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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_DeValois1982-peak_sf")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet-18-LC_w_sh_100_iter_m	.964
2	resnet50_imagenet_10_seed-0	.950
3	alexnet_training_seed_01	.943
4	resnet-18-LC_m	.941
5	resnet50_linf_4_robust	.935
6	alexnet_training_seed_08	.933
7	mobilenet_v2_1_0_192	.925
8	vonegrcnn_62e_nobn	.925
9	resnet50_linf_8_robust	.923
10	vonegrcnn_52e_full	.917
11	alexnet_reduced_aliasing_early_checkpoint	.911
12	vonegrcnn_47e	.908
13	vgg-19-keras	.901
14	resnet50_l2_3_robust	.897
15	resnet50_robust_l2_eps1	.895
16	resnet-18-LC_w_sh_100_iter_conv_init_m	.893
17	alexnet_training_seed_10	.888
18	resnet-18-LC_w_sh_1_iter_conv_init_m	.885
19	resnet50_robust_l2_eps3	.876
20	alexnet_training_seed_06	.873
21	voneresnet-50-non_stochastic	.872
22	BiT-S-R152x2	.872
23	nasnet_large-tf	.868
24	RN50	.865
25	xception	.862
26	inception_v1	.862
27	mobilenet_v2_1.0_96	.861
28	mobilenet_v2_0_75_160	.860
29	mobilenet_v2_0.75_160-tf	.859
30	vgg-16-keras	.857
31	alexnet_training_seed_04	.857
32	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.858
33	0.5x_resnet-18_LC_w_sh_100_iter	.855
34	densenet-201-keras	.854
35	artResNet18_1	.853
36	AlexNet_SIN	.852
37	resnet-50-robust	.852
38	effnetb1_272x240	.851
39	grcnn	.850
40	voneresnet-50	.847
41	resnet-50_untrained	.844
42	mobilenet_v2_1_0_224	.843
43	grcnn_109	.837
44	voneresnet-50-robust	.835
45	resnet50-moclr8deg	.835
46	VOneCORnet-S	.834
47	mobilenet_v2_0.75_96	.832
48	effnetv2m_custom384	.831
49	resnet-18-LC_d_w_sh_1x1_conv_init_m	.830
50	imagenet_l2_3_0	.829
51	omnivore_swinB	.829
52	resnet50_byol	.828
53	alexnet_training_seed_07	.826
54	inception_v4-tf	.825
55	pnasnet_large-tf	.822
56	cvt_cvt-13-384-in1k_4_LucyV4	.818
57	alexnet_training_seed_09	.817
58	convnext_tiny_imagenet_full_seed-0	.816
59	mobilenet_v2_0.75_224-tf	.815
60	densenet-201	.814
61	mobilenet_v2_0.5_224-tf	.812
62	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.812
63	densenet-169	.812
64	resnet-18-LC_w_sh_10_iter_m	.811
65	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.811
66	mobilenet_v2_0_5_224	.810
67	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.808
68	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.805
69	resnet_50_v1	.805
70	resnet-18-LC_w_sh_10_iter_conv_init_m	.804
71	resnet_50_v1_spiking_l4	.803
72	yudixie_resnet50_imagenet1kpret_0_240908	.804
73	resnet_50_v1_spiking_l3	.803
74	resnet_50_v1_spiking	.803
75	resnet50_simclr	.802
76	resnet50_tutorial	.803
77	resnet_50_v1_spiking_l4a	.803
78	alexnet	.802
79	alexnet	.802
80	alexnet	.802
81	resnet50-sup	.803
82	alexnet	.802
83	alexnet	.802
84	alexnet	.802
85	alexnet	.802
86	nasnet_mobile	.801
87	SWSL_resnext101_32x8d	.801
88	alexnet-baseline	.800
89	omnivore_swinT	.799
90	resnet50_moco_v2	.797
91	efficientnet_b0_imagenet_full	.798
92	densenet-169-keras	.796
93	resnet-18_untrained	.795
94	resnet-18-LC_conv_init_m	.795
95	omnivore_swinS	.794
96	focalnet_tiny_lrf_in1k	.793
97	grcnn_robust_v1	.792
98	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.791
99	konkle_alexnetgn_ipcl_ref01_primary_model	.790
100	resnet-50x2_untrained	.790
101	alexnet_training_seed_02	.788
102	vgg_16	.787
103	resnet50_FractalDB	.787
104	blt_vs	.787
105	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.783
106	resnet-152_v1-tf	.783
107	yudixie_resnet18_imagenet1kpret_0_240719	.783
108	mobilenet_v2_0_75_192	.782
109	imagenet_l2_10_0	.782
110	resnet50-SIN	.782
111	resnet-101_v2-tf	.779
112	convnext_large_imagenet_full_seed-0	.778
113	yudixie_resnet18_object_class_0_240719	.777
114	cvt_cvt-13-224-in1k_4_LucyV4	.776
115	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.774
116	resnet50-VITO-8deg-cc	.773
117	r3m_resnet18	.773
118	BiT-M-R101x3	.772
119	resnet50-barlow	.771
120	nasnet_mobile-tf	.770
121	resnet50-vicreg	.769
122	convnext_small_imagenet_1_seed-0	.769
123	BiT-M-R101x1	.769
124	alexnet_training_seed_03	.768
125	0.5x_resnet-18_LC_w_sh_10_iter	.768
126	BiT-S-R50x3	.766
127	resnet50_random_l2_perturb	.764
128	resnet50	.764
129	resnet50-meshes-lt-100-original-scratch	.763
130	alexnet_random_linf8_perturb	.764
131	vggface	.763
132	SWSL_resnet50	.762
133	resnet50-SIN_IN_IN	.759
134	0.5x_resnet-18_LC	.756
135	mobilenet_v2_0_75_224	.756
136	CLIP_resnet50	.755
137	CLIP_resnet50_float32	.754
138	resnet50-vicregl0p75	.755
139	my-model	.755
140	resnet-101_v1-tf	.752
141	convnext_tiny:in12k_ft_in1k	.752
142	AdvProp_efficientnet-b7	.749
143	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.749
144	mobilenet_v2_1_0_160	.747
145	densenet-121	.745
146	convnext_femto_ols:d1_in1k	.743
147	convnext_large:fb_in22k_ft_in1k	.742
148	BiT-M-R152x2	.742
149	alexnet_training_seed_05	.741
150	convnext_small_imagenet_10_seed-0	.741
151	BiT-S-R101x1	.741
152	resnet-50_v2-tf	.740
153	yudixie_resnet18_distance_reg_0_240719	.740
154	mobilenet_v2_1.3_224-tf	.740
155	resnet-152_v2-tf	.737
156	texture_shape_resnet50_trained_on_SIN	.737
157	resnet-152_v2_pytorch	.733
158	AdvProp_efficientnet-b2	.732
159	resnet50_random_linf8_perturb	.732
160	convnext_tiny_sup	.731
161	AT_efficientnet-b2	.731
162	convnext_small_imagenet_full_seed-0	.729
163	vonealexnet_gaussian_noise_std4_fixed	.728
164	yudixie_resnet50_random_0_240908	.727
165	cv_18_dagger_408_pretrained	.725
166	compact_vgg19_V4	.726
167	mobilenet_v2_0_5_192	.724
168	resnet_SIN_IN_FT_IN	.725
169	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.723
170	BiT-M-R50x1	.722
171	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.721
172	shufflenet_v2_x1_0	.721
173	inception_v3-tf	.720
174	mobilenet_v2_1_4_224	.719
175	mobilenet_v2_0.75_192-tf	.719
176	compact_resnet50_IT	.717
177	deit_base_imagenet_full_seed-0	.717
178	0.5x_resnet-18_LC_w_sh_1_iter	.716
179	inception_resnet_v2	.714
180	densenet-121-keras	.712
181	yudixie_resnet50_translation_rotation_0_240908	.710
182	mobilenet_v2_1_3_224	.708
183	BiT-S-R101x3	.708
184	AdvProp_efficientnet-b4	.707
185	deit_small_imagenet_100_seed-0	.706
186	resnet-50_v1-tf	.706
187	convnext_xlarge:fb_in22k_ft_in1k	.703
188	CLIP-RN50	.703
189	antialiased-rnext101_32x8d	.703
190	compact_resnet50_robust_V4	.702
191	yudixie_resnet50_rotation_reg_0_240908	.701
192	CORnetZ_CIFAR10_bs32_20_04	.702
193	alexnet_early_checkpoint	.700
194	BiT-S-R152x4	.698
195	BiT-S-R50x1	.698
196	r3m_resnet50_nocrop	.695
197	CORnetZ_CIFAR10	.694
198	inception_v2-tf	.695
199	yudixie_resnet50_object_class_0_240908	.692
200	resnet-18-LC_w_sh_1_iter_m	.692
201	yudixie_resnet50_category_class_0_240908	.691
202	grcnn_v2_text_noise	.691
203	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.690
204	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.690
205	BiT-M-R50x3	.690
206	compact_cornets_V4	.688
207	custom_model_cv_18_dagger_408	.687
208	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.687
209	yudixie_resnet50_distance_rotation_0_240908	.686
210	compact_cornets_IT	.687
211	efficientnet_b2_imagenet_full	.686
212	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.686
213	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.686
214	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.686
215	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.686
216	AdvProp_efficientnet-b8	.685
217	yudixie_resnet18_random_0_240719	.684
218	mobilenet_v2_0.5_192-tf	.681
219	vit_relpos_base_patch16_clsgap_224:sw_in1k	.681
220	yudixie_resnet50_distance_translation_0_240908	.680
221	mobilenet_v1_0.25_128	.677
222	alexnet_l2_3_robust	.676
223	mobilenet_v2_0.75_128	.675
224	mobilenet_v2_1.0_192-tf	.675
225	BiT-M-R152x4	.673
226	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.671
227	resnet50_ewconplus_wzc	.671
228	ViT_L_32_imagenet1k	.671
229	r3m_resnet50	.670
230	yudixie_resnet50_distance_reg_0_240908	.669
231	antialias-resnet152	.665
232	cvt_cvt-21-224-in1k_4_LucyV4	.655
233	yudixie_resnet50_distance_translation_rotation_0_240908	.653
234	vit_large_patch14_clip_224:openai_ft_in1k	.652
235	antialiased-r50	.651
236	yudixie_resnet18_distance_translation_0_240719	.650
237	yudixie_resnet18_category_class_0_240719	.650
238	FrankRobWobv0	.647
239	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.643
240	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.641
241	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.640
242	convnext_small_imagenet_100_seed-0	.638
243	deit_large_imagenet_full_seed-0	.638
244	resnet50-vicregl0p9	.636
245	efficientnet_b1_imagenet_full	.635
246	yudixie_resnet18_translation_rotation_0_240719	.635
247	cvt_cvt-21-384-in1k_4_LucyV4	.635
248	vit_base_patch16_clip_224:openai_ft_in1k	.634
249	resnet_101_v1	.628
250	AdvProp_efficientnet-b0	.627
251	resnet50-SIN_IN	.626
252	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.625
253	resnet50_primary_visual_cortex	.623
254	vgg_19	.622
255	r3m_resnet34	.620
256	mobilenet_v2_1.4_224-tf	.620
257	convnext_base_imagenet_full_seed-0	.619
258	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.615
259	compact_resnet50_V4	.614
260	compact_resnet50_robust_IT	.612
261	yolos_tiny	.608
262	mobilenet_v2_1_0_128	.605
263	Res2Net50_26w_4s	.605
264	yudixie_resnet50_translation_reg_0_240908	.602
265	inception_v3	.601
266	barlow-twins-resnet50	.597
267	mobilenet_v2_0.35_224	.595
268	mobilenet_v2_1.0_128-tf	.593
269	mobilenet_v2_1.0_224-tf	.592
270	mobilenet_v2_1.0_160-tf	.592
271	compact_vgg19_IT	.592
272	yudixie_resnet18_translation_reg_0_240719	.586
273	yudixie_resnet18_distance_translation_rotation_0_240719	.583
274	resnet-18-LC_untrained	.580
275	CORnet-Z	.579
276	alexnet_robust_correct	.579
277	resnet-18-LC_1st_conv_m	.576
278	resnet50-meshes-lt-100-original-pretrained	.572
279	CORnet-S	.570
280	ReAlnet07_ALL	.569
281	mobilenet_v2_1-4_224_pytorch	.567
282	AdvProp_efficientnet-b6	.564
283	ReAlnet10_ALL	.563
284	briaai_rmbg_1_4	.560
285	ReAlnet02_ALL	.560
286	resnet101	.558
287	yudixie_resnet18_rotation_reg_0_240719	.557
288	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.553
289	resnet18_imagenet21kP	.554
290	resnet50_imagenet_1_seed-0	.553
291	yudixie_resnet18_distance_rotation_0_240719	.552
292	mobilenet_v2_0.5_128	.550
293	vision_transformer_vit_large_patch16_224	.549
294	ReAlnet03_ALL	.548
295	ReAlnet06_ALL	.548
296	ReAlnet08_ALL	.547
297	ReAlnet05_ALL	.546
298	ReAlnet09_ALL	.546
299	ReAlnet01_ALL	.546
300	ReAlnet07	.544
301	resnext101_32x8d_wsl	.543
302	ReAlnet07_cornet	.543
303	ReAlnet04_ALL	.543
304	ReAlnet08	.541
305	ReAlnet08_cornet	.541
306	mobilenet_v2_0.5_160	.539
307	efficientnet-b7	.537
308	resnet50_pretrained_with_retinal_waves	.534
309	convnext_base:clip_laiona_augreg_ft_in1k_384	.529
310	ViT-B/32	.529
311	0.5x_resnet-18	.528
312	ReAlnet01_cornet	.527
313	ReAlnet01	.528
314	ReAlnet04	.525
315	ReAlnet04_cornet	.524
316	vit_large_patch14_clip_336:laion2b_ft_in1k	.524
317	ReAlnet09_cornet	.522
318	ReAlnet10_cornet	.522
319	ReAlnet09	.520
320	efficientnet_b0	.521
321	ReAlnet10	.521
322	resnet-18_test_m	.516
323	resnet50-simclr-vissl	.515
324	ReAlnet06_cornet	.513
325	ReAlnet06	.512
326	alexnet_linf_8_robust	.512
327	resnet_50_v2	.511
328	v1-pyr-nodown	.510
329	ReAlnet03	.509
330	ReAlnet03_cornet	.509
331	resnet101_ecoset_full	.506
332	vit_large_patch14_clip_224:laion2b_ft_in1k	.503
333	hmax	.503
334	resnet101_imagenet_full	.498
335	texture_shape_alexnet_trained_on_SIN	.497
336	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.493
337	cornet_s	.487
338	resnet50_imagenet_full	.486
339	ReAlnet02_cornet	.486
340	ReAlnet02	.485
341	ReAlnet05	.483
342	ReAlnet05_cornet	.483
343	resnet152_ecoset_full	.481
344	alexnet	.475
345	resnet50_ecoset_full	.472
346	resnet50-vitoimagevidnet8	.473
347	resnet18_imagenet_full	.472
348	resnet18_ecoset_full	.471
349	inception_v1-tf	.466
350	CLIP_ViT-B_32	.460
351	alexnet_random_l2_3_perturb	.444
352	resnet50	.443
353	resnet34_ecoset_full	.443
354	deit_small_imagenet_full_seed-0	.442
355	resnet50_imagenet_100_seed-0	.435
356	resnet152_imagenet_full	.429
357	mobilevit_small	.429
358	my-model	.425
359	resnet34_imagenet_full	.397
360	deit_small_imagenet_10_seed-0	.330
361	resnet50-cifar	.248
362	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.245
363	mobilenet_v2_0.35_160
364	resnet18-supervised
365	r3m_resnet34_nocrop
366	fitvid_trained_on_physion
367	openclip
368	resnext101_32x32d_wsl
369	resnext101_32x48d_wsl
370	resnet50_ImageNet
371	regnet_y_400mf
372	tv_efficientnet-b1
373	alexnet_ks_torevert
374	yudixie_resnet50_imagenet1kpret_0_240312
375	resnet_152_v1
376	resnext101_32x16d_wsl
377	densenet_201_pytorch
378	pnasnet_large_pytorch
379	inception_v3_pytorch
380	inception_v4
381	nasnet_large
382	deit_small_imagenet_1_seed-0
383	pnasnet_large
384	vonegrcnn_62e_nobn
385	resnet_101_v2
386	vonegrcnn_47e
387	vonegrcnn_52e_full
388	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_DeValois1982

Metric: peak_sf