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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	vonegrcnn_52e_full	.906
2	resnet50_imagenet_1_seed-0	.900
3	deit_large_imagenet_full_seed-0	.886
4	resnet50_imagenet_10_seed-0	.884
5	vonegrcnn_52e_full	.872
6	deit_small_imagenet_100_seed-0	.864
7	vonegrcnn_47e	.855
8	vgg_19	.818
9	blt_vs	.809
10	vonegrcnn_62e_nobn	.808
11	voneresnet-50-robust	.798
12	resnet-18-LC_w_sh_10_iter_m	.798
13	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.795
14	VOneCORnet-S	.793
15	voneresnet-50	.789
16	mobilenet_v2_0.5_160	.789
17	v1-pyr-nodown	.783
18	resnet-18-LC_m	.783
19	resnet50_FractalDB	.781
20	nasnet_large-tf	.774
21	resnet-18-LC_w_sh_1_iter_conv_init_m	.758
22	resnet-18-LC_w_sh_100_iter_m	.752
23	vit_large_patch14_clip_224:laion2b_ft_in1k	.749
24	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.747
25	voneresnet-50-non_stochastic	.726
26	vit_base_patch16_clip_224:openai_ft_in1k	.719
27	deit_small_imagenet_full_seed-0	.710
28	resnet50_pretrained_with_retinal_waves	.697
29	vision_transformer_vit_large_patch16_224	.695
30	resnet-18-LC_w_sh_10_iter_conv_init_m	.693
31	alexnet_l2_3_robust	.690
32	vggface	.688
33	BiT-M-R152x4	.686
34	deit_base_imagenet_full_seed-0	.677
35	BiT-S-R50x1	.676
36	yudixie_resnet50_object_class_0_240908	.671
37	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.671
38	yudixie_resnet50_category_class_0_240908	.670
39	resnet-18-LC_conv_init_m	.660
40	resnet-18-LC_untrained	.660
41	focalnet_tiny_lrf_in1k	.659
42	0.5x_resnet-18_LC_w_sh_10_iter	.656
43	mobilenet_v2_1.0_96	.656
44	resnet-18-LC_w_sh_100_iter_conv_init_m	.651
45	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.650
46	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.646
47	r3m_resnet50	.646
48	nasnet_mobile-tf	.644
49	resnet50_primary_visual_cortex	.643
50	yudixie_resnet50_distance_rotation_0_240908	.640
51	resnet-50_untrained	.638
52	BiT-S-R152x2	.633
53	resnet-18-LC_d_w_sh_1x1_conv_init_m	.627
54	r3m_resnet34	.626
55	alexnet_reduced_aliasing_early_checkpoint	.624
56	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.622
57	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.617
58	imagenet_l2_10_0	.617
59	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.615
60	resnet50-VITO-8deg-cc	.611
61	resnet-152_v2-tf	.610
62	omnivore_swinS	.607
63	resnet50_linf_8_robust	.607
64	pnasnet_large-tf	.604
65	my-model	.601
66	yolos_tiny	.602
67	r3m_resnet50_nocrop	.601
68	resnet50-moclr8deg	.598
69	yudixie_resnet18_object_class_0_240719	.598
70	shufflenet_v2_x1_0	.598
71	resnet50_robust_l2_eps1	.597
72	omnivore_swinT	.597
73	xception	.596
74	resnet-50x2_untrained	.588
75	resnet_50_v1_spiking_l4a	.588
76	resnet50-sup	.588
77	resnet_50_v1_spiking_l4	.587
78	yudixie_resnet50_imagenet1kpret_0_240908	.587
79	resnet_50_v1	.587
80	resnet_50_v1_spiking	.586
81	resnet_50_v1_spiking_l3	.586
82	resnet50_tutorial	.585
83	resnet50-vitoimagevidnet8	.581
84	resnet50_simclr	.579
85	convnext_large:fb_in22k_ft_in1k	.579
86	grcnn_v2_text_noise	.579
87	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.578
88	yudixie_resnet18_translation_rotation_0_240719	.578
89	vgg_16	.577
90	yudixie_resnet50_translation_rotation_0_240908	.576
91	resnet-152_v1-tf	.576
92	efficientnet_b1_imagenet_full	.575
93	resnet50-vicregl0p9	.573
94	yudixie_resnet50_random_0_240908	.573
95	yudixie_resnet50_rotation_reg_0_240908	.571
96	convnext_xlarge:fb_in22k_ft_in1k	.570
97	resnet50-barlow	.570
98	resnet-50_v1-tf	.570
99	BiT-M-R50x3	.570
100	resnet50_byol	.568
101	BiT-S-R101x3	.567
102	cv_18_dagger_408_pretrained	.567
103	inception_v1	.564
104	resnet-18_untrained	.565
105	yudixie_resnet18_distance_reg_0_240719	.564
106	resnet50-vicreg	.563
107	BiT-S-R152x4	.561
108	yudixie_resnet50_distance_translation_rotation_0_240908	.560
109	nasnet_mobile	.559
110	resnet34_ecoset_full	.559
111	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.559
112	vit_large_patch14_clip_224:openai_ft_in1k	.556
113	mobilenet_v2_1-4_224_pytorch	.553
114	r3m_resnet18	.552
115	cvt_cvt-21-384-in1k_4_LucyV4	.550
116	yudixie_resnet18_category_class_0_240719	.548
117	briaai_rmbg_1_4	.548
118	efficientnet_b0	.548
119	0.5x_resnet-18	.547
120	resnet50_l2_3_robust	.545
121	resnet-101_v2-tf	.545
122	grcnn	.544
123	resnet50_ewconplus_wzc	.544
124	resnet101_imagenet_full	.542
125	BiT-M-R101x3	.542
126	omnivore_swinB	.541
127	resnet50-simclr-vissl	.539
128	CLIP_ViT-B_32	.538
129	yudixie_resnet50_distance_translation_0_240908	.536
130	alexnet_training_seed_03	.536
131	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.536
132	resnet50-SIN_IN_IN	.535
133	densenet-121	.533
134	BiT-M-R101x1	.532
135	resnet50_linf_4_robust	.531
136	imagenet_l2_3_0	.530
137	BiT-S-R50x3	.530
138	yudixie_resnet18_distance_translation_rotation_0_240719	.529
139	grcnn_robust_v1	.528
140	resnet18_imagenet_full	.528
141	vonealexnet_gaussian_noise_std4_fixed	.526
142	resnet50_robust_l2_eps3	.527
143	alexnet_robust_correct	.524
144	barlow-twins-resnet50	.524
145	BiT-M-R50x1	.524
146	resnet-18_test_m	.524
147	inception_v4-tf	.522
148	densenet-121-keras	.519
149	mobilenet_v2_1.0_160-tf	.519
150	convnext_small_imagenet_10_seed-0	.517
151	resnet18_ecoset_full	.517
152	yudixie_resnet18_distance_translation_0_240719	.516
153	alexnet_training_seed_07	.516
154	yudixie_resnet18_imagenet1kpret_0_240719	.514
155	mobilenet_v2_1.0_128-tf	.514
156	yudixie_resnet18_random_0_240719	.512
157	resnet50_moco_v2	.511
158	resnet-152_v2_pytorch	.512
159	grcnn_109	.511
160	SWSL_resnet50	.509
161	yudixie_resnet18_translation_reg_0_240719	.509
162	alexnet_linf_8_robust	.508
163	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.504
164	resnet50_imagenet_full	.503
165	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.503
166	yudixie_resnet18_rotation_reg_0_240719	.501
167	resnet50	.502
168	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.501
169	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.501
170	resnet50-vicregl0p75	.499
171	convnext_small_imagenet_100_seed-0	.499
172	yudixie_resnet18_distance_rotation_0_240719	.498
173	BiT-S-R101x1	.496
174	resnet50_random_l2_perturb	.493
175	antialiased-rnext101_32x8d	.493
176	0.5x_resnet-18_LC_w_sh_100_iter	.493
177	cvt_cvt-13-224-in1k_4_LucyV4	.492
178	yudixie_resnet50_distance_reg_0_240908	.491
179	resnet50-SIN_IN	.491
