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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	vit_base_patch14_dinov2:lvd142m	.834
9	vgg_19	.818
10	vit_base_patch14_reg4_dinov2:lvd142m	.814
11	blt_vs	.809
12	vonegrcnn_62e_nobn	.808
13	voneresnet-50-robust	.798
14	resnet-18-LC_w_sh_10_iter_m	.798
15	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.795
16	VOneCORnet-S	.793
17	vit_base_patch16_dinov3:lvd1689m	.793
18	voneresnet-50	.789
19	mobilenet_v2_0.5_160	.789
20	v1-pyr-nodown	.783
21	resnet-18-LC_m	.783
22	resnet50_FractalDB	.781
23	nasnet_large-tf	.774
24	resnet-18-LC_w_sh_1_iter_conv_init_m	.758
25	resnet-18-LC_w_sh_100_iter_m	.752
26	vit_large_patch14_clip_224:laion2b_ft_in1k	.749
27	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.747
28	voneresnet-50-non_stochastic	.726
29	vit_base_patch16_clip_224:openai_ft_in1k	.719
30	deit_small_imagenet_full_seed-0	.710
31	resnet50_pretrained_with_retinal_waves	.697
32	vision_transformer_vit_large_patch16_224	.695
33	resnet-18-LC_w_sh_10_iter_conv_init_m	.693
34	alexnet_l2_3_robust	.690
35	vggface	.688
36	ReAlnet01_cornet_fov4	.687
37	BiT-M-R152x4	.686
38	deit_base_imagenet_full_seed-0	.677
39	BiT-S-R50x1	.676
40	yudixie_resnet50_object_class_0_240908	.671
41	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.671
42	yudixie_resnet50_category_class_0_240908	.670
43	resnet-18-LC_conv_init_m	.660
44	resnet-18-LC_untrained	.660
45	focalnet_tiny_lrf_in1k	.659
46	0.5x_resnet-18_LC_w_sh_10_iter	.656
47	mobilenet_v2_1.0_96	.656
48	resnet-18-LC_w_sh_100_iter_conv_init_m	.651
49	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.650
50	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.646
51	r3m_resnet50	.646
52	nasnet_mobile-tf	.644
53	resnet50_primary_visual_cortex	.643
54	yudixie_resnet50_distance_rotation_0_240908	.640
55	resnet-50_untrained	.638
56	BiT-S-R152x2	.633
57	resnet-18-LC_d_w_sh_1x1_conv_init_m	.627
58	r3m_resnet34	.626
59	alexnet_reduced_aliasing_early_checkpoint	.624
60	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.622
61	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.617
62	imagenet_l2_10_0	.617
63	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.615
64	resnet50-VITO-8deg-cc	.611
65	resnet-152_v2-tf	.610
66	omnivore_swinS	.607
67	resnet50_linf_8_robust	.607
68	pnasnet_large-tf	.604
69	my-model	.601
70	yolos_tiny	.602
71	r3m_resnet50_nocrop	.601
72	resnet50-moclr8deg	.598
73	yudixie_resnet18_object_class_0_240719	.598
74	shufflenet_v2_x1_0	.598
75	resnet50_robust_l2_eps1	.597
76	omnivore_swinT	.597
77	xception	.596
78	resnet-50x2_untrained	.588
79	resnet_50_v1_spiking_l4a	.588
80	resnet50-sup	.588
81	resnet_50_v1_spiking_l4	.587
82	yudixie_resnet50_imagenet1kpret_0_240908	.587
83	resnet_50_v1	.587
84	resnet_50_v1_spiking	.586
85	resnet_50_v1_spiking_l3	.586
86	resnet50_tutorial	.585
87	resnet50-vitoimagevidnet8	.581
88	resnet50_simclr	.579
89	convnext_large:fb_in22k_ft_in1k	.579
90	grcnn_v2_text_noise	.579
91	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.578
92	yudixie_resnet18_translation_rotation_0_240719	.578
93	vgg_16	.577
94	yudixie_resnet50_translation_rotation_0_240908	.576
95	resnet-152_v1-tf	.576
96	efficientnet_b1_imagenet_full	.575
97	resnet50-vicregl0p9	.573
98	yudixie_resnet50_random_0_240908	.573
99	yudixie_resnet50_rotation_reg_0_240908	.571
100	convnext_xlarge:fb_in22k_ft_in1k	.570
101	resnet50-barlow	.570
102	resnet-50_v1-tf	.570
103	BiT-M-R50x3	.570
104	resnet50_byol	.568
105	BiT-S-R101x3	.567
106	cv_18_dagger_408_pretrained	.567
107	inception_v1	.564
108	resnet-18_untrained	.565
109	yudixie_resnet18_distance_reg_0_240719	.564
110	resnet50-vicreg	.563
111	BiT-S-R152x4	.561
112	yudixie_resnet50_distance_translation_rotation_0_240908	.560
113	nasnet_mobile	.559
114	resnet34_ecoset_full	.559
115	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.559
116	vit_large_patch14_clip_224:openai_ft_in1k	.556
117	mobilenet_v2_1-4_224_pytorch	.553
118	r3m_resnet18	.552
119	cvt_cvt-21-384-in1k_4_LucyV4	.550
120	alexnet_training_seed_01_fov12	.550
121	yudixie_resnet18_category_class_0_240719	.548
122	briaai_rmbg_1_4	.548
123	efficientnet_b0	.548
124	0.5x_resnet-18	.547
125	resnet50_l2_3_robust	.545
126	resnet-101_v2-tf	.545
127	grcnn	.544
128	resnet50_ewconplus_wzc	.544
129	resnet101_imagenet_full	.542
130	BiT-M-R101x3	.542
131	omnivore_swinB	.541
132	resnet50-simclr-vissl	.539
133	CLIP_ViT-B_32	.538
134	yudixie_resnet50_distance_translation_0_240908	.536
135	alexnet_training_seed_03	.536
136	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.536
137	resnet50-SIN_IN_IN	.535
138	densenet-121	.533
139	BiT-M-R101x1	.532
140	resnet50_linf_4_robust	.531
141	imagenet_l2_3_0	.530
142	BiT-S-R50x3	.530
143	yudixie_resnet18_distance_translation_rotation_0_240719	.529
144	grcnn_robust_v1	.528
145	resnet18_imagenet_full	.528
146	vonealexnet_gaussian_noise_std4_fixed	.526
147	resnet50_robust_l2_eps3	.527
148	alexnet_robust_correct	.524
149	barlow-twins-resnet50	.524
150	BiT-M-R50x1	.524
151	resnet-18_test_m	.524
152	inception_v4-tf	.522
153	densenet-121-keras	.519
154	mobilenet_v2_1.0_160-tf	.519
155	convnext_small_imagenet_10_seed-0	.517
156	resnet18_ecoset_full	.517
157	yudixie_resnet18_distance_translation_0_240719	.516
158	alexnet_training_seed_07	.516
159	yudixie_resnet18_imagenet1kpret_0_240719	.514
160	mobilenet_v2_1.0_128-tf	.514
161	yudixie_resnet18_random_0_240719	.512
162	resnet50_moco_v2	.511
163	resnet-152_v2_pytorch	.512
164	grcnn_109	.511
165	SWSL_resnet50	.509
166	yudixie_resnet18_translation_reg_0_240719	.509
167	alexnet_linf_8_robust	.508
168	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.504
169	resnet50_imagenet_full	.503
170	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.503
171	yudixie_resnet18_rotation_reg_0_240719	.501
172	resnet50	.502
173	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.501
174	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.501
175	resnet50-vicregl0p75	.499
176	convnext_small_imagenet_100_seed-0	.499
177	yudixie_resnet18_distance_rotation_0_240719	.498
178	BiT-S-R101x1	.496
179	resnet50_random_l2_perturb	.493
180	antialiased-rnext101_32x8d	.493
181	0.5x_resnet-18_LC_w_sh_100_iter	.493
182	cvt_cvt-13-224-in1k_4_LucyV4	.492
183	yudixie_resnet50_distance_reg_0_240908	.491
184	resnet50-SIN_IN	.491
185	resnet-18-LC_1st_conv_m	.490
186	yudixie_resnet50_translation_reg_0_240908	.490
187	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.487
188	mobilenet_v2_1_0_128	.486
189	cvt_cvt-21-224-in1k_4_LucyV4	.485
190	custom_model_cv_18_dagger_408	.484
191	resnet50_random_linf8_perturb	.484
192	resnet_50_v2	.482
193	inception_v3	.481
194	vit_relpos_base_patch16_clsgap_224:sw_in1k	.480
195	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.480
196	convnext_tiny_sup	.479
197	0.5x_resnet-18_LC	.479
198	mobilenet_v2_0_5_224	.478
199	alexnet_fov4	.477
200	resnet-50_v2-tf	.477
201	ViT-B/32	.476
202	texture_shape_resnet50_trained_on_SIN	.475
203	alexnet	.472
204	alexnet_training_seed_04	.470
205	resnet34_imagenet_full	.469
206	RN50	.469
207	resnet50-SIN	.468
208	resnet101_ecoset_full	.469
209	convnext_large_imagenet_full_seed-0	.468
210	resnet50-cifar	.467
211	resnet101	.465
212	Res2Net50_26w_4s	.462
213	mobilenet_v2_1_4_224	.460
214	effnetv2m_custom384	.458
215	mobilenet_v2_0.75_192-tf	.457
216	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.457
217	alexnet_training_seed_09	.457
218	resnet152_imagenet_full	.453
219	resnet152_ecoset_full	.453
220	convnext_base:clip_laiona_augreg_ft_in1k_384	.452
221	alexnet_random_l2_3_perturb	.451
222	vgg-16-keras	.450
223	alexnet_training_seed_01_fov16	.449
224	alexnet_training_seed_02	.448
225	SWSL_resnext101_32x8d	.445
226	konkle_alexnetgn_ipcl_ref01_primary_model	.444
227	mobilenet_v2_0_75_224	.443
228	efficientnet_b0_imagenet_full	.441
229	mobilevit_small	.441
230	convnext_small_imagenet_1_seed-0	.440
231	resnet-101_v1-tf	.440
232	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.440
233	convnext_tiny:in12k_ft_in1k	.439
234	resnet18_imagenet21kP	.439
235	artResNet18_1	.436
236	resnet_SIN_IN_FT_IN	.436
237	vit_large_patch14_clip_336:laion2b_ft_in1k	.434
238	compact_vgg19_V4	.433
239	convnext_tiny_imagenet_full_seed-0	.432
240	0.5x_resnet-18_LC_w_sh_1_iter	.429
241	convnext_base_imagenet_full_seed-0	.429
242	resnet50_ecoset_full	.426
243	alexnet_training_seed_10	.421
244	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.421
245	CLIP_resnet50_float32	.420
246	CLIP_resnet50	.420
247	mobilenet_v2_0.5_224-tf	.418
248	densenet-201	.413
249	resnet-50-robust	.412
250	CORnet-Z	.410
251	FrankRobWobv0	.411
252	AdvProp_efficientnet-b6	.410
253	densenet-169	.410
254	compact_resnet50_IT	.409
255	compact_resnet50_V4	.408
256	resnet-18-LC_w_sh_1_iter_m	.408
257	resnext101_32x8d_wsl	.407
258	inception_v2-tf	.407
259	CORnetZ_CIFAR10_bs32_20_04	.405
260	mobilenet_v2_1.4_224-tf	.404
261	mobilenet_v2_1.3_224-tf	.403
262	densenet-169-keras	.403
263	AdvProp_efficientnet-b7	.403
264	alexnet_training_seed_01	.402
265	BiT-M-R152x2	.402
266	convnext_small_imagenet_full_seed-0	.397
267	alexnet_fov16	.394
268	alexnet	.388
269	alexnet	.387
270	alexnet	.387
271	alexnet	.387
272	alexnet	.387
273	alexnet	.387
274	alexnet_workflow_trigger	.387
275	alexnet-baseline	.387
276	alexnet	.387
277	alexnet	.387
278	AlexNet_SIN	.387
279	AdvProp_efficientnet-b8	.386
280	compact_resnet50_robust_IT	.385
281	inception_v3-tf	.383
282	mobilenet_v2_0.75_160-tf	.382
283	CLIP-RN50	.378
284	mobilenet_v2_0_75_192	.378
285	antialias-resnet152	.378
286	inception_v1-tf	.376
287	cvt_cvt-13-384-in1k_4_LucyV4	.374
288	antialiased-r50	.371
289	AlexNet_SIN_fov4	.369
290	mobilenet_v2_0.75_128	.368
291	densenet-201-keras	.366
292	texture_shape_alexnet_trained_on_SIN	.365
