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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	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.975
2	deit3_base_patch16_224_fb_in22k_ft_in1k	.973
3	vonegrcnn_52e_full	.906
4	resnet50_imagenet_1_seed-0	.900
5	deit_large_imagenet_full_seed-0	.886
6	resnet50_imagenet_10_seed-0	.884
7	vonegrcnn_52e_full	.872
8	deit_small_imagenet_100_seed-0	.864
9	resnet18_fair	.855
10	vonegrcnn_47e	.855
11	aughost_fair_resnet18	.840
12	vit_base_patch14_dinov2-lvd142m	.833
13	vgg_19	.818
14	vit_base_patch14_reg4_dinov2-lvd142m	.815
15	blt_vs	.809
16	vonegrcnn_62e_nobn	.808
17	voneresnet-50-robust	.798
18	resnet-18-LC_w_sh_10_iter_m	.798
19	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.795
20	VOneCORnet-S	.793
21	vit_base_patch16_dinov3-lvd1689m	.793
22	voneresnet-50	.789
23	mobilenet_v2_0.5_160	.789
24	v1-pyr-nodown	.783
25	resnet-18-LC_m	.783
26	resnet50_FractalDB	.781
27	nasnet_large-tf	.774
28	resnet-18-LC_w_sh_1_iter_conv_init_m	.758
29	resnet-18-LC_w_sh_100_iter_m	.752
30	vit_large_patch14_clip_224:laion2b_ft_in1k	.749
31	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.747
32	voneresnet-50-non_stochastic	.726
33	vit_base_patch16_clip_224:openai_ft_in1k	.719
34	deit_small_imagenet_full_seed-0	.710
35	resnet50_pretrained_with_retinal_waves	.697
36	vision_transformer_vit_large_patch16_224	.695
37	resnet-18-LC_w_sh_10_iter_conv_init_m	.693
38	alexnet_l2_3_robust	.690
39	vggface	.688
40	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.689
41	convnext_base_fb_in22k_ft_in1k	.687
42	ReAlnet01_cornet_fov4	.687
43	BiT-M-R152x4	.686
44	deit_base_imagenet_full_seed-0	.677
45	BiT-S-R50x1	.676
46	yudixie_resnet50_object_class_0_240908	.671
47	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.671
48	yudixie_resnet50_category_class_0_240908	.670
49	resnet-18-LC_conv_init_m	.660
50	resnet-18-LC_untrained	.660
51	focalnet_tiny_lrf_in1k	.659
52	0.5x_resnet-18_LC_w_sh_10_iter	.656
53	mobilenet_v2_1.0_96	.656
54	resnet-18-LC_w_sh_100_iter_conv_init_m	.651
55	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.650
56	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.646
57	r3m_resnet50	.646
58	nasnet_mobile-tf	.644
59	resnet50_primary_visual_cortex	.643
60	yudixie_resnet50_distance_rotation_0_240908	.640
61	resnet-50_untrained	.638
62	BiT-S-R152x2	.633
63	resnet-18-LC_d_w_sh_1x1_conv_init_m	.627
64	deepeisnn_ei_imagenet_resnet18	.627
65	r3m_resnet34	.626
66	alexnet_reduced_aliasing_early_checkpoint	.624
67	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.622
68	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.617
69	imagenet_l2_10_0	.617
70	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.615
71	resnet50-VITO-8deg-cc	.611
72	resnet-152_v2-tf	.610
73	deepeisnn_ei_cifar10_resnet18	.609
74	omnivore_swinS	.607
75	resnet50_linf_8_robust	.607
76	pnasnet_large-tf	.604
77	resnet50_st_wzc_seed5_2_323	.602
78	pnasnet_large_halfPrecision	.603
79	yudixie_wbh_object_class_scaling_0415_run_2	.602
80	my-model	.601
81	yolos_tiny	.602
82	resnet50_st_wzc_2_323	.602
83	r3m_resnet50_nocrop	.601
84	resnet50-moclr8deg	.598
85	yudixie_resnet18_object_class_0_240719	.598
86	shufflenet_v2_x1_0	.598
87	resnet50_robust_l2_eps1	.597
88	omnivore_swinT	.597
89	xception	.596
90	resnet-50x2_untrained	.588
91	resnet_50_v1_spiking_l4a	.588
92	resnet50-sup	.588
93	resnet_50_v1_spiking_l4	.587
94	yudixie_resnet50_imagenet1kpret_0_240908	.587
95	resnet_50_v1	.587
96	resnet_50_v1_spiking	.586
97	resnet_50_v1_spiking_l3	.586
98	resnet50_tutorial	.585
99	resnet50-vitoimagevidnet8	.581
100	resnet50_simclr	.579
101	convnext_large:fb_in22k_ft_in1k	.579
102	grcnn_v2_text_noise	.579
103	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.578
104	yudixie_resnet18_translation_rotation_0_240719	.578
105	vgg_16	.577
106	yudixie_resnet50_translation_rotation_0_240908	.576
107	resnet-152_v1-tf	.576
108	efficientnet_b1_imagenet_full	.575
109	resnet50-vicregl0p9	.573
110	yudixie_resnet50_random_0_240908	.573
111	yudixie_resnet50_rotation_reg_0_240908	.571
112	convnext_xlarge:fb_in22k_ft_in1k	.570
113	resnet50-barlow	.570
114	resnet-50_v1-tf	.570
115	BiT-M-R50x3	.570
116	resnet50_st_wzc_2	.569
117	resnet50_byol	.568
118	BiT-S-R101x3	.567
119	cv_18_dagger_408_pretrained	.567
120	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.565
121	inception_v1	.564
122	resnet-18_untrained	.565
123	yudixie_resnet18_distance_reg_0_240719	.564
124	deit3_base_patch16_224_fb_in1k	.563
125	resnet50-vicreg	.563
126	BiT-S-R152x4	.561
127	yudixie_resnet50_distance_translation_rotation_0_240908	.560
128	nasnet_mobile	.559
129	resnet34_ecoset_full	.559
130	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.559
131	vit_large_patch14_clip_224:openai_ft_in1k	.556
132	yudixie_wbh_object_class_scaling_0415_run_3	.553
133	resnet50_st_wzc_seed3_2_323	.553
134	mobilenet_v2_1-4_224_pytorch	.553
135	r3m_resnet18	.552
136	cvt_cvt-21-384-in1k_4_LucyV4	.550
137	alexnet_training_seed_01_fov12	.550
138	yudixie_resnet18_category_class_0_240719	.548
139	briaai_rmbg_1_4	.548
140	resnet50_st_wzc_seed6_2_323	.548
141	efficientnet_b0	.548
142	0.5x_resnet-18	.547
143	resnet50_l2_3_robust	.545
144	resnet-101_v2-tf	.545
145	grcnn	.544
146	resnet50_ewconplus_wzc	.544
147	resnet101_imagenet_full	.542
148	BiT-M-R101x3	.542
149	omnivore_swinB	.541
150	resnet50-simclr-vissl	.539
151	CLIP_ViT-B_32	.538
152	yudixie_resnet50_distance_translation_0_240908	.536
153	alexnet_training_seed_03	.536
154	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.536
155	resnet50-SIN_IN_IN	.535
156	densenet-121	.533
157	resnet50_ewconplus_wzc_seed2_2__323	.533
158	BiT-M-R101x1	.532
159	resnet50_linf_4_robust	.531
160	imagenet_l2_3_0	.530
161	BiT-S-R50x3	.530
162	yudixie_resnet18_distance_translation_rotation_0_240719	.529
163	grcnn_robust_v1	.528
164	resnet18_imagenet_full	.528
165	vonealexnet_gaussian_noise_std4_fixed	.526
166	resnet50_robust_l2_eps3	.527
167	alexnet_robust_correct	.524
168	barlow-twins-resnet50	.524
169	BiT-M-R50x1	.524
170	resnet-18_test_m	.524
171	resnet50_st_wzc_seed7_2_323	.524
172	resnet50_st_wzc_seed4_2_323	.524
173	inception_v4-tf	.522
174	resnet50_st_wzc_noseed_2_323	.519
175	densenet-121-keras	.519
176	mobilenet_v2_1.0_160-tf	.519
177	convnext_small_imagenet_10_seed-0	.517
178	resnet18_ecoset_full	.517
179	resnet50_ewconplus_wzc_2_323	.516
180	yudixie_resnet18_distance_translation_0_240719	.516
181	yudixie_wbh_object_class_scaling_0415_run_0	.516
182	alexnet_training_seed_07	.516
183	yudixie_wbh_object_class_scaling_0415_imagenet	.514
184	yudixie_resnet18_imagenet1kpret_0_240719	.514
185	mobilenet_v2_1.0_128-tf	.514
186	yudixie_resnet18_random_0_240719	.512
187	resnet50_moco_v2	.511
188	resnet-152_v2_pytorch	.512
189	grcnn_109	.511
190	SWSL_resnet50	.509
191	yudixie_resnet18_translation_reg_0_240719	.509
192	alexnet_linf_8_robust	.508
193	convnext_base_fb_in1k	.508
194	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.504
195	resnet50_imagenet_full	.503
196	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.503
197	yudixie_resnet18_rotation_reg_0_240719	.501
198	resnet50	.502
199	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.501
200	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.501
201	resnet50-vicregl0p75	.499
202	convnext_small_imagenet_100_seed-0	.499
203	yudixie_resnet18_distance_rotation_0_240719	.498
204	BiT-S-R101x1	.496
205	resnet50_random_l2_perturb	.493
206	antialiased-rnext101_32x8d	.493
207	0.5x_resnet-18_LC_w_sh_100_iter	.493
208	cvt_cvt-13-224-in1k_4_LucyV4	.492
209	yudixie_resnet50_distance_reg_0_240908	.491
210	resnet50-SIN_IN	.491
211	resnet-18-LC_1st_conv_m	.490
212	yudixie_resnet50_translation_reg_0_240908	.490
213	yudixie_wbh_object_class_scaling_0415_run_1	.488
214	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.487
215	mobilenet_v2_1_0_128	.486
216	cvt_cvt-21-224-in1k_4_LucyV4	.485
217	custom_model_cv_18_dagger_408	.484
218	resnet50_random_linf8_perturb	.484
219	resnet_50_v2	.482
220	inception_v3	.481
221	vit_relpos_base_patch16_clsgap_224:sw_in1k	.480
222	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.480
223	convnext_tiny_sup	.479
224	0.5x_resnet-18_LC	.479
225	mobilenet_v2_0_5_224	.478
226	alexnet_fov4	.477
227	resnet-50_v2-tf	.477
228	ViT-B/32	.476
229	resnet50_ewconplus_wzc_seed5_2__323	.476
230	texture_shape_resnet50_trained_on_SIN	.475
231	alexnet_training_seed_04	.470
232	resnet34_imagenet_full	.469
233	RN50	.469
234	resnet50-SIN	.468
235	resnet101_ecoset_full	.469
236	convnext_large_imagenet_full_seed-0	.468
237	resnet50-cifar	.467
238	resnet101	.465
239	Res2Net50_26w_4s	.462
240	mobilenet_v2_1_4_224	.460
241	effnetv2m_custom384	.458
242	resnet50_ewconplus_wzc_seed4_2_323	.458
243	mobilenet_v2_0.75_192-tf	.457
