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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_large_patch14_clip_336:laion2b_ft_in1k	.781
2	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.774
3	vit_base_patch16_clip_224:openai_ft_in1k	.750
4	efficientnet_b2_imagenet_full	.748
5	convnext_base:clip_laiona_augreg_ft_in1k_384	.747
6	effnetb1_272x240	.746
7	cvt_cvt-21-384-in1k_4_LucyV4	.746
8	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.745
9	vit_large_patch14_clip_224:openai_ft_in1k	.742
10	convnext_femto_ols:d1_in1k	.741
11	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.739
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.738
13	efficientnet_b1_imagenet_full	.737
14	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.735
15	resnet18_imagenet21kP	.734
16	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.734
17	cvt_cvt-13-384-in1k_4_LucyV4	.732
18	resnet50_ewconplus_wzc	.731
19	resnet_152_v2	.729
20	efficientnet_b0_imagenet_full	.727
21	vgg_19	.727
22	mobilenet_v2_1_0_224	.724
23	resnet-152_v2_pytorch	.718
24	resnet50-vicregl0p9	.717
25	ReAlnet01_cornet_fov4	.717
26	densenet-201	.716
27	cvt_cvt-21-224-in1k_4_LucyV4	.716
28	antialiased-r50	.715
29	cvt_cvt-13-224-in1k_4_LucyV4	.715
30	nasnet_large	.713
31	mobilenet_v2_1-4_224_pytorch	.712
32	mobilenet_v2_1_4_224	.712
33	resnext101_32x32d_wsl	.710
34	resnet_50_v2	.707
35	resnet_101_v1	.705
36	resnet101_ecoset_full	.705
37	BiT-S-R50x3	.703
38	resnet18_ecoset_full	.703
39	deit_base_imagenet_full_seed-0	.701
40	mobilenet_v2_1_3_224	.701
41	pnasnet_large_halfPrecision	.701
42	ReAlnet08_ALL	.699
43	ReAlnet06_ALL	.698
44	resnet152_imagenet_full	.697
45	ReAlnet04_ALL	.695
46	Res2Net50_26w_4s	.692
47	resnext101_32x16d_wsl	.692
48	ReAlnet10_ALL	.691
49	yudixie_resnet18_imagenet1kpret_0_240719	.690
50	vgg_16	.689
51	inception_v3	.688
52	ReAlnet09_ALL	.688
53	ReAlnet01_ALL	.688
54	mobilenet_v2_0_75_160	.688
55	resnet_50_v1_spiking_l4a	.687
56	convnext_large:fb_in22k_ft_in1k	.686
57	resnet50_ecoset_full	.686
58	densenet-121	.686
59	ReAlnet05_ALL	.685
60	ReAlnet02_ALL	.685
61	resnet50-vicregl0p75	.685
62	convnext_tiny_sup	.684
63	resnet_101_v2	.684
64	efficientnet_b0	.683
65	mobilenet_v2_1_0_192	.681
66	deit_small_imagenet_full_seed-0	.681
67	tv_efficientnet-b1	.681
68	mobilenet_v2_0_75_192	.680
69	resnet34_ecoset_full	.680
70	ReAlnet03_ALL	.680
71	cv_18_dagger_408_pretrained	.680
72	mobilenet_v2_0_5_224	.679
73	vonegrcnn_52e_full	.679
74	ReAlnet02_cornet	.678
75	ReAlnet02	.678
76	vonegrcnn_62e_nobn	.676
77	resnet_50_v1	.676
78	resnet50-sup	.676
79	regnet_y_400mf	.676
80	resnet-18_test_m	.674
81	deit_large_imagenet_full_seed-0	.674
82	grcnn	.672
83	yudixie_resnet50_imagenet1kpret_0_240908	.672
84	resnet50_eMMCR_Vanilla	.671
85	resnet50-SIN_IN_IN	.669
86	densenet-169	.669
87	inception_v1	.669
88	convnext_small_imagenet_full_seed-0	.668
89	vonegrcnn_47e	.668
90	ReAlnet07_ALL	.667
91	resnet_50_v1_spiking	.667
92	resnet50_tutorial	.667
93	nasnet_mobile	.667
94	ReAlnet06	.667
95	ReAlnet06_cornet	.667
96	resnet18_imagenet_full	.667
97	resnext101_32x8d_wsl	.667
98	convnext_tiny:in12k_ft_in1k	.666
99	BiT-S-R101x3	.666
100	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.666
101	convnext_small_imagenet_100_seed-0	.666
102	convnext_tiny_imagenet_full_seed-0	.665
103	xception	.663
104	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
105	resnet34_imagenet_full	.660
106	BiT-S-R50x1	.660
107	resnet50_imagenet_full	.659
108	inception_v3_pytorch	.659
109	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
110	resnet_SIN_IN_FT_IN	.657
111	ReAlnet05	.656
112	ReAlnet05_cornet	.656
113	convnext_xlarge:fb_in22k_ft_in1k	.656
114	shufflenet_v2_x1_0	.656
115	ReAlnet03	.656
116	ReAlnet03_cornet	.656
117	resnet101_imagenet_full	.654
118	ReAlnet04_cornet	.653
119	ReAlnet04	.653
120	resnet152_ecoset_full	.651
121	convnext_large_imagenet_full_seed-0	.650
122	ReAlnet09	.649
123	ReAlnet09_cornet	.649
124	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
125	resnet50-moclr8deg	.648
126	ReAlnet01_cornet	.648
127	ReAlnet01	.648
128	inception_v4	.648
129	ReAlnet07	.647
130	ReAlnet07_cornet	.647
131	ReAlnet08	.647
132	ReAlnet08_cornet	.647
133	resnet_152_v1	.646
134	ReAlnet10	.646
135	ReAlnet10_cornet	.646
136	resnet50-vicreg	.646
137	mobilenet_v2_0_5_192	.644
138	resnext101_32x48d_wsl	.644
139	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
140	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
141	resnet50-vitoimagevidnet8	.640
142	resnet50-VITO-8deg-cc	.640
143	resnet_50_v1_spiking_l4	.639
144	resnet50_imagenet_100_seed-0	.638
145	antialias-resnet152	.638
146	VOneCORnet-S	.638
147	convnext_base_imagenet_full_seed-0	.635
148	mobilenet_v2_1_0_160	.632
149	vit_base_patch16_dinov3-lvd1689m	.627
150	voneresnet-50-non_stochastic	.625
151	grcnn_109	.625
152	alexnet	.624
153	alexnet_ks_torevert	.624
154	alexnet_fov4	.618
155	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
156	BiT-S-R101x1	.617
157	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
158	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
159	yolos_tiny	.615
160	resnet_50_v1_spiking_l3	.613
161	resnet50-SIN_IN	.610
162	voneresnet-50	.609
163	compact_vgg19_IT	.607
164	deit_small_imagenet_100_seed-0	.606
165	ViT_L_32_imagenet1k	.604
166	resnet50-barlow	.596
167	compact_resnet50_robust_V4	.578
168	BiT-S-R152x2	.578
169	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
170	compact_resnet50_robust_IT	.569
171	mobilenet_v2_1_0_128	.568
172	AdvProp_efficientnet-b2	.565
173	AT_efficientnet-b2	.565
174	compact_cornets_IT	.561
175	compact_resnet50_IT	.553
176	focalnet_tiny_lrf_in1k	.550
177	yudixie_resnet50_object_class_0_240908	.550
178	yudixie_resnet50_distance_translation_0_240908	.549
179	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
180	yudixie_resnet18_distance_rotation_0_240719	.541
181	resnet50_robust_l2_eps1	.541
182	yudixie_resnet18_category_class_0_240719	.535
183	blt_vs	.534
184	yudixie_resnet18_object_class_0_240719	.531
185	yudixie_resnet18_translation_rotation_0_240719	.531
186	alexnet_fov12	.528
187	AlexNet_SIN_fov4	.525
188	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
189	yudixie_resnet50_distance_translation_rotation_0_240908	.521
190	yudixie_resnet18_distance_translation_0_240719	.521
191	yudixie_resnet50_category_class_0_240908	.520
192	compact_resnet50_V4	.507
193	efficientnet-b7	.507
194	yudixie_resnet50_rotation_reg_0_240908	.502
195	AlexNet_SIN	.499
196	yudixie_resnet50_translation_reg_0_240908	.498
197	yudixie_resnet50_distance_rotation_0_240908	.498
198	artResNet18_1	.494
199	yudixie_resnet18_translation_reg_0_240719	.484
200	yudixie_resnet18_rotation_reg_0_240719	.484
201	convnext_small_imagenet_10_seed-0	.484
202	yudixie_resnet18_distance_translation_rotation_0_240719	.482
203	voneresnet-50-robust	.482
204	resnet50-SIN	.479
205	alexnet_fov16	.478
206	resnet50_imagenet_10_seed-0	.478
207	yudixie_resnet50_translation_rotation_0_240908	.476
208	yudixie_resnet18_distance_reg_0_240719	.470
209	pnasnet_large_pytorch	.470
210	compact_vgg19_V4	.470
211	ReAlnet01_cornet_fov12	.462
212	yudixie_resnet18_random_0_240719	.462
213	AdvProp_efficientnet-b4	.461
214	yudixie_resnet50_distance_reg_0_240908	.450
215	compact_cornets_V4	.448
216	resnet-50-robust	.437
217	AlexNet_SIN_fov12	.416
218	AlexNet_SIN_fov16	.412
219	alexnet_training_seed_01_fov4	.407
220	AdvProp_efficientnet-b7	.395
221	ReAlnet01_cornet_fov16	.395
222	imagenet_l2_3_0	.395
223	resnet50_robust_l2_eps3	.395
224	AdvProp_efficientnet-b6	.383
225	alexnet_training_seed_10	.380
226	alexnet_training_seed_06	.379
227	alexnet_training_seed_02	.367
228	resnet50_primary_visual_cortex	.366
229	AdvProp_efficientnet-b8	.363
230	alexnet_training_seed_01	.358
231	alexnet_training_seed_09	.349
232	alexnet_training_seed_03	.349
233	alexnet_training_seed_07	.344
234	alexnet_training_seed_05	.343
235	yudixie_resnet50_random_0_240908	.341
236	alexnet_training_seed_04	.326
237	alexnet_training_seed_08	.325
238	resnet50_imagenet_1_seed-0	.323
239	briaai_rmbg_1_4	.323
240	deit_small_imagenet_1_seed-0	.296
241	convnext_small_imagenet_1_seed-0	.266
242	alexnet_training_seed_01_fov12	.237
243	deit_small_imagenet_10_seed-0	.210
244	alexnet_training_seed_01_fov16	.204
245	aughost_fair_resnet18	.067
246	mobilenet_v2_0_75_224
247	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
248	convnext_xxlarge:clip_laion2b_soup_ft_in1k
249	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
250	hmax
251	custom_model_cv_18_dagger_408
252	antialiased-rnext101_32x8d
253	PredRNN
254	SimVP
255	TAU
256	MIM
257	densenet_201_pytorch
258	ConvLSTM
259	pnasnet_large
260	omnivore_swinB
261	omnivore_swinS
262	BiT-S-R152x4
263	vit_base_patch14_dinov2-lvd142m
264	vit_base_patch14_reg4_dinov2-lvd142m

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

1.00.

Note that scores are relative to this ceiling.

Data: Maniquet2024

Metric: tasks_consistency