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

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio