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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	resnet50_ewconplus_wzc	.731
15	resnet_152_v2	.729
16	efficientnet_b0_imagenet_full	.727
17	vgg_19	.727
18	mobilenet_v2_1_0_224	.724
19	resnet-152_v2_pytorch	.718
20	resnet50-vicregl0p9	.717
21	densenet-201	.716
22	cvt_cvt-21-224-in1k_4_LucyV4	.716
23	antialiased-r50	.715
24	cvt_cvt-13-224-in1k_4_LucyV4	.715
25	nasnet_large	.713
26	mobilenet_v2_1-4_224_pytorch	.712
27	mobilenet_v2_1_4_224	.712
28	resnext101_32x32d_wsl	.710
29	resnet_50_v2	.707
30	resnet_101_v1	.705
31	resnet101_ecoset_full	.705
32	resnet18_ecoset_full	.703
33	deit_base_imagenet_full_seed-0	.701
34	ReAlnet08_ALL	.699
35	ReAlnet06_ALL	.698
36	resnet152_imagenet_full	.697
37	ReAlnet04_ALL	.695
38	Res2Net50_26w_4s	.692
39	resnext101_32x16d_wsl	.692
40	ReAlnet10_ALL	.691
41	yudixie_resnet18_imagenet1kpret_0_240719	.690
42	vgg_16	.689
43	inception_v3	.688
44	ReAlnet09_ALL	.688
45	ReAlnet01_ALL	.688
46	mobilenet_v2_0_75_160	.688
47	resnet_50_v1_spiking_l4a	.687
48	resnet50_ecoset_full	.686
49	ReAlnet05_ALL	.685
50	ReAlnet02_ALL	.685
51	resnet50-vicregl0p75	.685
52	convnext_tiny_sup	.684
53	resnet_101_v2	.684
54	efficientnet_b0	.683
55	deit_small_imagenet_full_seed-0	.681
56	tv_efficientnet-b1	.681
57	resnet34_ecoset_full	.680
58	ReAlnet03_ALL	.680
59	vonegrcnn_52e_full	.679
60	ReAlnet02_cornet	.678
61	ReAlnet02	.678
62	vonegrcnn_62e_nobn	.676
63	resnet_50_v1	.676
64	resnet50-sup	.676
65	regnet_y_400mf	.676
66	resnet-18_test_m	.674
67	deit_large_imagenet_full_seed-0	.674
68	grcnn	.672
69	yudixie_resnet50_imagenet1kpret_0_240908	.672
70	resnet50_eMMCR_Vanilla	.671
71	resnet50-SIN_IN_IN	.669
72	inception_v1	.669
73	convnext_small_imagenet_full_seed-0	.668
74	vonegrcnn_47e	.668
75	ReAlnet07_ALL	.667
76	resnet_50_v1_spiking	.667
77	resnet50_tutorial	.667
78	nasnet_mobile	.667
79	ReAlnet06	.667
80	ReAlnet06_cornet	.667
81	resnet18_imagenet_full	.667
82	resnext101_32x8d_wsl	.667
83	convnext_tiny:in12k_ft_in1k	.666
84	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.666
85	convnext_small_imagenet_100_seed-0	.666
86	convnext_tiny_imagenet_full_seed-0	.665
87	xception	.663
88	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
89	resnet34_imagenet_full	.660
90	BiT-S-R50x1	.660
91	resnet50_imagenet_full	.659
92	inception_v3_pytorch	.659
93	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
94	resnet_SIN_IN_FT_IN	.657
95	ReAlnet05	.656
96	ReAlnet05_cornet	.656
97	convnext_xlarge:fb_in22k_ft_in1k	.656
98	shufflenet_v2_x1_0	.656
99	ReAlnet03	.656
100	ReAlnet03_cornet	.656
101	resnet101_imagenet_full	.654
102	ReAlnet04_cornet	.653
103	ReAlnet04	.653
104	resnet152_ecoset_full	.651
105	convnext_large_imagenet_full_seed-0	.650
106	ReAlnet09	.649
107	ReAlnet09_cornet	.649
108	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
109	resnet50-moclr8deg	.648
110	ReAlnet01_cornet	.648
111	ReAlnet01	.648
112	inception_v4	.648
113	ReAlnet07	.647
114	ReAlnet07_cornet	.647
115	ReAlnet08	.647
116	ReAlnet08_cornet	.647
117	resnet_152_v1	.646
118	ReAlnet10	.646
119	ReAlnet10_cornet	.646
120	resnet50-vicreg	.646
121	resnext101_32x48d_wsl	.644
122	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
123	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
124	resnet50-vitoimagevidnet8	.640
125	resnet_50_v1_spiking_l4	.639
126	resnet50_imagenet_100_seed-0	.638
127	antialias-resnet152	.638
128	VOneCORnet-S	.638
129	convnext_base_imagenet_full_seed-0	.635
130	mobilenet_v2_1_0_160	.632
131	voneresnet-50-non_stochastic	.625
132	grcnn_109	.625
133	alexnet_ks_torevert	.624
134	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
135	BiT-S-R101x1	.617
136	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
