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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Sanghavi2020.V4-pls")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet01_cornet_fov4	.623
2	alexnet_fov4	.613
3	compact_resnet50_robust_V4	.601
4	custom_model_cv_18_dagger_408	.595
5	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.591
6	AdvProp_efficientnet-b4	.590
7	dinov2_vitg14_lc	.589
8	densenet-169-keras	.589
9	dinov2_vitg14_reg_lc	.589
10	densenet-121-keras	.589
11	resnet50	.589
12	ReAlnet07_cornet	.588
13	ReAlnet08_cornet	.587
14	ReAlnet02_cornet	.587
15	compact_resnet50_V4	.586
16	resnet50_ewconplus_wzc_seed2_2__323	.585
17	ReAlnet10_cornet	.585
18	yudixie_resnet50_imagenet1kpret_0_240908	.584
19	resnet_50_v1	.584
20	resnet50-sup	.584
21	resnet_50_v1_spiking	.584
22	resnet_50_v1_spiking_l4	.584
23	resnet_50_v1_spiking_l4a	.584
24	resnet50_tutorial	.584
25	resnet_50_v1_spiking_l3	.584
26	yudixie_resnet50_imagenet1kpret_0_240312	.584
27	ReAlnet03_cornet	.584
28	ReAlnet05_cornet	.583
29	efficientnet_b1_imagenet_full	.583
30	ReAlnet04_cornet	.583
31	densenet-201-keras	.582
32	ReAlnet09_cornet	.582
33	antialiased-r50	.582
34	vit_large_clip336_openai	.581
35	antialiased-rnext101_32x8d	.581
36	resnet50_ewconplus_wzc_seed6_2_323	.581
37	resnet50_ewconplus_wzc_seed5_2__323	.581
38	resnet50_random_linf8_perturb	.581
39	efficientnet_b2_imagenet_full	.581
40	SWSL_resnet50	.580
41	resnet34_ecoset_full	.580
42	focalnet_tiny_lrf_in1k	.580
43	ReAlnet06_cornet	.580
44	resnet50_random_l2_perturb	.580
45	resnet50-SIN_IN_IN	.579
46	resnet_SIN_IN_FT_IN	.579
47	resnet18-colorization	.579
48	ReAlnet01_cornet	.579
49	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.578
50	deit3_base_patch16_224_fb_in1k	.578
51	resnet-50_v1-tf	.578
52	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.578
53	inception_v1-tf	.577
54	deit3_base_patch16_224_fb_in22k_ft_in1k	.576
55	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.576
56	resnet50-SIN_IN	.576
57	resnet-101_v1-tf	.576
58	resnet50_linf_4_robust	.576
59	AdvProp_efficientnet-b8	.575
60	effnetv2m_custom384	.575
61	xception-keras	.575
62	ReAlnet02_ALL	.575
63	resnext101_32x8d_wsl	.575
64	ReAlnet01_ALL	.575
65	ReAlnet08_ALL	.575
66	ReAlnet09_ALL	.574
67	resnet-152_v1-tf	.574
68	vgg_16	.574
69	vonegrcnn_62e_nobn	.574
70	ReAlnet07_ALL	.574
71	resnet-18-LC_w_sh_100_iter_conv_init_m	.574
72	ReAlnet06_ALL	.574
73	vgg-16-keras	.574
74	vit_base_patch16_clip_224:openai_ft_in1k	.574
75	vonegrcnn_52e_full	.574
76	resnext101_32x16d_wsl	.574
77	resnet152_imagenet_full	.574
78	vision_transformer_vit_large_patch16_224	.574
79	ReAlnet04_ALL	.574
80	deit_large_imagenet_full_seed-0	.574
81	ReAlnet03_ALL	.574
82	BiT-M-R101x3	.573
83	vonegrcnn_47e	.573
84	ReAlnet10_ALL	.573
85	resnet-50_v2-tf	.573
86	ReAlnet05_ALL	.573
87	resnet50_ewconplus_wzc	.573
88	resnet152_ecoset_full	.572
89	resnet-18-LC_d_w_sh_1x1_conv_init_m	.572
90	effnetb1_272x240	.572
91	AdvProp_efficientnet-b6	.572
92	resnet50_imagenet_100_seed-0	.572
93	BiT-S-R101x3	.571
94	omnivore_swinB	.571
95	Res2Net50_26w_4s	.571
96	resnet50_hvs_wzc	.571
97	resnet101_ecoset_full	.571