244	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.457
245	alexnet_training_seed_09	.457
246	resnet152_imagenet_full	.453
247	resnet152_ecoset_full	.453
248	resnet50_ewconplus_wzc_seed7_2_323	.453
249	convnext_base:clip_laiona_augreg_ft_in1k_384	.452
250	alexnet_random_l2_3_perturb	.451
251	vgg-16-keras	.450
252	alexnet_training_seed_01_fov16	.449
253	alexnet_training_seed_02	.448
254	SWSL_resnext101_32x8d	.445
255	resnet50_ewconplus_wzc_seed6_2_323	.445
256	konkle_alexnetgn_ipcl_ref01_primary_model	.444
257	mobilenet_v2_0_75_224	.443
258	resnet50_ewconplus_noseed_2_323	.442
259	efficientnet_b0_imagenet_full	.441
260	mobilevit_small	.441
261	convnext_small_imagenet_1_seed-0	.440
262	resnet-101_v1-tf	.440
263	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.440
264	convnext_tiny:in12k_ft_in1k	.439
265	resnet18_imagenet21kP	.439
266	artResNet18_1	.436
267	resnet_SIN_IN_FT_IN	.436
268	vit_large_patch14_clip_336:laion2b_ft_in1k	.434
269	compact_vgg19_V4	.433
270	convnext_tiny_imagenet_full_seed-0	.432
271	0.5x_resnet-18_LC_w_sh_1_iter	.429
272	deepeisnn_ei_cifar10_vgg19	.429
273	convnext_base_imagenet_full_seed-0	.429
274	resnet50_ewconplus_wzc_seed3_2_323	.428
275	resnet50_ecoset_full	.426
276	alexnet_training_seed_10	.421
277	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.421
278	CLIP_resnet50_float32	.420
279	CLIP_resnet50	.420
280	mobilenet_v2_0.5_224-tf	.418
281	densenet-201	.413
282	resnet-50-robust	.412
283	CORnet-Z	.410
284	FrankRobWobv0	.411
285	AdvProp_efficientnet-b6	.410
286	densenet-169	.410
287	compact_resnet50_IT	.409
288	compact_resnet50_V4	.408
289	resnet-18-LC_w_sh_1_iter_m	.408
290	resnext101_32x8d_wsl	.407
291	inception_v2-tf	.407
292	CORnetZ_CIFAR10_bs32_20_04	.405
293	mobilenet_v2_1.4_224-tf	.404
294	mobilenet_v2_1.3_224-tf	.403
295	densenet-169-keras	.403
296	AdvProp_efficientnet-b7	.403
297	alexnet_training_seed_01	.402
298	BiT-M-R152x2	.402
299	convnext_small_imagenet_full_seed-0	.397
300	alexnet_fov16	.394
301	alexnet	.387
302	AlexNet_SIN	.387
303	AdvProp_efficientnet-b8	.386
304	compact_resnet50_robust_IT	.385
305	inception_v3-tf	.383
306	mobilenet_v2_0.75_160-tf	.382
307	CLIP-RN50	.378
308	mobilenet_v2_0_75_192	.378
309	antialias-resnet152	.378
310	inception_v1-tf	.376
311	cvt_cvt-13-384-in1k_4_LucyV4	.374
312	antialiased-r50	.371
313	AlexNet_SIN_fov4	.369
314	mobilenet_v2_0.75_128	.368
315	densenet-201-keras	.366
316	texture_shape_alexnet_trained_on_SIN	.365
317	mobilenet_v1_0.25_128	.363
318	AlexNet_SIN_fov12	.362
319	efficientnet_b2_imagenet_full	.359
320	mobilenet_v2_1.0_224-tf	.358
321	ReAlnet09_cornet	.351
322	ReAlnet09	.348
323	resnet50_imagenet_100_seed-0	.347
324	convnext_femto_ols:d1_in1k	.346
325	convnext_base_fb_in1k_gram_aligned_dinov3	.345
326	CORnet-S	.341
327	alexnet_fov12	.340
328	alexnet_random_linf8_perturb	.340
329	AdvProp_efficientnet-b2	.339
330	AT_efficientnet-b2	.338
331	cornet_s	.338
332	mobilenet_v2_0.75_224-tf	.336
333	ReAlnet02_cornet	.335
334	ReAlnet02	.334
335	ReAlnet04_ALL	.334
336	ReAlnet03_ALL	.333
337	ReAlnet01_ALL	.332
338	ReAlnet08_ALL	.329
339	ReAlnet10_ALL	.329
340	ReAlnet07_ALL	.329
341	ReAlnet06_ALL	.328
342	ReAlnet09_ALL	.328
343	ReAlnet02_ALL	.328
344	ReAlnet05_ALL	.327
345	effnetb1_272x240	.326
346	mobilenet_v2_1_3_224	.321
347	ReAlnet01	.318
348	ReAlnet01_cornet	.318
349	ReAlnet06	.316
350	ReAlnet06_cornet	.315
351	vgg-19-keras	.313
352	mobilenet_v2_0_5_192	.313
353	ReAlnet07_cornet	.312
354	ReAlnet07	.311
355	compact_cornets_IT	.309
356	mobilenet_v2_1_0_160	.308
357	ReAlnet03_cornet	.308
358	ReAlnet03	.307
359	ReAlnet04_cornet	.303
360	alexnet_training_seed_01_fov4	.302
361	ReAlnet04	.301
362	alexnet_early_checkpoint	.299
363	mobilenet_v2_0.75_96	.298
364	mobilenet_v2_0.5_192-tf	.297
365	ReAlnet08	.297
366	ReAlnet08_cornet	.297
367	alexnet_training_seed_08	.297
368	mobilenet_v2_0_75_160	.295
369	AdvProp_efficientnet-b0	.295
370	ReAlnet05	.293
371	ReAlnet05_cornet	.293
372	inception_resnet_v2	.291
373	compact_cornets_V4	.290
374	mobilenet_v2_0.35_224	.290
375	mobilenet_v2_1_0_192	.289
376	ReAlnet10_cornet	.288
377	ReAlnet10	.288
378	deit_small_imagenet_10_seed-0	.281
379	my-model	.281
380	ReAlnet01_cornet_fov12	.280
381	compact_resnet50_robust_V4	.278
382	AdvProp_efficientnet-b4	.276
383	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.273
384	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.272
385	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.272
386	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.271
387	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.271
388	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.271
389	efficientnet-b7	.271
390	mobilenet_v2_1.0_192-tf	.263
391	resnet50-meshes-lt-100-original-scratch	.253
392	CORnetZ_CIFAR10	.252
393	compact_vgg19_IT	.248
394	ReAlnet01_cornet_fov16	.245
395	alexnet_training_seed_05	.199
396	alexnet_training_seed_06	.198
397	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
398	mobilenet_v2_1_0_224	.198
399	AlexNet_SIN_fov16	.198
400	hmax	.199
401	mobilenet_v2_0.5_128	.198
402	resnet50	.198
403	resnet50-meshes-lt-100-original-pretrained	.198
404	mobilenet_v2_0.35_160
405	resnet18-supervised
406	fitvid_trained_on_physion
407	openclip
408	resnext101_32x32d_wsl
409	resnext101_32x48d_wsl
410	resnet50_ImageNet
411	regnet_y_400mf
412	tv_efficientnet-b1
413	alexnet_ks_torevert
414	yudixie_resnet50_imagenet1kpret_0_240312
415	resnet_152_v1
416	ViT_L_32_imagenet1k
417	resnext101_32x16d_wsl
418	densenet_201_pytorch
419	pnasnet_large_pytorch
420	inception_v3_pytorch
421	eBarlow_lmda_02_1000ep
422	inception_v4
423	nasnet_large
424	deit_small_imagenet_1_seed-0
425	pnasnet_large
426	vonegrcnn_62e_nobn
427	resnet_101_v2
428	vonegrcnn_47e
429	resnet_101_v1
430	resnet_152_v2
431	pr1_stl10_disentangled_alpha04
432	predictive_coding_pc

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: modulation_ratio