180	resnet-18-LC_1st_conv_m	.490
181	yudixie_resnet50_translation_reg_0_240908	.490
182	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.487
183	mobilenet_v2_1_0_128	.486
184	cvt_cvt-21-224-in1k_4_LucyV4	.485
185	custom_model_cv_18_dagger_408	.484
186	resnet50_random_linf8_perturb	.484
187	resnet_50_v2	.482
188	inception_v3	.481
189	vit_relpos_base_patch16_clsgap_224:sw_in1k	.480
190	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.480
191	convnext_tiny_sup	.479
192	0.5x_resnet-18_LC	.479
193	mobilenet_v2_0_5_224	.478
194	resnet-50_v2-tf	.477
195	ViT-B/32	.476
196	texture_shape_resnet50_trained_on_SIN	.475
197	alexnet	.472
198	alexnet_training_seed_04	.470
199	resnet34_imagenet_full	.469
200	RN50	.469
201	resnet50-SIN	.468
202	resnet101_ecoset_full	.469
203	convnext_large_imagenet_full_seed-0	.468
204	resnet50-cifar	.467
205	resnet101	.465
206	Res2Net50_26w_4s	.462
207	mobilenet_v2_1_4_224	.460
208	effnetv2m_custom384	.458
209	mobilenet_v2_0.75_192-tf	.457
210	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.457
211	alexnet_training_seed_09	.457
212	resnet152_imagenet_full	.453
213	resnet152_ecoset_full	.453
214	convnext_base:clip_laiona_augreg_ft_in1k_384	.452
215	alexnet_random_l2_3_perturb	.451
216	vgg-16-keras	.450
217	alexnet_training_seed_02	.448
218	SWSL_resnext101_32x8d	.445
219	konkle_alexnetgn_ipcl_ref01_primary_model	.444
220	mobilenet_v2_0_75_224	.443
221	efficientnet_b0_imagenet_full	.441
222	mobilevit_small	.441
223	convnext_small_imagenet_1_seed-0	.440
224	resnet-101_v1-tf	.440
225	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.440
226	convnext_tiny:in12k_ft_in1k	.439
227	resnet18_imagenet21kP	.439
228	artResNet18_1	.436
229	resnet_SIN_IN_FT_IN	.436
230	vit_large_patch14_clip_336:laion2b_ft_in1k	.434
231	compact_vgg19_V4	.433
232	convnext_tiny_imagenet_full_seed-0	.432
233	0.5x_resnet-18_LC_w_sh_1_iter	.429
234	convnext_base_imagenet_full_seed-0	.429
235	resnet50_ecoset_full	.426
236	alexnet_training_seed_10	.421
237	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.421
238	CLIP_resnet50_float32	.420
239	CLIP_resnet50	.420
240	mobilenet_v2_0.5_224-tf	.418
241	densenet-201	.413
242	resnet-50-robust	.412
243	CORnet-Z	.410
244	FrankRobWobv0	.411
245	AdvProp_efficientnet-b6	.410
246	densenet-169	.410
247	compact_resnet50_IT	.409
248	compact_resnet50_V4	.408
249	resnet-18-LC_w_sh_1_iter_m	.408
250	resnext101_32x8d_wsl	.407
251	inception_v2-tf	.407
252	CORnetZ_CIFAR10_bs32_20_04	.405
253	mobilenet_v2_1.4_224-tf	.404
254	mobilenet_v2_1.3_224-tf	.403
255	densenet-169-keras	.403
256	AdvProp_efficientnet-b7	.403
257	alexnet_training_seed_01	.402
258	BiT-M-R152x2	.402
259	convnext_small_imagenet_full_seed-0	.397
260	alexnet	.388
261	alexnet	.387
262	alexnet	.387
263	alexnet	.387
264	alexnet	.387
265	alexnet-baseline	.387
266	alexnet	.387
267	alexnet	.387
268	AlexNet_SIN	.387
269	AdvProp_efficientnet-b8	.386
270	compact_resnet50_robust_IT	.385
271	inception_v3-tf	.383
272	mobilenet_v2_0.75_160-tf	.382
273	CLIP-RN50	.378
274	mobilenet_v2_0_75_192	.378
275	antialias-resnet152	.378
276	inception_v1-tf	.376
277	cvt_cvt-13-384-in1k_4_LucyV4	.374
278	antialiased-r50	.371
279	mobilenet_v2_0.75_128	.368
280	densenet-201-keras	.366
281	texture_shape_alexnet_trained_on_SIN	.365
282	mobilenet_v1_0.25_128	.363
283	efficientnet_b2_imagenet_full	.359
284	mobilenet_v2_1.0_224-tf	.358
285	ReAlnet09_cornet	.351
286	ReAlnet09	.348
287	resnet50_imagenet_100_seed-0	.347
288	convnext_femto_ols:d1_in1k	.346
289	CORnet-S	.341
290	alexnet_random_linf8_perturb	.340
291	AdvProp_efficientnet-b2	.339
292	AT_efficientnet-b2	.338
293	cornet_s	.338
294	mobilenet_v2_0.75_224-tf	.336
295	ReAlnet02_cornet	.335
296	ReAlnet02	.334
297	ReAlnet04_ALL	.334
298	ReAlnet03_ALL	.333
299	ReAlnet01_ALL	.332
300	ReAlnet08_ALL	.329
301	ReAlnet10_ALL	.329
302	ReAlnet07_ALL	.329
303	ReAlnet06_ALL	.328
304	ReAlnet09_ALL	.328
305	ReAlnet02_ALL	.328
306	ReAlnet05_ALL	.327
307	effnetb1_272x240	.326
308	mobilenet_v2_1_3_224	.321
309	ReAlnet01	.318
310	ReAlnet01_cornet	.318
311	ReAlnet06	.316
312	ReAlnet06_cornet	.315
313	vgg-19-keras	.313
314	mobilenet_v2_0_5_192	.313
315	ReAlnet07_cornet	.312
316	ReAlnet07	.311
317	compact_cornets_IT	.309
318	mobilenet_v2_1_0_160	.308
319	ReAlnet03_cornet	.308
320	ReAlnet03	.307
321	ReAlnet04_cornet	.303
322	ReAlnet04	.301
323	alexnet_early_checkpoint	.299
324	mobilenet_v2_0.75_96	.298
325	mobilenet_v2_0.5_192-tf	.297
326	ReAlnet08	.297
327	ReAlnet08_cornet	.297
328	alexnet_training_seed_08	.297
329	mobilenet_v2_0_75_160	.295
330	AdvProp_efficientnet-b0	.295
331	ReAlnet05	.293
332	ReAlnet05_cornet	.293
333	inception_resnet_v2	.291
334	compact_cornets_V4	.290
335	mobilenet_v2_0.35_224	.290
336	mobilenet_v2_1_0_192	.289
337	ReAlnet10_cornet	.288
338	ReAlnet10	.288
339	deit_small_imagenet_10_seed-0	.281
340	my-model	.281
341	compact_resnet50_robust_V4	.278
342	AdvProp_efficientnet-b4	.276
343	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.273
344	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.272
345	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.272
346	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.271
347	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.271
348	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.271
349	efficientnet-b7	.271
350	mobilenet_v2_1.0_192-tf	.263
351	resnet50-meshes-lt-100-original-scratch	.253
352	CORnetZ_CIFAR10	.252
353	compact_vgg19_IT	.248
354	alexnet_training_seed_05	.199
355	alexnet_training_seed_06	.198
356	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
357	mobilenet_v2_1_0_224	.198
358	hmax	.199
359	mobilenet_v2_0.5_128	.198
360	resnet50	.198
361	resnet50-meshes-lt-100-original-pretrained	.198
362	mobilenet_v2_0.35_160
363	resnet18-supervised
364	fitvid_trained_on_physion
365	openclip
366	resnext101_32x32d_wsl
367	resnext101_32x48d_wsl
368	resnet50_ImageNet
369	regnet_y_400mf
370	tv_efficientnet-b1
371	alexnet_ks_torevert
372	yudixie_resnet50_imagenet1kpret_0_240312
373	resnet_152_v1
374	ViT_L_32_imagenet1k
375	resnext101_32x16d_wsl
376	densenet_201_pytorch
377	pnasnet_large_pytorch
378	inception_v3_pytorch
379	inception_v4
380	nasnet_large
381	deit_small_imagenet_1_seed-0
382	pnasnet_large
383	vonegrcnn_62e_nobn
384	resnet_101_v2
385	vonegrcnn_47e
386	resnet_101_v1
387	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio