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	yolos_tiny	.602
66	r3m_resnet50_nocrop	.601
67	resnet50-moclr8deg	.598
68	yudixie_resnet18_object_class_0_240719	.598
69	shufflenet_v2_x1_0	.598
70	resnet50_robust_l2_eps1	.597
71	omnivore_swinT	.597
72	xception	.596
73	resnet-50x2_untrained	.588
74	resnet50-sup	.588
75	yudixie_resnet50_imagenet1kpret_0_240908	.587
76	resnet_50_v1	.587
77	resnet50_tutorial	.585
78	resnet50-vitoimagevidnet8	.581
79	resnet50_simclr	.579
80	convnext_large:fb_in22k_ft_in1k	.579
81	grcnn_v2_text_noise	.579
82	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.578
83	yudixie_resnet18_translation_rotation_0_240719	.578
84	vgg_16	.577
85	yudixie_resnet50_translation_rotation_0_240908	.576
86	resnet-152_v1-tf	.576
87	efficientnet_b1_imagenet_full	.575
88	resnet50-vicregl0p9	.573
89	yudixie_resnet50_random_0_240908	.573
90	yudixie_resnet50_rotation_reg_0_240908	.571
91	convnext_xlarge:fb_in22k_ft_in1k	.570
92	resnet50-barlow	.570
93	resnet-50_v1-tf	.570
94	BiT-M-R50x3	.570
95	resnet50_byol	.568
96	BiT-S-R101x3	.567
97	cv_18_dagger_408_pretrained	.567
98	inception_v1	.564
99	resnet-18_untrained	.565
100	yudixie_resnet18_distance_reg_0_240719	.564
101	resnet50-vicreg	.563
102	BiT-S-R152x4	.561
103	yudixie_resnet50_distance_translation_rotation_0_240908	.560
104	nasnet_mobile	.559
105	resnet34_ecoset_full	.559
106	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.559
107	vit_large_patch14_clip_224:openai_ft_in1k	.556
108	mobilenet_v2_1-4_224_pytorch	.553
109	r3m_resnet18	.552
110	cvt_cvt-21-384-in1k_4_LucyV4	.550
111	yudixie_resnet18_category_class_0_240719	.548
112	briaai_rmbg_1_4	.548
113	efficientnet_b0	.548
114	0.5x_resnet-18	.547
115	resnet50_l2_3_robust	.545
116	resnet-101_v2-tf	.545
117	grcnn	.544
118	resnet101_imagenet_full	.542
119	BiT-M-R101x3	.542
120	omnivore_swinB	.541
121	resnet50-simclr-vissl	.539
122	CLIP_ViT-B_32	.538
123	yudixie_resnet50_distance_translation_0_240908	.536
124	alexnet_training_seed_03	.536
125	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.536
126	resnet50-SIN_IN_IN	.535
127	densenet-121	.533
128	BiT-M-R101x1	.532
129	resnet50_linf_4_robust	.531
130	imagenet_l2_3_0	.530
131	BiT-S-R50x3	.530
132	yudixie_resnet18_distance_translation_rotation_0_240719	.529
133	grcnn_robust_v1	.528
134	resnet18_imagenet_full	.528
135	vonealexnet_gaussian_noise_std4_fixed	.526
136	resnet50_robust_l2_eps3	.527
137	alexnet_robust_correct	.524
138	barlow-twins-resnet50	.524
139	BiT-M-R50x1	.524
140	resnet-18_test_m	.524
141	inception_v4-tf	.522
142	densenet-121-keras	.519
143	mobilenet_v2_1.0_160-tf	.519
144	convnext_small_imagenet_10_seed-0	.517
145	resnet18_ecoset_full	.517
146	yudixie_resnet18_distance_translation_0_240719	.516
147	alexnet_training_seed_07	.516
148	yudixie_resnet18_imagenet1kpret_0_240719	.514
149	mobilenet_v2_1.0_128-tf	.514
150	yudixie_resnet18_random_0_240719	.512
151	resnet50_moco_v2	.511
152	resnet-152_v2_pytorch	.512
153	grcnn_109	.511
154	SWSL_resnet50	.509
155	yudixie_resnet18_translation_reg_0_240719	.509
156	alexnet_linf_8_robust	.508
157	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.504
158	resnet50_imagenet_full	.503
159	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.503
160	yudixie_resnet18_rotation_reg_0_240719	.501
161	resnet50	.502
162	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.501
163	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.501
164	resnet50-vicregl0p75	.499
165	convnext_small_imagenet_100_seed-0	.499
166	yudixie_resnet18_distance_rotation_0_240719	.498
167	BiT-S-R101x1	.496
168	resnet50_random_l2_perturb	.493
169	antialiased-rnext101_32x8d	.493
170	0.5x_resnet-18_LC_w_sh_100_iter	.493
171	cvt_cvt-13-224-in1k_4_LucyV4	.492
172	yudixie_resnet50_distance_reg_0_240908	.491
173	resnet50-SIN_IN	.491
174	resnet-18-LC_1st_conv_m	.490
175	yudixie_resnet50_translation_reg_0_240908	.490
176	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.487
177	mobilenet_v2_1_0_128	.486
178	cvt_cvt-21-224-in1k_4_LucyV4	.485
179	custom_model_cv_18_dagger_408	.484
180	resnet50_random_linf8_perturb	.484
181	resnet_50_v2	.482
182	inception_v3	.481
183	vit_relpos_base_patch16_clsgap_224:sw_in1k	.480
184	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.480
185	convnext_tiny_sup	.479
186	0.5x_resnet-18_LC	.479
187	mobilenet_v2_0_5_224	.478
188	resnet-50_v2-tf	.477
189	ViT-B/32	.476
190	texture_shape_resnet50_trained_on_SIN	.475
191	alexnet	.472
192	alexnet_training_seed_04	.470
193	resnet34_imagenet_full	.469
194	RN50	.469
195	resnet50-SIN	.468
196	resnet101_ecoset_full	.469
197	convnext_large_imagenet_full_seed-0	.468
198	resnet50-cifar	.467
199	resnet101	.465
200	Res2Net50_26w_4s	.462
201	mobilenet_v2_1_4_224	.460
202	effnetv2m_custom384	.458
203	mobilenet_v2_0.75_192-tf	.457
204	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.457
205	alexnet_training_seed_09	.457
206	resnet152_imagenet_full	.453
207	resnet152_ecoset_full	.453
208	convnext_base:clip_laiona_augreg_ft_in1k_384	.452
209	alexnet_random_l2_3_perturb	.451
210	vgg-16-keras	.450
211	alexnet_training_seed_02	.448
212	SWSL_resnext101_32x8d	.445
213	konkle_alexnetgn_ipcl_ref01_primary_model	.444
214	mobilenet_v2_0_75_224	.443
215	efficientnet_b0_imagenet_full	.441
216	mobilevit_small	.441
217	convnext_small_imagenet_1_seed-0	.440
218	resnet-101_v1-tf	.440
219	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.440
220	convnext_tiny:in12k_ft_in1k	.439
221	resnet18_imagenet21kP	.439
222	artResNet18_1	.436
223	resnet_SIN_IN_FT_IN	.436
224	vit_large_patch14_clip_336:laion2b_ft_in1k	.434
225	convnext_tiny_imagenet_full_seed-0	.432
226	0.5x_resnet-18_LC_w_sh_1_iter	.429
227	convnext_base_imagenet_full_seed-0	.429
228	resnet50_ecoset_full	.426
229	alexnet_training_seed_10	.421
230	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.421
231	CLIP_resnet50_float32	.420
232	CLIP_resnet50	.420
233	mobilenet_v2_0.5_224-tf	.418
234	densenet-201	.413
235	resnet-50-robust	.412
236	CORnet-Z	.410
237	FrankRobWobv0	.411
238	AdvProp_efficientnet-b6	.410
239	densenet-169	.410
240	resnet-18-LC_w_sh_1_iter_m	.408
241	resnext101_32x8d_wsl	.407
242	inception_v2-tf	.407
243	CORnetZ_CIFAR10_bs32_20_04	.405
244	mobilenet_v2_1.4_224-tf	.404
245	mobilenet_v2_1.3_224-tf	.403
246	densenet-169-keras	.403
247	AdvProp_efficientnet-b7	.403
248	alexnet_training_seed_01	.402
249	BiT-M-R152x2	.402
250	convnext_small_imagenet_full_seed-0	.397
251	alexnet	.388
252	alexnet	.387
253	alexnet	.387
254	alexnet	.387
255	alexnet	.387
256	alexnet-baseline	.387
257	alexnet	.387
258	alexnet	.387
259	AlexNet_SIN	.387
260	AdvProp_efficientnet-b8	.386
261	inception_v3-tf	.383
262	mobilenet_v2_0.75_160-tf	.382
263	CLIP-RN50	.378
264	mobilenet_v2_0_75_192	.378
265	antialias-resnet152	.378
266	inception_v1-tf	.376
267	cvt_cvt-13-384-in1k_4_LucyV4	.374
268	antialiased-r50	.371
269	mobilenet_v2_0.75_128	.368
270	densenet-201-keras	.366
271	texture_shape_alexnet_trained_on_SIN	.365
272	mobilenet_v1_0.25_128	.363
273	efficientnet_b2_imagenet_full	.359
274	mobilenet_v2_1.0_224-tf	.358
275	ReAlnet09_cornet	.351
276	ReAlnet09	.348
277	resnet50_imagenet_100_seed-0	.347
278	convnext_femto_ols:d1_in1k	.346
279	CORnet-S	.341
280	alexnet_random_linf8_perturb	.340
281	AdvProp_efficientnet-b2	.339
282	AT_efficientnet-b2	.338
283	cornet_s	.338
284	mobilenet_v2_0.75_224-tf	.336
285	ReAlnet02_cornet	.335
286	ReAlnet02	.334
287	effnetb1_272x240	.326
288	mobilenet_v2_1_3_224	.321
289	ReAlnet01	.318
290	ReAlnet_incorrect	.318
291	ReAlnet01_cornet	.318
292	ReAlnet06	.316
293	ReAlnet06_cornet	.315
294	vgg-19-keras	.313
295	mobilenet_v2_0_5_192	.313
296	ReAlnet07_cornet	.312
297	ReAlnet07	.311
298	mobilenet_v2_1_0_160	.308
299	ReAlnet03_cornet	.308
300	ReAlnet03	.307
301	ReAlnet04_cornet	.303
302	ReAlnet04	.301
303	alexnet_early_checkpoint	.299
304	mobilenet_v2_0.75_96	.298
305	mobilenet_v2_0.5_192-tf	.297
306	ReAlnet08	.297
307	ReAlnet08_cornet	.297
308	alexnet_training_seed_08	.297
309	mobilenet_v2_0_75_160	.295
310	AdvProp_efficientnet-b0	.295
311	ReAlnet05	.293
312	ReAlnet05_cornet	.293
313	inception_resnet_v2	.291
314	mobilenet_v2_0.35_224	.290
315	mobilenet_v2_1_0_192	.289
316	ReAlnet10_cornet	.288
317	ReAlnet10	.288
318	deit_small_imagenet_10_seed-0	.281
319	my-model	.281
320	AdvProp_efficientnet-b4	.276
321	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.273
322	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.272
323	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.272
324	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.271
325	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.271
326	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.271
327	efficientnet-b7	.271
328	mobilenet_v2_1.0_192-tf	.263
329	resnet50-meshes-lt-100-original-scratch	.253
330	CORnetZ_CIFAR10	.252
331	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
332	mobilenet_v2_1_0_224	.198
333	alexnet_training_seed_05	.199
334	alexnet_training_seed_06	.198
335	resnet50	.198
336	resnet50-meshes-lt-100-original-pretrained	.198
337	mobilenet_v2_0.5_128	.198
338	hmax	.199
339	mobilenet_v2_0.35_160	X
340	resnet18-supervised	X
341	inception_v4	X
342	resnet_152_v1	X
343	yudixie_resnet50_imagenet1kpret_0_240312	X
344	alexnet_ks_torevert	X
345	tv_efficientnet-b1	X
346	regnet_y_400mf	X
347	nasnet_large	X
348	deit_small_imagenet_1_seed-0	X
349	resnet50_ImageNet	X
350	pnasnet_large	X
351	resnext101_32x48d_wsl	X
352	resnext101_32x32d_wsl	X
353	ViT_L_32_imagenet1k	X
354	vonegrcnn_62e_nobn	X
355	resnet_101_v2	X
356	resnext101_32x16d_wsl	X
357	vonegrcnn_47e	X
358	densenet_201_pytorch	X
359	pnasnet_large_pytorch	X
360	inception_v3_pytorch	X
361	openclip	X
362	resnet_101_v1	X
363	resnet_152_v2	X
364	fitvid_trained_on_physion	X

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio