Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Maniquet2024-tasks_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_base_patch16_clip_224:openai_ft_in1k	.750
2	efficientnet_b2_imagenet_full	.748
3	effnetb1_272x240	.746
4	cvt_cvt-21-384-in1k_4_LucyV4	.746
5	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.745
6	convnext_femto_ols:d1_in1k	.741
7	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.739
8	vit_large_patch14_clip_224:laion2b_ft_in1k	.738
9	efficientnet_b1_imagenet_full	.737
10	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.735
11	resnet18_imagenet21kP	.734
12	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.734
13	cvt_cvt-13-384-in1k_4_LucyV4	.732
14	resnet_152_v2	.729
15	efficientnet_b0_imagenet_full	.727
16	vgg_19	.727
17	mobilenet_v2_1_0_224	.724
18	resnet-152_v2_pytorch	.718
19	resnet50-vicregl0p9	.717
20	densenet-201	.716
21	cvt_cvt-21-224-in1k_4_LucyV4	.716
22	antialiased-r50	.715
23	cvt_cvt-13-224-in1k_4_LucyV4	.715
24	nasnet_large	.713
25	mobilenet_v2_1-4_224_pytorch	.712
26	mobilenet_v2_1_4_224	.712
27	resnext101_32x32d_wsl	.710
28	resnet_50_v2	.707
29	resnet_101_v1	.705
30	resnet101_ecoset_full	.705
31	resnet18_ecoset_full	.703
32	deit_base_imagenet_full_seed-0	.701
33	resnet152_imagenet_full	.697
34	Res2Net50_26w_4s	.692
35	resnext101_32x16d_wsl	.692
36	yudixie_resnet18_imagenet1kpret_0_240719	.690
37	vgg_16	.689
38	inception_v3	.688
39	mobilenet_v2_0_75_160	.688
40	resnet_50_v1_spiking_l4a	.687
41	resnet50_ecoset_full	.686
42	resnet50-vicregl0p75	.685
43	convnext_tiny_sup	.684
44	resnet_101_v2	.684
45	efficientnet_b0	.683
46	deit_small_imagenet_full_seed-0	.681
47	tv_efficientnet-b1	.681
48	resnet34_ecoset_full	.680
49	vonegrcnn_52e_full	.679
50	ReAlnet02_cornet	.678
51	ReAlnet02	.678
52	vonegrcnn_62e_nobn	.676
53	resnet_50_v1	.676
54	resnet50-sup	.676
55	regnet_y_400mf	.676
56	resnet-18_test_m	.674
57	deit_large_imagenet_full_seed-0	.674
58	grcnn	.672
59	yudixie_resnet50_imagenet1kpret_0_240908	.672
60	resnet50_eMMCR_Vanilla	.671
61	resnet50-SIN_IN_IN	.669
62	inception_v1	.669
63	convnext_small_imagenet_full_seed-0	.668
64	vonegrcnn_47e	.668
65	resnet_50_v1_spiking	.667
66	resnet50_tutorial	.667
67	nasnet_mobile	.667
68	ReAlnet06	.667
69	ReAlnet06_cornet	.667
70	resnet18_imagenet_full	.667
71	resnext101_32x8d_wsl	.667
72	convnext_tiny:in12k_ft_in1k	.666
73	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.666
74	convnext_small_imagenet_100_seed-0	.666
75	convnext_tiny_imagenet_full_seed-0	.665
76	xception	.663
77	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
78	resnet34_imagenet_full	.660
79	BiT-S-R50x1	.660
80	resnet50_imagenet_full	.659
81	inception_v3_pytorch	.659
82	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
83	resnet_SIN_IN_FT_IN	.657
84	ReAlnet05_cornet	.656
85	ReAlnet05	.656
86	convnext_xlarge:fb_in22k_ft_in1k	.656
87	shufflenet_v2_x1_0	.656
88	ReAlnet03_cornet	.656
89	ReAlnet03	.656
90	resnet101_imagenet_full	.654
91	ReAlnet04_cornet	.653
92	ReAlnet04	.653
93	resnet152_ecoset_full	.651
94	convnext_large_imagenet_full_seed-0	.650
95	ReAlnet09_cornet	.649
96	ReAlnet09	.649
97	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
98	resnet50-moclr8deg	.648
99	ReAlnet01	.648
100	ReAlnet01_cornet	.648
101	inception_v4	.648
102	ReAlnet07	.647
103	ReAlnet07_cornet	.647
104	ReAlnet08_cornet	.647
105	ReAlnet08	.647
106	resnet_152_v1	.646
107	ReAlnet10	.646
108	ReAlnet10_cornet	.646
109	resnet50-vicreg	.646
110	resnext101_32x48d_wsl	.644
111	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
112	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
113	resnet50-vitoimagevidnet8	.640
114	resnet_50_v1_spiking_l4	.639
115	resnet50_imagenet_100_seed-0	.638
116	antialias-resnet152	.638
117	VOneCORnet-S	.638
118	convnext_base_imagenet_full_seed-0	.635
119	mobilenet_v2_1_0_160	.632
120	voneresnet-50-non_stochastic	.625
121	grcnn_109	.625
122	alexnet_ks_torevert	.624
123	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
124	BiT-S-R101x1	.617
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
126	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
127	yolos_tiny	.615
128	resnet_50_v1_spiking_l3	.613
129	resnet50-SIN_IN	.610
130	voneresnet-50	.609
131	deit_small_imagenet_100_seed-0	.606
132	ViT_L_32_imagenet1k	.604
133	resnet50-barlow	.596
134	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
135	mobilenet_v2_1_0_128	.568
136	AT_efficientnet-b2	.565
137	AdvProp_efficientnet-b2	.565
138	focalnet_tiny_lrf_in1k	.550
139	yudixie_resnet50_object_class_0_240908	.550
140	yudixie_resnet50_distance_translation_0_240908	.549
141	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
142	yudixie_resnet18_distance_rotation_0_240719	.541
143	resnet50_robust_l2_eps1	.541
144	yudixie_resnet18_category_class_0_240719	.535
145	blt_vs	.534
146	yudixie_resnet18_object_class_0_240719	.531
147	yudixie_resnet18_translation_rotation_0_240719	.531
148	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
149	yudixie_resnet50_distance_translation_rotation_0_240908	.521
150	yudixie_resnet18_distance_translation_0_240719	.521
151	yudixie_resnet50_category_class_0_240908	.520
152	efficientnet-b7	.507
153	yudixie_resnet50_rotation_reg_0_240908	.502
154	AlexNet_SIN	.499
155	yudixie_resnet50_translation_reg_0_240908	.498
156	yudixie_resnet50_distance_rotation_0_240908	.498
157	artResNet18_1	.494
158	yudixie_resnet18_translation_reg_0_240719	.484
159	yudixie_resnet18_rotation_reg_0_240719	.484
160	convnext_small_imagenet_10_seed-0	.484
161	yudixie_resnet18_distance_translation_rotation_0_240719	.482
162	resnet50-SIN	.479
163	resnet50_imagenet_10_seed-0	.478
164	yudixie_resnet50_translation_rotation_0_240908	.476
165	yudixie_resnet18_distance_reg_0_240719	.470
166	pnasnet_large_pytorch	.470
167	yudixie_resnet18_random_0_240719	.462
168	AdvProp_efficientnet-b4	.461
169	yudixie_resnet50_distance_reg_0_240908	.450
170	resnet-50-robust	.437
171	imagenet_l2_3_0	.395
172	resnet50_robust_l2_eps3	.395
173	AdvProp_efficientnet-b6	.383
174	alexnet_training_seed_10	.380
175	alexnet_training_seed_06	.379
176	alexnet_training_seed_02	.367
177	resnet50_primary_visual_cortex	.366
178	alexnet_training_seed_01	.358
179	alexnet_training_seed_09	.349
180	alexnet_training_seed_03	.349
181	alexnet_training_seed_07	.344
182	alexnet_training_seed_05	.343
183	yudixie_resnet50_random_0_240908	.341
184	alexnet_training_seed_04	.326
185	alexnet_training_seed_08	.325
186	resnet50_imagenet_1_seed-0	.323
187	briaai_rmbg_1_4	.323
188	deit_small_imagenet_1_seed-0	.296
189	convnext_small_imagenet_1_seed-0	.266
190	deit_small_imagenet_10_seed-0	.210
191	convnext_large:fb_in22k_ft_in1k
192	pnasnet_large
193	vit_large_patch14_clip_336:openai_ft_in12k_in1k
194	vit_large_patch14_clip_336:laion2b_ft_in1k
195	ConvLSTM
196	densenet_201_pytorch
197	MIM
198	TAU
199	SimVP
200	PredRNN
201	mobilenet_v2_1_3_224
202	antialiased-rnext101_32x8d
203	custom_model_cv_18_dagger_408
204	omnivore_swinB
205	voneresnet-50-robust
206	hmax
207	AdvProp_efficientnet-b7
208	BiT-S-R50x3
209	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
210	resnet50-VITO-8deg-cc
211	mobilenet_v2_0_5_224
212	mobilenet_v2_0_75_192
213	AdvProp_efficientnet-b8
214	omnivore_swinS
215	BiT-S-R152x4
216	BiT-S-R101x3
217	mobilenet_v2_0_5_192
218	BiT-S-R152x2
219	convnext_xxlarge:clip_laion2b_soup_ft_in1k
220	mobilenet_v2_1_0_192
221	vit_large_patch14_clip_224:openai_ft_in1k
222	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
223	mobilenet_v2_0_75_224
224	densenet-169
225	densenet-121
226	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: tasks_consistency