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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("SanghaviJozwik2020.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	.536
2	FrankRobWobv0	.528
3	custom_model_cv_18_dagger_408	.520
4	alexnet_fov4	.511
5	resnet50	.507
6	resnet50_ewconplus_wzc_seed6_2_323	.507
7	resnet50_random_l2_perturb	.507
8	SWSL_resnet50	.505
9	resnet50_ewconplus_wzc_seed5_2__323	.502
10	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.499
11	cv_18_dagger_408_pretrained	.498
12	dinov2_vitg14_lc	.497
13	vit_base_patch16_clip_224:openai_ft_in1k	.496
14	resnet50_ewconplus_wzc_seed2_2__323	.496
15	antialiased-r50	.495
16	resnet50_st_wzc_seed6_2_323	.494
17	texture_shape_resnet50_trained_on_SIN	.493
18	resnext101_32x16d_wsl	.493
19	antialiased-rnext101_32x8d	.493
20	resnet50_random_linf8_perturb	.492
21	SWSL_resnext101_32x8d	.492
22	vit_relpos_base_patch16_clsgap_224:sw_in1k	.492
23	dinov2_vitl14_reg_lc	.492
24	dinov2_vitl14_reg_lc_featread	.492
25	resnet50-SIN_IN_IN	.492
26	resnet_SIN_IN_FT_IN	.492
27	yudixie_resnet50_imagenet1kpret_0_240312	.491
28	resnet50_tutorial	.491
29	resnet_50_v1	.491
30	resnet50-sup	.491
31	yudixie_resnet50_imagenet1kpret_0_240908	.491
32	resnet_50_v1_spiking	.491
33	resnet_50_v1_spiking_l4	.491
34	resnet_50_v1_spiking_l4a	.491
35	resnet_50_v1_spiking_l3	.491
36	resnet152_imagenet_full	.491
37	resnet101_imagenet_full	.491
38	dinov2_vitg14_reg_lc	.490
39	vit_large_clip336_openai	.490
40	resnet152_ecoset_full	.489
41	resnet_50_v2	.489
42	ReAlnet10_cornet	.489
43	ReAlnet08_cornet	.488
44	resnet101_ecoset_full	.488
45	nasnet_mobile	.488
46	resnet-101_v1-tf	.488
47	deit_small_imagenet_full_seed-0	.487
48	resnet50-SIN_IN	.487
49	cvt_cvt-13-224-in1k_4_LucyV4	.486
50	ReAlnet02_cornet	.486
51	Res2Net50_26w_4s	.486
52	deit_large_imagenet_full_seed-0	.485
53	cvt_cvt-21-384-in1k_4_LucyV4	.485
54	resnet50_linf_4_robust	.485
55	ReAlnet07_cornet	.485
56	ReAlnet06_cornet	.485
57	resnet50_st_wzc_seed7_2_323	.485
58	ReAlnet05_cornet	.484
59	ReAlnet09_cornet	.484
60	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.484
61	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.484
62	deit3_base_patch16_224_fb_in1k	.484
63	ReAlnet04_cornet	.484
64	ReAlnet01_cornet	.484
65	resnet-152_v1-tf	.483
66	ReAlnet03_cornet	.483
67	resnet101	.483
68	resnext101_32x8d_wsl	.483
69	resnet50_ewconplus_wzc	.483
70	AdvProp_efficientnet-b4	.483
71	effnetb1_272x240	.483
72	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.483
73	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.482
74	pnasnet_large-tf	.482
75	cvt_cvt-13-384-in1k_4_LucyV4	.481
76	convnext_tiny_imagenet_full_seed-0	.481
77	resnet50_st_wzc_seed3_2_323	.481
78	deit3_base_patch16_224_fb_in22k_ft_in1k	.481
79	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.481
80	resnet50-VITO-8deg-cc	.481
81	resnet50_hvs_wzc	.480
82	vision_transformer_vit_large_patch16_224	.480
83	CLIP-RN50	.480
84	inception_v2-tf	.479
85	ReAlnet07_ALL	.479
86	resnet50_l2_3_robust	.478
87	cvt_cvt-21-224-in1k_4_LucyV4	.478
88	resnet50_moco_v2	.478
89	ReAlnet08_ALL	.477
90	dinov2_vitl14_lc_res336	.477
91	ReAlnet05_ALL	.477
92	resnet-50_v1-tf	.477
93	ReAlnet02_ALL	.477
94	ReAlnet01_ALL	.476
95	ReAlnet09_ALL	.476
96	eBarlow_lmda_02_1000ep	.476
97	ReAlnet03_ALL	.476
98	ReAlnet10_ALL	.476
99	ReAlnet06_ALL	.476
100	convnext_xlarge:fb_in22k_ft_in1k	.476
101	voneresnet-50	.476
102	ReAlnet04_ALL	.475
103	omnivore_swinT	.475
104	resnet18_imagenet_full	.475
105	omnivore_swinB	.475
106	resnet50_primary_visual_cortex	.475
107	mobilenet_v2_1_3_224	.475
108	resnet18_ecoset_full	.475
109	vonegrcnn_62e_nobn	.474
110	densenet-201	.474
111	omnivore_swinS	.474
112	densenet-121-keras	.473
113	AlexNet_SIN_fov4	.473
114	xception-keras	.473
115	cornet_s	.473
116	convnext_small_imagenet_full_seed-0	.473
117	vonegrcnn_52e_full	.473
118	convnext_large_imagenet_full_seed-0	.472
119	vonegrcnn_47e	.472
120	CORnet-S	.472
121	AdvProp_efficientnet-b0	.472
122	tv_efficientnet-b1	.472
123	focalnet_tiny_lrf_in1k	.472
124	resnet50-vicreg	.472
125	convnext_large:fb_in22k_ft_in1k	.471
126	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.471
127	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.471
128	resnext101_32x32d_wsl	.471
129	resnet50_byol	.471
130	densenet-169	.470
131	convnext_large_in22k_supervised	.470
132	mobilenet_v2_1_0_224	.470
133	efficientnet_b2_imagenet_full	.470
134	barlow-twins-resnet50	.470
135	nasnet_mobile-tf	.470
136	resnext101_32x48d_wsl	.470
137	resnet50_imagenet_100_seed-0	.470
138	inception_v1-tf	.470
139	mobilenet_v1_0.75_128	.470
140	effnetv2m_custom384	.469
141	resnet50-barlow	.469
142	resnet50-vicregl0p9	.469
143	CLIP_resnet50_float32	.469
144	CLIP_resnet50	.469
145	efficientnet_b0	.469
146	voneresnet-50-robust	.469
147	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.469
148	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.469
149	CLIP_ViT-B_32	.469
150	resnet50_st_wzc_2	.469
151	convnext_base_imagenet_full_seed-0	.469
152	mobilenet_v2_1_0_192	.469
153	VOneCORnet-S	.469
154	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.469
155	voneresnet-50-non_stochastic	.469
156	resnet50-moclr8deg	.469
157	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.468
158	resnet-18-LC_w_sh_10_iter_conv_init_m	.468
159	ViT-B/32	.467
160	convnext_femto_ols:d1_in1k	.467
161	dinov2_vitl14_lc	.467
162	antialias-resnet152	.467
163	convnext_tiny:in12k_ft_in1k	.466
164	densenet-169-keras	.466
165	AT_efficientnet-b7	.466
166	mobilenet_v1_1.0_224	.466
167	resnet-18-LC_w_sh_1_iter_conv_init_m	.465
168	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.465
169	AdvProp_efficientnet-b6	.465
170	resnet-50_v2-tf	.464
171	AdvProp_efficientnet-b7	.464
172	regnet_y_400mf	.464
173	resnet34_imagenet_full	.464
174	AT_efficientnet-b2	.464
175	convnext_tiny_sup	.464
176	resnet18_imagenet21kP	.463
177	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.463
178	resnet50-simclr-vissl	.463
179	mobilenet_v1_1.0_160	.463
180	resnet50-vicregl0p75	.463
181	resnet50_robust_l2_eps1	.463
182	nasnet_large-tf	.463
183	AdvProp_efficientnet-b8	.462
184	inception_v4-tf	.462
185	resnet-18-LC_w_sh_100_iter_conv_init_m	.462
186	inception_resnet_v2	.462
187	mobilenet_v1_0.5_128	.462
188	mobilenet_v1_1.0_128	.461
189	0.5x_resnet-18_LC_w_sh_10_iter	.461
190	resnet-101_v2-tf	.461
191	mobilenet_v1_1.0_192	.461
192	mobilenet_v1_0.75_224	.461
193	mobilenet_v1_0.75_160	.461
194	AdvProp_efficientnet-b2	.460
195	grcnn_109	.460
196	resnet50_linf_8_robust	.460
197	resnet-18-LC_w_sh_100_iter_m	.460
198	mobilenet_v1_0.5_192	.460
199	AT_efficientnet-b4	.459
200	resnet50-SIN	.459
201	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.459
202	resnet50_robust_l2_eps3	.459
203	resnet-18-LC_w_sh_1_iter_m	.459
204	inception_v3-tf	.459
205	convnext_large_clip_laion2b_finelayers	.459
206	convnext_xxlarge_clip	.459
207	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.459
208	resnet50-vitoimagevidnet8	.458
209	inception_v3	.458
210	resnet50_imagenet_full	.458
211	0.5x_resnet-18_LC_w_sh_1_iter	.458
212	imagenet_l2_3_0	.457
213	mobilenet_v2_1_4_224	.457
214	inception_v1	.457
215	yudixie_resnet18_imagenet1kpret_0_240719	.456
216	yudixie_wbh_object_class_scaling_0415_imagenet	.456
217	densenet-201-keras	.456
218	mobilenet_v2_1.3_224-tf	.456
219	efficientnet_b0_imagenet_full	.456
220	grcnn_v2_text_noise	.455
221	resnet50_ecoset_full	.455
222	vit_base_patch14_dinov2-lvd142m	.455
223	ReAlnet01_cornet_fov12	.455
224	mobilenet_v2_0.75_224-tf	.455
225	resnet34_ecoset_full	.455
226	grcnn	.455
227	mobilenet_v2_1.4_224-tf	.454
228	mobilenet_v2_0_75_192	.454
229	mobilenet_v1_0.5_160	.454
230	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.453
231	resnet-18-LC_d_w_sh_1x1_conv_init_m	.453
232	resnet-18	.452
233	vit_base_patch16_dinov3-lvd1689m	.452
234	mobilenet_v1_0.5_224	.452
235	resnet50_simclr	.452
236	efficientnet_b1_imagenet_full	.452
237	grcnn_robust_v1	.452
238	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.452
239	mobilenet_v2_0_75_224	.452
240	mobilenet_v2_1.0_224-tf	.452
241	resnet-152_v2-tf	.451
242	mobilenet_v2_1_0_160	.449
243	convnext_small_imagenet_100_seed-0	.448
244	vit_large_patch14_clip_224:openai_ft_in1k	.448
245	mobilenet_v1_0.75_192	.448
246	mobilenet_v2_0_75_160	.447
247	mobilenet_v1_0.25_224	.447
248	shufflenet_v2_x1_0	.446
249	alexnet_random_linf8_perturb	.446
250	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.446
251	vit_large_patch14_clip_224:laion2b_ft_in1k	.446
252	mobilenet_v2_1_0_128	.445
253	mobilenet_v2_0_5_224	.445
254	RN50	.445
255	alexnet_random_l2_3_perturb	.444
256	mobilenet_v1_0.25_192	.444
257	BiT-M-R101x3	.444
258	blt_vs	.443
259	mobilenet_v2_0_5_192	.442
260	ReAlnet08	.442
261	convnext_base:clip_laiona_augreg_ft_in1k_384	.442
262	vgg_16	.441
263	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.441
264	deepeisnn_ei_imagenet_resnet18	.441
265	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.441
266	resnet18-supervised	.440
267	BiT-M-R50x1	.440
268	mobilenet_v2_0.5_224-tf	.439
269	deit_base_imagenet_full_seed-0	.439
270	densenet-121	.439
271	BiT-S-R50x3	.439
272	0.5x_resnet-18	.439
273	vgg_19	.439
274	resnet-18_test_m	.439
275	konkle_alexnetgn_ipcl_ref01_primary_model	.438
276	efficientnet-b7	.438
277	ReAlnet10	.438
278	mobilenet_v2_0.75_160-tf	.438
279	vit_large_patch14_clip_336:laion2b_ft_in1k	.437
280	ReAlnet02	.437
281	BiT-M-R101x1	.436
282	ReAlnet06	.435
283	vgg-16-keras	.435
284	ReAlnet05	.435
285	0.5x_resnet-18_LC_w_sh_100_iter	.435
286	mobilenet_v2_1.0_96	.434
287	mobilenet_v2_1.0_128-tf	.434
288	ReAlnet09	.434
289	ReAlnet07	.433
290	ReAlnet01	.432
291	RN50	.432
292	ReAlnet04	.431
293	resnet-50-pytorch	.431
294	resnet-50-robust	.431
295	ReAlnet03	.430
296	mobilenet_v2_0.75_96	.430
297	resnet-18-LC_w_sh_10_iter_m	.430
298	resnet18-instance_recognition	.430
299	resnet18_fair	.430
300	CORnet-Z	.430
301	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.429
302	yudixie_resnet50_object_class_0_240908	.429
303	resnet18-local_aggregation	.427
304	mobilenet_v1_0.25_160	.427
305	vonealexnet_gaussian_noise_std4_fixed	.427
306	imagenet_l2_10_0	.427
307	BiT-S-R101x3	.426
308	mobilenet_v1_0.25_128	.426
309	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.426
310	mobilenet_v2_0.5_128	.425
311	squeezenet1_0	.424
312	mobilenet_v2_1.0_160-tf	.424
313	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.424
314	mobilenet_v2_1.0_192-tf	.423
315	mobilenet_v2_0.75_128	.422
316	BiT-S-R50x1	.422
317	alexnet	.421
318	alexnet_ks_torevert	.421
319	BiT-M-R152x4	.421
320	squeezenet1_1	.421
321	mobilenet_v2_0.35_224	.421
322	BiT-M-R50x3	.420
323	resnet18-colorization	.420
324	yudixie_resnet50_category_class_0_240908	.419
325	alexnet_l2_3_robust	.418
326	convnext_base_fb_in22k_ft_in1k	.418
327	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.418
328	vgg-19-keras	.417
329	BiT-S-R101x1	.416
330	yolos_tiny	.416
331	mobilenet_v2_0.75_192-tf	.415
332	ReAlnet01_cornet_fov16	.415
333	resnet18-relative_position	.415
334	mobilenet_v2_0.5_96	.414
335	yudixie_resnet50_distance_translation_rotation_0_240908	.414
336	BiT-M-R152x2	.414
337	yudixie_resnet50_translation_rotation_0_240908	.413
338	vit_base_patch14_reg4_dinov2-lvd142m	.413
339	texture_shape_alexnet_trained_on_SIN	.413
340	yudixie_resnet50_distance_rotation_0_240908	.413
341	deit_small_imagenet_100_seed-0	.411
342	yudixie_resnet50_distance_translation_0_240908	.411
343	yudixie_resnet50_translation_reg_0_240908	.409
344	resnet18-contrastive_multiview	.408
345	resnet-34	.408
346	mobilenet_v2_0.5_160	.407
347	mobilenet_v2_0.35_192	.405
348	resnet-18-LC_m	.405
349	yudixie_resnet18_object_class_0_240719	.405
350	mobilenet_v2_0.35_96	.404
351	mobilenet_v2_0.35_160	.403
352	mobilenet_v2_0.5_192-tf	.402
353	AlexNet_SIN	.402
354	alexnet_reduced_aliasing_early_checkpoint	.399
355	0.5x_resnet-18_LC	.399
356	BiT-S-R152x2	.399
357	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.399
358	convnext_base_fb_in1k	.399
359	convnext_base_fb_in1k_gram_aligned_dinov3	.399
360	yudixie_resnet50_distance_reg_0_240908	.399
361	artResNet18_1	.396
362	alexnet_early_checkpoint	.396
363	r3m_resnet18	.393
364	yudixie_resnet50_rotation_reg_0_240908	.391
365	r3m_resnet50	.391
366	resnet18-deepcluster	.390
367	r3m_resnet34	.389
368	BiT-S-R152x4	.389
369	compact_resnet50_IT	.386
370	yudixie_resnet18_category_class_0_240719	.386
371	ViT_L_32_imagenet1k	.380
372	mobilenet_v2_0.35_128	.379
373	resnet50-cifar	.378
374	yudixie_resnet18_distance_rotation_0_240719	.377
375	alexnet_training_seed_01_fov4	.377
376	compact_resnet50_robust_V4	.376
377	mobilenet_v2_1-4_224_pytorch	.370
378	yudixie_resnet18_distance_translation_0_240719	.368
379	yudixie_resnet18_distance_translation_rotation_0_240719	.366
380	resnet18-depth_prediction	.366
381	yudixie_resnet18_distance_reg_0_240719	.365
382	compact_resnet50_robust_IT	.362
383	resnet-18-LC_1st_conv_m	.361
384	prednet	.360
385	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.360
386	yudixie_resnet18_translation_rotation_0_240719	.358
387	alexnet_linf_8_robust	.357
388	yudixie_resnet18_translation_reg_0_240719	.356
389	vggface	.354
390	AlexNet_SIN_fov12	.347
391	yudixie_resnet18_rotation_reg_0_240719	.347
392	resnet-18-LC_conv_init_m	.347
393	deepeisnn_ei_cifar10_resnet18	.347
394	alexnet_fov12	.338
395	deepeisnn_ei_cifar10_vgg19	.337
396	resnet50_imagenet_10_seed-0	.336
397	compact_vgg19_V4	.335
398	my-model	.334
399	compact_resnet50_V4	.331
400	alexnet_robust_correct	.325
401	aughost_fair_resnet18	.308
402	AlexNet_SIN_fov16	.307
403	alexnet_fov16	.306
404	compact_cornets_IT	.296
405	compact_cornets_V4	.288
406	resnet18-contrastive_predictive	.285
407	yudixie_wbh_object_class_scaling_0415_run_0	.285
408	briaai_rmbg_1_4	.280
409	compact_vgg19_IT	.278
410	yudixie_wbh_object_class_scaling_0415_run_1	.273
411	alexnet_training_seed_02	.271
412	alexnet_training_seed_06	.271
413	CORnetZ_CIFAR10	.266
414	yudixie_wbh_object_class_scaling_0415_run_2	.265
415	alexnet_training_seed_01	.265
416	resnet18-autoencoder	.265
417	alexnet_training_seed_09	.265
418	alexnet_training_seed_10	.264
419	alexnet_training_seed_07	.262
420	alexnet_training_seed_05	.260
421	resnet-50x2_untrained	.260
422	alexnet_training_seed_04	.258
423	alexnet_training_seed_08	.258
424	alexnet_training_seed_03	.257
425	yudixie_wbh_object_class_scaling_0415_run_3	.241
426	resnet-50_untrained	.241
427	CORnetZ_CIFAR10_bs32_20_04	.240
428	alexnet_training_seed_01_fov12	.238
429	yudixie_resnet50_random_0_240908	.237
430	resnet-18_untrained	.236
431	yudixie_resnet18_random_0_240719	.235
432	v1-pyr-nodown	.235
433	resnet50_pretrained_with_retinal_waves	.227
434	hmax	.216
435	alexnet_training_seed_01_fov16	.210
436	resnet-18-LC_untrained	.193
437	resnet50_imagenet_1_seed-0	.191
438	resnet50_FractalDB	.186
439	resnet-152_v2_pytorch	.184
440	pr1_stl10_disentangled_alpha04	.181
441	predictive_coding_pc	.155
442	convnext_small_imagenet_10_seed-0	.066
443	deit_small_imagenet_10_seed-0	.053
444	deit_small_imagenet_1_seed-0	.037
445	convnext_small_imagenet_1_seed-0	.035
446	pixels	.024
447	my-model	.020
448	pnasnet_large_halfPrecision
449	eva02_large_in22k
450	vit_huge_clip336_laion

Benchmark bibtex

@misc{Sanghavi_Jozwik_DiCarlo_2021,
  title={SanghaviJozwik2020},
  url={osf.io/fhy36},
  DOI={10.17605/OSF.IO/FHY36},
  publisher={OSF},
  author={Sanghavi, Sachi and Jozwik, Kamila M and DiCarlo, James J},
  year={2021},
  month={Nov}
}

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: SanghaviJozwik2020.V4

4916 stimuli recordings from 50 sites in V4

Metric: pls