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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	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.763
6	vit_base_patch16_clip_224:openai_ft_in1k	.750
7	efficientnet_b2_imagenet_full	.748
8	convnext_base:clip_laiona_augreg_ft_in1k_384	.747
9	effnetb1_272x240	.746
10	cvt_cvt-21-384-in1k_4_LucyV4	.746
11	resnet50_hvs_wzc	.746
12	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.745
13	vit_large_patch14_clip_224:openai_ft_in1k	.742
14	dinov2_vitl14_lc	.742
15	convnext_femto_ols:d1_in1k	.741
16	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.739
17	vit_large_patch14_clip_224:laion2b_ft_in1k	.738
18	efficientnet_b1_imagenet_full	.737
19	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.735
20	resnet18_imagenet21kP	.734
21	resnet50_st_wzc_seed3_2_323	.734
22	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.734
23	cvt_cvt-13-384-in1k_4_LucyV4	.732
24	resnet50_st_wzc_seed7_2_323	.731
25	resnet50_ewconplus_wzc	.731
26	resnet_152_v2	.729
27	resnet50_ewconplus_wzc_seed2_2__323	.729
28	efficientnet_b0_imagenet_full	.727
29	vgg_19	.727
30	mobilenet_v2_1_0_224	.724
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_wzc_seed6_2_323	.711
42	resnext101_32x32d_wsl	.710
43	resnet50_st_wzc_seed6_2_323	.709
44	resnet_50_v2	.707
45	resnet_101_v1	.705
46	resnet101_ecoset_full	.705
47	BiT-S-R50x3	.703
48	resnet18_ecoset_full	.703
49	deit_base_imagenet_full_seed-0	.701
50	mobilenet_v2_1_3_224	.701
51	pnasnet_large_halfPrecision	.701
52	yudixie_wbh_object_class_scaling_0415_imagenet	.700
53	ijepa_vith14_linhead	.700
54	ReAlnet08_ALL	.699
55	ReAlnet06_ALL	.698
56	resnet152_imagenet_full	.697
57	ReAlnet04_ALL	.695
58	Res2Net50_26w_4s	.692
59	resnext101_32x16d_wsl	.692
60	ReAlnet10_ALL	.691
61	yudixie_resnet18_imagenet1kpret_0_240719	.690
62	vgg_16	.689
63	inception_v3	.688
64	ReAlnet09_ALL	.688
65	ReAlnet01_ALL	.688
66	mobilenet_v2_0_75_160	.688
67	resnet_50_v1_spiking_l4a	.687
68	convnext_large:fb_in22k_ft_in1k	.686
69	resnet50_ecoset_full	.686
70	densenet-121	.686
71	ReAlnet05_ALL	.685
72	ReAlnet02_ALL	.685
73	resnet50-vicregl0p75	.685
74	convnext_tiny_sup	.684
75	resnet_101_v2	.684
76	efficientnet_b0	.683
77	resnet50_ewconplus_wzc_seed5_2__323	.683
78	convnext_large_in22k_supervised	.681
79	mobilenet_v2_1_0_192	.681
80	convnext_large_clip_laion2b_finelayers	.681
81	deit_small_imagenet_full_seed-0	.681
82	tv_efficientnet-b1	.681
83	mobilenet_v2_0_75_192	.680
84	resnet34_ecoset_full	.680
85	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.680
86	ReAlnet03_ALL	.680
87	cv_18_dagger_408_pretrained	.680
88	mobilenet_v2_0_5_224	.679
89	vonegrcnn_52e_full	.679
90	ReAlnet02_cornet	.678
91	ReAlnet02	.678
92	vonegrcnn_62e_nobn	.676
93	resnet_50_v1	.676
94	resnet50-sup	.676
95	regnet_y_400mf	.676
96	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.675
97	resnet50_ijepacnn_linhead	.674
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	resnet50_cpc_linhead	.666
119	BiT-S-R101x3	.666
120	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.666
121	convnext_small_imagenet_100_seed-0	.666
122	convnext_tiny_imagenet_full_seed-0	.665
123	xception	.663
124	eicircuit_resnet18_bn_20260919	.661
125	vit_relpos_base_patch16_clsgap_224:sw_in1k	.661
126	resnet34_imagenet_full	.660
127	BiT-S-R50x1	.660
128	resnet50_imagenet_full	.659
129	inception_v3_pytorch	.659
130	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
131	resnet_SIN_IN_FT_IN	.657
132	ReAlnet05_cornet	.656
133	ReAlnet05	.656
134	convnext_xlarge:fb_in22k_ft_in1k	.656
135	shufflenet_v2_x1_0	.656
