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

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio