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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	eBarlow_lmda_02_1000ep	.495
2	resnet50-vicreg	.489
3	resnet50-barlow	.489
4	resnet50-vicregl0p9	.488
5	AdvProp_efficientnet-b7	.488
6	barlow-twins-resnet50	.487
7	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.487
8	AT_efficientnet-b7	.486
9	vonegrcnn_52e_full	.485
10	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.485
11	custom_model_cv_18_dagger_408	.483
12	vonegrcnn_62e_nobn	.483
13	AT_efficientnet-b2	.483
14	AdvProp_efficientnet-b4	.483
15	resnet50-vicregl0p75	.483
16	AT_efficientnet-b4	.482
17	AdvProp_efficientnet-b6	.482
18	AdvProp_efficientnet-b2	.482
19	AdvProp_efficientnet-b8	.481
20	konkle_alexnetgn_ipcl_ref01_primary_model	.481
21	efficientnet_b1_imagenet_full	.480
22	efficientnet_b2_imagenet_full	.480
23	AdvProp_efficientnet-b0	.480
24	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.480
25	efficientnet_b0_imagenet_full	.479
26	tv_efficientnet-b1	.479
27	resnet-18-LC_w_sh_10_iter_m	.479
28	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.479
29	convnext_tiny_imagenet_full_seed-0	.479
30	effnetv2m_custom384	.478
31	resnet-18-LC_w_sh_10_iter_conv_init_m	.478
32	0.5x_resnet-18_LC_w_sh_1_iter	.478
33	mobilenet_v2_1.0_192-tf	.477
34	resnet-152_v1-tf	.477
35	antialiased-rnext101_32x8d	.476
36	ReAlnet02_cornet	.476
37	resnet-101_v1-tf	.476
38	resnet-18-LC_w_sh_1_iter_conv_init_m	.476
39	resnet50_ewconplus_wzc	.475
40	resnet50-SIN_IN	.475
41	convnext_xxlarge_clip	.475
42	densenet-201-keras	.475
43	ReAlnet06_cornet	.474
44	efficientnet-b7	.474
45	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.474
46	mobilenet_v2_1.3_224-tf	.474
47	convnext_small_imagenet_full_seed-0	.473
48	mobilenet_v2_1.0_160-tf	.473
49	resnet50-moclr8deg	.473
50	mobilenet_v2_1.4_224-tf	.473
51	resnet-50_v1-tf	.473
52	ReAlnet03_cornet	.473
53	ReAlnet08_cornet	.473
54	ReAlnet04_cornet	.473
55	ReAlnet01_cornet	.473
56	resnet101_ecoset_full	.473
57	resnet50_ewconplus_wzc_seed5_2__323	.472
58	resnet50_ewconplus_wzc_seed6_2_323	.472
59	mobilenet_v2_0.75_224-tf	.471
60	ReAlnet07_cornet	.471
61	resnet50-simclr-vissl	.471
62	ReAlnet10_cornet	.471
63	resnet18-instance_recognition	.471
64	vit_relpos_base_patch16_clsgap_224:sw_in1k	.471
65	resnet50_hvs_wzc	.471
66	resnet50_robust_l2_eps1	.471
67	resnet50-SIN	.471
68	resnet-18-LC_w_sh_100_iter_conv_init_m	.470
69	effnetb1_272x240	.470
70	nasnet_mobile	.470
71	resnet50_imagenet_full	.470
72	mobilenet_v2_0.75_160-tf	.470
73	ReAlnet09_cornet	.470
74	convnext_xlarge:fb_in22k_ft_in1k	.469
75	ReAlnet09_ALL	.469
76	convnext_tiny:in12k_ft_in1k	.469
77	ReAlnet06_ALL	.469
78	vonegrcnn_47e	.469
79	convnext_base_imagenet_full_seed-0	.469
80	mobilenet_v2_1.0_128-tf	.469
81	ReAlnet05_cornet	.469
82	resnet18-local_aggregation	.469
83	convnext_base:clip_laiona_augreg_ft_in1k_384	.469
84	voneresnet-50	.469
85	antialiased-r50	.468
86	densenet-169-keras	.468
87	efficientnet_b0	.468
88	resnet50_ewconplus_wzc_seed2_2__323	.468
89	resnet-101_v2-tf	.468
90	ReAlnet04_ALL	.468
91	ReAlnet03_ALL	.468
92	vonegrcnn_47e	.468
93	resnet50-SIN_IN_IN	.468
94	voneresnet-50-robust	.468
95	densenet-169	.468
96	mobilenet_v2_0.5_192-tf	.468
97	ReAlnet02_ALL	.468
98	ReAlnet10_ALL	.468
99	resnet-18-LC_w_sh_1_iter_m	.468
100	ReAlnet01_ALL	.467
101	0.5x_resnet-18_LC_w_sh_10_iter	.467
102	mobilenet_v2_0.75_128	.467
103	grcnn	.467
104	ReAlnet05_ALL	.467
105	mobilenet_v2_0.75_192-tf	.467
106	resnet_SIN_IN_FT_IN	.467
107	mobilenet_v2_0.35_192	.467
108	ReAlnet08_ALL	.467
109	nasnet_mobile-tf	.467
110	convnext_tiny_sup	.467
111	mobilenet_v2_1_0_160	.466
112	mobilenet_v2_1-4_224_pytorch	.466
113	resnet-18-LC_w_sh_100_iter_m	.466
114	resnet50_ecoset_full	.466
115	convnext_large_imagenet_full_seed-0	.466
116	resnet-50_v2-tf	.466
117	ReAlnet07_ALL	.465
118	resnet152_ecoset_full	.465
119	mobilenet_v2_1_0_192	.465
120	mobilenet_v2_1_4_224	.465
121	resnet_50_v1_spiking	.465
122	resnet_50_v1	.465
123	resnet50-sup	.465
124	convnext_large_clip_laion2b_finelayers	.465
125	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.465
126	mobilenet_v2_1_0_224	.465
127	voneresnet-50-non_stochastic	.465
128	mobilenet_v2_0.5_160	.464
129	CORnet-S	.464
130	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.464
131	mobilenet_v2_0.5_224-tf	.464
132	mobilenet_v2_1.0_224-tf	.464
133	mobilenet_v1_0.75_192	.464
134	mobilenet_v1_1.0_192	.463
135	resnet50_imagenet_100_seed-0	.463
136	resnet50_random_linf8_perturb	.463
137	shufflenet_v2_x1_0	.463
138	mobilenet_v1_1.0_160	.463
139	resnet-152_v2-tf	.462
140	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.462
141	resnet101_imagenet_full	.462
142	nasnet_large-tf	.462
143	mobilenet_v1_1.0_224	.461
144	grcnn_robust_v1	.461
145	mobilenet_v1_1.0_128	.461
146	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.460
147	densenet-121-keras	.460
148	resnet50_st_wzc_2	.460
149	convnext_large:fb_in22k_ft_in1k	.460
150	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.459
151	resnet34_ecoset_full	.459
152	mobilenet_v2_1_3_224	.459
153	densenet-121	.459
154	BiT-M-R50x3	.459
155	resnet152_imagenet_full	.459
156	resnet50-vitoimagevidnet8	.459
157	mobilenet_v1_0.75_160	.458
158	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.458
159	BiT-M-R50x1	.458
160	inception_v2-tf	.458
161	resnet50_st_wzc_seed7_2_323	.458
162	resnet_50_v1_spiking_l4	.458
163	resnet_50_v1_spiking_l4a	.458
164	resnet50_tutorial	.458
165	yudixie_resnet50_imagenet1kpret_0_240908	.458
166	yudixie_resnet50_imagenet1kpret_0_240312	.458
167	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.457
168	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.457
169	BiT-S-R50x3	.457
170	grcnn_109	.457
171	mobilenet_v2_0_75_192	.457
172	mobilenet_v2_1_0_128	.457
173	Res2Net50_26w_4s	.457
174	mobilenet_v2_0_5_192	.457
175	mobilenet_v2_0_75_160	.457
176	resnet18-supervised	.456
177	mobilenet_v2_0.35_160	.456
178	antialias-resnet152	.456
179	resnet50_st_wzc_seed6_2_323	.456
180	mobilenet_v2_0_75_224	.456
181	seresnext50_32x4d	.456
182	resnet50_linf_4_robust	.455
183	resnet50	.455
184	mobilenet_v2_0.5_128	.455
185	vit_large_patch14_clip_224:openai_ft_in1k	.455
186	resnet50_random_l2_perturb	.455
187	resnet50_moco_v2	.454
188	mobilenet_v2_0.35_224	.454
189	deit_large_imagenet_full_seed-0	.454
190	inception_v1-tf	.454
191	CLIP-RN50	.454
192	inception_v3-tf	.453
193	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.453
194	mobilenet_v1_0.75_224	.453
195	resnet50_l2_3_robust	.453
196	cv_18_dagger_408_pretrained	.453
197	deit_base_imagenet_full_seed-0	.453
198	ReAlnet01_cornet_fov12	.453
199	focalnet_tiny_lrf_in1k	.453
200	yudixie_resnet18_imagenet1kpret_0_240719	.453
201	yudixie_wbh_object_class_scaling_0415_imagenet	.453
202	mobilenet_v1_0.75_128	.452
203	resnet101	.452
204	resnet34_imagenet_full	.452
205	BiT-S-R50x1	.452
206	grcnn_v2_text_noise	.452
207	regnet_y_400mf	.452
208	inception_v3	.452
209	dinov2_vitg14_reg_lc	.452
210	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.452
211	resnet50_robust_l2_eps3	.451
212	regnety_032	.451
213	eicircuit_resnet18_bn_20260919	.451
214	BiT-S-R152x4	.451
215	convnext_femto_ols:d1_in1k	.451
216	compact_resnet50_robust_IT	.450
217	inception_resnet_v2	.450
218	resnet50_st_wzc_seed3_2_323	.450
219	resnet18_ecoset_full	.450
220	dinov2_vitg14_lc	.449
221	vit_large_patch14_clip_224:laion2b_ft_in1k	.449
222	resnet18_imagenet_full	.449
223	cvt_cvt-13-224-in1k_4_LucyV4	.449
224	omnivore_swinS	.449
225	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.449
226	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.448
227	resnext101_32x8d_wsl	.448
228	RN50	.448
229	omnivore_swinB	.448
230	ReAlnet01_cornet_fov4	.448
231	texture_shape_resnet50_trained_on_SIN	.447
232	compact_resnet50_IT	.447
233	dinov2_vitl14_reg_lc	.447
234	dinov2_vitl14_reg_lc_featread	.447
235	0.5x_resnet-18	.446
236	resnet-18_test_m	.446
237	resnet-18	.446
238	vgg-16-keras	.446
239	inception_v1	.446
240	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.446
241	resnet18_imagenet21kP	.445
242	BiT-M-R101x3	.445
243	BiT-M-R152x4	.444
244	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.444
245	cvt_cvt-21-224-in1k_4_LucyV4	.444
246	resnet18-contrastive_multiview	.444
247	vgg_16	.443
248	ViT-B/32	.443
249	omnivore_swinT	.443
250	resnet-18-LC_1st_conv_m	.443
251	inception_v4-tf	.442
252	SWSL_resnet50	.442
253	vit_large_patch14_clip_336:laion2b_ft_in1k	.442
254	resnet50-VITO-8deg-cc	.442
255	cvt_cvt-13-384-in1k_4_LucyV4	.442
256	dinov2_vitl14_lc	.442
257	resnet18-deepcluster	.442
258	compact_vgg19_IT	.441
259	CLIP_ViT-B_32	.441
260	resnet50_simclr	.441
261	mobilenet_v1_0.5_192	.441
262	mobilenet_v2_1.0_96	.441
263	xception-keras	.441
264	resnet-18-LC_d_w_sh_1x1_conv_init_m	.441
265	CLIP_resnet50_float32	.441
266	CLIP_resnet50	.441
267	resnet-50-pytorch	.440
268	resnet-50-robust	.440
269	vit_base_patch16_clip_224:openai_ft_in1k	.440
270	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.440
271	deit3_base_patch16_224_fb_in1k	.440
272	compact_cornets_IT	.440
273	alexnet_random_l2_3_perturb	.440
274	mobilenet_v1_0.5_224	.440
275	resnet-34	.438
276	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.438
277	squeezenet1_1	.438
278	vgg-19-keras	.438
279	mobilenet_v2_0.75_96	.437
280	pnasnet_large-tf	.437
281	yudixie_resnet50_object_class_0_240908	.436
282	mobilenet_v1_0.5_128	.436
283	deit_small_imagenet_full_seed-0	.436
284	BiT-M-R152x2	.436
285	resnext101_32x48d_wsl	.436
286	0.5x_resnet-18_LC_w_sh_100_iter	.436