137	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
138	yolos_tiny	.615
139	resnet_50_v1_spiking_l3	.613
140	resnet50-SIN_IN	.610
141	voneresnet-50	.609
142	compact_vgg19_IT	.607
143	deit_small_imagenet_100_seed-0	.606
144	ViT_L_32_imagenet1k	.604
145	resnet50-barlow	.596
146	compact_resnet50_robust_V4	.578
147	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
148	compact_resnet50_robust_IT	.569
149	mobilenet_v2_1_0_128	.568
150	AdvProp_efficientnet-b2	.565
151	AT_efficientnet-b2	.565
152	compact_cornets_IT	.561
153	compact_resnet50_IT	.553
154	focalnet_tiny_lrf_in1k	.550
155	yudixie_resnet50_object_class_0_240908	.550
156	yudixie_resnet50_distance_translation_0_240908	.549
157	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
158	yudixie_resnet18_distance_rotation_0_240719	.541
159	resnet50_robust_l2_eps1	.541
160	yudixie_resnet18_category_class_0_240719	.535
161	blt_vs	.534
162	yudixie_resnet18_object_class_0_240719	.531
163	yudixie_resnet18_translation_rotation_0_240719	.531
164	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
165	yudixie_resnet50_distance_translation_rotation_0_240908	.521
166	yudixie_resnet18_distance_translation_0_240719	.521
167	yudixie_resnet50_category_class_0_240908	.520
168	compact_resnet50_V4	.507
169	efficientnet-b7	.507
170	yudixie_resnet50_rotation_reg_0_240908	.502
171	AlexNet_SIN	.499
172	yudixie_resnet50_translation_reg_0_240908	.498
173	yudixie_resnet50_distance_rotation_0_240908	.498
174	artResNet18_1	.494
175	yudixie_resnet18_translation_reg_0_240719	.484
176	yudixie_resnet18_rotation_reg_0_240719	.484
177	convnext_small_imagenet_10_seed-0	.484
178	yudixie_resnet18_distance_translation_rotation_0_240719	.482
179	resnet50-SIN	.479
180	resnet50_imagenet_10_seed-0	.478
181	yudixie_resnet50_translation_rotation_0_240908	.476
182	yudixie_resnet18_distance_reg_0_240719	.470
183	pnasnet_large_pytorch	.470
184	compact_vgg19_V4	.470
185	yudixie_resnet18_random_0_240719	.462
186	AdvProp_efficientnet-b4	.461
187	yudixie_resnet50_distance_reg_0_240908	.450
188	compact_cornets_V4	.448
189	resnet-50-robust	.437
190	imagenet_l2_3_0	.395
191	resnet50_robust_l2_eps3	.395
192	AdvProp_efficientnet-b6	.383
193	alexnet_training_seed_10	.380
194	alexnet_training_seed_06	.379
195	alexnet_training_seed_02	.367
196	resnet50_primary_visual_cortex	.366
197	alexnet_training_seed_01	.358
198	alexnet_training_seed_09	.349
199	alexnet_training_seed_03	.349
200	alexnet_training_seed_07	.344
201	alexnet_training_seed_05	.343
202	yudixie_resnet50_random_0_240908	.341
203	alexnet_training_seed_04	.326
204	alexnet_training_seed_08	.325
205	resnet50_imagenet_1_seed-0	.323
206	briaai_rmbg_1_4	.323
207	deit_small_imagenet_1_seed-0	.296
208	convnext_small_imagenet_1_seed-0	.266
209	deit_small_imagenet_10_seed-0	.210
210	BiT-S-R50x3
211	mobilenet_v2_0_75_224
212	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
213	vit_large_patch14_clip_224:openai_ft_in1k
214	mobilenet_v2_1_0_192
215	convnext_xxlarge:clip_laion2b_soup_ft_in1k
216	mobilenet_v2_0_5_192
217	mobilenet_v2_0_75_192
218	mobilenet_v2_0_5_224
219	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
220	hmax
221	custom_model_cv_18_dagger_408
222	antialiased-rnext101_32x8d
223	PredRNN
224	SimVP
225	TAU
226	MIM
227	densenet_201_pytorch
228	ConvLSTM
229	vit_large_patch14_clip_336:laion2b_ft_in1k
230	vit_large_patch14_clip_336:openai_ft_in12k_in1k
231	pnasnet_large
232	mobilenet_v2_1_3_224
233	omnivore_swinB
234	voneresnet-50-robust
235	AdvProp_efficientnet-b7
236	resnet50-VITO-8deg-cc
237	AdvProp_efficientnet-b8
238	omnivore_swinS
239	BiT-S-R152x4
240	BiT-S-R101x3
241	BiT-S-R152x2
242	densenet-169
243	densenet-121
244	convnext_base:clip_laiona_augreg_ft_in1k_384
245	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