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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	eBarlow_lmda_02_1000ep	.581
3	AT_efficientnet-b7	.569
4	resnet50-vicregl0p9	.566
5	resnet-18-LC_w_sh_100_iter_conv_init_m	.565
6	resnet50_robust_l2_eps1	.565
7	resnet-18-LC_w_sh_10_iter_m	.563
8	voneresnet-50-robust	.563
9	barlow-twins-resnet50	.562
10	resnet-18-LC_w_sh_100_iter_m	.562
11	resnet50-barlow	.560
12	resnet-50-robust	.560
13	resnet-50-pytorch	.560
14	resnet50-vicreg	.560
15	resnet-18-LC_w_sh_1_iter_conv_init_m	.557
16	AdvProp_efficientnet-b6	.554
17	custom_model_cv_18_dagger_408	.553
18	AdvProp_efficientnet-b7	.552
19	resnet-18-LC_d_w_sh_1x1_conv_init_m	.552
20	resnet-18-LC_w_sh_10_iter_conv_init_m	.552
21	alexnet_robust_correct	.550
22	convnext_tiny_imagenet_full_seed-0	.550
23	resnet50-vicregl0p75	.548
24	convnext_small_imagenet_full_seed-0	.548
25	AdvProp_efficientnet-b8	.548
26	resnet50-simclr-vissl	.546
27	0.5x_resnet-18_LC_w_sh_1_iter	.546
28	AT_efficientnet-b4	.543
29	0.5x_resnet-18_LC_w_sh_10_iter	.541
30	AT_efficientnet-b2	.541
31	AdvProp_efficientnet-b4	.538
32	vonegrcnn_62e_nobn	.538
33	vonegrcnn_52e_full	.538
34	convnext_base_imagenet_full_seed-0	.538
35	mobilenet_v2_0.5_160	.537
36	voneresnet-50	.536
37	ReAlnet01_cornet_fov12	.536
38	resnet50_random_linf8_perturb	.536
39	mobilenet_v2_0.75_128	.535
40	convnext_large_imagenet_full_seed-0	.535
41	mobilenet_v2_1.0_96	.535
42	konkle_alexnetgn_ipcl_ref01_primary_model	.534
43	resnet50_hvs_wzc	.534
44	alexnet_l2_3_robust	.534
45	mobilenet_v2_0.75_96	.534
46	resnet50_random_l2_perturb	.533
47	resnet50_l2_3_robust	.532
48	AdvProp_efficientnet-b2	.532
49	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.532
50	mobilenet_v2_0.5_96	.531
51	resnet-50_v1-tf	.530
52	vit_relpos_base_patch16_clsgap_224:sw_in1k	.530
53	resnet-152_v1-tf	.529
54	tv_efficientnet-b1	.529
55	imagenet_l2_10_0	.529
56	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.529
57	resnet18-instance_recognition	.529
58	ReAlnet01_cornet_fov16	.529
59	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.527
60	convnext_xlarge:fb_in22k_ft_in1k	.527
61	mobilenet_v1_1.0_128	.527
62	resnet50-VITO-8deg-cc	.527
63	resnet-18-LC_w_sh_1_iter_m	.527
64	mobilenet_v2_1.0_128-tf	.527
65	vision_transformer_vit_large_patch16_224	.525
66	mobilenet_v2_0.5_128	.525
67	AdvProp_efficientnet-b0	.525
68	resnet50-vitoimagevidnet8	.524
69	resnet50_imagenet_full	.524
70	Res2Net50_26w_4s	.523
71	vonegrcnn_47e	.523
72	effnetv2m_custom384	.523
73	mobilenet_v2_0.35_160	.523
74	resnet50_ecoset_full	.523
75	mobilenet_v2_1_0_128	.522
76	resnet50-moclr8deg	.521
77	resnet50_linf_8_robust	.521
78	imagenet_l2_3_0	.521
79	resnet-101_v1-tf	.520
80	0.5x_resnet-18_LC_w_sh_100_iter	.520
81	ReAlnet08_cornet	.520
82	cvt_cvt-13-224-in1k_4_LucyV4	.519
83	deit_base_imagenet_full_seed-0	.519
84	mobilenet_v2_0.35_96	.519
85	mobilenet_v2_0.75_160-tf	.519
86	resnet50_ewconplus_wzc	.519
87	cv_18_dagger_408_pretrained	.518
88	ReAlnet10_cornet	.518
89	vonegrcnn_47e	.518
90	ReAlnet03_cornet	.518
91	resnet101	.517
92	resnet50	.517
93	voneresnet-50-non_stochastic	.517
94	mobilenet_v2_0_75_160	.516
95	ReAlnet06_cornet	.515
96	ReAlnet07_cornet	.515
97	resnet50_imagenet_100_seed-0	.515
98	nasnet_mobile	.515
99	ReAlnet02_cornet	.515
100	mobilenet_v2_0.5_192-tf	.515
101	resnet50_linf_4_robust	.514
102	yudixie_resnet50_imagenet1kpret_0_240312	.514
103	resnet_50_v1_spiking_l4	.514
104	resnet_50_v1_spiking_l4a	.514
105	resnet50_tutorial	.514
106	yudixie_resnet50_imagenet1kpret_0_240908	.514
107	resnet50-SIN_IN_IN	.514
108	efficientnet_b0_imagenet_full	.513
109	ReAlnet04_cornet	.513
110	mobilenet_v2_0.75_192-tf	.513
111	ReAlnet09_cornet	.513
112	efficientnet_b1_imagenet_full	.513
113	omnivore_swinT	.512
114	ViT_L_32_imagenet1k	.512
115	mobilenet_v1_1.0_160	.512
116	ReAlnet01_cornet	.512
117	mobilenet_v2_1_4_224	.512
118	resnet-18-LC_1st_conv_m	.511
119	mobilenet_v2_1-4_224_pytorch	.511
120	ReAlnet05_cornet	.510
121	resnet-18-LC_m	.510
122	efficientnet-b7	.509
123	mobilenet_v2_0.35_128	.508
124	resnet152_imagenet_full	.507
125	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.507
126	resnet50-SIN_IN	.507
127	mobilenet_v2_1.0_160-tf	.506
128	mobilenet_v2_1.3_224-tf	.506
129	antialias-resnet152	.506
130	convnext_tiny:in12k_ft_in1k	.506
131	regnet_y_400mf	.505
132	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.505
133	resnext101_32x8d_wsl	.504
134	mobilenet_v2_0.35_192	.504
135	mobilenet_v2_0.5_224-tf	.504
136	resnet101_ecoset_full	.504
137	cvt_cvt-21-224-in1k_4_LucyV4	.504
138	resnet-50_v2-tf	.503
139	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.503
140	omnivore_swinB	.503
141	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.503
142	resnet-101_v2-tf	.503
143	mobilenet_v2_1_0_160	.503
144	BiT-S-R152x4	.503
145	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.503
146	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.502
147	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.502
148	mobilenet_v2_0.75_224-tf	.502
149	mobilenet_v1_0.75_160	.501
150	mobilenet_v2_0_5_192	.501
151	mobilenet_v2_1.4_224-tf	.501
152	efficientnet_b2_imagenet_full	.501
153	CORnet-S	.501
154	ViT-B/32	.500
155	effnetb1_272x240	.500
156	mobilenet_v2_1.0_192-tf	.500
157	grcnn	.500
158	efficientnet_b0	.500
159	mobilenet_v2_0_75_192	.499
160	focalnet_tiny_lrf_in1k	.499
161	cvt_cvt-13-384-in1k_4_LucyV4	.499
162	alexnet_linf_8_robust	.499
163	CLIP_ViT-B_32	.498
164	inception_v1-tf	.498
165	ReAlnet03_ALL	.498
166	ReAlnet07_ALL	.498
167	ReAlnet10_ALL	.498
168	ReAlnet01_ALL	.498
169	BiT-M-R152x4	.497
170	resnet152_ecoset_full	.497
171	ReAlnet02_ALL	.497
172	ReAlnet08_ALL	.497
173	cvt_cvt-21-384-in1k_4_LucyV4	.496
174	omnivore_swinS	.496
175	resnet34_ecoset_full	.496
176	FrankRobWobv0	.496
177	mobilenet_v1_0.5_192	.496
178	ReAlnet04_ALL	.496
179	0.5x_resnet-18_LC	.495
180	antialiased-r50	.495
181	ReAlnet06_ALL	.495
182	mobilenet_v2_0.35_224	.495
183	convnext_base:clip_laiona_augreg_ft_in1k_384	.495
184	ReAlnet09_ALL	.495
185	ReAlnet05_ALL	.495
186	antialiased-rnext101_32x8d	.494
187	densenet-201-keras	.494
188	densenet-201	.493
189	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.493
190	mobilenet_v2_1_0_224	.493
191	mobilenet_v1_0.25_160	.493
192	0.5x_resnet-18	.492
193	resnet-18-LC_conv_init_m	.492
194	mobilenet_v2_1.0_224-tf	.492
195	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.491
196	mobilenet_v2_1_0_192	.491
197	shufflenet_v2_x1_0	.491
198	deit_large_imagenet_full_seed-0	.491
199	convnext_large:fb_in22k_ft_in1k	.490
200	densenet-169	.489
201	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.489
202	resnet50_st_wzc_2	.489
203	mobilenet_v2_0_75_224	.487
204	resnet101_imagenet_full	.487
205	grcnn_robust_v1	.487
206	mobilenet_v1_0.75_192	.487
207	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.487
208	grcnn_109	.486
209	mobilenet_v1_1.0_192	.486
210	nasnet_mobile-tf	.484
211	deit_small_imagenet_full_seed-0	.484
212	resnet50-SIN	.484
213	nasnet_large-tf	.483
214	yudixie_resnet18_imagenet1kpret_0_240719	.483
215	mobilenet_v1_0.5_128	.483
216	densenet-169-keras	.482
