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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	resnet50_st_wzc_2	.776
3	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.774
4	vit_base_patch16_clip_224:openai_ft_in1k	.750
5	efficientnet_b2_imagenet_full	.748
6	convnext_base:clip_laiona_augreg_ft_in1k_384	.747
7	effnetb1_272x240	.746
8	cvt_cvt-21-384-in1k_4_LucyV4	.746
9	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.745
10	resnet50_st_wzc_seed4_2_323	.743
11	vit_large_patch14_clip_224:openai_ft_in1k	.742
12	convnext_femto_ols:d1_in1k	.741
13	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.739
14	vit_large_patch14_clip_224:laion2b_ft_in1k	.738
15	efficientnet_b1_imagenet_full	.737
16	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.735
17	resnet18_imagenet21kP	.734
18	resnet50_st_wzc_seed3_2_323	.734
19	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.734
20	cvt_cvt-13-384-in1k_4_LucyV4	.732
21	resnet50_st_wzc_seed7_2_323	.731
22	resnet50_ewconplus_wzc	.731
23	resnet_152_v2	.729
24	resnet50_ewconplus_wzc_seed2_2__323	.729
25	efficientnet_b0_imagenet_full	.727
26	vgg_19	.727
27	mobilenet_v2_1_0_224	.724
28	resnet50_st_wzc_noseed_2_323	.721
29	resnet50_st_wzc_2_323	.720
30	resnet50_ewconplus_wzc_2_323	.720
31	resnet-152_v2_pytorch	.718
32	resnet50-vicregl0p9	.717
33	ReAlnet01_cornet_fov4	.717
34	densenet-201	.716
35	cvt_cvt-21-224-in1k_4_LucyV4	.716
36	antialiased-r50	.715
37	cvt_cvt-13-224-in1k_4_LucyV4	.715
38	nasnet_large	.713
39	mobilenet_v2_1-4_224_pytorch	.712
40	mobilenet_v2_1_4_224	.712
41	resnet50_ewconplus_noseed_2_323	.711
42	resnet50_ewconplus_wzc_seed6_2_323	.711
43	resnext101_32x32d_wsl	.710
44	resnet50_st_wzc_seed6_2_323	.709
45	resnet_50_v2	.707
46	resnet50_ewconplus_wzc_seed7_2_323	.705
47	resnet_101_v1	.705
48	resnet101_ecoset_full	.705
49	BiT-S-R50x3	.703
50	resnet18_ecoset_full	.703
51	deit_base_imagenet_full_seed-0	.701
52	mobilenet_v2_1_3_224	.701
53	pnasnet_large_halfPrecision	.701
54	yudixie_wbh_object_class_scaling_0415_imagenet	.700
55	resnet50_ewconplus_wzc_seed3_2_323	.699
56	ReAlnet08_ALL	.699
57	ReAlnet06_ALL	.698
58	resnet152_imagenet_full	.697
59	ReAlnet04_ALL	.695
60	Res2Net50_26w_4s	.692
61	resnext101_32x16d_wsl	.692
62	ReAlnet10_ALL	.691
63	yudixie_resnet18_imagenet1kpret_0_240719	.690
64	vgg_16	.689
65	inception_v3	.688
66	ReAlnet09_ALL	.688
67	ReAlnet01_ALL	.688
68	mobilenet_v2_0_75_160	.688
69	resnet_50_v1_spiking_l4a	.687
70	convnext_large:fb_in22k_ft_in1k	.686
71	resnet50_ecoset_full	.686
72	densenet-121	.686
73	resnet50_st_wzc_seed5_2_323	.686
74	ReAlnet05_ALL	.685
75	ReAlnet02_ALL	.685
76	resnet50-vicregl0p75	.685
77	convnext_tiny_sup	.684
78	resnet_101_v2	.684
79	resnet50_ewconplus_wzc_seed4_2_323	.684
80	efficientnet_b0	.683
81	resnet50_ewconplus_wzc_seed5_2__323	.683
82	mobilenet_v2_1_0_192	.681
83	deit_small_imagenet_full_seed-0	.681
84	tv_efficientnet-b1	.681
85	mobilenet_v2_0_75_192	.680
86	resnet34_ecoset_full	.680
87	ReAlnet03_ALL	.680
88	cv_18_dagger_408_pretrained	.680
89	mobilenet_v2_0_5_224	.679
90	vonegrcnn_52e_full	.679
91	ReAlnet02_cornet	.678
92	ReAlnet02	.678
93	vonegrcnn_62e_nobn	.676
94	resnet_50_v1	.676
95	resnet50-sup	.676
96	regnet_y_400mf	.676
97	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.675
98	resnet-18_test_m	.674
99	deit_large_imagenet_full_seed-0	.674
100	predictive_coding_pc	.673
101	grcnn	.672
102	yudixie_resnet50_imagenet1kpret_0_240908	.672
103	resnet50_eMMCR_Vanilla	.671
104	resnet50-SIN_IN_IN	.669
105	densenet-169	.669
106	inception_v1	.669
107	convnext_small_imagenet_full_seed-0	.668
108	vonegrcnn_47e	.668
109	ReAlnet07_ALL	.667
110	resnet_50_v1_spiking	.667
111	resnet50_tutorial	.667
112	nasnet_mobile	.667
113	ReAlnet06_cornet	.667
114	ReAlnet06	.667
115	resnet18_imagenet_full	.667
116	resnext101_32x8d_wsl	.667
117	convnext_tiny:in12k_ft_in1k	.666
118	BiT-S-R101x3	.666
119	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.666
120	convnext_small_imagenet_100_seed-0	.666
121	convnext_tiny_imagenet_full_seed-0	.665
122	xception	.663
123	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
124	resnet34_imagenet_full	.660
125	BiT-S-R50x1	.660
126	resnet50_imagenet_full	.659
127	inception_v3_pytorch	.659
128	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
129	resnet_SIN_IN_FT_IN	.657
130	ReAlnet05_cornet	.656
131	ReAlnet05	.656
132	convnext_xlarge:fb_in22k_ft_in1k	.656
133	shufflenet_v2_x1_0	.656
134	ReAlnet03_cornet	.656
135	ReAlnet03	.656
136	resnet101_imagenet_full	.654
137	ReAlnet04_cornet	.653
138	ReAlnet04	.653
139	resnet152_ecoset_full	.651
140	convnext_large_imagenet_full_seed-0	.650
141	ReAlnet09_cornet	.649
142	ReAlnet09	.649
143	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
144	resnet50-moclr8deg	.648
145	ReAlnet01_cornet	.648
146	ReAlnet01	.648
147	inception_v4	.648
148	ReAlnet07_cornet	.647
149	ReAlnet07	.647
150	ReAlnet08_cornet	.647
151	ReAlnet08	.647
152	resnet_152_v1	.646
153	ReAlnet10_cornet	.646
154	ReAlnet10	.646
155	resnet50-vicreg	.646
156	mobilenet_v2_0_5_192	.644
157	resnext101_32x48d_wsl	.644
158	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
159	deepeisnn_ei_imagenet_resnet18	.643
160	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
161	resnet50-vitoimagevidnet8	.640
162	resnet50-VITO-8deg-cc	.640
163	resnet_50_v1_spiking_l4	.639
164	resnet50_imagenet_100_seed-0	.638
165	antialias-resnet152	.638
166	VOneCORnet-S	.638
167	convnext_base_imagenet_full_seed-0	.635
168	mobilenet_v2_1_0_160	.632
169	vit_base_patch16_dinov3-lvd1689m	.627
170	voneresnet-50-non_stochastic	.625
171	grcnn_109	.625
172	alexnet_ks_torevert	.624
173	alexnet	.624
174	deit3_base_patch16_224_fb_in22k_ft_in1k	.624
175	alexnet_fov4	.618
176	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
177	BiT-S-R101x1	.617
178	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
179	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
180	yolos_tiny	.615
181	resnet_50_v1_spiking_l3	.613
182	convnext_base_fb_in1k	.613
183	resnet50-SIN_IN	.610
184	voneresnet-50	.609
185	convnext_base_fb_in1k_gram_aligned_dinov3	.609
186	compact_vgg19_IT	.607
187	deit_small_imagenet_100_seed-0	.606
188	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.604
189	ViT_L_32_imagenet1k	.604
190	resnet50-barlow	.596
191	compact_resnet50_robust_V4	.578
192	BiT-S-R152x2	.578
193	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
194	yudixie_wbh_object_class_scaling_0415_run_0	.569
195	compact_resnet50_robust_IT	.569
196	mobilenet_v2_1_0_128	.568
197	AT_efficientnet-b2	.565
198	AdvProp_efficientnet-b2	.565
199	compact_cornets_IT	.561
200	yudixie_wbh_object_class_scaling_0415_run_3	.554
201	compact_resnet50_IT	.553
202	focalnet_tiny_lrf_in1k	.550
203	yudixie_resnet50_object_class_0_240908	.550
204	yudixie_resnet50_distance_translation_0_240908	.549
205	convnext_base_fb_in22k_ft_in1k	.547
