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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	ReAlnet08_ALL	.699
34	ReAlnet06_ALL	.698
35	resnet152_imagenet_full	.697
36	ReAlnet04_ALL	.695
37	Res2Net50_26w_4s	.692
38	resnext101_32x16d_wsl	.692
39	ReAlnet10_ALL	.691
40	yudixie_resnet18_imagenet1kpret_0_240719	.690
41	vgg_16	.689
42	inception_v3	.688
43	ReAlnet09_ALL	.688
44	ReAlnet01_ALL	.688
45	mobilenet_v2_0_75_160	.688
46	resnet_50_v1_spiking_l4a	.687
47	resnet50_ecoset_full	.686
48	ReAlnet05_ALL	.685
49	ReAlnet02_ALL	.685
50	resnet50-vicregl0p75	.685
51	convnext_tiny_sup	.684
52	resnet_101_v2	.684
53	efficientnet_b0	.683
54	deit_small_imagenet_full_seed-0	.681
55	tv_efficientnet-b1	.681
56	resnet34_ecoset_full	.680
57	ReAlnet03_ALL	.680
58	vonegrcnn_52e_full	.679
59	ReAlnet02_cornet	.678
60	ReAlnet02	.678
61	vonegrcnn_62e_nobn	.676
62	resnet_50_v1	.676
63	resnet50-sup	.676
64	regnet_y_400mf	.676
65	resnet-18_test_m	.674
66	deit_large_imagenet_full_seed-0	.674
67	grcnn	.672
68	yudixie_resnet50_imagenet1kpret_0_240908	.672
69	resnet50_eMMCR_Vanilla	.671
70	resnet50-SIN_IN_IN	.669
71	inception_v1	.669
72	convnext_small_imagenet_full_seed-0	.668
73	vonegrcnn_47e	.668
74	ReAlnet07_ALL	.667
75	resnet_50_v1_spiking	.667
76	resnet50_tutorial	.667
77	nasnet_mobile	.667
78	ReAlnet06	.667
79	ReAlnet06_cornet	.667
80	resnet18_imagenet_full	.667
81	resnext101_32x8d_wsl	.667
82	convnext_tiny:in12k_ft_in1k	.666
83	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.666
84	convnext_small_imagenet_100_seed-0	.666
85	convnext_tiny_imagenet_full_seed-0	.665
86	xception	.663
87	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
88	resnet34_imagenet_full	.660
89	BiT-S-R50x1	.660
90	resnet50_imagenet_full	.659
91	inception_v3_pytorch	.659
92	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
93	resnet_SIN_IN_FT_IN	.657
94	ReAlnet05	.656
95	ReAlnet05_cornet	.656
96	convnext_xlarge:fb_in22k_ft_in1k	.656
97	shufflenet_v2_x1_0	.656
98	ReAlnet03	.656
99	ReAlnet03_cornet	.656
100	resnet101_imagenet_full	.654
101	ReAlnet04_cornet	.653
102	ReAlnet04	.653
103	resnet152_ecoset_full	.651
104	convnext_large_imagenet_full_seed-0	.650
105	ReAlnet09	.649
106	ReAlnet09_cornet	.649
107	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
108	resnet50-moclr8deg	.648
109	ReAlnet01_cornet	.648
110	ReAlnet01	.648
111	inception_v4	.648
112	ReAlnet07	.647
113	ReAlnet07_cornet	.647
114	ReAlnet08	.647
115	ReAlnet08_cornet	.647
116	resnet_152_v1	.646
117	ReAlnet10	.646
118	ReAlnet10_cornet	.646
119	resnet50-vicreg	.646
120	resnext101_32x48d_wsl	.644
121	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
122	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
123	resnet50-vitoimagevidnet8	.640
124	resnet_50_v1_spiking_l4	.639
125	resnet50_imagenet_100_seed-0	.638
126	antialias-resnet152	.638
127	VOneCORnet-S	.638
128	convnext_base_imagenet_full_seed-0	.635
129	mobilenet_v2_1_0_160	.632
130	voneresnet-50-non_stochastic	.625
131	grcnn_109	.625
132	alexnet_ks_torevert	.624
133	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
134	BiT-S-R101x1	.617
135	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
136	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
137	yolos_tiny	.615
138	resnet_50_v1_spiking_l3	.613
139	resnet50-SIN_IN	.610
140	voneresnet-50	.609
141	deit_small_imagenet_100_seed-0	.606