98	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.571
99	AT_efficientnet-b7	.570
100	resnet18_imagenet_full	.570
101	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.570
102	voneresnet-50	.570
103	resnet50_l2_3_robust	.570
104	0.5x_resnet-18_LC_w_sh_10_iter	.569
105	compact_vgg19_V4	.569
106	AdvProp_efficientnet-b7	.569
107	resnet18_imagenet21kP	.569
108	yudixie_resnet18_imagenet1kpret_0_240719	.569
109	yudixie_wbh_object_class_scaling_0415_imagenet	.569
110	convnext_xxlarge_clip	.569
111	inception_resnet_v2	.568
112	resnet18_ecoset_full	.568
113	konkle_alexnetgn_ipcl_ref01_primary_model	.568
114	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.568
115	inception_v2-tf	.568
116	dinov2_vitl14_reg_lc	.568
117	dinov2_vitl14_reg_lc_featread	.568
118	voneresnet-50-robust	.568
119	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.568
120	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.567
121	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.567
122	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.567
123	AdvProp_efficientnet-b2	.567
124	mobilenet_v1_1.0_160	.567
125	convnext_tiny_imagenet_full_seed-0	.567
126	AdvProp_efficientnet-b0	.567
127	resnext101_32x48d_wsl	.567
128	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.567
129	efficientnet_b0_imagenet_full	.567
130	efficientnet_b0	.566
131	AT_efficientnet-b2	.566
132	mobilenet_v2_1_4_224	.566
133	resnet101_imagenet_full	.566
134	BiT-M-R152x2	.566
135	texture_shape_resnet50_trained_on_SIN	.566
136	resnet34_imagenet_full	.566
137	resnet-18-LC_w_sh_100_iter_m	.565
138	SWSL_resnext101_32x8d	.565
139	resnet-18-LC_w_sh_1_iter_m	.565
140	convnext_small_imagenet_full_seed-0	.565
141	omnivore_swinS	.565
142	resnet50_imagenet_full	.565
143	convnext_base_imagenet_full_seed-0	.565
144	resnet-152_v2-tf	.565
145	antialias-resnet152	.564
146	CORnet-S	.564
147	compact_resnet50_IT	.564
148	cv_18_dagger_408_pretrained	.564
149	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.564
150	dinov2_vitl14_lc	.563
151	BiT-S-R50x3	.563
152	omnivore_swinT	.563
153	resnet50_robust_l2_eps1	.563
154	BiT-M-R152x4	.563
155	resnet101	.563
156	resnet50_st_wzc_seed6_2_323	.563
157	vit_relpos_base_patch16_clsgap_224:sw_in1k	.563
158	dinov2_vitl14_lc_res336	.562
159	resnet-101_v2-tf	.562
160	voneresnet-50-non_stochastic	.562
161	mobilenet_v1_1.0_224	.562
162	CLIP-RN50	.562
163	resnet-18	.562
164	resnet-18-LC_w_sh_1_iter_conv_init_m	.562
165	FrankRobWobv0	.562
166	resnet-18-LC_w_sh_10_iter_conv_init_m	.561
167	convnext_tiny_sup	.561
168	grcnn_v2_text_noise	.561
169	VOneCORnet-S	.561
170	resnet50_moco_v2	.561
171	mobilenet_v2_1.0_128-tf	.561
172	convnext_femto_ols:d1_in1k	.561
173	BiT-S-R152x2	.561
174	resnet50_st_wzc_seed7_2_323	.561
175	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.560
176	nasnet_mobile	.560
177	BiT-M-R101x1	.560
178	resnext101_32x32d_wsl	.560
179	mobilenet_v2_1_3_224	.559
180	AT_efficientnet-b4	.559
181	resnet50-vicreg	.559
182	densenet-169	.559
183	regnet_y_400mf	.559
184	AlexNet_SIN_fov4	.558
185	barlow-twins-resnet50	.558
186	inception_v3-tf	.558
187	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.558
188	resnet50_st_wzc_2	.558
189	deit_small_imagenet_full_seed-0	.557
190	nasnet_large-tf	.557
191	densenet-201	.557
192	grcnn_109	.557
193	resnet50_ecoset_full	.557
194	resnet50_st_wzc_seed3_2_323	.557
195	resnet50-vicregl0p9	.557
196	BiT-M-R50x1	.556
197	resnet-18_test_m	.556
198	resnet18-local_aggregation	.556
199	mobilenet_v2_1.3_224-tf	.556
200	convnext_large_clip_laion2b_finelayers	.556
201	cvt_cvt-21-384-in1k_4_LucyV4	.555
202	cvt_cvt-13-384-in1k_4_LucyV4	.554
203	resnet50-vicregl0p75	.554
204	resnet50-VITO-8deg-cc	.554
205	pnasnet_large-tf	.554
206	0.5x_resnet-18_LC_w_sh_100_iter	.554
207	grcnn	.554
208	resnet_50_v2	.554
209	tv_efficientnet-b1	.554
210	resnet50-barlow	.553
211	mobilenet_v2_1_0_128	.553
212	alexnet_random_linf8_perturb	.553
213	densenet-121	.553
214	vgg_19	.553
215	inception_v3	.553
216	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.553
217	mobilenet_v1_0.75_128	.553
218	mobilenet_v2_0_75_224	.553
219	RN50	.552
220	convnext_tiny:in12k_ft_in1k	.552
221	alexnet_random_l2_3_perturb	.552
222	deit_base_imagenet_full_seed-0	.552
223	vonealexnet_gaussian_noise_std4_fixed	.552
224	ViT-B/32	.552
225	CLIP_resnet50	.552
226	CLIP_resnet50_float32	.552
227	CLIP_ViT-B_32	.551
228	cornet_s	.551
229	mobilenet_v2_1.4_224-tf	.551
230	mobilenet_v2_1_0_192	.550
231	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.550
232	mobilenet_v2_1.0_160-tf	.550
233	fixres_resnext101_32x48d_wsl	.550
234	cvt_cvt-13-224-in1k_4_LucyV4	.550
235	mobilenet_v1_1.0_192	.550
236	compact_resnet50_robust_IT	.549
237	mobilenet_v2_0.75_224-tf	.549
238	mobilenet_v1_0.75_160	.549
239	mobilenet_v2_1_0_224	.549
240	resnet50-moclr8deg	.549
241	compact_cornets_V4	.549
242	mobilenet_v2_0_75_192	.549
243	grcnn_robust_v1	.549
244	convnext_large_in22k_supervised	.548
245	convnext_large_imagenet_full_seed-0	.548
246	resnet18-instance_recognition	.548
247	BiT-S-R101x1	.548
248	BiT-S-R50x1	.548
249	alexnet	.548
250	alexnet_ks_torevert	.548
251	convnext_xlarge:fb_in22k_ft_in1k	.548
252	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.548
253	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.548
254	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.548
255	convnext_base:clip_laiona_augreg_ft_in1k_384	.547
256	vgg-19-keras	.547
257	BiT-M-R50x3	.547
258	resnet50-simclr-vissl	.546
259	mobilenet_v2_1.0_192-tf	.546
260	mobilenet_v1_1.0_128	.546
261	0.5x_resnet-18_LC_w_sh_1_iter	.546
262	imagenet_l2_3_0	.546
263	0.5x_resnet-18	.546
264	vit_large_patch14_clip_224:openai_ft_in1k	.545
265	mobilenet_v1_0.75_192	.545
266	resnet50-vitoimagevidnet8	.545
267	resnet50_robust_l2_eps3	.545
268	yudixie_resnet50_object_class_0_240908	.544
269	mobilenet_v1_0.75_224	.544
270	resnet50_byol	.544
271	resnet50_byol	.544
272	resnet50-SIN	.544
273	mobilenet_v2_0.75_192-tf	.544
274	cvt_cvt-21-224-in1k_4_LucyV4	.542
275	nasnet_mobile-tf	.542
276	vit_base_patch16_dinov3-lvd1689m	.541
277	efficientnet-b7	.541
278	mobilenet_v2_0.75_160-tf	.541
279	mobilenet_v2_1.0_224-tf	.541
280	inception_v4-tf	.541
281	mobilenet_v2_1_0_160	.541
282	mobilenet_v2_0.75_128	.540
283	squeezenet1_0	.540
284	BiT-S-R152x4	.540
285	yudixie_resnet50_translation_reg_0_240908	.540
286	vit_base_patch14_dinov2-lvd142m	.540
287	yudixie_resnet50_category_class_0_240908	.539
288	CORnet-Z	.539
289	resnet-18-LC_1st_conv_m	.538
290	mobilenet_v2_0_5_192	.538
291	ReAlnet01_cornet_fov12	.538
292	resnet18-relative_position	.537
293	resnet-18-LC_m	.537
294	vit_base_patch14_reg4_dinov2-lvd142m	.537
295	mobilenet_v1_0.5_192	.537
296	resnet-34	.537
297	vit_large_patch14_clip_224:laion2b_ft_in1k	.536
298	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.536
299	mobilenet_v2_0_5_224	.536
300	shufflenet_v2_x1_0	.536
301	squeezenet1_1	.535
302	RN50	.535
303	inception_v1	.535
304	resnet-18-LC_w_sh_10_iter_m	.535
305	resnet18-contrastive_multiview	.535
306	mobilenet_v2_0.5_224-tf	.534
307	mobilenet_v2_0.5_128	.534
308	yudixie_resnet50_distance_translation_0_240908	.534
309	mobilenet_v1_0.25_224	.533
310	mobilenet_v1_0.5_224	.533
311	convnext_small_imagenet_100_seed-0	.532
312	resnet50_linf_8_robust	.532
313	mobilenet_v2_0_75_160	.531
314	yudixie_resnet50_translation_rotation_0_240908	.531
315	yudixie_resnet50_distance_reg_0_240908	.531
316	resnet18-supervised	.531
317	yudixie_resnet50_distance_translation_rotation_0_240908	.531
318	yudixie_resnet50_distance_rotation_0_240908	.530
319	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.530
320	resnet18_fair	.530
321	mobilenet_v2_1.0_96	.529
322	vit_large_patch14_clip_336:laion2b_ft_in1k	.528
323	mobilenet_v2_0.5_192-tf	.528
324	convnext_large:fb_in22k_ft_in1k	.528
325	mobilenet_v1_0.5_160	.528
326	0.5x_resnet-18_LC	.527
327	resnet-18-LC_conv_init_m	.527
328	mobilenet_v1_0.5_128	.524
329	mobilenet_v2_0.35_224	.523
330	alexnet_reduced_aliasing_early_checkpoint	.522
331	resnet18-deepcluster	.522
332	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.521
333	resnet50_primary_visual_cortex	.520
334	mobilenet_v2_0.5_160	.520
335	resnet50_simclr	.520
336	r3m_resnet18	.519
337	ReAlnet10	.519
338	r3m_resnet34	.517
339	mobilenet_v2_0.35_192	.517
340	ReAlnet02	.515
341	yudixie_resnet18_category_class_0_240719	.515
342	ReAlnet08	.515
343	alexnet_early_checkpoint	.515
344	mobilenet_v2_0.75_96	.514
345	ReAlnet07	.513
346	texture_shape_alexnet_trained_on_SIN	.512
347	ReAlnet03	.510
348	yudixie_resnet50_rotation_reg_0_240908	.510
349	AlexNet_SIN	.509
350	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.509
351	ReAlnet06	.509
352	imagenet_l2_10_0	.509
353	ReAlnet01	.508
354	deepeisnn_ei_imagenet_resnet18	.508
355	ReAlnet09	.507
356	ReAlnet04	.506
357	mobilenet_v1_0.25_192	.506
358	ReAlnet05	.505
359	mobilenet_v2_0.5_96	.503
360	yudixie_resnet18_object_class_0_240719	.502
361	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.500
362	alexnet_l2_3_robust	.500
363	convnext_base_fb_in22k_ft_in1k	.500
364	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.500
365	yolos_tiny	.499
366	mobilenet_v1_0.25_160	.499
367	mobilenet_v2_0.35_160	.497
368	yudixie_resnet18_distance_translation_rotation_0_240719	.497
369	compact_cornets_IT	.494
370	ReAlnet01_cornet_fov16	.493
371	resnet-50-pytorch	.491
372	resnet-50-robust	.491
373	r3m_resnet50	.491
374	vggface	.489
375	yudixie_resnet18_translation_rotation_0_240719	.489
376	yudixie_resnet18_distance_reg_0_240719	.488
377	yudixie_resnet18_distance_rotation_0_240719	.485
378	yudixie_resnet18_translation_reg_0_240719	.485
379	artResNet18_1	.484
380	ViT_L_32_imagenet1k	.483
381	r3m_resnet50_nocrop	.479
382	compact_vgg19_IT	.479
383	convnext_base_fb_in1k	.477
384	convnext_base_fb_in1k_gram_aligned_dinov3	.477
385	mobilenet_v2_0.35_96	.476
386	yudixie_resnet18_rotation_reg_0_240719	.476
387	yudixie_resnet18_distance_translation_0_240719	.475
388	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.474
389	deit_small_imagenet_100_seed-0	.474
390	mobilenet_v1_0.25_128	.462
391	my-model	.462
392	mobilenet_v2_0.35_128	.457
393	resnet50_imagenet_10_seed-0	.457
394	mobilenet_v2_1-4_224_pytorch	.455
395	AlexNet_SIN_fov12	.447
396	prednet	.446
397	resnet18-depth_prediction	.443
398	alexnet_training_seed_01_fov4	.442
399	resnet50-cifar	.418
400	alexnet_linf_8_robust	.413
401	alexnet_fov12	.412
402	aughost_fair_resnet18	.410
403	deepeisnn_ei_cifar10_vgg19	.405
404	deepeisnn_ei_cifar10_resnet18	.403
405	alexnet_robust_correct	.399
406	resnet18-contrastive_predictive	.394
407	yudixie_wbh_object_class_scaling_0415_run_2	.389
408	alexnet_fov16	.378
409	AlexNet_SIN_fov16	.374
410	resnet18-autoencoder	.367
411	alexnet_training_seed_02	.366
412	briaai_rmbg_1_4	.364
413	v1-pyr-nodown	.362
414	resnet-152_v2_pytorch	.358
415	alexnet_training_seed_06	.356
416	yudixie_wbh_object_class_scaling_0415_run_0	.351
417	alexnet_training_seed_09	.350
418	CORnetZ_CIFAR10	.349
419	alexnet_training_seed_01	.347
420	yudixie_wbh_object_class_scaling_0415_run_1	.346
421	alexnet_training_seed_03	.345
422	alexnet_training_seed_07	.344
423	alexnet_training_seed_08	.343
424	alexnet_training_seed_10	.342
425	alexnet_training_seed_05	.341
426	resnet50_pretrained_with_retinal_waves	.340
427	CORnetZ_CIFAR10_bs32_20_04	.340
428	alexnet_training_seed_04	.336
429	yudixie_resnet50_random_0_240908	.331
430	mobilevit_small	.330
431	resnet-18_untrained	.329
432	resnet50_FractalDB	.328
433	resnet-50_untrained	.327
434	alexnet_training_seed_01_fov12	.322
435	resnet-50x2_untrained	.321
436	yudixie_wbh_object_class_scaling_0415_run_3	.320
437	yudixie_resnet18_random_0_240719	.317
438	alexnet_training_seed_01_fov16	.304
439	resnet50_imagenet_1_seed-0	.298
440	resnet-18-LC_untrained	.270
441	pr1_stl10_disentangled_alpha04	.215
442	predictive_coding_pc	.189
443	deit_small_imagenet_10_seed-0	.156
444	convnext_small_imagenet_10_seed-0	.121
445	fitvid_trained_on_physion	.117
446	deit_small_imagenet_1_seed-0	.102
447	convnext_small_imagenet_1_seed-0	.092
448	pixels	.075
449	my-model	.075
450	pnasnet_large_halfPrecision
451	eva02_large_in22k
452	vit_huge_clip336_laion

Benchmark bibtex

@misc{Sanghavi_DiCarlo_2021,
  title={Sanghavi2020},
  url={osf.io/chwdk},
  DOI={10.17605/OSF.IO/CHWDK},
  publisher={OSF},
  author={Sanghavi, Sachi and DiCarlo, James J},
  year={2021},
  month={Nov}
}

Ceiling

0.89.

Note that scores are relative to this ceiling.

Data: Sanghavi2020.V4

5760 stimuli recordings from 47 sites in V4

Metric: pls