287	BiT-S-R101x3	.435
288	RN50	.435
289	resnet_101_v2	.435
290	CORnet-Z	.435
291	cvt_cvt-21-384-in1k_4_LucyV4	.435
292	mobilenet_v2_0_5_224	.434
293	convnext_small_imagenet_100_seed-0	.434
294	mobilenet_v1_0.5_160	.434
295	deit3_base_patch16_224_fb_in22k_ft_in1k	.434
296	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.434
297	resnext101_32x32d_wsl	.434
298	yudixie_resnet50_category_class_0_240908	.433
299	resnet_50_v2	.433
300	ViT_L_32_imagenet1k	.432
301	resnext101_32x16d_wsl	.432
302	vit_large_clip336_openai	.431
303	alexnet	.430
304	alexnet_ks_torevert	.430
305	pnasnet_large_halfPrecision	.430
306	vision_transformer_vit_large_patch16_224	.430
307	BiT-M-R101x1	.430
308	imagenet_l2_3_0	.430
309	BiT-S-R152x2	.429
310	alexnet_random_linf8_perturb	.429
311	resnet50_byol	.429
312	ReAlnet10	.428
313	mobilenet_v2_0.35_128	.428
314	mobilenet_v1_0.25_224	.428
315	SWSL_resnext101_32x8d	.427
316	ReAlnet02	.427
317	ReAlnet01	.426
318	ReAlnet07	.426
319	ReAlnet06	.425
320	alexnet_fov4	.425
321	ReAlnet08	.425
322	ReAlnet04	.425
323	ReAlnet03	.424
324	mobilenet_v1_0.25_192	.424
325	ReAlnet09	.424
326	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.424
327	resnet50_linf_8_robust	.423
328	ReAlnet05	.422
329	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.422
330	BiT-S-R101x1	.422
331	mobilenet_v1_0.25_160	.422
332	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.422
333	alexnet_l2_3_robust	.422
334	convnext_base_fb_in1k_gram_aligned_dinov3	.421
335	VOneCORnet-S	.421
336	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.421
337	mobilenet_v2_0.5_96	.421
338	ReAlnet01_cornet_fov16	.421
339	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.420
340	alexnet_reduced_aliasing_early_checkpoint	.420
341	fixres_resnext101_32x48d_wsl	.419
342	vit_base_patch14_dinov2-lvd142m	.418
343	AlexNet_SIN	.416
344	cornet_s	.416
345	yudixie_resnet18_object_class_0_240719	.415
346	FrankRobWobv0	.415
347	yudixie_resnet18_category_class_0_240719	.415
348	vgg_19	.415
349	yudixie_resnet50_distance_translation_rotation_0_240908	.414
350	resnet_50_v1_spiking_l3	.413
351	resnet-18-LC_m	.413
352	yolos_tiny	.412
353	blt_vs	.412
354	yudixie_resnet50_translation_rotation_0_240908	.411
355	yudixie_resnet50_distance_translation_0_240908	.409
356	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.408
357	0.5x_resnet-18_LC	.408
358	resnet-18-LC_conv_init_m	.408
359	vit_base_patch16_dinov3-lvd1689m	.407
360	mobilenet_v2_0.35_96	.407
361	mobilenet_v1_0.25_128	.405
362	resnet18-colorization	.405
363	squeezenet1_0	.403
364	AlexNet_SIN_fov4	.403
365	alexnet_robust_correct	.402
366	alexnet_early_checkpoint	.400
367	alexnet_fov12	.400
368	texture_shape_alexnet_trained_on_SIN	.400
369	convnext_large_in22k_supervised	.397
370	vonealexnet_gaussian_noise_std4_fixed	.397
371	deepeisnn_ei_imagenet_resnet18	.395
372	yudixie_resnet50_distance_reg_0_240908	.392
373	imagenet_l2_10_0	.390
374	resnet18-relative_position	.389
375	resnet-152_v2_pytorch	.389
376	yudixie_resnet50_rotation_reg_0_240908	.388
377	yudixie_resnet50_translation_reg_0_240908	.384
378	convnext_base_fb_in22k_ft_in1k	.382
379	AlexNet_SIN_fov12	.379
