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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	resnet50-vicreg	.563
2	resnet50-barlow	.563
3	resnet50-vicregl0p9	.562
4	AdvProp_efficientnet-b7	.562
5	barlow-twins-resnet50	.561
6	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.560
7	AT_efficientnet-b7	.560
8	vonegrcnn_52e_full	.559
9	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.558
10	custom_model_cv_18_dagger_408	.556
11	vonegrcnn_62e_nobn	.556
12	AT_efficientnet-b2	.556
13	AdvProp_efficientnet-b4	.556
14	resnet50-vicregl0p75	.556
15	AT_efficientnet-b4	.555
16	AdvProp_efficientnet-b6	.555
17	AdvProp_efficientnet-b2	.555
18	AdvProp_efficientnet-b8	.554
19	konkle_alexnetgn_ipcl_ref01_primary_model	.553
20	efficientnet_b1_imagenet_full	.553
21	efficientnet_b2_imagenet_full	.553
22	AdvProp_efficientnet-b0	.553
23	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.553
24	efficientnet_b0_imagenet_full	.552
25	tv_efficientnet-b1	.552
26	resnet-18-LC_w_sh_10_iter_m	.551
27	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.551
28	convnext_tiny_imagenet_full_seed-0	.551
29	effnetv2m_custom384	.551
30	resnet-18-LC_w_sh_10_iter_conv_init_m	.550
31	0.5x_resnet-18_LC_w_sh_1_iter	.550
32	mobilenet_v2_1.0_192-tf	.549
33	resnet-152_v1-tf	.549
34	antialiased-rnext101_32x8d	.548
35	ReAlnet02_cornet	.548
36	resnet-101_v1-tf	.548
37	resnet-18-LC_w_sh_1_iter_conv_init_m	.548
38	resnet50_ewconplus_wzc	.547
39	resnet50-SIN_IN	.547
40	densenet-201-keras	.547
41	ReAlnet06_cornet	.546
42	efficientnet-b7	.546
43	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.546
44	mobilenet_v2_1.3_224-tf	.546
45	convnext_small_imagenet_full_seed-0	.545
46	mobilenet_v2_1.0_160-tf	.545
47	resnet50-moclr8deg	.545
48	mobilenet_v2_1.4_224-tf	.545
49	resnet-50_v1-tf	.545
50	ReAlnet03_cornet	.545
51	ReAlnet08_cornet	.545
52	ReAlnet04_cornet	.544
53	ReAlnet01_cornet	.544
54	resnet101_ecoset_full	.544
55	mobilenet_v2_0.75_224-tf	.543
56	ReAlnet07_cornet	.543
57	resnet50-simclr-vissl	.543
58	ReAlnet10_cornet	.543
59	resnet18-instance_recognition	.543
60	vit_relpos_base_patch16_clsgap_224:sw_in1k	.542
61	resnet50_robust_l2_eps1	.542
62	resnet50-SIN	.542
63	resnet-18-LC_w_sh_100_iter_conv_init_m	.542
64	effnetb1_272x240	.542
65	nasnet_mobile	.541
66	resnet50_imagenet_full	.541
67	mobilenet_v2_0.75_160-tf	.541
68	ReAlnet09_cornet	.541
69	convnext_xlarge:fb_in22k_ft_in1k	.541
70	ReAlnet09_ALL	.541
71	convnext_tiny:in12k_ft_in1k	.540
72	ReAlnet06_ALL	.540
73	vonegrcnn_47e	.540
74	convnext_base_imagenet_full_seed-0	.540
75	mobilenet_v2_1.0_128-tf	.540
76	ReAlnet05_cornet	.540
77	resnet18-local_aggregation	.540
78	convnext_base:clip_laiona_augreg_ft_in1k_384	.540
79	voneresnet-50	.540
80	antialiased-r50	.540
81	densenet-169-keras	.540
82	efficientnet_b0	.539
83	resnet-101_v2-tf	.539
84	ReAlnet04_ALL	.539
85	ReAlnet03_ALL	.539
86	vonegrcnn_47e	.539
87	resnet50-SIN_IN_IN	.539
88	voneresnet-50-robust	.539
89	densenet-169	.539
90	mobilenet_v2_0.5_192-tf	.539
91	ReAlnet02_ALL	.539
92	ReAlnet10_ALL	.539
93	resnet-18-LC_w_sh_1_iter_m	.539
94	ReAlnet01_ALL	.538
95	0.5x_resnet-18_LC_w_sh_10_iter	.538
96	mobilenet_v2_0.75_128	.538
97	grcnn	.538
98	ReAlnet05_ALL	.538
99	mobilenet_v2_0.75_192-tf	.538
100	resnet_SIN_IN_FT_IN	.538
101	mobilenet_v2_0.35_192	.538
102	ReAlnet08_ALL	.537
103	nasnet_mobile-tf	.537
104	convnext_tiny_sup	.537
105	mobilenet_v2_1_0_160	.537
106	mobilenet_v2_1-4_224_pytorch	.537
107	resnet-18-LC_w_sh_100_iter_m	.537
108	resnet50_ecoset_full	.536
109	convnext_large_imagenet_full_seed-0	.536
110	resnet-50_v2-tf	.536
111	ReAlnet07_ALL	.536
112	resnet152_ecoset_full	.536
113	mobilenet_v2_1_0_192	.536
114	mobilenet_v2_1_4_224	.536
115	resnet_50_v1_spiking	.536
116	resnet_50_v1	.536
117	resnet50-sup	.536
118	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.535
119	mobilenet_v2_1_0_224	.535
120	voneresnet-50-non_stochastic	.535
121	mobilenet_v2_0.5_160	.535
122	CORnet-S	.534
123	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.534
124	mobilenet_v2_0.5_224-tf	.534
125	mobilenet_v2_1.0_224-tf	.534
126	mobilenet_v1_0.75_192	.534
127	mobilenet_v1_1.0_192	.533
128	resnet50_imagenet_100_seed-0	.533
129	resnet50_random_linf8_perturb	.533
130	shufflenet_v2_x1_0	.533
131	mobilenet_v1_1.0_160	.533
132	resnet-152_v2-tf	.532
133	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.532
134	resnet101_imagenet_full	.532
135	nasnet_large-tf	.532
136	mobilenet_v1_1.0_224	.531
137	grcnn_robust_v1	.531
138	mobilenet_v1_1.0_128	.531
139	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.530
140	densenet-121-keras	.530
141	convnext_large:fb_in22k_ft_in1k	.529
142	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.529
143	resnet34_ecoset_full	.529
144	mobilenet_v2_1_3_224	.529
145	densenet-121	.529
146	BiT-M-R50x3	.528
147	resnet152_imagenet_full	.528
148	resnet50-vitoimagevidnet8	.528
149	mobilenet_v1_0.75_160	.528
150	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.528
151	BiT-M-R50x1	.528
152	inception_v2-tf	.527
153	resnet_50_v1_spiking_l4	.527
154	resnet_50_v1_spiking_l4a	.527
155	resnet50_tutorial	.527
156	yudixie_resnet50_imagenet1kpret_0_240908	.527
157	yudixie_resnet50_imagenet1kpret_0_240312	.527
158	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.527
159	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.527
160	BiT-S-R50x3	.527
161	grcnn_109	.527
162	mobilenet_v2_0_75_192	.527
163	mobilenet_v2_1_0_128	.526
164	Res2Net50_26w_4s	.526
165	mobilenet_v2_0_5_192	.526
166	mobilenet_v2_0_75_160	.526
167	resnet18-supervised	.526
168	mobilenet_v2_0.35_160	.525
169	antialias-resnet152	.525
170	mobilenet_v2_0_75_224	.525
171	resnet50_linf_4_robust	.524
172	resnet50	.524
173	mobilenet_v2_0.5_128	.524
174	vit_large_patch14_clip_224:openai_ft_in1k	.524
175	resnet50_random_l2_perturb	.524
176	resnet50_moco_v2	.523
177	mobilenet_v2_0.35_224	.523
178	deit_large_imagenet_full_seed-0	.523
179	inception_v1-tf	.523
180	CLIP-RN50	.522
181	inception_v3-tf	.522
182	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.522
183	mobilenet_v1_0.75_224	.522
184	resnet50_l2_3_robust	.522
185	cv_18_dagger_408_pretrained	.522
186	deit_base_imagenet_full_seed-0	.522
187	ReAlnet01_cornet_fov12	.521
188	focalnet_tiny_lrf_in1k	.521
189	yudixie_resnet18_imagenet1kpret_0_240719	.521
190	mobilenet_v1_0.75_128	.521
191	resnet101	.521
192	resnet34_imagenet_full	.521
193	BiT-S-R50x1	.521
194	grcnn_v2_text_noise	.520
195	regnet_y_400mf	.520
196	inception_v3	.520
197	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.520
198	resnet50_robust_l2_eps3	.520
199	BiT-S-R152x4	.519
200	convnext_femto_ols:d1_in1k	.519
201	compact_resnet50_robust_IT	.518
202	inception_resnet_v2	.518
203	resnet18_ecoset_full	.518
204	vit_large_patch14_clip_224:laion2b_ft_in1k	.517
205	resnet18_imagenet_full	.517
206	cvt_cvt-13-224-in1k_4_LucyV4	.517
207	omnivore_swinS	.517
208	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.517
209	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.516
210	resnext101_32x8d_wsl	.516
211	RN50	.516
212	omnivore_swinB	.516
213	ReAlnet01_cornet_fov4	.516
214	texture_shape_resnet50_trained_on_SIN	.515
215	compact_resnet50_IT	.515
216	0.5x_resnet-18	.514
217	resnet-18_test_m	.514
218	resnet-18	.514
219	vgg-16-keras	.514
220	inception_v1	.513
221	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.513
222	resnet18_imagenet21kP	.513
223	BiT-M-R101x3	.513
224	BiT-M-R152x4	.512
225	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.512
226	cvt_cvt-21-224-in1k_4_LucyV4	.512
227	resnet18-contrastive_multiview	.511
228	vgg_16	.511
229	ViT-B/32	.511
230	omnivore_swinT	.510
231	resnet-18-LC_1st_conv_m	.510
232	inception_v4-tf	.510
233	SWSL_resnet50	.509
234	vit_large_patch14_clip_336:laion2b_ft_in1k	.509
235	resnet50-VITO-8deg-cc	.509
236	cvt_cvt-13-384-in1k_4_LucyV4	.509
237	resnet18-deepcluster	.509
238	compact_vgg19_IT	.508
239	CLIP_ViT-B_32	.508
240	resnet50_simclr	.508
241	mobilenet_v1_0.5_192	.508
242	mobilenet_v2_1.0_96	.508
243	xception-keras	.508
244	resnet-18-LC_d_w_sh_1x1_conv_init_m	.508
245	CLIP_resnet50_float32	.507
246	CLIP_resnet50	.507
247	resnet-50-pytorch	.507
248	resnet-50-robust	.507
249	vit_base_patch16_clip_224:openai_ft_in1k	.507
250	compact_cornets_IT	.507
251	alexnet_random_l2_3_perturb	.506
252	mobilenet_v1_0.5_224	.506
253	resnet-34	.505
254	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.505
255	squeezenet1_1	.504
256	vgg-19-keras	.504
257	mobilenet_v2_0.75_96	.503
258	pnasnet_large-tf	.503
259	yudixie_resnet50_object_class_0_240908	.503
260	mobilenet_v1_0.5_128	.503
261	deit_small_imagenet_full_seed-0	.502
262	BiT-M-R152x2	.502
263	resnext101_32x48d_wsl	.502
264	0.5x_resnet-18_LC_w_sh_100_iter	.502
265	BiT-S-R101x3	.502
266	RN50	.501
267	resnet_101_v2	.501
268	CORnet-Z	.501
269	cvt_cvt-21-384-in1k_4_LucyV4	.501
270	mobilenet_v2_0_5_224	.500
271	convnext_small_imagenet_100_seed-0	.500
272	mobilenet_v1_0.5_160	.500
273	resnext101_32x32d_wsl	.500
274	yudixie_resnet50_category_class_0_240908	.499
275	resnet_50_v2	.498
276	ViT_L_32_imagenet1k	.497
277	resnext101_32x16d_wsl	.497
278	alexnet	.496
279	alexnet	.496
280	alexnet	.496
281	alexnet	.496
282	alexnet	.496
283	alexnet	.496
284	alexnet	.496
285	alexnet-baseline	.496
286	alexnet	.496
287	alexnet	.496
288	alexnet_ks_torevert	.496
289	vision_transformer_vit_large_patch16_224	.495
290	BiT-M-R101x1	.495
291	imagenet_l2_3_0	.495
292	BiT-S-R152x2	.494
293	alexnet_random_linf8_perturb	.494
294	resnet50_byol	.494
295	ReAlnet10	.493
296	mobilenet_v2_0.35_128	.493
297	mobilenet_v1_0.25_224	.493
298	SWSL_resnext101_32x8d	.492
299	ReAlnet02	.492
300	ReAlnet01	.491
301	ReAlnet07	.491
302	ReAlnet06	.490
303	alexnet_fov4	.490
304	ReAlnet08	.490
305	ReAlnet04	.489
306	ReAlnet03	.489
307	mobilenet_v1_0.25_192	.489
308	ReAlnet09	.489
309	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.488
310	resnet50_linf_8_robust	.487
311	ReAlnet05	.486
312	BiT-S-R101x1	.486
313	mobilenet_v1_0.25_160	.486
314	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.486
315	alexnet_l2_3_robust	.486
316	alexnet	.485
317	VOneCORnet-S	.485
318	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.485
319	mobilenet_v2_0.5_96	.485
320	ReAlnet01_cornet_fov16	.485
321	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.484
322	alexnet_reduced_aliasing_early_checkpoint	.483
323	fixres_resnext101_32x48d_wsl	.483
324	AlexNet_SIN	.479
325	cornet_s	.479
326	yudixie_resnet18_object_class_0_240719	.479
327	FrankRobWobv0	.478
328	yudixie_resnet18_category_class_0_240719	.477
329	vgg_19	.477
330	yudixie_resnet50_distance_translation_rotation_0_240908	.477
331	resnet_50_v1_spiking_l3	.476
332	resnet-18-LC_m	.476
333	yolos_tiny	.475
334	blt_vs	.475
335	yudixie_resnet50_translation_rotation_0_240908	.473
336	yudixie_resnet50_distance_translation_0_240908	.471
337	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.470
338	0.5x_resnet-18_LC	.470
339	resnet-18-LC_conv_init_m	.469
340	mobilenet_v2_0.35_96	.468
341	mobilenet_v1_0.25_128	.467
342	resnet18-colorization	.466
343	squeezenet1_0	.464
344	AlexNet_SIN_fov4	.464
345	alexnet_robust_correct	.463
346	alexnet_early_checkpoint	.461
347	alexnet_fov12	.461
348	texture_shape_alexnet_trained_on_SIN	.461
349	vonealexnet_gaussian_noise_std4_fixed	.457
350	yudixie_resnet50_distance_reg_0_240908	.452
351	imagenet_l2_10_0	.449
352	resnet18-relative_position	.448
353	resnet-152_v2_pytorch	.448
354	yudixie_resnet50_rotation_reg_0_240908	.447
355	yudixie_resnet50_translation_reg_0_240908	.442
356	AlexNet_SIN_fov12	.437
357	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.435
358	yudixie_resnet18_translation_rotation_0_240719	.434
359	resnet18-contrastive_predictive	.433
360	alexnet_fov16	.431
361	alexnet_linf_8_robust	.431
362	yudixie_resnet18_distance_translation_rotation_0_240719	.425
363	yudixie_resnet18_rotation_reg_0_240719	.422
364	yudixie_resnet18_distance_rotation_0_240719	.422
365	vggface	.419
366	yudixie_resnet50_distance_rotation_0_240908	.418
367	yudixie_resnet18_translation_reg_0_240719	.417
368	yudixie_resnet18_distance_translation_0_240719	.414
369	AlexNet_SIN_fov16	.409
370	resnet18-depth_prediction	.408
371	deit_small_imagenet_100_seed-0	.402
372	yudixie_resnet18_distance_reg_0_240719	.402
373	r3m_resnet34	.400
374	briaai_rmbg_1_4	.398
375	r3m_resnet18	.393
376	r3m_resnet50	.391
377	artResNet18_1	.380
378	resnet50	.380
379	r3m_resnet50_nocrop	.375
380	resnet50-cifar	.359
381	resnet50_primary_visual_cortex	.355
382	prednet	.351
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.350
384	my-model	.346
385	resnet50_imagenet_10_seed-0	.329
386	CORnetZ_CIFAR10	.316
387	compact_resnet50_V4	.309
388	compact_resnet50_robust_V4	.307
389	alexnet_training_seed_08	.296
390	v1-pyr-nodown	.295
391	alexnet_training_seed_03	.295
392	compact_vgg19_V4	.293
393	alexnet_training_seed_01	.293
394	alexnet_training_seed_02	.292
395	alexnet_training_seed_05	.291
396	alexnet_training_seed_04	.288
397	alexnet_training_seed_07	.288
398	alexnet_training_seed_09	.286
399	alexnet_training_seed_06	.282
400	alexnet_training_seed_10	.282
401	alexnet_training_seed_01_fov12	.282
402	resnet50_FractalDB	.280
403	mobilevit_small	.276
404	CORnetZ_CIFAR10_bs32_20_04	.276
405	resnet18-autoencoder	.276
406	alexnet_training_seed_01_fov4	.275
407	compact_cornets_V4	.274
408	alexnet_training_seed_01_fov16	.274
409	resnet50_imagenet_1_seed-0	.224
410	resnet-18_untrained	.223
411	yudixie_resnet18_random_0_240719	.221
412	resnet-50x2_untrained	.221
413	resnet50-meshes-lt-100-original-pretrained	.218
414	resnet50-meshes-lt-100-original-scratch	.217
415	resnet-50_untrained	.214
416	convnext_small_imagenet_10_seed-0	.208
417	yudixie_resnet50_random_0_240908	.201
418	resnet50_pretrained_with_retinal_waves	.183
419	resnet-18-LC_untrained	.179
420	fitvid_trained_on_physion	.166
421	deit_small_imagenet_10_seed-0	.118
422	deit_small_imagenet_1_seed-0	.095
423	convnext_small_imagenet_1_seed-0	.093
424	pixels	.072
425	my-model	.071
426	AT_efficientnet-b0
427	efficientnet-b0
428	efficientnet-b6
429	efficientnet-b2
430	efficientnet-b4
431	resnet18-simclr
432	openclip
433	resnet50_ImageNet
434	resnet_152_v1
435	densenet-201
436	ConvLSTM
437	PredRNN
438	SimVP
439	TAU
440	MIM
441	hmax
442	densenet_201_pytorch
443	pnasnet_large_pytorch
444	inception_v3_pytorch
445	inception_v4
446	xception
447	nasnet_large
448	pnasnet_large
449	vonegrcnn_62e_nobn
450	resnet_101_v1
451	resnet_152_v2
452	vonegrcnn_52e_full

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