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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	.585
2	AT_efficientnet-b7	.569
3	resnet50-vicregl0p9	.566
4	resnet-18-LC_w_sh_100_iter_conv_init_m	.565
5	resnet50_robust_l2_eps1	.565
6	resnet-18-LC_w_sh_10_iter_m	.563
7	voneresnet-50-robust	.563
8	barlow-twins-resnet50	.562
9	resnet-18-LC_w_sh_100_iter_m	.562
10	resnet50-barlow	.560
11	resnet-50-robust	.560
12	resnet-50-pytorch	.560
13	resnet50-vicreg	.560
14	resnet-18-LC_w_sh_1_iter_conv_init_m	.557
15	AdvProp_efficientnet-b6	.554
16	custom_model_cv_18_dagger_408	.553
17	AdvProp_efficientnet-b7	.552
18	resnet-18-LC_d_w_sh_1x1_conv_init_m	.552
19	resnet-18-LC_w_sh_10_iter_conv_init_m	.552
20	alexnet_robust_correct	.550
21	convnext_tiny_imagenet_full_seed-0	.550
22	resnet50-vicregl0p75	.548
23	convnext_small_imagenet_full_seed-0	.548
24	AdvProp_efficientnet-b8	.548
25	resnet50-simclr-vissl	.546
26	0.5x_resnet-18_LC_w_sh_1_iter	.546
27	AT_efficientnet-b4	.543
28	0.5x_resnet-18_LC_w_sh_10_iter	.541
29	AT_efficientnet-b2	.541
30	AdvProp_efficientnet-b4	.538
31	vonegrcnn_62e_nobn	.538
32	vonegrcnn_52e_full	.538
33	convnext_base_imagenet_full_seed-0	.538
34	mobilenet_v2_0.5_160	.537
35	voneresnet-50	.536
36	ReAlnet01_cornet_fov12	.536
37	resnet50_random_linf8_perturb	.536
38	mobilenet_v2_0.75_128	.535
39	convnext_large_imagenet_full_seed-0	.535
40	mobilenet_v2_1.0_96	.535
41	konkle_alexnetgn_ipcl_ref01_primary_model	.534
42	alexnet_l2_3_robust	.534
43	mobilenet_v2_0.75_96	.534
44	resnet50_random_l2_perturb	.533
45	resnet50_l2_3_robust	.532
46	AdvProp_efficientnet-b2	.532
47	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.532
48	mobilenet_v2_0.5_96	.531
49	resnet-50_v1-tf	.530
50	vit_relpos_base_patch16_clsgap_224:sw_in1k	.530
51	resnet-152_v1-tf	.529
52	tv_efficientnet-b1	.529
53	imagenet_l2_10_0	.529
54	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.529
55	resnet18-instance_recognition	.529
56	ReAlnet01_cornet_fov16	.529
57	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.527
58	convnext_xlarge:fb_in22k_ft_in1k	.527
59	mobilenet_v1_1.0_128	.527
60	resnet50-VITO-8deg-cc	.527
61	resnet-18-LC_w_sh_1_iter_m	.527
62	mobilenet_v2_1.0_128-tf	.527
63	vision_transformer_vit_large_patch16_224	.525
64	mobilenet_v2_0.5_128	.525
65	AdvProp_efficientnet-b0	.525
66	resnet50-vitoimagevidnet8	.524
67	resnet50_imagenet_full	.524
68	Res2Net50_26w_4s	.523
69	vonegrcnn_47e	.523
70	effnetv2m_custom384	.523
71	mobilenet_v2_0.35_160	.523
72	resnet50_ecoset_full	.523
73	mobilenet_v2_1_0_128	.522
74	resnet50-moclr8deg	.521
75	resnet50_linf_8_robust	.521
76	imagenet_l2_3_0	.521
77	resnet-101_v1-tf	.520
78	0.5x_resnet-18_LC_w_sh_100_iter	.520
79	ReAlnet08_cornet	.520
80	cvt_cvt-13-224-in1k_4_LucyV4	.519
81	deit_base_imagenet_full_seed-0	.519
82	mobilenet_v2_0.35_96	.519
83	mobilenet_v2_0.75_160-tf	.519
84	resnet50_ewconplus_wzc	.519
85	cv_18_dagger_408_pretrained	.518
86	ReAlnet10_cornet	.518
87	vonegrcnn_47e	.518
88	ReAlnet03_cornet	.518
89	resnet101	.517
90	resnet50	.517
91	voneresnet-50-non_stochastic	.517
92	mobilenet_v2_0_75_160	.516
93	ReAlnet06_cornet	.515
94	ReAlnet07_cornet	.515
95	resnet50_imagenet_100_seed-0	.515
96	nasnet_mobile	.515
97	ReAlnet02_cornet	.515
98	mobilenet_v2_0.5_192-tf	.515
99	resnet50_linf_4_robust	.514
100	yudixie_resnet50_imagenet1kpret_0_240312	.514
101	resnet_50_v1_spiking_l4	.514
102	resnet_50_v1_spiking_l4a	.514
103	resnet50_tutorial	.514
104	yudixie_resnet50_imagenet1kpret_0_240908	.514
105	resnet50-SIN_IN_IN	.514
106	efficientnet_b0_imagenet_full	.513
107	ReAlnet04_cornet	.513
108	mobilenet_v2_0.75_192-tf	.513
109	ReAlnet09_cornet	.513
110	efficientnet_b1_imagenet_full	.513
111	omnivore_swinT	.512
112	ViT_L_32_imagenet1k	.512
113	mobilenet_v1_1.0_160	.512
114	ReAlnet01_cornet	.512
115	mobilenet_v2_1_4_224	.512
116	resnet-18-LC_1st_conv_m	.511
117	mobilenet_v2_1-4_224_pytorch	.511
118	ReAlnet05_cornet	.510
119	resnet-18-LC_m	.510
120	efficientnet-b7	.509
121	mobilenet_v2_0.35_128	.508
122	resnet152_imagenet_full	.507
123	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.507
124	resnet50-SIN_IN	.507
125	mobilenet_v2_1.0_160-tf	.506
126	mobilenet_v2_1.3_224-tf	.506
127	antialias-resnet152	.506
128	convnext_tiny:in12k_ft_in1k	.506
129	regnet_y_400mf	.505
130	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.505
131	resnext101_32x8d_wsl	.504
132	mobilenet_v2_0.35_192	.504
133	mobilenet_v2_0.5_224-tf	.504
134	resnet101_ecoset_full	.504
135	cvt_cvt-21-224-in1k_4_LucyV4	.504
136	resnet-50_v2-tf	.503
137	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.503
138	omnivore_swinB	.503
139	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.503
140	resnet-101_v2-tf	.503
141	mobilenet_v2_1_0_160	.503
142	BiT-S-R152x4	.503
143	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.503
144	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.502
145	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.502
146	mobilenet_v2_0.75_224-tf	.502
147	mobilenet_v1_0.75_160	.501
148	mobilenet_v2_0_5_192	.501
149	mobilenet_v2_1.4_224-tf	.501
150	efficientnet_b2_imagenet_full	.501
151	CORnet-S	.501
152	ViT-B/32	.500
153	effnetb1_272x240	.500
154	mobilenet_v2_1.0_192-tf	.500
155	grcnn	.500
156	efficientnet_b0	.500
157	mobilenet_v2_0_75_192	.499
158	focalnet_tiny_lrf_in1k	.499
159	cvt_cvt-13-384-in1k_4_LucyV4	.499
160	alexnet_linf_8_robust	.499
161	CLIP_ViT-B_32	.498
162	inception_v1-tf	.498
163	ReAlnet03_ALL	.498
164	ReAlnet07_ALL	.498
165	ReAlnet10_ALL	.498
166	ReAlnet01_ALL	.498
167	BiT-M-R152x4	.497
168	resnet152_ecoset_full	.497
169	ReAlnet02_ALL	.497
170	ReAlnet08_ALL	.497
171	cvt_cvt-21-384-in1k_4_LucyV4	.496
172	omnivore_swinS	.496
173	resnet34_ecoset_full	.496
174	FrankRobWobv0	.496
175	mobilenet_v1_0.5_192	.496
176	ReAlnet04_ALL	.496
177	0.5x_resnet-18_LC	.495
178	antialiased-r50	.495
179	ReAlnet06_ALL	.495
180	mobilenet_v2_0.35_224	.495
181	convnext_base:clip_laiona_augreg_ft_in1k_384	.495
182	ReAlnet09_ALL	.495
183	ReAlnet05_ALL	.495
184	antialiased-rnext101_32x8d	.494
185	densenet-201-keras	.494
186	densenet-201	.493
187	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.493
188	mobilenet_v2_1_0_224	.493
189	mobilenet_v1_0.25_160	.493
190	0.5x_resnet-18	.492
191	resnet-18-LC_conv_init_m	.492
192	mobilenet_v2_1.0_224-tf	.492
193	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.491
194	mobilenet_v2_1_0_192	.491
195	shufflenet_v2_x1_0	.491
196	deit_large_imagenet_full_seed-0	.491
197	convnext_large:fb_in22k_ft_in1k	.490
198	densenet-169	.489
199	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.489
200	mobilenet_v2_0_75_224	.487
201	resnet101_imagenet_full	.487
202	grcnn_robust_v1	.487
203	mobilenet_v1_0.75_192	.487
204	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.487
205	grcnn_109	.486
206	mobilenet_v1_1.0_192	.486
207	nasnet_mobile-tf	.484
208	deit_small_imagenet_full_seed-0	.484
209	resnet50-SIN	.484
210	nasnet_large-tf	.483
211	yudixie_resnet18_imagenet1kpret_0_240719	.483
212	mobilenet_v1_0.5_128	.483
213	densenet-169-keras	.482
214	mobilenet_v1_0.25_192	.482
215	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.482
216	SWSL_resnet50	.481
217	mobilenet_v1_0.75_224	.481
218	resnet_50_v1_spiking	.480
219	resnet50-sup	.480
220	resnet_50_v1	.480
221	convnext_small_imagenet_100_seed-0	.479
222	inception_v1	.478
223	resnet-152_v2-tf	.478
224	mobilenet_v2_1_3_224	.477
225	alexnet_random_l2_3_perturb	.476
226	mobilenet_v1_1.0_224	.476
227	texture_shape_resnet50_trained_on_SIN	.475
228	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
229	resnet18_imagenet_full	.473
230	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.473
231	mobilenet_v1_0.75_128	.473
232	mobilenet_v1_0.5_160	.473
233	densenet-121	.472
234	resnet_SIN_IN_FT_IN	.472
235	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.472
236	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.472
237	resnet18_ecoset_full	.471
238	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.471
239	CLIP_resnet50_float32	.471
240	CLIP_resnet50	.471
241	alexnet_ks_torevert	.471
242	alexnet	.471
243	alexnet	.471
244	alexnet	.471
245	alexnet	.471
246	alexnet	.471
247	alexnet	.471
248	alexnet	.471
249	alexnet-baseline	.471
250	alexnet	.471
251	alexnet	.471
252	grcnn_v2_text_noise	.471
253	vit_large_patch14_clip_224:laion2b_ft_in1k	.468
254	ReAlnet01_cornet_fov4	.468
255	resnet50_simclr	.468
256	resnet18-deepcluster	.468
257	densenet-121-keras	.468
258	resnet18-local_aggregation	.467
259	resnet18_imagenet21kP	.467
260	RN50	.466
261	mobilenet_v1_0.5_224	.466
262	resnet_50_v1_spiking_l3	.466
263	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.466
264	alexnet_random_linf8_perturb	.465
265	BiT-S-R152x2	.465
266	mobilenet_v2_0_5_224	.465
267	vit_large_patch14_clip_224:openai_ft_in1k	.464
268	mobilenet_v1_0.25_128	.463
269	CORnet-Z	.462
270	deit_small_imagenet_100_seed-0	.459
271	CLIP-RN50	.459
272	convnext_femto_ols:d1_in1k	.458
273	mobilenet_v1_0.25_224	.457
274	resnet18-supervised	.457
275	BiT-S-R50x3	.457
276	BiT-M-R50x3	.456
277	squeezenet1_1	.456
278	vonealexnet_gaussian_noise_std4_fixed	.456
279	inception_resnet_v2	.453
280	resnet50_moco_v2	.453
281	vgg-16-keras	.452
282	resnet_50_v2	.450
283	alexnet	.450
284	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.448
285	vgg_16	.447
286	resnet34_imagenet_full	.446
287	xception-keras	.446
288	resnext101_32x48d_wsl	.446
289	resnet50_byol	.445
290	alexnet_fov12	.445
291	alexnet_fov4	.445
292	convnext_tiny_sup	.443
293	RN50	.442
294	inception_v2-tf	.442
295	vgg_19	.441
296	alexnet_reduced_aliasing_early_checkpoint	.440
297	BiT-M-R50x1	.440
298	vit_base_patch16_clip_224:openai_ft_in1k	.439
299	resnext101_32x16d_wsl	.439
300	r3m_resnet18	.439
301	resnet-18_test_m	.439
302	resnet-18	.439
303	inception_v3	.437
304	resnet50_primary_visual_cortex	.437
305	VOneCORnet-S	.436
306	AlexNet_SIN_fov16	.434
307	vgg-19-keras	.433
308	briaai_rmbg_1_4	.431
309	artResNet18_1	.431
310	resnet-34	.428
311	alexnet_fov16	.428
312	resnext101_32x32d_wsl	.428
313	alexnet_early_checkpoint	.427
314	blt_vs	.427
315	BiT-S-R50x1	.425
316	AlexNet_SIN	.424
317	inception_v3-tf	.423
318	r3m_resnet34	.423
319	vit_large_patch14_clip_336:laion2b_ft_in1k	.423
320	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.423
321	yudixie_resnet50_object_class_0_240908	.420
322	resnet18-colorization	.419
323	SWSL_resnext101_32x8d	.418
324	AlexNet_SIN_fov12	.416
325	resnet18-contrastive_multiview	.414
326	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.411
327	texture_shape_alexnet_trained_on_SIN	.410
328	pnasnet_large-tf	.410
329	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.403
330	yolos_tiny	.402
331	resnet18-relative_position	.398
332	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.397
333	yudixie_resnet50_category_class_0_240908	.396
334	BiT-M-R101x1	.391
335	AlexNet_SIN_fov4	.390
336	BiT-M-R101x3	.388
337	BiT-S-R101x1	.386
338	BiT-S-R101x3	.384
339	inception_v4-tf	.381
340	BiT-M-R152x2	.380
341	ReAlnet06	.378
342	ReAlnet05	.376
343	ReAlnet07	.376
344	ReAlnet04	.374
345	ReAlnet10	.373
346	ReAlnet01	.373
347	ReAlnet03	.372
348	ReAlnet08	.372
349	squeezenet1_0	.371
350	ReAlnet09	.371
351	resnet18-contrastive_predictive	.370
352	resnet50_imagenet_10_seed-0	.370
353	ReAlnet02	.369
354	r3m_resnet50	.366
355	yudixie_resnet50_distance_reg_0_240908	.364
356	vggface	.364
357	yudixie_resnet50_translation_rotation_0_240908	.363
358	yudixie_resnet18_category_class_0_240719	.362
359	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.362
360	cornet_s	.361
361	resnet50-cifar	.360
362	yudixie_resnet50_distance_translation_rotation_0_240908	.357
363	yudixie_resnet18_object_class_0_240719	.356
364	yudixie_resnet50_distance_rotation_0_240908	.356
365	resnet-152_v2_pytorch	.355
366	yudixie_resnet50_distance_translation_0_240908	.344
367	yudixie_resnet50_translation_reg_0_240908	.343
368	yudixie_resnet50_rotation_reg_0_240908	.343
369	resnet18-depth_prediction	.338
370	CORnetZ_CIFAR10	.333
371	resnet50	.330
372	alexnet_training_seed_02	.322
373	alexnet_training_seed_06	.320
374	alexnet_training_seed_03	.317
375	alexnet_training_seed_08	.315
376	alexnet_training_seed_05	.311
377	alexnet_training_seed_01	.309
378	alexnet_training_seed_10	.306
379	alexnet_training_seed_09	.304
380	alexnet_training_seed_04	.302
381	resnet50_FractalDB	.301
382	compact_resnet50_robust_IT	.300
383	alexnet_training_seed_01_fov12	.300
384	compact_resnet50_IT	.298
385	prednet	.297
386	mobilevit_small	.295
387	alexnet_training_seed_07	.295
388	yudixie_resnet18_translation_rotation_0_240719	.294
389	compact_cornets_IT	.288
390	my-model	.283
391	alexnet_training_seed_01_fov16	.279
392	yudixie_resnet18_distance_rotation_0_240719	.277
393	yudixie_resnet18_distance_translation_0_240719	.272
394	compact_vgg19_IT	.271
395	yudixie_resnet18_translation_reg_0_240719	.268
396	yudixie_resnet18_distance_translation_rotation_0_240719	.267
397	resnet-50x2_untrained	.265
398	yudixie_resnet18_rotation_reg_0_240719	.259
399	alexnet_training_seed_01_fov4	.257
400	convnext_small_imagenet_10_seed-0	.256
401	yudixie_resnet18_distance_reg_0_240719	.255
402	CORnetZ_CIFAR10_bs32_20_04	.250
403	resnet50_imagenet_1_seed-0	.250
404	yudixie_resnet50_random_0_240908	.243
405	yudixie_resnet18_random_0_240719	.237
406	resnet50-meshes-lt-100-original-scratch	.237
407	resnet-18_untrained	.223
408	resnet18-autoencoder	.220
409	resnet50_pretrained_with_retinal_waves	.207
410	resnet-50_untrained	.204
411	resnet-18-LC_untrained	.192
412	compact_cornets_V4	.192
413	v1-pyr-nodown	.190
414	resnet50-meshes-lt-100-original-pretrained	.175
415	compact_vgg19_V4	.145
416	compact_resnet50_robust_V4	.129
417	compact_resnet50_V4	.118
418	deit_small_imagenet_1_seed-0	.056
419	deit_small_imagenet_10_seed-0	.053
420	convnext_small_imagenet_1_seed-0	.041
421	pixels	.038
422	my-model	.022
423	AT_efficientnet-b0
424	fixres_resnext101_32x48d_wsl
425	efficientnet-b0
426	efficientnet-b6
427	efficientnet-b2
428	efficientnet-b4
429	resnet18-simclr
430	r3m_resnet50_nocrop
431	fitvid_trained_on_physion
432	openclip
433	resnet50_ImageNet
434	resnet_152_v1
435	resnet_101_v2
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_152_v2
451	resnet_101_v1
452	vonegrcnn_52e_full

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