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

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio