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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	dinov2_vitl14_reg_lc	.789
2	vit_large_patch14_clip_336:laion2b_ft_in1k	.781
3	resnet50_st_wzc_2	.776
4	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.774
5	vit_base_patch16_clip_224:openai_ft_in1k	.750
6	efficientnet_b2_imagenet_full	.748
7	convnext_base:clip_laiona_augreg_ft_in1k_384	.747
8	effnetb1_272x240	.746
9	cvt_cvt-21-384-in1k_4_LucyV4	.746
10	resnet50_hvs_wzc	.746
11	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.745
12	vit_large_patch14_clip_224:openai_ft_in1k	.742
13	dinov2_vitl14_lc	.742
14	convnext_femto_ols:d1_in1k	.741
15	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.739
16	vit_large_patch14_clip_224:laion2b_ft_in1k	.738
17	efficientnet_b1_imagenet_full	.737
18	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.735
19	resnet18_imagenet21kP	.734
20	resnet50_st_wzc_seed3_2_323	.734
21	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.734
22	cvt_cvt-13-384-in1k_4_LucyV4	.732
23	resnet50_st_wzc_seed7_2_323	.731
24	resnet50_ewconplus_wzc	.731
25	resnet_152_v2	.729
26	resnet50_ewconplus_wzc_seed2_2__323	.729
27	efficientnet_b0_imagenet_full	.727
28	vgg_19	.727
29	mobilenet_v2_1_0_224	.724
30	resnet-152_v2_pytorch	.718
31	resnet50-vicregl0p9	.717
32	ReAlnet01_cornet_fov4	.717
33	densenet-201	.716
34	cvt_cvt-21-224-in1k_4_LucyV4	.716
35	antialiased-r50	.715
36	cvt_cvt-13-224-in1k_4_LucyV4	.715
37	nasnet_large	.713
38	mobilenet_v2_1-4_224_pytorch	.712
39	mobilenet_v2_1_4_224	.712
40	resnet50_ewconplus_wzc_seed6_2_323	.711
41	resnext101_32x32d_wsl	.710
42	resnet50_st_wzc_seed6_2_323	.709
43	resnet_50_v2	.707
44	resnet_101_v1	.705
45	resnet101_ecoset_full	.705
46	BiT-S-R50x3	.703
47	resnet18_ecoset_full	.703
48	deit_base_imagenet_full_seed-0	.701
49	mobilenet_v2_1_3_224	.701
50	pnasnet_large_halfPrecision	.701
51	yudixie_wbh_object_class_scaling_0415_imagenet	.700
52	ReAlnet08_ALL	.699
53	ReAlnet06_ALL	.698
54	resnet152_imagenet_full	.697
55	ReAlnet04_ALL	.695
56	Res2Net50_26w_4s	.692
57	resnext101_32x16d_wsl	.692
58	ReAlnet10_ALL	.691
59	yudixie_resnet18_imagenet1kpret_0_240719	.690
60	vgg_16	.689
61	inception_v3	.688
62	ReAlnet09_ALL	.688
63	ReAlnet01_ALL	.688
64	mobilenet_v2_0_75_160	.688
65	resnet_50_v1_spiking_l4a	.687
66	convnext_large:fb_in22k_ft_in1k	.686
67	resnet50_ecoset_full	.686
68	densenet-121	.686
69	ReAlnet05_ALL	.685
70	ReAlnet02_ALL	.685
71	resnet50-vicregl0p75	.685
72	convnext_tiny_sup	.684
73	resnet_101_v2	.684
74	efficientnet_b0	.683
75	resnet50_ewconplus_wzc_seed5_2__323	.683
76	convnext_large_in22k_supervised	.681
77	mobilenet_v2_1_0_192	.681
78	convnext_large_clip_laion2b_finelayers	.681
79	deit_small_imagenet_full_seed-0	.681
80	tv_efficientnet-b1	.681
81	mobilenet_v2_0_75_192	.680
82	resnet34_ecoset_full	.680
83	ReAlnet03_ALL	.680
84	cv_18_dagger_408_pretrained	.680
85	mobilenet_v2_0_5_224	.679
86	vonegrcnn_52e_full	.679
87	ReAlnet02_cornet	.678
88	ReAlnet02	.678
89	vonegrcnn_62e_nobn	.676
90	resnet_50_v1	.676
91	resnet50-sup	.676
92	regnet_y_400mf	.676
93	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.675
94	resnet-18_test_m	.674
95	deit_large_imagenet_full_seed-0	.674
96	predictive_coding_pc	.673
97	grcnn	.672
98	yudixie_resnet50_imagenet1kpret_0_240908	.672
99	resnet50_eMMCR_Vanilla	.671
100	resnet50-SIN_IN_IN	.669
101	densenet-169	.669
102	inception_v1	.669
103	convnext_small_imagenet_full_seed-0	.668
104	vonegrcnn_47e	.668
105	ReAlnet07_ALL	.667
106	resnet_50_v1_spiking	.667
107	resnet50_tutorial	.667
108	nasnet_mobile	.667
109	ReAlnet06_cornet	.667
110	ReAlnet06	.667
111	resnet18_imagenet_full	.667
112	resnext101_32x8d_wsl	.667
113	convnext_tiny:in12k_ft_in1k	.666
114	BiT-S-R101x3	.666
115	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.666
116	convnext_small_imagenet_100_seed-0	.666
117	convnext_tiny_imagenet_full_seed-0	.665
118	xception	.663
119	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
120	resnet34_imagenet_full	.660
121	BiT-S-R50x1	.660
122	resnet50_imagenet_full	.659
123	inception_v3_pytorch	.659
124	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
125	resnet_SIN_IN_FT_IN	.657
126	ReAlnet05_cornet	.656
127	ReAlnet05	.656
128	convnext_xlarge:fb_in22k_ft_in1k	.656
129	shufflenet_v2_x1_0	.656
130	ReAlnet03_cornet	.656
131	ReAlnet03	.656
132	resnet101_imagenet_full	.654
133	ReAlnet04_cornet	.653
134	ReAlnet04	.653
135	resnet152_ecoset_full	.651
136	convnext_large_imagenet_full_seed-0	.650
137	ReAlnet09_cornet	.649
138	ReAlnet09	.649
139	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
140	resnet50-moclr8deg	.648
141	ReAlnet01_cornet	.648
142	ReAlnet01	.648
143	inception_v4	.648
144	ReAlnet07_cornet	.647
145	ReAlnet07	.647
146	ReAlnet08_cornet	.647
147	ReAlnet08	.647
148	resnet_152_v1	.646
149	ReAlnet10_cornet	.646
150	ReAlnet10	.646
151	resnet50-vicreg	.646
152	mobilenet_v2_0_5_192	.644
153	resnext101_32x48d_wsl	.644
154	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
155	deepeisnn_ei_imagenet_resnet18	.643
156	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
157	resnet50-vitoimagevidnet8	.640
158	resnet50-VITO-8deg-cc	.640
159	resnet_50_v1_spiking_l4	.639
160	resnet50_imagenet_100_seed-0	.638
161	antialias-resnet152	.638
162	VOneCORnet-S	.638
163	convnext_base_imagenet_full_seed-0	.635
164	mobilenet_v2_1_0_160	.632
165	vit_base_patch16_dinov3-lvd1689m	.627
166	voneresnet-50-non_stochastic	.625
167	grcnn_109	.625
168	alexnet_ks_torevert	.624
169	alexnet	.624
170	deit3_base_patch16_224_fb_in22k_ft_in1k	.624
171	alexnet_fov4	.618
172	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
173	BiT-S-R101x1	.617
174	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
175	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
176	yolos_tiny	.615
177	resnet_50_v1_spiking_l3	.613
178	convnext_base_fb_in1k	.613
179	resnet50-SIN_IN	.610
180	voneresnet-50	.609
181	convnext_base_fb_in1k_gram_aligned_dinov3	.609
182	compact_vgg19_IT	.607
183	deit_small_imagenet_100_seed-0	.606
184	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.604
185	ViT_L_32_imagenet1k	.604
186	resnet50-barlow	.596
187	compact_resnet50_robust_V4	.578
188	BiT-S-R152x2	.578
189	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
190	yudixie_wbh_object_class_scaling_0415_run_0	.569
191	compact_resnet50_robust_IT	.569
192	mobilenet_v2_1_0_128	.568
193	AT_efficientnet-b2	.565
194	AdvProp_efficientnet-b2	.565
195	compact_cornets_IT	.561
196	yudixie_wbh_object_class_scaling_0415_run_3	.554
197	compact_resnet50_IT	.553
198	focalnet_tiny_lrf_in1k	.550
199	yudixie_resnet50_object_class_0_240908	.550
200	yudixie_resnet50_distance_translation_0_240908	.549
201	convnext_base_fb_in22k_ft_in1k	.547
202	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
203	yudixie_resnet18_distance_rotation_0_240719	.541
204	resnet50_robust_l2_eps1	.541
205	yudixie_wbh_object_class_scaling_0415_run_1	.541
206	persistence_mem	.536
207	deit3_base_patch16_224_fb_in1k	.536
208	yudixie_resnet18_category_class_0_240719	.535
209	blt_vs	.534
210	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.534
211	yudixie_resnet18_object_class_0_240719	.531
212	yudixie_resnet18_translation_rotation_0_240719	.531
213	alexnet_fov12	.528
214	AlexNet_SIN_fov4	.525
215	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
216	yudixie_wbh_object_class_scaling_0415_run_2	.522
217	yudixie_resnet50_distance_translation_rotation_0_240908	.521
218	yudixie_resnet18_distance_translation_0_240719	.521
219	yudixie_resnet50_category_class_0_240908	.520
220	compact_resnet50_V4	.507
221	efficientnet-b7	.507
222	yudixie_resnet50_rotation_reg_0_240908	.502
223	AlexNet_SIN	.499
224	yudixie_resnet50_translation_reg_0_240908	.498
225	yudixie_resnet50_distance_rotation_0_240908	.498
226	artResNet18_1	.494
227	persistence_memory	.491
228	resnet18_fair	.484
229	yudixie_resnet18_translation_reg_0_240719	.484
230	yudixie_resnet18_rotation_reg_0_240719	.484
231	convnext_small_imagenet_10_seed-0	.484
232	vonealexnet	.483
233	yudixie_resnet18_distance_translation_rotation_0_240719	.482
234	voneresnet-50-robust	.482
235	resnet50-SIN	.479
236	alexnet_fov16	.478
237	resnet50_imagenet_10_seed-0	.478
238	yudixie_resnet50_translation_rotation_0_240908	.476
239	yudixie_resnet18_distance_reg_0_240719	.470
240	pnasnet_large_pytorch	.470
241	compact_vgg19_V4	.470
242	ReAlnet01_cornet_fov12	.462
243	yudixie_resnet18_random_0_240719	.462
244	AdvProp_efficientnet-b4	.461
245	yudixie_resnet50_distance_reg_0_240908	.450
246	compact_cornets_V4	.448
247	resnet-50-robust	.437
248	pr1_stl10_disentangled_alpha04	.433
249	AlexNet_SIN_fov12	.416
250	AlexNet_SIN_fov16	.412
251	alexnet_training_seed_01_fov4	.407
252	AdvProp_efficientnet-b7	.395
253	ReAlnet01_cornet_fov16	.395
254	resnet50_robust_l2_eps3	.395
255	imagenet_l2_3_0	.395
256	AdvProp_efficientnet-b6	.383
257	alexnet_training_seed_10	.380
258	alexnet_training_seed_06	.379
259	alexnet_training_seed_02	.367
260	resnet50_primary_visual_cortex	.366
261	AdvProp_efficientnet-b8	.363
262	alexnet_training_seed_01	.358
263	alexnet_training_seed_09	.349
264	alexnet_training_seed_03	.349
265	alexnet_training_seed_07	.344
266	alexnet_training_seed_05	.343
267	yudixie_resnet50_random_0_240908	.341
268	alexnet_training_seed_04	.326
269	alexnet_training_seed_08	.325
270	resnet50_imagenet_1_seed-0	.323
271	briaai_rmbg_1_4	.323
272	deit_small_imagenet_1_seed-0	.296
273	convnext_small_imagenet_1_seed-0	.266
274	alexnet_training_seed_01_fov12	.237
275	deepeisnn_ei_cifar10_vgg19	.217
276	deit_small_imagenet_10_seed-0	.210
277	alexnet_training_seed_01_fov16	.204
278	deepeisnn_ei_cifar10_resnet18	.125
279	vit_huge_clip336_laion
280	convnext_xxlarge_clip
281	vit_large_clip336_openai
282	dinov2_vitl14_reg_lc_featread
283	dinov2_vitl14_lc_res336
284	eva02_large_in22k
285	dinov2_vitg14_reg_lc
286	dinov2_vitg14_lc
287	vit_base_patch14_reg4_dinov2-lvd142m
288	vit_base_patch14_dinov2-lvd142m
289	pnasnet_large
290	mobilenet_v2_0_75_224
291	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
292	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
293	omnivore_swinS
294	omnivore_swinB
295	convnext_xxlarge:clip_laion2b_soup_ft_in1k
296	BiT-S-R152x4
297	ConvLSTM
298	densenet_201_pytorch
299	MIM
300	TAU
301	SimVP
302	PredRNN
303	antialiased-rnext101_32x8d
304	custom_model_cv_18_dagger_408
305	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