380	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.378
381	yudixie_resnet18_translation_rotation_0_240719	.377
382	resnet18-contrastive_predictive	.376
383	alexnet_fov16	.375
384	alexnet_linf_8_robust	.374
385	yudixie_resnet18_distance_translation_rotation_0_240719	.369
386	yudixie_resnet18_rotation_reg_0_240719	.366
387	yudixie_resnet18_distance_rotation_0_240719	.366
388	vggface	.364
389	yudixie_resnet50_distance_rotation_0_240908	.363
390	yudixie_resnet18_translation_reg_0_240719	.362
391	yudixie_resnet18_distance_translation_0_240719	.359
392	convnext_base_fb_in1k	.359
393	AlexNet_SIN_fov16	.355
394	resnet18-depth_prediction	.354
395	deit_small_imagenet_100_seed-0	.349
396	yudixie_resnet18_distance_reg_0_240719	.349
397	r3m_resnet34	.347
398	briaai_rmbg_1_4	.345
399	r3m_resnet18	.341
400	r3m_resnet50	.339
401	resnet18_fair	.335
402	artResNet18_1	.330
403	r3m_resnet50_nocrop	.325
404	resnet50-cifar	.311
405	resnet50_primary_visual_cortex	.308
406	prednet	.305
407	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.304
408	my-model	.300
409	deepeisnn_ei_cifar10_resnet18	.300
410	predictive_coding_pc	.299
411	deepeisnn_ei_cifar10_vgg19	.289
412	pr1_stl10_disentangled_alpha04	.288
413	resnet50_imagenet_10_seed-0	.285
414	CORnetZ_CIFAR10	.274
415	compact_resnet50_V4	.268
416	compact_resnet50_robust_V4	.266
417	alexnet_training_seed_08	.257
418	v1-pyr-nodown	.256
419	alexnet_training_seed_03	.256
420	compact_vgg19_V4	.255
421	alexnet_training_seed_01	.254
422	alexnet_training_seed_02	.253
423	alexnet_training_seed_05	.253
424	alexnet_training_seed_04	.250
425	alexnet_training_seed_07	.250
426	alexnet_training_seed_09	.248
427	alexnet_training_seed_06	.245
428	alexnet_training_seed_10	.245
429	alexnet_training_seed_01_fov12	.245
430	resnet50_FractalDB	.243
431	gabor_filter_mean_abs_4x4_v2	.242
432	mobilevit_small	.240
433	CORnetZ_CIFAR10_bs32_20_04	.239
434	resnet18-autoencoder	.239
435	alexnet_training_seed_01_fov4	.239
436	compact_cornets_V4	.238
437	alexnet_training_seed_01_fov16	.238
438	yudixie_wbh_object_class_scaling_0415_run_2	.226
439	gabor_filter_entropy_4x4_v2_forcedCosine	.224
440	gabor_filter_entropy_4x4_v2	.223
441	gabor_filter_entropy_4x4	.213
442	yudixie_wbh_object_class_scaling_0415_run_3	.201
443	yudixie_wbh_object_class_scaling_0415_run_0	.198
444	resnet50_imagenet_1_seed-0	.195
445	yudixie_wbh_object_class_scaling_0415_run_1	.194
446	resnet-18_untrained	.194
447	yudixie_resnet18_random_0_240719	.192
448	resnet-50x2_untrained	.192
449	resnet-50_untrained	.186
450	convnext_small_imagenet_10_seed-0	.181
451	yudixie_resnet50_random_0_240908	.174
452	resnet50_pretrained_with_retinal_waves	.159
453	resnet-18-LC_untrained	.155
454	fitvid_trained_on_physion	.144
455	deit_small_imagenet_10_seed-0	.103
456	deit_small_imagenet_1_seed-0	.083
457	convnext_small_imagenet_1_seed-0	.081
458	pixels	.062
459	my-model	.062
460	vit_base_patch14_reg4_dinov2-lvd142m
461	eva02_large_in22k
462	dinov2_vitl14_lc_res336
463	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

0.87.

Note that scores are relative to this ceiling.

Data: Sanghavi2020.IT

5760 stimuli recordings from 88 sites in IT

Metric: pls