142	ViT_L_32_imagenet1k	.604
143	resnet50-barlow	.596
144	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
145	mobilenet_v2_1_0_128	.568
146	AdvProp_efficientnet-b2	.565
147	AT_efficientnet-b2	.565
148	focalnet_tiny_lrf_in1k	.550
149	yudixie_resnet50_object_class_0_240908	.550
150	yudixie_resnet50_distance_translation_0_240908	.549
151	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
152	yudixie_resnet18_distance_rotation_0_240719	.541
153	resnet50_robust_l2_eps1	.541
154	yudixie_resnet18_category_class_0_240719	.535
155	blt_vs	.534
156	yudixie_resnet18_object_class_0_240719	.531
157	yudixie_resnet18_translation_rotation_0_240719	.531
158	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
159	yudixie_resnet50_distance_translation_rotation_0_240908	.521
160	yudixie_resnet18_distance_translation_0_240719	.521
161	yudixie_resnet50_category_class_0_240908	.520
162	efficientnet-b7	.507
163	yudixie_resnet50_rotation_reg_0_240908	.502
164	AlexNet_SIN	.499
165	yudixie_resnet50_translation_reg_0_240908	.498
166	yudixie_resnet50_distance_rotation_0_240908	.498
167	artResNet18_1	.494
168	yudixie_resnet18_translation_reg_0_240719	.484
169	yudixie_resnet18_rotation_reg_0_240719	.484
170	convnext_small_imagenet_10_seed-0	.484
171	yudixie_resnet18_distance_translation_rotation_0_240719	.482
172	resnet50-SIN	.479
173	resnet50_imagenet_10_seed-0	.478
174	yudixie_resnet50_translation_rotation_0_240908	.476
175	yudixie_resnet18_distance_reg_0_240719	.470
176	pnasnet_large_pytorch	.470
177	yudixie_resnet18_random_0_240719	.462
178	AdvProp_efficientnet-b4	.461
179	yudixie_resnet50_distance_reg_0_240908	.450
180	resnet-50-robust	.437
181	imagenet_l2_3_0	.395
182	resnet50_robust_l2_eps3	.395
183	AdvProp_efficientnet-b6	.383
184	alexnet_training_seed_10	.380
185	alexnet_training_seed_06	.379
186	alexnet_training_seed_02	.367
187	resnet50_primary_visual_cortex	.366
188	alexnet_training_seed_01	.358
189	alexnet_training_seed_09	.349
190	alexnet_training_seed_03	.349
191	alexnet_training_seed_07	.344
192	alexnet_training_seed_05	.343
193	yudixie_resnet50_random_0_240908	.341
194	alexnet_training_seed_04	.326
195	alexnet_training_seed_08	.325
196	resnet50_imagenet_1_seed-0	.323
197	briaai_rmbg_1_4	.323
198	deit_small_imagenet_1_seed-0	.296
199	convnext_small_imagenet_1_seed-0	.266
200	deit_small_imagenet_10_seed-0	.210
201	BiT-S-R50x3
202	mobilenet_v2_0_75_224
203	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
204	vit_large_patch14_clip_224:openai_ft_in1k
205	mobilenet_v2_1_0_192
206	convnext_xxlarge:clip_laion2b_soup_ft_in1k
207	mobilenet_v2_0_5_192
208	mobilenet_v2_0_75_192
209	mobilenet_v2_0_5_224
210	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
211	hmax
212	custom_model_cv_18_dagger_408
213	antialiased-rnext101_32x8d
214	PredRNN
215	SimVP
216	TAU
217	MIM
218	densenet_201_pytorch
219	ConvLSTM
220	vit_large_patch14_clip_336:laion2b_ft_in1k
221	vit_large_patch14_clip_336:openai_ft_in12k_in1k
222	pnasnet_large
223	mobilenet_v2_1_3_224
224	omnivore_swinB
225	voneresnet-50-robust
226	AdvProp_efficientnet-b7
227	resnet50-VITO-8deg-cc
228	AdvProp_efficientnet-b8
229	omnivore_swinS
230	BiT-S-R152x4
231	BiT-S-R101x3
232	BiT-S-R152x2
233	densenet-169
234	densenet-121
235	convnext_base:clip_laiona_augreg_ft_in1k_384
236	convnext_large:fb_in22k_ft_in1k

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