206	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
207	yudixie_resnet18_distance_rotation_0_240719	.541
208	resnet50_robust_l2_eps1	.541
209	yudixie_wbh_object_class_scaling_0415_run_1	.541
210	deit3_base_patch16_224_fb_in1k	.536
211	yudixie_resnet18_category_class_0_240719	.535
212	blt_vs	.534
213	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.534
214	yudixie_resnet18_object_class_0_240719	.531
215	yudixie_resnet18_translation_rotation_0_240719	.531
216	alexnet_fov12	.528
217	AlexNet_SIN_fov4	.525
218	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
219	yudixie_wbh_object_class_scaling_0415_run_2	.522
220	yudixie_resnet50_distance_translation_rotation_0_240908	.521
221	yudixie_resnet18_distance_translation_0_240719	.521
222	yudixie_resnet50_category_class_0_240908	.520
223	compact_resnet50_V4	.507
224	efficientnet-b7	.507
225	yudixie_resnet50_rotation_reg_0_240908	.502
226	AlexNet_SIN	.499
227	yudixie_resnet50_translation_reg_0_240908	.498
228	yudixie_resnet50_distance_rotation_0_240908	.498
229	artResNet18_1	.494
230	resnet18_fair	.484
231	yudixie_resnet18_translation_reg_0_240719	.484
232	yudixie_resnet18_rotation_reg_0_240719	.484
233	convnext_small_imagenet_10_seed-0	.484
234	yudixie_resnet18_distance_translation_rotation_0_240719	.482
235	voneresnet-50-robust	.482
236	resnet50-SIN	.479
237	alexnet_fov16	.478
238	resnet50_imagenet_10_seed-0	.478
239	yudixie_resnet50_translation_rotation_0_240908	.476
240	yudixie_resnet18_distance_reg_0_240719	.470
241	pnasnet_large_pytorch	.470
242	compact_vgg19_V4	.470
243	ReAlnet01_cornet_fov12	.462
244	yudixie_resnet18_random_0_240719	.462
245	AdvProp_efficientnet-b4	.461
246	yudixie_resnet50_distance_reg_0_240908	.450
247	compact_cornets_V4	.448
248	resnet-50-robust	.437
249	pr1_stl10_disentangled_alpha04	.433
250	AlexNet_SIN_fov12	.416
251	AlexNet_SIN_fov16	.412
252	alexnet_training_seed_01_fov4	.407
253	AdvProp_efficientnet-b7	.395
254	ReAlnet01_cornet_fov16	.395
255	resnet50_robust_l2_eps3	.395
256	imagenet_l2_3_0	.395
257	AdvProp_efficientnet-b6	.383
258	alexnet_training_seed_10	.380
259	alexnet_training_seed_06	.379
260	alexnet_training_seed_02	.367
261	resnet50_primary_visual_cortex	.366
262	AdvProp_efficientnet-b8	.363
263	alexnet_training_seed_01	.358
264	alexnet_training_seed_09	.349
265	alexnet_training_seed_03	.349
266	alexnet_training_seed_07	.344
267	alexnet_training_seed_05	.343
268	yudixie_resnet50_random_0_240908	.341
269	alexnet_training_seed_04	.326
270	alexnet_training_seed_08	.325
271	resnet50_imagenet_1_seed-0	.323
272	briaai_rmbg_1_4	.323
273	deit_small_imagenet_1_seed-0	.296
274	convnext_small_imagenet_1_seed-0	.266
275	alexnet_training_seed_01_fov12	.237
276	deepeisnn_ei_cifar10_vgg19	.217
277	deit_small_imagenet_10_seed-0	.210
278	alexnet_training_seed_01_fov16	.204
279	deepeisnn_ei_cifar10_resnet18	.125
280	aughost_fair_resnet18	.067
281	vit_base_patch14_reg4_dinov2-lvd142m
282	vit_base_patch14_dinov2-lvd142m
283	pnasnet_large
284	mobilenet_v2_0_75_224
285	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
286	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
287	omnivore_swinS
288	omnivore_swinB
289	convnext_xxlarge:clip_laion2b_soup_ft_in1k
290	BiT-S-R152x4
291	ConvLSTM
292	densenet_201_pytorch
293	MIM
294	TAU
295	SimVP
296	PredRNN
297	antialiased-rnext101_32x8d
298	custom_model_cv_18_dagger_408
299	hmax

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