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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	aughost_fair_resnet18	.840
13	vit_base_patch14_dinov2-lvd142m	.833
14	dinov2_vitl14_lc	.822
15	vgg_19	.818
16	vit_base_patch14_reg4_dinov2-lvd142m	.815
17	blt_vs	.809
18	vonegrcnn_62e_nobn	.808
19	voneresnet-50-robust	.798
20	resnet-18-LC_w_sh_10_iter_m	.798
21	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.795
22	VOneCORnet-S	.793
23	vit_base_patch16_dinov3-lvd1689m	.793
24	dinov2_vitl14_reg_lc	.789
25	voneresnet-50	.789
26	mobilenet_v2_0.5_160	.789
27	v1-pyr-nodown	.783
28	resnet-18-LC_m	.783
29	resnet50_FractalDB	.781
30	dinov2_vitl14_lc_res336	.779
31	nasnet_large-tf	.774
32	resnet-18-LC_w_sh_1_iter_conv_init_m	.758
33	resnet-18-LC_w_sh_100_iter_m	.752
34	vit_large_patch14_clip_224:laion2b_ft_in1k	.749
35	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.747
36	voneresnet-50-non_stochastic	.726
37	vit_base_patch16_clip_224:openai_ft_in1k	.719
38	deit_small_imagenet_full_seed-0	.710
39	resnet50_pretrained_with_retinal_waves	.697
40	vision_transformer_vit_large_patch16_224	.695
41	resnet-18-LC_w_sh_10_iter_conv_init_m	.693
42	alexnet_l2_3_robust	.690
43	vggface	.688
44	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.689
45	convnext_base_fb_in22k_ft_in1k	.687
46	ReAlnet01_cornet_fov4	.687
47	BiT-M-R152x4	.686
48	deit_base_imagenet_full_seed-0	.677
49	BiT-S-R50x1	.676
50	yudixie_resnet50_object_class_0_240908	.671
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.671
52	yudixie_resnet50_category_class_0_240908	.670
53	resnet-18-LC_conv_init_m	.660
54	resnet-18-LC_untrained	.660
55	focalnet_tiny_lrf_in1k	.659
56	0.5x_resnet-18_LC_w_sh_10_iter	.656
57	mobilenet_v2_1.0_96	.656
58	resnet-18-LC_w_sh_100_iter_conv_init_m	.651
59	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.650
60	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.646
61	r3m_resnet50	.646
62	nasnet_mobile-tf	.644
63	resnet50_primary_visual_cortex	.643
64	yudixie_resnet50_distance_rotation_0_240908	.640
65	resnet-50_untrained	.638
66	BiT-S-R152x2	.633
67	resnet-18-LC_d_w_sh_1x1_conv_init_m	.627
68	deepeisnn_ei_imagenet_resnet18	.627
69	r3m_resnet34	.626
70	alexnet_reduced_aliasing_early_checkpoint	.624
71	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.622
72	resnet50_hvs_wzc	.617
73	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.617
74	imagenet_l2_10_0	.617
75	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.615
76	resnet50-VITO-8deg-cc	.611
77	resnet-152_v2-tf	.610
78	deepeisnn_ei_cifar10_resnet18	.609
79	omnivore_swinS	.607
80	resnet50_linf_8_robust	.607
81	pnasnet_large-tf	.604
82	pnasnet_large_halfPrecision	.603
83	yudixie_wbh_object_class_scaling_0415_run_2	.602
84	my-model	.601
85	yolos_tiny	.602
86	r3m_resnet50_nocrop	.601
87	resnet50-moclr8deg	.598
88	yudixie_resnet18_object_class_0_240719	.598
89	shufflenet_v2_x1_0	.598
90	resnet50_robust_l2_eps1	.597
91	omnivore_swinT	.597
92	xception	.596
93	resnet-50x2_untrained	.588
94	resnet_50_v1_spiking_l4a	.588
95	resnet50-sup	.588
96	resnet_50_v1_spiking_l4	.587
97	yudixie_resnet50_imagenet1kpret_0_240908	.587
98	resnet_50_v1	.587
99	resnet_50_v1_spiking	.586
100	resnet_50_v1_spiking_l3	.586
101	resnet50_tutorial	.585
102	eva02_large_in22k	.584
103	resnet50-vitoimagevidnet8	.581
104	resnet50_simclr	.579
105	convnext_large:fb_in22k_ft_in1k	.579
106	grcnn_v2_text_noise	.579
107	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.578
108	yudixie_resnet18_translation_rotation_0_240719	.578
109	vgg_16	.577
110	yudixie_resnet50_translation_rotation_0_240908	.576
111	resnet-152_v1-tf	.576
112	efficientnet_b1_imagenet_full	.575
113	resnet50-vicregl0p9	.573
114	yudixie_resnet50_random_0_240908	.573
115	yudixie_resnet50_rotation_reg_0_240908	.571
116	convnext_xlarge:fb_in22k_ft_in1k	.570
117	resnet50-barlow	.570
118	resnet-50_v1-tf	.570
119	BiT-M-R50x3	.570
120	resnet50_st_wzc_2	.569
121	resnet50_byol	.568
122	BiT-S-R101x3	.567
123	cv_18_dagger_408_pretrained	.567
124	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.565
125	inception_v1	.564
126	resnet-18_untrained	.565
127	yudixie_resnet18_distance_reg_0_240719	.564
128	deit3_base_patch16_224_fb_in1k	.563
129	resnet50-vicreg	.563
130	BiT-S-R152x4	.561
131	yudixie_resnet50_distance_translation_rotation_0_240908	.560
132	nasnet_mobile	.559
133	resnet34_ecoset_full	.559
134	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.559
135	vit_large_patch14_clip_224:openai_ft_in1k	.556
136	convnext_xxlarge_clip	.555
137	yudixie_wbh_object_class_scaling_0415_run_3	.553
138	resnet50_st_wzc_seed3_2_323	.553
139	mobilenet_v2_1-4_224_pytorch	.553
140	r3m_resnet18	.552
141	cvt_cvt-21-384-in1k_4_LucyV4	.550
142	alexnet_training_seed_01_fov12	.550
143	yudixie_resnet18_category_class_0_240719	.548
144	briaai_rmbg_1_4	.548
145	resnet50_st_wzc_seed6_2_323	.548
146	efficientnet_b0	.548
147	0.5x_resnet-18	.547
148	resnet50_l2_3_robust	.545
149	resnet-101_v2-tf	.545
150	grcnn	.544
151	resnet50_ewconplus_wzc	.544
152	resnet101_imagenet_full	.542
153	BiT-M-R101x3	.542
154	omnivore_swinB	.541
155	resnet50-simclr-vissl	.539
156	vit_large_clip336_openai	.539
157	CLIP_ViT-B_32	.538
158	yudixie_resnet50_distance_translation_0_240908	.536
159	alexnet_training_seed_03	.536
160	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.536
161	resnet50-SIN_IN_IN	.535
162	densenet-121	.533
163	resnet50_ewconplus_wzc_seed2_2__323	.533
164	BiT-M-R101x1	.532
165	resnet50_linf_4_robust	.531
166	imagenet_l2_3_0	.530
167	BiT-S-R50x3	.530
168	yudixie_resnet18_distance_translation_rotation_0_240719	.529
169	grcnn_robust_v1	.528
170	resnet18_imagenet_full	.528
171	vonealexnet_gaussian_noise_std4_fixed	.526
172	vit_huge_clip336_laion	.527
173	resnet50_robust_l2_eps3	.527
174	alexnet_robust_correct	.524
175	barlow-twins-resnet50	.524
176	BiT-M-R50x1	.524
177	resnet-18_test_m	.524
178	resnet50_st_wzc_seed7_2_323	.524
179	inception_v4-tf	.522
180	densenet-121-keras	.519
181	mobilenet_v2_1.0_160-tf	.519
182	convnext_small_imagenet_10_seed-0	.517
183	resnet18_ecoset_full	.517
184	yudixie_resnet18_distance_translation_0_240719	.516
185	yudixie_wbh_object_class_scaling_0415_run_0	.516
186	alexnet_training_seed_07	.516
187	yudixie_wbh_object_class_scaling_0415_imagenet	.514
188	yudixie_resnet18_imagenet1kpret_0_240719	.514
189	mobilenet_v2_1.0_128-tf	.514
190	yudixie_resnet18_random_0_240719	.512
191	resnet50_moco_v2	.511
192	resnet-152_v2_pytorch	.512
193	grcnn_109	.511
194	SWSL_resnet50	.509
195	yudixie_resnet18_translation_reg_0_240719	.509
196	alexnet_linf_8_robust	.508
197	convnext_base_fb_in1k	.508
198	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.504
199	resnet50_imagenet_full	.503
200	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.503
201	yudixie_resnet18_rotation_reg_0_240719	.501
202	resnet50	.502
203	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.501
204	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.501
205	resnet50-vicregl0p75	.499
206	convnext_small_imagenet_100_seed-0	.499
207	convnext_large_clip_laion2b_finelayers	.499
208	yudixie_resnet18_distance_rotation_0_240719	.498
209	BiT-S-R101x1	.496
210	resnet50_random_l2_perturb	.493
211	antialiased-rnext101_32x8d	.493
212	0.5x_resnet-18_LC_w_sh_100_iter	.493
213	cvt_cvt-13-224-in1k_4_LucyV4	.492
214	yudixie_resnet50_distance_reg_0_240908	.491
215	resnet50-SIN_IN	.491
216	resnet-18-LC_1st_conv_m	.490
217	yudixie_resnet50_translation_reg_0_240908	.490
218	yudixie_wbh_object_class_scaling_0415_run_1	.488
219	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.487
220	mobilenet_v2_1_0_128	.486
221	cvt_cvt-21-224-in1k_4_LucyV4	.485
222	custom_model_cv_18_dagger_408	.484
223	resnet50_random_linf8_perturb	.484
224	resnet_50_v2	.482
225	inception_v3	.481
226	vit_relpos_base_patch16_clsgap_224:sw_in1k	.480
227	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.480
228	convnext_tiny_sup	.479
229	0.5x_resnet-18_LC	.479
230	mobilenet_v2_0_5_224	.478
231	alexnet_fov4	.477
232	resnet-50_v2-tf	.477
233	ViT-B/32	.476
234	resnet50_ewconplus_wzc_seed5_2__323	.476
235	texture_shape_resnet50_trained_on_SIN	.475
236	alexnet_training_seed_04	.470
237	resnet34_imagenet_full	.469
238	RN50	.469
239	resnet50-SIN	.468
240	resnet101_ecoset_full	.469
241	convnext_large_imagenet_full_seed-0	.468
242	resnet50-cifar	.467
243	resnet101	.465
244	Res2Net50_26w_4s	.462
245	mobilenet_v2_1_4_224	.460
246	effnetv2m_custom384	.458
247	mobilenet_v2_0.75_192-tf	.457
248	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.457
249	alexnet_training_seed_09	.457
250	resnet152_imagenet_full	.453
251	resnet152_ecoset_full	.453
252	convnext_base:clip_laiona_augreg_ft_in1k_384	.452
253	alexnet_random_l2_3_perturb	.451
254	vgg-16-keras	.450
255	alexnet_training_seed_01_fov16	.449
256	alexnet_training_seed_02	.448
257	SWSL_resnext101_32x8d	.445
258	resnet50_ewconplus_wzc_seed6_2_323	.445
259	konkle_alexnetgn_ipcl_ref01_primary_model	.444
260	mobilenet_v2_0_75_224	.443
261	efficientnet_b0_imagenet_full	.441
262	mobilevit_small	.441
263	convnext_small_imagenet_1_seed-0	.440
264	resnet-101_v1-tf	.440
265	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.440
266	convnext_tiny:in12k_ft_in1k	.439
267	resnet18_imagenet21kP	.439
268	artResNet18_1	.436
269	resnet_SIN_IN_FT_IN	.436
270	vit_large_patch14_clip_336:laion2b_ft_in1k	.434
271	compact_vgg19_V4	.433
272	convnext_tiny_imagenet_full_seed-0	.432
273	0.5x_resnet-18_LC_w_sh_1_iter	.429
274	deepeisnn_ei_cifar10_vgg19	.429
275	convnext_base_imagenet_full_seed-0	.429
276	resnet50_ecoset_full	.426
277	alexnet_training_seed_10	.421
278	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.421
279	CLIP_resnet50_float32	.420
280	CLIP_resnet50	.420
281	mobilenet_v2_0.5_224-tf	.418
282	densenet-201	.413
283	resnet-50-robust	.412
284	CORnet-Z	.410
285	FrankRobWobv0	.411
286	AdvProp_efficientnet-b6	.410
287	densenet-169	.410
288	compact_resnet50_IT	.409
289	compact_resnet50_V4	.408
290	resnet-18-LC_w_sh_1_iter_m	.408
291	resnext101_32x8d_wsl	.407
292	inception_v2-tf	.407
293	CORnetZ_CIFAR10_bs32_20_04	.405
294	mobilenet_v2_1.4_224-tf	.404
295	mobilenet_v2_1.3_224-tf	.403
296	densenet-169-keras	.403
297	AdvProp_efficientnet-b7	.403
298	alexnet_training_seed_01	.402
299	BiT-M-R152x2	.402
300	convnext_small_imagenet_full_seed-0	.397
301	convnext_large_in22k_supervised	.397
302	alexnet_fov16	.394
303	alexnet	.387
304	AlexNet_SIN	.387
305	AdvProp_efficientnet-b8	.386
306	compact_resnet50_robust_IT	.385
307	inception_v3-tf	.383
308	mobilenet_v2_0.75_160-tf	.382
309	CLIP-RN50	.378
310	mobilenet_v2_0_75_192	.378
311	antialias-resnet152	.378
312	inception_v1-tf	.376
313	cvt_cvt-13-384-in1k_4_LucyV4	.374
314	antialiased-r50	.371
315	AlexNet_SIN_fov4	.369
316	mobilenet_v2_0.75_128	.368
317	densenet-201-keras	.366
318	texture_shape_alexnet_trained_on_SIN	.365
319	mobilenet_v1_0.25_128	.363
320	AlexNet_SIN_fov12	.362
321	efficientnet_b2_imagenet_full	.359
322	mobilenet_v2_1.0_224-tf	.358
323	ReAlnet09_cornet	.351
324	ReAlnet09	.348
325	resnet50_imagenet_100_seed-0	.347
326	convnext_femto_ols:d1_in1k	.346
327	convnext_base_fb_in1k_gram_aligned_dinov3	.345
328	CORnet-S	.341
329	alexnet_fov12	.340
330	alexnet_random_linf8_perturb	.340
331	AdvProp_efficientnet-b2	.339
332	AT_efficientnet-b2	.338
333	cornet_s	.338
334	mobilenet_v2_0.75_224-tf	.336
335	ReAlnet02_cornet	.335
336	ReAlnet02	.334
337	ReAlnet04_ALL	.334
338	ReAlnet03_ALL	.333
339	ReAlnet01_ALL	.332
340	ReAlnet08_ALL	.329
341	ReAlnet10_ALL	.329
342	ReAlnet07_ALL	.329
343	ReAlnet06_ALL	.328
344	ReAlnet09_ALL	.328
345	ReAlnet02_ALL	.328
346	ReAlnet05_ALL	.327
347	effnetb1_272x240	.326
348	mobilenet_v2_1_3_224	.321
349	ReAlnet01	.318
350	ReAlnet01_cornet	.318
351	ReAlnet06	.316
352	ReAlnet06_cornet	.315
353	vgg-19-keras	.313
354	mobilenet_v2_0_5_192	.313
355	ReAlnet07_cornet	.312
356	ReAlnet07	.311
357	compact_cornets_IT	.309
358	mobilenet_v2_1_0_160	.308
359	ReAlnet03_cornet	.308
360	ReAlnet03	.307
361	ReAlnet04_cornet	.303
362	alexnet_training_seed_01_fov4	.302
363	ReAlnet04	.301
364	alexnet_early_checkpoint	.299
365	mobilenet_v2_0.75_96	.298
366	mobilenet_v2_0.5_192-tf	.297
367	ReAlnet08	.297
368	ReAlnet08_cornet	.297
369	alexnet_training_seed_08	.297
370	mobilenet_v2_0_75_160	.295
371	AdvProp_efficientnet-b0	.295
372	ReAlnet05	.293
373	ReAlnet05_cornet	.293
374	inception_resnet_v2	.291
375	compact_cornets_V4	.290
376	mobilenet_v2_0.35_224	.290
377	mobilenet_v2_1_0_192	.289
378	ReAlnet10_cornet	.288
379	ReAlnet10	.288
380	deit_small_imagenet_10_seed-0	.281
381	my-model	.281
382	ReAlnet01_cornet_fov12	.280
383	compact_resnet50_robust_V4	.278
384	AdvProp_efficientnet-b4	.276
385	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.273
386	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.272
387	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.272
388	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.271
389	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.271
390	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.271
391	efficientnet-b7	.271
392	mobilenet_v2_1.0_192-tf	.263
393	CORnetZ_CIFAR10	.252
394	compact_vgg19_IT	.248
395	ReAlnet01_cornet_fov16	.245
396	alexnet_training_seed_05	.199
397	alexnet_training_seed_06	.198
398	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
399	mobilenet_v2_1_0_224	.198
400	AlexNet_SIN_fov16	.198
401	hmax	.199
402	mobilenet_v2_0.5_128	.198
403	mobilenet_v2_0.35_160
404	resnet18-supervised
405	fitvid_trained_on_physion
406	openclip
407	resnext101_32x32d_wsl
408	resnext101_32x48d_wsl
409	resnet50_ImageNet
410	regnet_y_400mf
411	tv_efficientnet-b1
412	alexnet_ks_torevert
413	yudixie_resnet50_imagenet1kpret_0_240312
414	resnet_152_v1
415	ViT_L_32_imagenet1k
416	resnext101_32x16d_wsl
417	densenet_201_pytorch
418	pnasnet_large_pytorch
419	inception_v3_pytorch
420	eBarlow_lmda_02_1000ep
421	inception_v4
422	nasnet_large
423	deit_small_imagenet_1_seed-0
424	pnasnet_large
425	vonegrcnn_62e_nobn
426	resnet_101_v2
427	vonegrcnn_47e
428	resnet_101_v1
429	resnet_152_v2
430	pr1_stl10_disentangled_alpha04
431	predictive_coding_pc
432	dinov2_vitg14_reg_lc
433	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