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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	eicircuit_resnet18_bn_20260919	.661
120	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
121	resnet34_imagenet_full	.660
122	BiT-S-R50x1	.660
123	resnet50_imagenet_full	.659
124	inception_v3_pytorch	.659
125	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
126	resnet_SIN_IN_FT_IN	.657
127	ReAlnet05_cornet	.656
128	ReAlnet05	.656
129	convnext_xlarge:fb_in22k_ft_in1k	.656
130	shufflenet_v2_x1_0	.656
131	ReAlnet03_cornet	.656
132	ReAlnet03	.656
133	resnet101_imagenet_full	.654
134	ReAlnet04_cornet	.653
135	ReAlnet04	.653
136	resnet152_ecoset_full	.651
137	convnext_large_imagenet_full_seed-0	.650
138	ReAlnet09_cornet	.649
139	ReAlnet09	.649
140	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
141	resnet50-moclr8deg	.648
142	ReAlnet01_cornet	.648
143	ReAlnet01	.648
144	inception_v4	.648
145	ReAlnet07_cornet	.647
146	ReAlnet07	.647
147	ReAlnet08_cornet	.647
148	ReAlnet08	.647
149	resnet_152_v1	.646
150	ReAlnet10_cornet	.646
151	ReAlnet10	.646
152	resnet50-vicreg	.646
153	mobilenet_v2_0_5_192	.644
154	resnext101_32x48d_wsl	.644
155	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
156	deepeisnn_ei_imagenet_resnet18	.643
157	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
158	resnet50-vitoimagevidnet8	.640
159	resnet50-VITO-8deg-cc	.640
160	resnet_50_v1_spiking_l4	.639
161	resnet50_imagenet_100_seed-0	.638
162	antialias-resnet152	.638
163	VOneCORnet-S	.638
164	convnext_base_imagenet_full_seed-0	.635
165	mobilenet_v2_1_0_160	.632
166	regnety_032	.632
167	vit_base_patch16_dinov3-lvd1689m	.627
168	voneresnet-50-non_stochastic	.625
169	grcnn_109	.625
170	alexnet_ks_torevert	.624
171	alexnet	.624
172	deit3_base_patch16_224_fb_in22k_ft_in1k	.624
173	alexnet_fov4	.618
174	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
175	BiT-S-R101x1	.617
176	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
177	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
178	yolos_tiny	.615
179	resnet_50_v1_spiking_l3	.613
180	convnext_base_fb_in1k	.613
181	resnet50-SIN_IN	.610
182	voneresnet-50	.609
183	convnext_base_fb_in1k_gram_aligned_dinov3	.609
184	compact_vgg19_IT	.607
185	deit_small_imagenet_100_seed-0	.606
186	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.604
187	ViT_L_32_imagenet1k	.604
188	resnet50-barlow	.596
189	seresnext50_32x4d	.592
190	compact_resnet50_robust_V4	.578
191	BiT-S-R152x2	.578
192	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
193	yudixie_wbh_object_class_scaling_0415_run_0	.569
194	compact_resnet50_robust_IT	.569
195	mobilenet_v2_1_0_128	.568
196	AT_efficientnet-b2	.565
197	AdvProp_efficientnet-b2	.565
198	compact_cornets_IT	.561
199	yudixie_wbh_object_class_scaling_0415_run_3	.554
200	compact_resnet50_IT	.553
201	focalnet_tiny_lrf_in1k	.550
202	yudixie_resnet50_object_class_0_240908	.550
203	yudixie_resnet50_distance_translation_0_240908	.549
204	convnext_base_fb_in22k_ft_in1k	.547
205	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
206	gabor_filter_entropy_4x4	.541
207	gabor_filter_entropy_4x4_v2_forcedCosine	.541
208	yudixie_resnet18_distance_rotation_0_240719	.541
209	resnet50_robust_l2_eps1	.541
210	yudixie_wbh_object_class_scaling_0415_run_1	.541
211	persistence_mem	.536
212	deit3_base_patch16_224_fb_in1k	.536
213	yudixie_resnet18_category_class_0_240719	.535
214	blt_vs	.534
215	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.534
216	yudixie_resnet18_object_class_0_240719	.531
217	yudixie_resnet18_translation_rotation_0_240719	.531
218	alexnet_fov12	.528
219	gabor_filter_entropy_4x4_v2	.527
220	AlexNet_SIN_fov4	.525
221	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
222	yudixie_wbh_object_class_scaling_0415_run_2	.522
223	yudixie_resnet50_distance_translation_rotation_0_240908	.521
224	yudixie_resnet18_distance_translation_0_240719	.521
225	yudixie_resnet50_category_class_0_240908	.520
226	compact_resnet50_V4	.507
227	efficientnet-b7	.507
228	yudixie_resnet50_rotation_reg_0_240908	.502
229	AlexNet_SIN	.499
230	yudixie_resnet50_translation_reg_0_240908	.498
231	yudixie_resnet50_distance_rotation_0_240908	.498
232	artResNet18_1	.494
233	persistence_memory	.491
234	resnet18_fair	.484
235	yudixie_resnet18_translation_reg_0_240719	.484
236	yudixie_resnet18_rotation_reg_0_240719	.484
237	convnext_small_imagenet_10_seed-0	.484
238	vonealexnet	.483
239	yudixie_resnet18_distance_translation_rotation_0_240719	.482
240	voneresnet-50-robust	.482
241	resnet50-SIN	.479
242	alexnet_fov16	.478
243	resnet50_imagenet_10_seed-0	.478
244	yudixie_resnet50_translation_rotation_0_240908	.476
245	yudixie_resnet18_distance_reg_0_240719	.470
246	pnasnet_large_pytorch	.470
247	compact_vgg19_V4	.470
248	ReAlnet01_cornet_fov12	.462
249	yudixie_resnet18_random_0_240719	.462
250	AdvProp_efficientnet-b4	.461
251	yudixie_resnet50_distance_reg_0_240908	.450
252	compact_cornets_V4	.448
253	resnet-50-robust	.437
254	pr1_stl10_disentangled_alpha04	.433
255	AlexNet_SIN_fov12	.416
256	AlexNet_SIN_fov16	.412
257	alexnet_training_seed_01_fov4	.407
258	AdvProp_efficientnet-b7	.395
259	ReAlnet01_cornet_fov16	.395
260	resnet50_robust_l2_eps3	.395
261	imagenet_l2_3_0	.395
262	AdvProp_efficientnet-b6	.383
263	alexnet_training_seed_10	.380
264	alexnet_training_seed_06	.379
265	alexnet_training_seed_02	.367
266	resnet50_primary_visual_cortex	.366
267	AdvProp_efficientnet-b8	.363
268	alexnet_training_seed_01	.358
269	alexnet_training_seed_09	.349
270	alexnet_training_seed_03	.349
271	alexnet_training_seed_07	.344
272	alexnet_training_seed_05	.343
273	yudixie_resnet50_random_0_240908	.341
274	alexnet_training_seed_04	.326
275	alexnet_training_seed_08	.325
276	resnet50_imagenet_1_seed-0	.323
277	briaai_rmbg_1_4	.323
278	deit_small_imagenet_1_seed-0	.296
279	gabor_filter_mean_abs_4x4_v2	.292
280	convnext_small_imagenet_1_seed-0	.266
281	alexnet_training_seed_01_fov12	.237
282	deepeisnn_ei_cifar10_vgg19	.217
283	deit_small_imagenet_10_seed-0	.210
284	alexnet_training_seed_01_fov16	.204
285	deepeisnn_ei_cifar10_resnet18	.125
286	vit_huge_clip336_laion
287	convnext_xxlarge_clip
288	vit_large_clip336_openai
289	dinov2_vitl14_reg_lc_featread
290	dinov2_vitl14_lc_res336
291	eva02_large_in22k
292	dinov2_vitg14_reg_lc
293	dinov2_vitg14_lc
294	vit_base_patch14_reg4_dinov2-lvd142m
295	vit_base_patch14_dinov2-lvd142m
296	pnasnet_large
297	mobilenet_v2_0_75_224
298	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
299	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
300	omnivore_swinS
301	omnivore_swinB
302	convnext_xxlarge:clip_laion2b_soup_ft_in1k
303	BiT-S-R152x4
304	ConvLSTM
305	densenet_201_pytorch
306	MIM
307	TAU
308	SimVP
309	PredRNN
310	antialiased-rnext101_32x8d
311	custom_model_cv_18_dagger_408
312	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