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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_Ringach2002-modulation_ratio")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.975
2	deit3_base_patch16_224_fb_in22k_ft_in1k	.973
3	vonegrcnn_52e_full	.906
4	resnet50_imagenet_1_seed-0	.900
5	deit_large_imagenet_full_seed-0	.886
6	resnet50_imagenet_10_seed-0	.884
7	vonegrcnn_52e_full	.872
8	deit_small_imagenet_100_seed-0	.864
9	resnet18_fair	.855
10	vonegrcnn_47e	.855
11	dinov2_vitg14_lc	.848
12	vit_base_patch14_dinov2-lvd142m	.833
13	dinov2_vitl14_lc	.822
14	vgg_19	.818
15	vit_base_patch14_reg4_dinov2-lvd142m	.815
16	blt_vs	.809
17	vonegrcnn_62e_nobn	.808
18	voneresnet-50-robust	.798
19	resnet-18-LC_w_sh_10_iter_m	.798
20	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.795
21	VOneCORnet-S	.793
22	vit_base_patch16_dinov3-lvd1689m	.793
23	dinov2_vitl14_reg_lc	.789
24	voneresnet-50	.789
25	mobilenet_v2_0.5_160	.789
26	v1-pyr-nodown	.783
27	resnet-18-LC_m	.783
28	resnet50_FractalDB	.781
29	dinov2_vitl14_lc_res336	.779
30	nasnet_large-tf	.774
31	resnet-18-LC_w_sh_1_iter_conv_init_m	.758
32	resnet-18-LC_w_sh_100_iter_m	.752
33	vit_large_patch14_clip_224:laion2b_ft_in1k	.749
34	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.747
35	voneresnet-50-non_stochastic	.726
36	vit_base_patch16_clip_224:openai_ft_in1k	.719
37	deit_small_imagenet_full_seed-0	.710
38	resnet50_pretrained_with_retinal_waves	.697
39	vision_transformer_vit_large_patch16_224	.695
40	resnet-18-LC_w_sh_10_iter_conv_init_m	.693
41	alexnet_l2_3_robust	.690
42	vggface	.688
43	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.689
44	convnext_base_fb_in22k_ft_in1k	.687
45	ReAlnet01_cornet_fov4	.687
46	BiT-M-R152x4	.686
47	deit_base_imagenet_full_seed-0	.677
48	BiT-S-R50x1	.676
49	yudixie_resnet50_object_class_0_240908	.671
50	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.671
51	yudixie_resnet50_category_class_0_240908	.670
52	resnet-18-LC_conv_init_m	.660
53	resnet-18-LC_untrained	.660
54	focalnet_tiny_lrf_in1k	.659
55	0.5x_resnet-18_LC_w_sh_10_iter	.656
56	mobilenet_v2_1.0_96	.656
57	resnet-18-LC_w_sh_100_iter_conv_init_m	.651
58	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.650
59	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.646
60	r3m_resnet50	.646
61	nasnet_mobile-tf	.644
62	resnet50_primary_visual_cortex	.643
63	yudixie_resnet50_distance_rotation_0_240908	.640
64	resnet-50_untrained	.638
65	BiT-S-R152x2	.633
66	resnet-18-LC_d_w_sh_1x1_conv_init_m	.627
67	deepeisnn_ei_imagenet_resnet18	.627
68	r3m_resnet34	.626
69	alexnet_reduced_aliasing_early_checkpoint	.624
70	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.622
71	resnet50_hvs_wzc	.617
72	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.617
73	imagenet_l2_10_0	.617
74	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.615
75	resnet50-VITO-8deg-cc	.611
76	resnet-152_v2-tf	.610
77	deepeisnn_ei_cifar10_resnet18	.609
78	omnivore_swinS	.607
79	resnet50_linf_8_robust	.607
80	pnasnet_large-tf	.604
81	pnasnet_large_halfPrecision	.603
82	yudixie_wbh_object_class_scaling_0415_run_2	.602
83	my-model	.601
84	yolos_tiny	.602
85	r3m_resnet50_nocrop	.601
86	resnet50-moclr8deg	.598
87	yudixie_resnet18_object_class_0_240719	.598
88	shufflenet_v2_x1_0	.598
89	resnet50_robust_l2_eps1	.597
90	omnivore_swinT	.597
91	xception	.596
92	resnet-50x2_untrained	.588
93	resnet_50_v1_spiking_l4a	.588
94	resnet50-sup	.588
95	resnet_50_v1_spiking_l4	.587
96	yudixie_resnet50_imagenet1kpret_0_240908	.587
97	resnet_50_v1	.587
98	resnet_50_v1_spiking	.586
99	resnet_50_v1_spiking_l3	.586
100	resnet50_tutorial	.585
101	eva02_large_in22k	.584
102	resnet50-vitoimagevidnet8	.581
103	resnet50_simclr	.579
104	convnext_large:fb_in22k_ft_in1k	.579
105	grcnn_v2_text_noise	.579
106	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.578
107	yudixie_resnet18_translation_rotation_0_240719	.578
108	vgg_16	.577
109	yudixie_resnet50_translation_rotation_0_240908	.576
110	resnet-152_v1-tf	.576
111	efficientnet_b1_imagenet_full	.575
112	resnet50-vicregl0p9	.573
113	yudixie_resnet50_random_0_240908	.573
114	yudixie_resnet50_rotation_reg_0_240908	.571
115	convnext_xlarge:fb_in22k_ft_in1k	.570
116	resnet50-barlow	.570
117	resnet-50_v1-tf	.570
118	BiT-M-R50x3	.570
119	resnet50_st_wzc_2	.569
120	resnet50_byol	.568
121	BiT-S-R101x3	.567
122	cv_18_dagger_408_pretrained	.567
123	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.565
124	inception_v1	.564
125	resnet-18_untrained	.565
126	yudixie_resnet18_distance_reg_0_240719	.564
127	deit3_base_patch16_224_fb_in1k	.563
128	resnet50-vicreg	.563
129	BiT-S-R152x4	.561
130	yudixie_resnet50_distance_translation_rotation_0_240908	.560
131	nasnet_mobile	.559
132	resnet34_ecoset_full	.559
133	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.559
134	vit_large_patch14_clip_224:openai_ft_in1k	.556
135	convnext_xxlarge_clip	.555
136	yudixie_wbh_object_class_scaling_0415_run_3	.553
137	resnet50_st_wzc_seed3_2_323	.553
138	mobilenet_v2_1-4_224_pytorch	.553
139	r3m_resnet18	.552
140	cvt_cvt-21-384-in1k_4_LucyV4	.550
141	alexnet_training_seed_01_fov12	.550
142	yudixie_resnet18_category_class_0_240719	.548
143	briaai_rmbg_1_4	.548
144	resnet50_st_wzc_seed6_2_323	.548
145	efficientnet_b0	.548
146	0.5x_resnet-18	.547
147	resnet50_l2_3_robust	.545
148	resnet-101_v2-tf	.545
149	grcnn	.544
150	resnet50_ewconplus_wzc	.544
151	resnet101_imagenet_full	.542
152	BiT-M-R101x3	.542
153	omnivore_swinB	.541
154	resnet50-simclr-vissl	.539
155	vit_large_clip336_openai	.539
156	CLIP_ViT-B_32	.538
157	yudixie_resnet50_distance_translation_0_240908	.536
158	alexnet_training_seed_03	.536
159	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.536
160	resnet50-SIN_IN_IN	.535
161	densenet-121	.533
162	resnet50_ewconplus_wzc_seed2_2__323	.533
163	BiT-M-R101x1	.532
164	resnet50_linf_4_robust	.531
165	imagenet_l2_3_0	.530
166	BiT-S-R50x3	.530
167	yudixie_resnet18_distance_translation_rotation_0_240719	.529
168	grcnn_robust_v1	.528
169	resnet18_imagenet_full	.528
170	vonealexnet_gaussian_noise_std4_fixed	.526
171	vit_huge_clip336_laion	.527
172	resnet50_robust_l2_eps3	.527
173	alexnet_robust_correct	.524
174	barlow-twins-resnet50	.524
175	BiT-M-R50x1	.524
176	resnet-18_test_m	.524
177	resnet50_st_wzc_seed7_2_323	.524
178	inception_v4-tf	.522
179	densenet-121-keras	.519
180	mobilenet_v2_1.0_160-tf	.519
181	convnext_small_imagenet_10_seed-0	.517
182	resnet18_ecoset_full	.517
183	yudixie_resnet18_distance_translation_0_240719	.516
184	yudixie_wbh_object_class_scaling_0415_run_0	.516
185	alexnet_training_seed_07	.516
186	yudixie_wbh_object_class_scaling_0415_imagenet	.514
187	yudixie_resnet18_imagenet1kpret_0_240719	.514
188	mobilenet_v2_1.0_128-tf	.514
189	yudixie_resnet18_random_0_240719	.512
190	resnet50_moco_v2	.511
191	resnet-152_v2_pytorch	.512
192	grcnn_109	.511
193	SWSL_resnet50	.509
194	yudixie_resnet18_translation_reg_0_240719	.509
195	alexnet_linf_8_robust	.508
196	convnext_base_fb_in1k	.508
197	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.504
198	resnet50_imagenet_full	.503
199	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.503
200	yudixie_resnet18_rotation_reg_0_240719	.501
201	resnet50	.502
202	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.501
203	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.501
204	resnet50-vicregl0p75	.499
205	convnext_small_imagenet_100_seed-0	.499
206	convnext_large_clip_laion2b_finelayers	.499
207	yudixie_resnet18_distance_rotation_0_240719	.498
208	BiT-S-R101x1	.496
209	resnet50_random_l2_perturb	.493
210	antialiased-rnext101_32x8d	.493
211	0.5x_resnet-18_LC_w_sh_100_iter	.493
212	cvt_cvt-13-224-in1k_4_LucyV4	.492
213	yudixie_resnet50_distance_reg_0_240908	.491
214	resnet50-SIN_IN	.491
215	resnet-18-LC_1st_conv_m	.490
216	yudixie_resnet50_translation_reg_0_240908	.490
217	yudixie_wbh_object_class_scaling_0415_run_1	.488
218	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.487
219	mobilenet_v2_1_0_128	.486
220	cvt_cvt-21-224-in1k_4_LucyV4	.485
221	custom_model_cv_18_dagger_408	.484
222	resnet50_random_linf8_perturb	.484
223	resnet_50_v2	.482
224	inception_v3	.481
225	vit_relpos_base_patch16_clsgap_224:sw_in1k	.480
226	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.480
227	convnext_tiny_sup	.479
228	0.5x_resnet-18_LC	.479
229	mobilenet_v2_0_5_224	.478
230	alexnet_fov4	.477
231	resnet-50_v2-tf	.477
232	ViT-B/32	.476
233	resnet50_ewconplus_wzc_seed5_2__323	.476
234	texture_shape_resnet50_trained_on_SIN	.475
235	alexnet_training_seed_04	.470
236	resnet34_imagenet_full	.469
237	RN50	.469
238	resnet50-SIN	.468
239	resnet101_ecoset_full	.469
240	convnext_large_imagenet_full_seed-0	.468
241	resnet50-cifar	.467
242	resnet101	.465
243	Res2Net50_26w_4s	.462
244	mobilenet_v2_1_4_224	.460
245	effnetv2m_custom384	.458
246	mobilenet_v2_0.75_192-tf	.457
247	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.457
248	alexnet_training_seed_09	.457
249	resnet152_imagenet_full	.453
250	resnet152_ecoset_full	.453
251	convnext_base:clip_laiona_augreg_ft_in1k_384	.452
252	alexnet_random_l2_3_perturb	.451
253	vgg-16-keras	.450
254	alexnet_training_seed_01_fov16	.449
255	alexnet_training_seed_02	.448
256	SWSL_resnext101_32x8d	.445
257	resnet50_ewconplus_wzc_seed6_2_323	.445
258	konkle_alexnetgn_ipcl_ref01_primary_model	.444
259	mobilenet_v2_0_75_224	.443
260	efficientnet_b0_imagenet_full	.441
261	mobilevit_small	.441
262	convnext_small_imagenet_1_seed-0	.440
263	resnet-101_v1-tf	.440
264	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.440
265	convnext_tiny:in12k_ft_in1k	.439
266	resnet18_imagenet21kP	.439
267	artResNet18_1	.436
268	resnet_SIN_IN_FT_IN	.436
269	vit_large_patch14_clip_336:laion2b_ft_in1k	.434
270	compact_vgg19_V4	.433
271	convnext_tiny_imagenet_full_seed-0	.432
272	0.5x_resnet-18_LC_w_sh_1_iter	.429
273	deepeisnn_ei_cifar10_vgg19	.429
274	convnext_base_imagenet_full_seed-0	.429
275	resnet50_ecoset_full	.426
276	alexnet_training_seed_10	.421
277	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.421
278	CLIP_resnet50_float32	.420
279	CLIP_resnet50	.420
280	mobilenet_v2_0.5_224-tf	.418
281	densenet-201	.413
282	resnet-50-robust	.412
283	CORnet-Z	.410
284	FrankRobWobv0	.411
285	AdvProp_efficientnet-b6	.410
286	densenet-169	.410
287	compact_resnet50_IT	.409
288	compact_resnet50_V4	.408
289	resnet-18-LC_w_sh_1_iter_m	.408
290	resnext101_32x8d_wsl	.407
291	inception_v2-tf	.407
292	CORnetZ_CIFAR10_bs32_20_04	.405
293	mobilenet_v2_1.4_224-tf	.404
294	mobilenet_v2_1.3_224-tf	.403
295	densenet-169-keras	.403
296	AdvProp_efficientnet-b7	.403
297	alexnet_training_seed_01	.402
298	BiT-M-R152x2	.402
299	convnext_small_imagenet_full_seed-0	.397
300	convnext_large_in22k_supervised	.397
301	alexnet_fov16	.394
302	alexnet	.387
303	AlexNet_SIN	.387
304	AdvProp_efficientnet-b8	.386
305	compact_resnet50_robust_IT	.385
306	inception_v3-tf	.383
307	mobilenet_v2_0.75_160-tf	.382
308	CLIP-RN50	.378
309	mobilenet_v2_0_75_192	.378
310	antialias-resnet152	.378
311	inception_v1-tf	.376
312	cvt_cvt-13-384-in1k_4_LucyV4	.374
313	antialiased-r50	.371
314	AlexNet_SIN_fov4	.369
315	mobilenet_v2_0.75_128	.368
316	densenet-201-keras	.366
317	texture_shape_alexnet_trained_on_SIN	.365
318	mobilenet_v1_0.25_128	.363
319	AlexNet_SIN_fov12	.362
320	efficientnet_b2_imagenet_full	.359
321	mobilenet_v2_1.0_224-tf	.358
322	ReAlnet09_cornet	.351
323	ReAlnet09	.348
324	resnet50_imagenet_100_seed-0	.347
325	convnext_femto_ols:d1_in1k	.346
326	convnext_base_fb_in1k_gram_aligned_dinov3	.345
327	CORnet-S	.341
328	alexnet_fov12	.340
329	alexnet_random_linf8_perturb	.340
330	AdvProp_efficientnet-b2	.339
331	AT_efficientnet-b2	.338
332	cornet_s	.338
333	mobilenet_v2_0.75_224-tf	.336
334	ReAlnet02_cornet	.335
335	ReAlnet02	.334
336	ReAlnet04_ALL	.334
337	ReAlnet03_ALL	.333
338	ReAlnet01_ALL	.332
339	ReAlnet08_ALL	.329
340	ReAlnet10_ALL	.329
341	ReAlnet07_ALL	.329
342	ReAlnet06_ALL	.328
343	ReAlnet09_ALL	.328
344	ReAlnet02_ALL	.328
345	ReAlnet05_ALL	.327
346	effnetb1_272x240	.326
347	mobilenet_v2_1_3_224	.321
348	ReAlnet01	.318
349	ReAlnet01_cornet	.318
350	ReAlnet06	.316
351	ReAlnet06_cornet	.315
352	vgg-19-keras	.313
353	mobilenet_v2_0_5_192	.313
354	ReAlnet07_cornet	.312
355	ReAlnet07	.311
356	compact_cornets_IT	.309
357	mobilenet_v2_1_0_160	.308
358	ReAlnet03_cornet	.308
359	ReAlnet03	.307
360	ReAlnet04_cornet	.303
361	alexnet_training_seed_01_fov4	.302
362	ReAlnet04	.301
363	alexnet_early_checkpoint	.299
364	mobilenet_v2_0.75_96	.298
365	mobilenet_v2_0.5_192-tf	.297
366	ReAlnet08	.297
367	ReAlnet08_cornet	.297
368	alexnet_training_seed_08	.297
369	mobilenet_v2_0_75_160	.295
370	AdvProp_efficientnet-b0	.295
371	ReAlnet05	.293
372	ReAlnet05_cornet	.293
373	inception_resnet_v2	.291
374	compact_cornets_V4	.290
375	mobilenet_v2_0.35_224	.290
376	mobilenet_v2_1_0_192	.289
377	ReAlnet10_cornet	.288
378	ReAlnet10	.288
379	deit_small_imagenet_10_seed-0	.281
380	my-model	.281
381	ReAlnet01_cornet_fov12	.280
382	compact_resnet50_robust_V4	.278
383	AdvProp_efficientnet-b4	.276
384	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.273
385	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.272
386	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.272
387	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.271
388	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.271
389	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.271
390	efficientnet-b7	.271
391	mobilenet_v2_1.0_192-tf	.263
392	CORnetZ_CIFAR10	.252
393	compact_vgg19_IT	.248
394	ReAlnet01_cornet_fov16	.245
395	alexnet_training_seed_05	.199
396	alexnet_training_seed_06	.198
397	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
398	mobilenet_v2_1_0_224	.198
399	AlexNet_SIN_fov16	.198
400	hmax	.199
401	mobilenet_v2_0.5_128	.198
402	mobilenet_v2_0.35_160
403	resnet18-supervised
404	fitvid_trained_on_physion
405	openclip
406	resnext101_32x32d_wsl
407	resnext101_32x48d_wsl
408	resnet50_ImageNet
409	regnet_y_400mf
410	tv_efficientnet-b1
411	alexnet_ks_torevert
412	yudixie_resnet50_imagenet1kpret_0_240312
413	resnet_152_v1
414	ViT_L_32_imagenet1k
415	resnext101_32x16d_wsl
416	densenet_201_pytorch
417	pnasnet_large_pytorch
418	inception_v3_pytorch
419	eBarlow_lmda_02_1000ep
420	inception_v4
421	nasnet_large
422	deit_small_imagenet_1_seed-0
423	pnasnet_large
424	vonegrcnn_62e_nobn
425	resnet_101_v2
426	vonegrcnn_47e
427	resnet_101_v1
428	resnet_152_v2
429	pr1_stl10_disentangled_alpha04
430	predictive_coding_pc
431	dinov2_vitg14_reg_lc
432	dinov2_vitl14_reg_lc_featread

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio