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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("SanghaviJozwik2020.IT-pls")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50_robust_l2_eps3	.503
2	eBarlow_lmda_02_1000ep	.500
3	AT_efficientnet-b7	.490
4	resnet50-vicregl0p9	.487
5	resnet-18-LC_w_sh_100_iter_conv_init_m	.486
6	resnet50_robust_l2_eps1	.486
7	resnet-18-LC_w_sh_10_iter_m	.485
8	voneresnet-50-robust	.485
9	barlow-twins-resnet50	.484
10	resnet-18-LC_w_sh_100_iter_m	.483
11	resnet50-barlow	.482
12	resnet-50-robust	.482
13	resnet-50-pytorch	.482
14	resnet50-vicreg	.482
15	resnet-18-LC_w_sh_1_iter_conv_init_m	.480
16	AdvProp_efficientnet-b6	.476
17	custom_model_cv_18_dagger_408	.476
18	AdvProp_efficientnet-b7	.475
19	resnet-18-LC_d_w_sh_1x1_conv_init_m	.475
20	resnet-18-LC_w_sh_10_iter_conv_init_m	.475
21	alexnet_robust_correct	.473
22	convnext_tiny_imagenet_full_seed-0	.473
23	resnet50-vicregl0p75	.472
24	convnext_small_imagenet_full_seed-0	.471
25	AdvProp_efficientnet-b8	.471
26	resnet50-simclr-vissl	.470
27	0.5x_resnet-18_LC_w_sh_1_iter	.470
28	AT_efficientnet-b4	.467
29	0.5x_resnet-18_LC_w_sh_10_iter	.466
30	AT_efficientnet-b2	.466
31	resnet50_ewconplus_wzc_seed5_2__323	.465
32	resnet50_ewconplus_wzc_seed6_2_323	.464
33	AdvProp_efficientnet-b4	.463
34	vonegrcnn_62e_nobn	.463
35	vonegrcnn_52e_full	.463
36	convnext_base_imagenet_full_seed-0	.463
37	resnet50_ewconplus_wzc_seed2_2__323	.463
38	mobilenet_v2_0.5_160	.462
39	voneresnet-50	.462
40	ReAlnet01_cornet_fov12	.461
41	resnet50_random_linf8_perturb	.461
42	mobilenet_v2_0.75_128	.460
43	convnext_large_imagenet_full_seed-0	.460
44	mobilenet_v2_1.0_96	.460
45	convnext_large_clip_laion2b_finelayers	.460
46	convnext_large_clip_laion2b_finelayers	.460
47	konkle_alexnetgn_ipcl_ref01_primary_model	.459
48	resnet50_hvs_wzc	.459
49	alexnet_l2_3_robust	.459
50	mobilenet_v2_0.75_96	.459
51	dinov2_vitg14_lc	.458
52	resnet50_random_l2_perturb	.458
53	resnet50_l2_3_robust	.458
54	AdvProp_efficientnet-b2	.458
55	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.458
56	mobilenet_v2_0.5_96	.457
57	resnet-50_v1-tf	.456
58	vit_relpos_base_patch16_clsgap_224:sw_in1k	.456
59	resnet-152_v1-tf	.455
60	tv_efficientnet-b1	.455
61	imagenet_l2_10_0	.455
62	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.455
63	resnet18-instance_recognition	.455
64	ReAlnet01_cornet_fov16	.455
65	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.454
66	convnext_xlarge:fb_in22k_ft_in1k	.454
67	mobilenet_v1_1.0_128	.453
68	resnet50-VITO-8deg-cc	.453
69	resnet-18-LC_w_sh_1_iter_m	.453
70	mobilenet_v2_1.0_128-tf	.453
71	vision_transformer_vit_large_patch16_224	.452
72	mobilenet_v2_0.5_128	.452
73	AdvProp_efficientnet-b0	.451
74	resnet50-vitoimagevidnet8	.451
75	resnet50_imagenet_full	.451
76	Res2Net50_26w_4s	.450
77	vonegrcnn_47e	.450
78	effnetv2m_custom384	.450
79	mobilenet_v2_0.35_160	.450
80	resnet50_ecoset_full	.450
81	resnet50_st_wzc_seed6_2_323	.450
82	mobilenet_v2_1_0_128	.449
83	resnet50-moclr8deg	.449
84	resnet50_linf_8_robust	.449
85	imagenet_l2_3_0	.448
86	resnet-101_v1-tf	.448
87	0.5x_resnet-18_LC_w_sh_100_iter	.447
88	ReAlnet08_cornet	.447