293	mobilenet_v1_0.25_128	.363
294	AlexNet_SIN_fov12	.362
295	efficientnet_b2_imagenet_full	.359
296	mobilenet_v2_1.0_224-tf	.358
297	ReAlnet09_cornet	.351
298	ReAlnet09	.348
299	resnet50_imagenet_100_seed-0	.347
300	convnext_femto_ols:d1_in1k	.346
301	CORnet-S	.341
302	alexnet_fov12	.340
303	alexnet_random_linf8_perturb	.340
304	AdvProp_efficientnet-b2	.339
305	AT_efficientnet-b2	.338
306	cornet_s	.338
307	mobilenet_v2_0.75_224-tf	.336
308	ReAlnet02_cornet	.335
309	ReAlnet02	.334
310	ReAlnet04_ALL	.334
311	ReAlnet03_ALL	.333
312	ReAlnet01_ALL	.332
313	ReAlnet08_ALL	.329
314	ReAlnet10_ALL	.329
315	ReAlnet07_ALL	.329
316	ReAlnet06_ALL	.328
317	ReAlnet09_ALL	.328
318	ReAlnet02_ALL	.328
319	ReAlnet05_ALL	.327
320	effnetb1_272x240	.326
321	mobilenet_v2_1_3_224	.321
322	ReAlnet01	.318
323	ReAlnet01_cornet	.318
324	ReAlnet06	.316
325	ReAlnet06_cornet	.315
326	vgg-19-keras	.313
327	mobilenet_v2_0_5_192	.313
328	ReAlnet07_cornet	.312
329	ReAlnet07	.311
330	compact_cornets_IT	.309
331	mobilenet_v2_1_0_160	.308
332	ReAlnet03_cornet	.308
333	ReAlnet03	.307
334	ReAlnet04_cornet	.303
335	alexnet_training_seed_01_fov4	.302
336	ReAlnet04	.301
337	alexnet_early_checkpoint	.299
338	mobilenet_v2_0.75_96	.298
339	mobilenet_v2_0.5_192-tf	.297
340	ReAlnet08	.297
341	ReAlnet08_cornet	.297
342	alexnet_training_seed_08	.297
343	mobilenet_v2_0_75_160	.295
344	AdvProp_efficientnet-b0	.295
345	ReAlnet05	.293
346	ReAlnet05_cornet	.293
347	inception_resnet_v2	.291
348	compact_cornets_V4	.290
349	mobilenet_v2_0.35_224	.290
350	mobilenet_v2_1_0_192	.289
351	ReAlnet10_cornet	.288
352	ReAlnet10	.288
353	deit_small_imagenet_10_seed-0	.281
354	my-model	.281
355	ReAlnet01_cornet_fov12	.280
356	compact_resnet50_robust_V4	.278
357	AdvProp_efficientnet-b4	.276
358	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.273
359	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.272
360	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.272
361	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.271
362	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.271
363	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.271
364	efficientnet-b7	.271
365	mobilenet_v2_1.0_192-tf	.263
366	resnet50-meshes-lt-100-original-scratch	.253
367	CORnetZ_CIFAR10	.252
368	compact_vgg19_IT	.248
369	ReAlnet01_cornet_fov16	.245
370	alexnet_training_seed_05	.199
371	alexnet_training_seed_06	.198
372	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
373	mobilenet_v2_1_0_224	.198
374	AlexNet_SIN_fov16	.198
375	hmax	.199
376	mobilenet_v2_0.5_128	.198
377	resnet50	.198
378	resnet50-meshes-lt-100-original-pretrained	.198
379	mobilenet_v2_0.35_160
380	resnet18-supervised
381	fitvid_trained_on_physion
382	openclip
383	resnext101_32x32d_wsl
384	resnext101_32x48d_wsl
385	resnet50_ImageNet
386	regnet_y_400mf
387	tv_efficientnet-b1
388	alexnet_ks_torevert
389	yudixie_resnet50_imagenet1kpret_0_240312
390	resnet_152_v1
391	ViT_L_32_imagenet1k
392	resnext101_32x16d_wsl
393	densenet_201_pytorch
394	pnasnet_large_pytorch
395	inception_v3_pytorch
396	inception_v4
397	nasnet_large
398	deit_small_imagenet_1_seed-0
399	pnasnet_large
400	vonegrcnn_62e_nobn
401	resnet_101_v2
402	vonegrcnn_47e
403	resnet_101_v1
404	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio