlnet05_ALL	.946
15	resnet_50_v1_spiking_l3	.944
16	predictive_coding_pc	.928
17	resnet50-sup	.928
18	resnet_50_v1	.928
19	mobilenet_v2_0_75_160	.928
20	shufflenet_v2_x1_0	.917
21	mobilenet_v2_0_5_192	.896
22	mobilenet_v2_0_5_224	.893
23	mobilenet_v2_1_0_128	.892
24	alexnet_fov4	.890
25	mobilenet_v2_0_75_192	.886
26	yudixie_wbh_object_class_scaling_0415_imagenet	.886
27	resnet_50_v1_spiking_l4	.878
28	ReAlnet07_cornet	.873
29	ReAlnet07	.873
30	deepeisnn_ei_cifar10_resnet18	.867
31	resnet_SIN_IN_FT_IN	.858
32	deit_small_imagenet_100_seed-0	.850
33	ReAlnet09_cornet	.832
34	ReAlnet09	.832
35	ReAlnet10_cornet	.832
36	ReAlnet10	.832
37	resnet_101_v1	.831
38	vit_large_patch14_clip_224:openai_ft_in1k	.826
39	vonegrcnn_62e_nobn	.823
40	pnasnet_large_halfPrecision	.822
41	resnet_50_v1_spiking_l4a	.821
42	yudixie_resnet18_random_0_240719	.820
43	ReAlnet01_cornet_fov4	.818
44	ReAlnet08_cornet	.813
45	ReAlnet08	.813
46	ReAlnet01_cornet	.808
47	ReAlnet01	.808
48	VOneCORnet-S	.804
49	vonegrcnn_47e	.803
50	ReAlnet03_cornet	.798
51	ReAlnet03	.798
52	densenet_201_pytorch	.794
53	vonegrcnn_52e_full	.793
54	resnet50-SIN_IN_IN	.785
55	resnet-18_test_m	.763
56	ReAlnet06_cornet	.759
57	ReAlnet06	.759
58	antialias-resnet152	.753
59	ReAlnet02_cornet	.753
60	ReAlnet02	.753
61	ReAlnet05_cornet	.751
62	ReAlnet05	.751
63	gabor_filter_mean_abs_4x4_v2	.749
64	yudixie_resnet18_imagenet1kpret_0_240719	.748
65	yudixie_resnet50_imagenet1kpret_0_240908	.743
66	ReAlnet04_cornet	.738
67	ReAlnet04	.738
68	mobilenet_v2_1_0_160	.737
69	resnet50_tutorial	.736
70	vgg_19	.729
71	compact_cornets_V4	.710
72	grcnn	.708
73	effnetb1_272x240	.708
74	compact_cornets_IT	.695
75	resnet50_st_wzc_seed6_2_323	.689
76	resnet50_imagenet_100_seed-0	.679
77	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.678
78	mobilenet_v2_1_0_224	.677
79	deit_small_imagenet_full_seed-0	.669
80	resnet50_primary_visual_cortex	.668
81	compact_resnet50_robust_IT	.665
82	convnext_xlarge:fb_in22k_ft_in1k	.665
83	antialiased-r50	.662
84	resnet50_robust_l2_eps1	.651
85	vgg_16	.645
86	convnext_tiny:in12k_ft_in1k	.640
87	yudixie_wbh_object_class_scaling_0415_run_1	.634
88	mobilenet_v2_1_4_224	.632
89	mobilenet_v2_1-4_224_pytorch	.632
90	Res2Net50_26w_4s	.630
91	artResNet18_1	.626
92	yudixie_wbh_object_class_scaling_0415_run_2	.622
93	convnext_large_in22k_supervised	.607
94	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.607
95	yudixie_resnet50_random_0_240908	.603
96	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.600
97	yudixie_resnet18_category_class_0_240719	.598
98	ReAlnet01_cornet_fov12	.596
99	deit3_base_patch16_224_fb_in22k_ft_in1k	.587
100	yudixie_resnet50_object_class_0_240908	.586
101	resnet50_ewconplus_wzc_seed2_2__323	.583
102	alexnet_training_seed_03	.571
103	alexnet_training_seed_01_fov4	.569
104	ReAlnet01_cornet_fov16	.568
105	deepeisnn_ei_cifar10_vgg19	.565
106	resnet50_st_wzc_seed3_2_323	.564
107	yudixie_resnet50_distance_translation_rotation_0_240908	.562
108	alexnet_training_seed_01_fov16	.562
109	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.562
110	yudixie_wbh_object_class_scaling_0415_run_3	.561
111	resnet18_fair	.560
112	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.559
113	resnet50_hvs_wzc	.556
114	convnext_small_imagenet_10_seed-0	.555
115	alexnet_training_seed_10	.555
116	convnext_large:fb_in22k_ft_in1k	.553
117	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.553
118	resnet50_imagenet_full	.552
119	resnet18_ecoset_full	.547
120	inception_v4	.546
121	gabor_filter_entropy_4x4	.543
122	yudixie_wbh_object_class_scaling_0415_run_0	.541
123	mobilenet_v2_1_0_192	.541
124	vit_base_patch16_clip_224:openai_ft_in1k	.540
125	resnet18_imagenet21kP	.538
126	voneresnet-50	.535
127	regnet_y_400mf	.533
128	deit_small_imagenet_1_seed-0	.533
129	alexnet_training_seed_01	.530
130	voneresnet-50-non_stochastic	.529
131	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.524
132	resnet50-vitoimagevidnet8	.520
133	resnet50-VITO-8deg-cc	.520
134	alexnet_training_seed_07	.516
135	nasnet_large	.513
136	efficientnet_b2_imagenet_full	.508
137	yudixie_resnet18_translation_rotation_0_240719	.508
138	gabor_filter_entropy_4x4_v2_forcedCosine	.504
139	resnet50_ewconplus_wzc_seed6_2_323	.502
140	resnet50_st_wzc_2	.502
141	alexnet_training_seed_08	.502
142	resnet_152_v1	.500
143	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.498
144	yudixie_resnet50_category_class_0_240908	.498
145	resnet50_ewconplus_wzc	.497
146	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.496
147	deit_base_imagenet_full_seed-0	.496
148	resnet152_imagenet_full	.495
149	alexnet_training_seed_01_fov12	.494
150	BiT-S-R50x3	.490
151	resnet50_imagenet_10_seed-0	.490
152	alexnet_training_seed_09	.488
153	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.487
154	resnet50_st_wzc_seed7_2_323	.486
155	yudixie_resnet50_distance_rotation_0_240908	.486
156	AlexNet_SIN_fov4	.482
157	compact_vgg19_IT	.482
158	convnext_femto_ols:d1_in1k	.480
159	compact_resnet50_V4	.475
160	resnet50-SIN_IN	.473
161	alexnet_training_seed_06	.473
162	vit_relpos_base_patch16_clsgap_224:sw_in1k	.472
163	persistence_memory	.465
164	alexnet_ks_torevert	.460
165	alexnet	.460
166	compact_vgg19_V4	.459
167	alexnet_fov16	.456
168	compact_resnet50_robust_V4	.454
169	persistence_mem	.453
170	efficientnet_b0_imagenet_full	.453
171	AlexNet_SIN_fov16	.453
172	mobilenet_v2_1_3_224	.452
173	alexnet_training_seed_04	.450
174	alexnet_fov12	.448
175	gabor_filter_entropy_4x4_v2	.446
176	alexnet_training_seed_05	.444
177	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.441
178	vonealexnet	.440
179	nasnet_mobile	.438
180	briaai_rmbg_1_4	.437
181	densenet-201	.436
182	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.436
183	convnext_small_imagenet_100_seed-0	.435
184	AlexNet_SIN_fov12	.431
185	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.430
186	deit_large_imagenet_full_seed-0	.428
187	cvt_cvt-21-384-in1k_4_LucyV4	.424
188	yudixie_resnet18_rotation_reg_0_240719	.419
189	yudixie_resnet18_object_class_0_240719	.418
190	alexnet_training_seed_02	.418
191	vit_large_patch14_clip_224:laion2b_ft_in1k	.416
192	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
193	resnet_152_v2	.410
194	densenet-169	.409
195	resnet34_ecoset_full	.407
196	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.407
197	resnet18_imagenet_full	.406
198	convnext_base_fb_in22k_ft_in1k	.406
199	densenet-121	.403
200	convnext_small_imagenet_full_seed-0	.395
201	xception	.392
202	pr1_stl10_disentangled_alpha04	.387
203	pnasnet_large_pytorch	.381
204	convnext_large_imagenet_full_seed-0	.375
205	resnet50-SIN	.371
206	resnet50_ewconplus_wzc_seed5_2__323	.367
207	deit_small_imagenet_10_seed-0	.365
208	yudixie_resnet50_distance_translation_0_240908	.365
209	resnet101_imagenet_full	.362
210	resnet50_imagenet_1_seed-0	.358
211	BiT-S-R101x3	.355
212	inception_v1	.351
213	AT_efficientnet-b2	.348
214	AdvProp_efficientnet-b2	.348
215	resnext101_32x16d_wsl	.346
216	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.345
217	imagenet_l2_3_0	.341
218	resnet50_robust_l2_eps3	.341
219	yudixie_resnet50_translation_rotation_0_240908	.341
220	cvt_cvt-13-224-in1k_4_LucyV4	.340
221	yudixie_resnet18_distance_rotation_0_240719	.337
222	yudixie_resnet18_translation_reg_0_240719	.326
223	tv_efficientnet-b1	.326
224	yudixie_resnet50_translation_reg_0_240908	.324
225	inception_v3	.324
226	yudixie_resnet18_distance_translation_rotation_0_240719	.323
227	AlexNet_SIN	.322
228	convnext_large_clip_laion2b_finelayers	.317
229	AdvProp_efficientnet-b8	.315
230	resnet152_ecoset_full	.314
231	compact_resnet50_IT	.312
232	resnet-50-robust	.305
233	yudixie_resnet18_distance_translation_0_240719	.302
234	resnet101_ecoset_full	.298
235	convnext_base_fb_in1k	.293
236	efficientnet_b1_imagenet_full	.289
237	resnet50_ecoset_full	.287
238	blt_vs	.284
239	AdvProp_efficientnet-b7	.281
240	dinov2_vitl14_reg_lc	.280
241	yudixie_resnet50_distance_reg_0_240908	.280
242	efficientnet-b7	.277
243	convnext_base:clip_laiona_augreg_ft_in1k_384	.270
244	BiT-S-R101x1	.270
245	inception_v3_pytorch	.262
246	convnext_base_fb_in1k_gram_aligned_dinov3	.258
247	dinov2_vitl14_lc	.257
248	cvt_cvt-13-384-in1k_4_LucyV4	.255
249	cv_18_dagger_408_pretrained	.249
250	AdvProp_efficientnet-b4	.247
251	resnet_101_v2	.239
252	voneresnet-50-robust	.238
253	yudixie_resnet50_rotation_reg_0_240908	.232
254	grcnn_109	.227
255	resnext101_32x32d_wsl	.220
256	resnet-152_v2_pytorch	.212
257	dinov2_vitg14_lc	.209
258	AdvProp_efficientnet-b6	.206
259	efficientnet_b0	.197
260	BiT-S-R50x1	.195
261	resnext101_32x8d_wsl	.195
262	convnext_tiny_imagenet_full_seed-0	.194
263	resnet34_imagenet_full	.192
264	resnet_50_v2	.189
265	convnext_small_imagenet_1_seed-0	.186
266	vit_large_patch14_clip_336:laion2b_ft_in1k	.183
267	regnety_032	.179
268	BiT-S-R152x2	.175
269	yudixie_resnet18_distance_reg_0_240719	.167
270	resnet50_eMMCR_Vanilla	.164
271	vit_base_patch16_dinov3-lvd1689m	.163
272	convnext_base_imagenet_full_seed-0	.162
273	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.156
274	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.138
275	resnet50-vicregl0p9	.123
276	resnext101_32x48d_wsl	.106
277	cvt_cvt-21-224-in1k_4_LucyV4	.100
278	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.098
279	focalnet_tiny_lrf_in1k	.092
280	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.085
281	deit3_base_patch16_224_fb_in1k	.065
282	convnext_tiny_sup	.062
283	seresnext50_32x4d	.060
284	resnet50-vicreg	.019
285	resnet50-vicregl0p75	.000
286	resnet50-moclr8deg	.000
287	resnet50-barlow	.000
288	dinov2_vitl14_reg_lc_featread
289	dinov2_vitl14_lc_res336
290	eva02_large_in22k
291	dinov2_vitg14_reg_lc
292	vit_base_patch14_reg4_dinov2-lvd142m
293	vit_base_patch14_dinov2-lvd142m
294	pnasnet_large
295	mobilenet_v2_0_75_224
296	vit_huge_clip336_laion
297	convnext_xxlarge_clip
298	vit_large_clip336_openai
299	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
300	omnivore_swinS
301	omnivore_swinB
302	convnext_xxlarge:clip_laion2b_soup_ft_in1k
303	BiT-S-R152x4
304	ViT_L_32_imagenet1k
305	MIM
306	TAU
307	SimVP
308	PredRNN
309	ConvLSTM
310	antialiased-rnext101_32x8d
311	hmax
312	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