136	ReAlnet03_cornet	.656
137	ReAlnet03	.656
138	resnet101_imagenet_full	.654
139	resnet50_barlowtwins_linhead	.654
140	ReAlnet04_cornet	.653
141	ReAlnet04	.653
142	resnet152_ecoset_full	.651
143	resnet50_simsiam_linhead	.650
144	convnext_large_imagenet_full_seed-0	.650
145	ReAlnet09_cornet	.649
146	ReAlnet09	.649
147	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.649
148	resnet50-moclr8deg	.648
149	ReAlnet01_cornet	.648
150	ReAlnet01	.648
151	inception_v4	.648
152	ReAlnet07_cornet	.647
153	ReAlnet07	.647
154	ReAlnet08_cornet	.647
155	ReAlnet08	.647
156	resnet_152_v1	.646
157	ReAlnet10_cornet	.646
158	ReAlnet10	.646
159	resnet50_supervised_linhead	.646
160	resnet50-vicreg	.646
161	mobilenet_v2_0_5_192	.644
162	resnext101_32x48d_wsl	.644
163	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.644
164	deepeisnn_ei_imagenet_resnet18	.643
165	mehrer2020_alexnet_training_seed_04	.643
166	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.642
167	resnet50-vitoimagevidnet8	.640
168	resnet50-VITO-8deg-cc	.640
169	resnet_50_v1_spiking_l4	.639
170	mehrer2020_alexnet_training_seed_05	.638
171	resnet50_imagenet_100_seed-0	.638
172	antialias-resnet152	.638
173	VOneCORnet-S	.638
174	convnext_base_imagenet_full_seed-0	.635
175	mehrer2020_alexnet_training_seed_06	.635
176	mobilenet_v2_1_0_160	.632
177	regnety_032	.632
178	vit_base_patch16_dinov3-lvd1689m	.627
179	resnet50_vicreg_linhead	.627
180	voneresnet-50-non_stochastic	.625
181	grcnn_109	.625
182	alexnet_ks_torevert	.624
183	alexnet	.624
184	deit3_base_patch16_224_fb_in22k_ft_in1k	.624
185	mehrer2020_alexnet_training_seed_01_fov4	.623
186	mehrer2020_alexnet_training_seed_09	.622
187	alexnet_fov4	.618
188	resnet50_byol_linhead	.618
189	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.617
190	BiT-S-R101x1	.617
191	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.616
192	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.616
193	yolos_tiny	.615
194	resnet_50_v1_spiking_l3	.613
195	convnext_base_fb_in1k	.613
196	resnet50-SIN_IN	.610
197	voneresnet-50	.609
198	convnext_base_fb_in1k_gram_aligned_dinov3	.609
199	compact_vgg19_IT	.607
200	deit_small_imagenet_100_seed-0	.606
201	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.604
202	ViT_L_32_imagenet1k	.604
203	mehrer2020_alexnet_training_seed_10	.602
204	resnet50_simclr_linhead_01norm	.598
205	mehrer2020_alexnet_training_seed_03	.598
206	resnet50-barlow	.596
207	mehrer2020_alexnet_training_seed_01	.595
208	seresnext50_32x4d	.592
209	mehrer2020_alexnet_training_seed_08	.590
210	mehrer2020_alexnet_training_seed_07	.582
211	compact_resnet50_robust_V4	.578
212	BiT-S-R152x2	.578
213	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.576
214	mehrer2020_alexnet_training_seed_02	.575
215	yudixie_wbh_object_class_scaling_0415_run_0	.569
216	compact_resnet50_robust_IT	.569
217	mobilenet_v2_1_0_128	.568
218	AT_efficientnet-b2	.565
219	AdvProp_efficientnet-b2	.565
220	compact_cornets_IT	.561
221	yudixie_wbh_object_class_scaling_0415_run_3	.554
222	compact_resnet50_IT	.553
223	focalnet_tiny_lrf_in1k	.550
224	yudixie_resnet50_object_class_0_240908	.550
225	yudixie_resnet50_distance_translation_0_240908	.549
226	convnext_base_fb_in22k_ft_in1k	.547
227	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.545
228	gabor_filter_entropy_4x4	.541
229	gabor_filter_entropy_4x4_v2_forcedCosine	.541
230	yudixie_resnet18_distance_rotation_0_240719	.541
231	resnet50_robust_l2_eps1	.541
232	yudixie_wbh_object_class_scaling_0415_run_1	.541
233	persistence_mem	.536
234	deit3_base_patch16_224_fb_in1k	.536
235	yudixie_resnet18_category_class_0_240719	.535
236	blt_vs	.534
237	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.534
238	yudixie_resnet18_object_class_0_240719	.531
239	yudixie_resnet18_translation_rotation_0_240719	.531
240	alexnet_fov12	.528
241	gabor_filter_entropy_4x4_v2	.527
242	AlexNet_SIN_fov4	.525
243	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.525
244	yudixie_wbh_object_class_scaling_0415_run_2	.522
245	yudixie_resnet50_distance_translation_rotation_0_240908	.521
246	yudixie_resnet18_distance_translation_0_240719	.521
247	yudixie_resnet50_category_class_0_240908	.520
248	hmax	.508
249	compact_resnet50_V4	.507
250	efficientnet-b7	.507
251	custom_model_cv_18_dagger_408	.505
252	yudixie_resnet50_rotation_reg_0_240908	.502
253	AlexNet_SIN	.499
254	yudixie_resnet50_translation_reg_0_240908	.498
255	yudixie_resnet50_distance_rotation_0_240908	.498
256	artResNet18_1	.494
257	persistence_memory	.491
258	resnet18_fair	.484
259	yudixie_resnet18_translation_reg_0_240719	.484
260	yudixie_resnet18_rotation_reg_0_240719	.484
261	convnext_small_imagenet_10_seed-0	.484
262	vonealexnet	.483
263	yudixie_resnet18_distance_translation_rotation_0_240719	.482
264	voneresnet-50-robust	.482
265	resnet50-SIN	.479
266	alexnet_fov16	.478
267	resnet50_imagenet_10_seed-0	.478
268	yudixie_resnet50_translation_rotation_0_240908	.476
269	yudixie_resnet18_distance_reg_0_240719	.470
270	pnasnet_large_pytorch	.470
271	compact_vgg19_V4	.470
272	ReAlnet01_cornet_fov12	.462
273	yudixie_resnet18_random_0_240719	.462
274	AdvProp_efficientnet-b4	.461
275	yudixie_resnet50_distance_reg_0_240908	.450
276	compact_cornets_V4	.448
277	mehrer2020_alexnet_training_seed_01_fov12	.448
278	vith14_untrained_linhead	.444
279	resnet50_untrained_linhead	.438
280	resnet-50-robust	.437
281	pr1_stl10_disentangled_alpha04	.433
282	AlexNet_SIN_fov12	.416
283	mehrer2020_alexnet_training_seed_01_fov16	.414
284	AlexNet_SIN_fov16	.412
285	alexnet_training_seed_01_fov4	.407
286	AdvProp_efficientnet-b7	.395
287	ReAlnet01_cornet_fov16	.395
288	resnet50_robust_l2_eps3	.395
289	imagenet_l2_3_0	.395
290	AdvProp_efficientnet-b6	.383
291	alexnet_training_seed_10	.380
292	alexnet_training_seed_06	.379
293	alexnet_training_seed_02	.367
294	resnet50_primary_visual_cortex	.366
295	AdvProp_efficientnet-b8	.363
296	alexnet_training_seed_01	.358
297	alexnet_training_seed_09	.349
298	alexnet_training_seed_03	.349
299	alexnet_training_seed_07	.344
300	alexnet_training_seed_05	.343
301	yudixie_resnet50_random_0_240908	.341
302	alexnet_training_seed_04	.326
303	alexnet_training_seed_08	.325
304	resnet50_imagenet_1_seed-0	.323
305	briaai_rmbg_1_4	.323
306	deit_small_imagenet_1_seed-0	.296
307	gabor_filter_mean_abs_4x4_v2	.292
308	convnext_small_imagenet_1_seed-0	.266
309	alexnet_training_seed_01_fov12	.237
310	deepeisnn_ei_cifar10_vgg19	.217
311	deit_small_imagenet_10_seed-0	.210
312	alexnet_training_seed_01_fov16	.204
313	deepeisnn_ei_cifar10_resnet18	.125
314	vit_huge_clip336_laion
315	convnext_xxlarge_clip
316	vit_large_clip336_openai
317	dinov2_vitl14_reg_lc_featread
318	dinov2_vitl14_lc_res336
319	eva02_large_in22k
320	dinov2_vitg14_reg_lc
321	dinov2_vitg14_lc
322	vit_base_patch14_reg4_dinov2-lvd142m
323	vit_base_patch14_dinov2-lvd142m
324	pnasnet_large
325	vith14_supervised_linhead
326	mobilenet_v2_0_75_224
327	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k
328	omnivore_swinS
329	omnivore_swinB
330	BiT-S-R152x4
331	ConvLSTM
332	densenet_201_pytorch
333	MIM
334	TAU
335	SimVP
336	PredRNN
337	antialiased-rnext101_32x8d

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