217	mobilenet_v1_0.25_192	.482
218	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.482
219	SWSL_resnet50	.481
220	mobilenet_v1_0.75_224	.481
221	resnet_50_v1_spiking	.480
222	resnet50-sup	.480
223	resnet_50_v1	.480
224	convnext_small_imagenet_100_seed-0	.479
225	inception_v1	.478
226	resnet-152_v2-tf	.478
227	mobilenet_v2_1_3_224	.477
228	alexnet_random_l2_3_perturb	.476
229	mobilenet_v1_1.0_224	.476
230	texture_shape_resnet50_trained_on_SIN	.475
231	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
232	resnet18_imagenet_full	.473
233	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.473
234	mobilenet_v1_0.75_128	.473
235	mobilenet_v1_0.5_160	.473
236	densenet-121	.472
237	resnet_SIN_IN_FT_IN	.472
238	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.472
239	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.472
240	resnet18_ecoset_full	.471
241	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.471
242	CLIP_resnet50_float32	.471
243	CLIP_resnet50	.471
244	alexnet_ks_torevert	.471
245	alexnet	.471
246	grcnn_v2_text_noise	.471
247	vit_large_patch14_clip_224:laion2b_ft_in1k	.468
248	ReAlnet01_cornet_fov4	.468
249	resnet50_simclr	.468
250	resnet18-deepcluster	.468
251	densenet-121-keras	.468
252	resnet18-local_aggregation	.467
253	resnet18_imagenet21kP	.467
254	RN50	.466
255	mobilenet_v1_0.5_224	.466
256	resnet_50_v1_spiking_l3	.466
257	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.466
258	alexnet_random_linf8_perturb	.465
259	BiT-S-R152x2	.465
260	mobilenet_v2_0_5_224	.465
261	vit_large_patch14_clip_224:openai_ft_in1k	.464
262	mobilenet_v1_0.25_128	.463
263	CORnet-Z	.462
264	deit_small_imagenet_100_seed-0	.459
265	CLIP-RN50	.459
266	convnext_femto_ols:d1_in1k	.458
267	mobilenet_v1_0.25_224	.457
268	resnet18-supervised	.457
269	BiT-S-R50x3	.457
270	BiT-M-R50x3	.456
271	squeezenet1_1	.456
272	vonealexnet_gaussian_noise_std4_fixed	.456
273	inception_resnet_v2	.453
274	resnet50_moco_v2	.453
275	vgg-16-keras	.452
276	resnet_50_v2	.450
277	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.448
278	vgg_16	.447
279	resnet34_imagenet_full	.446
280	xception-keras	.446
281	resnext101_32x48d_wsl	.446
282	resnet50_byol	.445
283	alexnet_fov12	.445
284	alexnet_fov4	.445
285	convnext_tiny_sup	.443
286	RN50	.442
287	inception_v2-tf	.442
288	vgg_19	.441
289	alexnet_reduced_aliasing_early_checkpoint	.440
290	BiT-M-R50x1	.440
291	vit_base_patch16_clip_224:openai_ft_in1k	.439
292	resnext101_32x16d_wsl	.439
293	r3m_resnet18	.439
294	resnet-18_test_m	.439
295	resnet-18	.439
296	inception_v3	.437
297	resnet50_primary_visual_cortex	.437
298	VOneCORnet-S	.436
299	AlexNet_SIN_fov16	.434
300	vgg-19-keras	.433
301	briaai_rmbg_1_4	.431
302	artResNet18_1	.431
303	resnet-34	.428
304	alexnet_fov16	.428
305	resnext101_32x32d_wsl	.428
306	alexnet_early_checkpoint	.427
307	blt_vs	.427
308	BiT-S-R50x1	.425
309	AlexNet_SIN	.424
310	inception_v3-tf	.423
311	r3m_resnet34	.423
312	vit_large_patch14_clip_336:laion2b_ft_in1k	.423
313	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.423
314	yudixie_resnet50_object_class_0_240908	.420
315	resnet18-colorization	.419
316	SWSL_resnext101_32x8d	.418
317	AlexNet_SIN_fov12	.416
318	resnet18-contrastive_multiview	.414
319	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.411
320	texture_shape_alexnet_trained_on_SIN	.410
321	pnasnet_large-tf	.410
322	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.403
323	yolos_tiny	.402
324	resnet18-relative_position	.398
325	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.397
326	yudixie_resnet50_category_class_0_240908	.396
327	BiT-M-R101x1	.391
328	AlexNet_SIN_fov4	.390
329	BiT-M-R101x3	.388
330	BiT-S-R101x1	.386
331	BiT-S-R101x3	.384
332	inception_v4-tf	.381
333	BiT-M-R152x2	.380
334	ReAlnet06	.378
335	ReAlnet05	.376
336	ReAlnet07	.376
337	ReAlnet04	.374
338	ReAlnet10	.373
339	ReAlnet01	.373
340	ReAlnet03	.372
341	ReAlnet08	.372
342	squeezenet1_0	.371
343	ReAlnet09	.371
344	resnet18-contrastive_predictive	.370
345	resnet50_imagenet_10_seed-0	.370
346	ReAlnet02	.369
347	r3m_resnet50	.366
348	yudixie_resnet50_distance_reg_0_240908	.364
349	vggface	.364
350	yudixie_resnet50_translation_rotation_0_240908	.363
351	yudixie_resnet18_category_class_0_240719	.362
352	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.362
353	cornet_s	.361
354	resnet50-cifar	.360
355	yudixie_resnet50_distance_translation_rotation_0_240908	.357
356	yudixie_resnet18_object_class_0_240719	.356
357	yudixie_resnet50_distance_rotation_0_240908	.356
358	resnet-152_v2_pytorch	.355
359	yudixie_resnet50_distance_translation_0_240908	.344
360	yudixie_resnet50_translation_reg_0_240908	.343
361	yudixie_resnet50_rotation_reg_0_240908	.343
362	resnet18-depth_prediction	.338
363	CORnetZ_CIFAR10	.333
364	alexnet_training_seed_02	.322
365	alexnet_training_seed_06	.320
366	alexnet_training_seed_03	.317
367	alexnet_training_seed_08	.315
368	alexnet_training_seed_05	.311
369	alexnet_training_seed_01	.309
370	alexnet_training_seed_10	.306
371	alexnet_training_seed_09	.304
372	alexnet_training_seed_04	.302
373	resnet50_FractalDB	.301
374	compact_resnet50_robust_IT	.300
375	alexnet_training_seed_01_fov12	.300
376	compact_resnet50_IT	.298
377	prednet	.297
378	mobilevit_small	.295
379	alexnet_training_seed_07	.295
380	yudixie_resnet18_translation_rotation_0_240719	.294
381	compact_cornets_IT	.288
382	my-model	.283
383	alexnet_training_seed_01_fov16	.279
384	yudixie_resnet18_distance_rotation_0_240719	.277
385	yudixie_resnet18_distance_translation_0_240719	.272
386	compact_vgg19_IT	.271
387	yudixie_resnet18_translation_reg_0_240719	.268
388	yudixie_resnet18_distance_translation_rotation_0_240719	.267
389	resnet-50x2_untrained	.265
390	yudixie_resnet18_rotation_reg_0_240719	.259
391	alexnet_training_seed_01_fov4	.257
392	convnext_small_imagenet_10_seed-0	.256
393	yudixie_resnet18_distance_reg_0_240719	.255
394	CORnetZ_CIFAR10_bs32_20_04	.250
395	resnet50_imagenet_1_seed-0	.250
396	yudixie_resnet50_random_0_240908	.243
397	yudixie_resnet18_random_0_240719	.237
398	resnet-18_untrained	.223
399	resnet18-autoencoder	.220
400	resnet50_pretrained_with_retinal_waves	.207
401	resnet-50_untrained	.204
402	resnet-18-LC_untrained	.192
403	compact_cornets_V4	.192
404	v1-pyr-nodown	.190
405	compact_vgg19_V4	.145
406	compact_resnet50_robust_V4	.129
407	compact_resnet50_V4	.118
408	deit_small_imagenet_1_seed-0	.056
409	deit_small_imagenet_10_seed-0	.053
410	convnext_small_imagenet_1_seed-0	.041
411	pixels	.038
412	my-model	.022
413	AT_efficientnet-b0
414	fixres_resnext101_32x48d_wsl
415	efficientnet-b0
416	efficientnet-b6
417	efficientnet-b2
418	efficientnet-b4
419	resnet18-simclr
420	r3m_resnet50_nocrop
421	fitvid_trained_on_physion
422	openclip
423	resnet50_ImageNet
424	resnet_152_v1
425	resnet_101_v2
426	ConvLSTM
427	PredRNN
428	SimVP
429	TAU
430	MIM
431	hmax
432	densenet_201_pytorch
433	pnasnet_large_pytorch
434	inception_v3_pytorch
435	inception_v4
436	xception
437	nasnet_large
438	pnasnet_large
439	vonegrcnn_62e_nobn
440	resnet_152_v2
441	resnet_101_v1
442	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