89	cvt_cvt-13-224-in1k_4_LucyV4	.447
90	deit_base_imagenet_full_seed-0	.447
91	mobilenet_v2_0.35_96	.446
92	mobilenet_v2_0.75_160-tf	.446
93	resnet50_ewconplus_wzc	.446
94	cv_18_dagger_408_pretrained	.446
95	ReAlnet10_cornet	.446
96	vonegrcnn_47e	.445
97	ReAlnet03_cornet	.445
98	resnet101	.445
99	resnet50	.445
100	voneresnet-50-non_stochastic	.445
101	mobilenet_v2_0_75_160	.444
102	ReAlnet06_cornet	.443
103	ReAlnet07_cornet	.443
104	resnet50_imagenet_100_seed-0	.443
105	nasnet_mobile	.443
106	ReAlnet02_cornet	.443
107	mobilenet_v2_0.5_192-tf	.443
108	resnet50_linf_4_robust	.442
109	yudixie_resnet50_imagenet1kpret_0_240312	.442
110	resnet_50_v1_spiking_l4	.442
111	resnet_50_v1_spiking_l4a	.442
112	resnet50_tutorial	.442
113	yudixie_resnet50_imagenet1kpret_0_240908	.442
114	resnet50-SIN_IN_IN	.442
115	efficientnet_b0_imagenet_full	.442
116	ReAlnet04_cornet	.441
117	dinov2_vitg14_reg_lc	.441
118	mobilenet_v2_0.75_192-tf	.441
119	ReAlnet09_cornet	.441
120	efficientnet_b1_imagenet_full	.441
121	omnivore_swinT	.441
122	ViT_L_32_imagenet1k	.441
123	mobilenet_v1_1.0_160	.440
124	ReAlnet01_cornet	.440
125	mobilenet_v2_1_4_224	.440
126	convnext_xxlarge_clip	.440
127	resnet-18-LC_1st_conv_m	.440
128	mobilenet_v2_1-4_224_pytorch	.439
129	ReAlnet05_cornet	.439
130	resnet-18-LC_m	.438
131	efficientnet-b7	.438
132	seresnext50_32x4d	.438
133	resnet50_st_wzc_seed3_2_323	.438
134	mobilenet_v2_0.35_128	.437
135	resnet50_st_wzc_seed7_2_323	.437
136	resnet152_imagenet_full	.437
137	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.436
138	resnet50-SIN_IN	.436
139	mobilenet_v2_1.0_160-tf	.435
140	mobilenet_v2_1.3_224-tf	.435
141	antialias-resnet152	.435
142	convnext_tiny:in12k_ft_in1k	.435
143	regnet_y_400mf	.435
144	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.434
145	resnext101_32x8d_wsl	.434
146	mobilenet_v2_0.35_192	.434
147	mobilenet_v2_0.5_224-tf	.434
148	resnet101_ecoset_full	.434
149	cvt_cvt-21-224-in1k_4_LucyV4	.433
150	vit_large_clip336_openai	.433
151	resnet-50_v2-tf	.433
152	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.433
153	omnivore_swinB	.433
154	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.433
155	resnet-101_v2-tf	.433
156	mobilenet_v2_1_0_160	.433
157	BiT-S-R152x4	.433
158	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.432
159	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.432
160	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.432
161	mobilenet_v2_0.75_224-tf	.432
162	mobilenet_v1_0.75_160	.431
163	mobilenet_v2_0_5_192	.431
164	mobilenet_v2_1.4_224-tf	.431
165	efficientnet_b2_imagenet_full	.431
166	CORnet-S	.431
167	ViT-B/32	.430
168	effnetb1_272x240	.430
169	mobilenet_v2_1.0_192-tf	.430
170	grcnn	.430
171	efficientnet_b0	.430
172	mobilenet_v2_0_75_192	.429
173	focalnet_tiny_lrf_in1k	.429
174	cvt_cvt-13-384-in1k_4_LucyV4	.429
175	alexnet_linf_8_robust	.429
176	CLIP_ViT-B_32	.429
177	inception_v1-tf	.429
178	ReAlnet03_ALL	.429
179	ReAlnet07_ALL	.428
180	ReAlnet10_ALL	.428
181	ReAlnet01_ALL	.428
182	BiT-M-R152x4	.428
183	resnet152_ecoset_full	.428
184	ReAlnet02_ALL	.428
185	ReAlnet08_ALL	.427
186	cvt_cvt-21-384-in1k_4_LucyV4	.427
187	omnivore_swinS	.427
188	resnet34_ecoset_full	.427
189	FrankRobWobv0	.427
190	mobilenet_v1_0.5_192	.427
191	ReAlnet04_ALL	.426
192	0.5x_resnet-18_LC	.426
193	antialiased-r50	.426
194	ReAlnet06_ALL	.426
195	mobilenet_v2_0.35_224	.426
196	convnext_base:clip_laiona_augreg_ft_in1k_384	.426
197	ReAlnet09_ALL	.426
198	ReAlnet05_ALL	.426
199	antialiased-rnext101_32x8d	.425
200	densenet-201-keras	.425
201	densenet-201	.425
202	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.424
203	mobilenet_v2_1_0_224	.424
204	mobilenet_v1_0.25_160	.424
205	dinov2_vitl14_reg_lc	.424
206	dinov2_vitl14_reg_lc_featread	.424
207	0.5x_resnet-18	.424
208	resnet-18-LC_conv_init_m	.423
209	mobilenet_v2_1.0_224-tf	.423
210	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.423
211	mobilenet_v2_1_0_192	.423
212	shufflenet_v2_x1_0	.423
213	deit_large_imagenet_full_seed-0	.422
214	convnext_large:fb_in22k_ft_in1k	.422
215	densenet-169	.421
216	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.421
217	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.420
218	deit3_base_patch16_224_fb_in1k	.420
219	resnet50_st_wzc_2	.420
220	mobilenet_v2_0_75_224	.419
221	resnet101_imagenet_full	.419
222	grcnn_robust_v1	.419
223	mobilenet_v1_0.75_192	.419
224	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.419
225	grcnn_109	.418
226	mobilenet_v1_1.0_192	.418
227	dinov2_vitl14_lc	.418
228	nasnet_mobile-tf	.417
229	deit_small_imagenet_full_seed-0	.416
230	resnet50-SIN	.416
231	nasnet_large-tf	.416
232	yudixie_resnet18_imagenet1kpret_0_240719	.416
233	yudixie_wbh_object_class_scaling_0415_imagenet	.416
234	mobilenet_v1_0.5_128	.415
235	densenet-169-keras	.414
236	mobilenet_v1_0.25_192	.414
237	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.414
238	SWSL_resnet50	.414
239	mobilenet_v1_0.75_224	.414
240	resnet_50_v1_spiking	.413
241	resnet50-sup	.413
242	resnet_50_v1	.413
243	convnext_small_imagenet_100_seed-0	.412
244	inception_v1	.412
245	resnet-152_v2-tf	.411
246	mobilenet_v2_1_3_224	.411
247	dinov2_vitl14_lc_res336	.409
248	alexnet_random_l2_3_perturb	.409
249	mobilenet_v1_1.0_224	.409
250	texture_shape_resnet50_trained_on_SIN	.409
251	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.407
252	resnet18_imagenet_full	.407
253	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.407
254	mobilenet_v1_0.75_128	.407
255	mobilenet_v1_0.5_160	.407
256	densenet-121	.406
257	resnet_SIN_IN_FT_IN	.406
258	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.406
259	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.406
260	resnet18_ecoset_full	.406
261	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.406
262	CLIP_resnet50_float32	.405
263	CLIP_resnet50	.405
264	alexnet_ks_torevert	.405
265	alexnet	.405
266	grcnn_v2_text_noise	.405
267	vit_large_patch14_clip_224:laion2b_ft_in1k	.403
268	ReAlnet01_cornet_fov4	.403
269	resnet50_simclr	.403
270	resnet18-deepcluster	.402
271	densenet-121-keras	.402
272	resnet18-local_aggregation	.402
273	resnet18_imagenet21kP	.401
274	RN50	.401
275	mobilenet_v1_0.5_224	.401
276	resnet_50_v1_spiking_l3	.401
277	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.401
278	alexnet_random_linf8_perturb	.400
279	BiT-S-R152x2	.400
280	mobilenet_v2_0_5_224	.400
281	vit_large_patch14_clip_224:openai_ft_in1k	.399
282	mobilenet_v1_0.25_128	.399
283	CORnet-Z	.397
284	deit_small_imagenet_100_seed-0	.395
285	CLIP-RN50	.395
286	convnext_femto_ols:d1_in1k	.394
287	mobilenet_v1_0.25_224	.394
288	resnet18-supervised	.393
289	BiT-S-R50x3	.393
290	BiT-M-R50x3	.393
291	squeezenet1_1	.392
292	vonealexnet_gaussian_noise_std4_fixed	.392
293	deepeisnn_ei_imagenet_resnet18	.392
294	inception_resnet_v2	.390
295	resnet50_moco_v2	.390
296	vgg-16-keras	.389
297	eicircuit_resnet18_bn_20260919	.389
298	resnet_50_v2	.387
299	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.385
300	vgg_16	.384
301	resnet34_imagenet_full	.384
302	xception-keras	.384
303	resnext101_32x48d_wsl	.383
304	resnet50_byol	.383
305	alexnet_fov12	.383
306	alexnet_fov4	.383
307	convnext_tiny_sup	.381
308	RN50	.381
309	inception_v2-tf	.380
310	vgg_19	.379
311	alexnet_reduced_aliasing_early_checkpoint	.379
312	BiT-M-R50x1	.378
313	vit_base_patch16_clip_224:openai_ft_in1k	.378
314	resnext101_32x16d_wsl	.377
315	r3m_resnet18	.377
316	resnet-18_test_m	.377
317	resnet-18	.377
318	inception_v3	.376
319	resnet50_primary_visual_cortex	.376
320	regnety_032	.375
321	VOneCORnet-S	.375
322	AlexNet_SIN_fov16	.374
323	vgg-19-keras	.373
324	briaai_rmbg_1_4	.371
325	artResNet18_1	.371
326	resnet-34	.368
327	alexnet_fov16	.368
328	resnext101_32x32d_wsl	.368
329	alexnet_early_checkpoint	.368
330	blt_vs	.367
331	convnext_base_fb_in1k_gram_aligned_dinov3	.366
332	BiT-S-R50x1	.366
333	AlexNet_SIN	.365
334	inception_v3-tf	.364
335	r3m_resnet34	.364
336	vit_large_patch14_clip_336:laion2b_ft_in1k	.364
337	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.364
338	pnasnet_large_halfPrecision	.363
339	yudixie_resnet50_object_class_0_240908	.362
340	resnet18-colorization	.361
341	SWSL_resnext101_32x8d	.360
342	resnet18_fair	.360
343	AlexNet_SIN_fov12	.358
344	resnet18-contrastive_multiview	.356
345	vit_base_patch14_dinov2-lvd142m	.356
346	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.354
347	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.353
348	texture_shape_alexnet_trained_on_SIN	.353
349	pnasnet_large-tf	.353
350	vit_base_patch16_dinov3-lvd1689m	.350
351	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.347
352	yolos_tiny	.346
353	pr1_stl10_disentangled_alpha04	.345
354	resnet18-relative_position	.342
355	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.342
356	yudixie_resnet50_category_class_0_240908	.340
357	BiT-M-R101x1	.337
358	AlexNet_SIN_fov4	.335
359	BiT-M-R101x3	.334
360	BiT-S-R101x1	.332
361	BiT-S-R101x3	.330
362	inception_v4-tf	.328
363	BiT-M-R152x2	.327
364	ReAlnet06	.325
365	ReAlnet05	.323
366	ReAlnet07	.323
367	ReAlnet04	.322
368	ReAlnet10	.321
369	ReAlnet01	.321
370	ReAlnet03	.320
371	ReAlnet08	.320
372	squeezenet1_0	.320
373	ReAlnet09	.319
374	resnet18-contrastive_predictive	.318
375	resnet50_imagenet_10_seed-0	.318
376	ReAlnet02	.317
377	r3m_resnet50	.315
378	yudixie_resnet50_distance_reg_0_240908	.313
379	vggface	.313
380	yudixie_resnet50_translation_rotation_0_240908	.313
381	vit_base_patch14_reg4_dinov2-lvd142m	.312
382	yudixie_resnet18_category_class_0_240719	.312
383	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.311
384	cornet_s	.311
385	resnet50-cifar	.310
386	deepeisnn_ei_cifar10_resnet18	.309
387	predictive_coding_pc	.308
388	yudixie_resnet50_distance_translation_rotation_0_240908	.307
389	yudixie_resnet18_object_class_0_240719	.306
390	yudixie_resnet50_distance_rotation_0_240908	.306
391	deit3_base_patch16_224_fb_in22k_ft_in1k	.305
392	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.305
393	resnet-152_v2_pytorch	.305
394	yudixie_resnet50_distance_translation_0_240908	.296
395	yudixie_resnet50_translation_reg_0_240908	.295
396	yudixie_resnet50_rotation_reg_0_240908	.295
397	resnet18-depth_prediction	.291
398	deepeisnn_ei_cifar10_vgg19	.289
399	CORnetZ_CIFAR10	.286
400	alexnet_training_seed_02	.277
401	alexnet_training_seed_06	.276
402	alexnet_training_seed_03	.273
403	alexnet_training_seed_08	.271
404	alexnet_training_seed_05	.268
405	alexnet_training_seed_01	.266
406	alexnet_training_seed_10	.263
407	alexnet_training_seed_09	.262
408	alexnet_training_seed_04	.260
409	resnet50_FractalDB	.259
410	compact_resnet50_robust_IT	.258
411	alexnet_training_seed_01_fov12	.258
412	compact_resnet50_IT	.256
413	prednet	.256
414	mobilevit_small	.254
415	alexnet_training_seed_07	.254
416	yudixie_resnet18_translation_rotation_0_240719	.253
417	compact_cornets_IT	.247
418	my-model	.243
419	alexnet_training_seed_01_fov16	.240
420	yudixie_resnet18_distance_rotation_0_240719	.238
421	yudixie_resnet18_distance_translation_0_240719	.234
422	compact_vgg19_IT	.233
423	yudixie_resnet18_translation_reg_0_240719	.230
424	yudixie_resnet18_distance_translation_rotation_0_240719	.230
425	resnet-50x2_untrained	.228
426	yudixie_resnet18_rotation_reg_0_240719	.223
427	alexnet_training_seed_01_fov4	.221
428	convnext_small_imagenet_10_seed-0	.220
429	yudixie_resnet18_distance_reg_0_240719	.219
430	CORnetZ_CIFAR10_bs32_20_04	.215
431	resnet50_imagenet_1_seed-0	.215
432	convnext_base_fb_in22k_ft_in1k	.212
433	yudixie_resnet50_random_0_240908	.209
434	convnext_large_in22k_supervised	.208
435	yudixie_wbh_object_class_scaling_0415_run_0	.205
436	yudixie_resnet18_random_0_240719	.204
437	yudixie_wbh_object_class_scaling_0415_run_2	.197
438	resnet-18_untrained	.191
439	resnet18-autoencoder	.190
440	yudixie_wbh_object_class_scaling_0415_run_1	.187
441	gabor_filter_mean_abs_4x4_v2	.182
442	yudixie_wbh_object_class_scaling_0415_run_3	.181
443	resnet50_pretrained_with_retinal_waves	.178
444	convnext_base_fb_in1k	.176
445	resnet-50_untrained	.176
446	resnet-18-LC_untrained	.166
447	compact_cornets_V4	.165
448	v1-pyr-nodown	.163
449	compact_vgg19_V4	.124
450	gabor_filter_entropy_4x4	.124
451	gabor_filter_entropy_4x4_v2_forcedCosine	.122
452	gabor_filter_entropy_4x4_v2	.117
453	compact_resnet50_robust_V4	.111
454	compact_resnet50_V4	.102
455	deit_small_imagenet_1_seed-0	.049
456	deit_small_imagenet_10_seed-0	.045
457	convnext_small_imagenet_1_seed-0	.036
458	pixels	.033
459	my-model	.019
460	eva02_large_in22k
461	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

0.86.

Note that scores are relative to this ceiling.

Data: SanghaviJozwik2020.IT

4916 stimuli recordings from 26 sites in IT

Metric: pls