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

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio