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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("SanghaviJozwik2020.V4-pls")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet01_cornet_fov4	.556
2	FrankRobWobv0	.548
3	custom_model_cv_18_dagger_408	.540
4	alexnet_fov4	.530
5	resnet50	.527
6	resnet50_random_l2_perturb	.526
7	SWSL_resnet50	.524
8	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.518
9	cv_18_dagger_408_pretrained	.518
10	vit_base_patch16_clip_224:openai_ft_in1k	.515
11	antialiased-r50	.514
12	texture_shape_resnet50_trained_on_SIN	.512
13	resnext101_32x16d_wsl	.512
14	antialiased-rnext101_32x8d	.512
15	resnet50_random_linf8_perturb	.511
16	SWSL_resnext101_32x8d	.511
17	vit_relpos_base_patch16_clsgap_224:sw_in1k	.510
18	resnet50-SIN_IN_IN	.510
19	resnet_SIN_IN_FT_IN	.510
20	yudixie_resnet50_imagenet1kpret_0_240312	.510
21	resnet50_tutorial	.510
22	resnet_50_v1	.510
23	resnet50-sup	.510
24	yudixie_resnet50_imagenet1kpret_0_240908	.510
25	resnet_50_v1_spiking	.510
26	resnet_50_v1_spiking_l4	.510
27	resnet_50_v1_spiking_l4a	.510
28	resnet_50_v1_spiking_l3	.510
29	resnet152_imagenet_full	.510
30	resnet101_imagenet_full	.509
31	resnet152_ecoset_full	.508
32	resnet_50_v2	.508
33	ReAlnet10_cornet	.508
34	ReAlnet08_cornet	.507
35	resnet101_ecoset_full	.507
36	nasnet_mobile	.507
37	resnet-101_v1-tf	.506
38	deit_small_imagenet_full_seed-0	.506
39	resnet50-SIN_IN	.506
40	cvt_cvt-13-224-in1k_4_LucyV4	.504
41	ReAlnet02_cornet	.504
42	Res2Net50_26w_4s	.504
43	deit_large_imagenet_full_seed-0	.504
44	cvt_cvt-21-384-in1k_4_LucyV4	.503
45	resnet50_linf_4_robust	.503
46	ReAlnet07_cornet	.503
47	ReAlnet06_cornet	.503
48	ReAlnet05_cornet	.503
49	ReAlnet09_cornet	.503
50	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.503
51	ReAlnet04_cornet	.503
52	ReAlnet01_cornet	.502
53	resnet-152_v1-tf	.502
54	ReAlnet03_cornet	.502
55	resnet101	.502
56	resnext101_32x8d_wsl	.502
57	resnet50_ewconplus_wzc	.502
58	AdvProp_efficientnet-b4	.502
59	effnetb1_272x240	.501
60	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.501
61	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.501
62	pnasnet_large-tf	.500
63	cvt_cvt-13-384-in1k_4_LucyV4	.500
64	convnext_tiny_imagenet_full_seed-0	.500
65	resnet50-VITO-8deg-cc	.499
66	vision_transformer_vit_large_patch16_224	.499
67	CLIP-RN50	.498
68	inception_v2-tf	.497
69	ReAlnet07_ALL	.497
70	resnet50_l2_3_robust	.496
71	cvt_cvt-21-224-in1k_4_LucyV4	.496
72	resnet50_moco_v2	.496
73	ReAlnet08_ALL	.496
74	ReAlnet05_ALL	.495
75	resnet-50_v1-tf	.495
76	ReAlnet02_ALL	.495
77	ReAlnet01_ALL	.495
78	ReAlnet09_ALL	.495
79	ReAlnet03_ALL	.494
80	ReAlnet10_ALL	.494
81	ReAlnet06_ALL	.494
82	convnext_xlarge:fb_in22k_ft_in1k	.494
83	voneresnet-50	.494
84	ReAlnet04_ALL	.494
85	omnivore_swinT	.493
86	resnet18_imagenet_full	.493
87	omnivore_swinB	.493
88	resnet50_primary_visual_cortex	.493
89	mobilenet_v2_1_3_224	.493
90	resnet18_ecoset_full	.493
91	vonegrcnn_62e_nobn	.492
92	densenet-201	.492
93	omnivore_swinS	.492
94	densenet-121-keras	.492
95	AlexNet_SIN_fov4	.491
96	xception-keras	.491
97	cornet_s	.491
98	convnext_small_imagenet_full_seed-0	.491
99	vonegrcnn_52e_full	.491
100	convnext_large_imagenet_full_seed-0	.490
101	vonegrcnn_47e	.490
102	CORnet-S	.490
103	AdvProp_efficientnet-b0	.490
104	tv_efficientnet-b1	.490
105	focalnet_tiny_lrf_in1k	.490
106	resnet50-vicreg	.490
107	convnext_large:fb_in22k_ft_in1k	.490
108	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.489
109	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.489
110	resnext101_32x32d_wsl	.489
111	resnet50_byol	.489
112	densenet-169	.488
113	mobilenet_v2_1_0_224	.488
114	efficientnet_b2_imagenet_full	.488
115	barlow-twins-resnet50	.488
116	nasnet_mobile-tf	.488
117	resnext101_32x48d_wsl	.488
118	resnet50_imagenet_100_seed-0	.488
119	inception_v1-tf	.488
120	mobilenet_v1_0.75_128	.488
121	effnetv2m_custom384	.487
122	resnet50-barlow	.487
123	resnet50-vicregl0p9	.487
124	CLIP_resnet50_float32	.487
125	CLIP_resnet50	.487
126	efficientnet_b0	.487
127	voneresnet-50-robust	.487
128	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.487
129	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.487
130	CLIP_ViT-B_32	.487
131	convnext_base_imagenet_full_seed-0	.487
132	mobilenet_v2_1_0_192	.487
133	VOneCORnet-S	.487
134	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.487
135	voneresnet-50-non_stochastic	.487
136	resnet50-moclr8deg	.487
137	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.486
138	resnet-18-LC_w_sh_10_iter_conv_init_m	.486
139	ViT-B/32	.485
140	convnext_femto_ols:d1_in1k	.485
141	antialias-resnet152	.485
142	convnext_tiny:in12k_ft_in1k	.484
143	densenet-169-keras	.484
144	AT_efficientnet-b7	.483
145	mobilenet_v1_1.0_224	.483
146	resnet-18-LC_w_sh_1_iter_conv_init_m	.483
147	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.483
148	AdvProp_efficientnet-b6	.483
149	resnet-50_v2-tf	.482
150	AdvProp_efficientnet-b7	.482
151	regnet_y_400mf	.482
152	resnet34_imagenet_full	.482
153	AT_efficientnet-b2	.482
154	convnext_tiny_sup	.481
155	resnet18_imagenet21kP	.481
156	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.481
157	resnet50-simclr-vissl	.481
158	mobilenet_v1_1.0_160	.481
159	resnet50-vicregl0p75	.481
160	resnet50_robust_l2_eps1	.481
161	nasnet_large-tf	.480
162	AdvProp_efficientnet-b8	.480
163	inception_v4-tf	.480
164	resnet-18-LC_w_sh_100_iter_conv_init_m	.480
165	inception_resnet_v2	.480
166	mobilenet_v1_0.5_128	.480
167	mobilenet_v1_1.0_128	.479
168	0.5x_resnet-18_LC_w_sh_10_iter	.479
169	resnet-101_v2-tf	.479
170	mobilenet_v1_1.0_192	.479
171	mobilenet_v1_0.75_224	.479
172	mobilenet_v1_0.75_160	.479
173	AdvProp_efficientnet-b2	.478
174	grcnn_109	.478
175	resnet50_linf_8_robust	.478
176	resnet-18-LC_w_sh_100_iter_m	.477
177	mobilenet_v1_0.5_192	.477
178	AT_efficientnet-b4	.477
179	resnet50-SIN	.477
180	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.477
181	resnet50_robust_l2_eps3	.476
182	resnet-18-LC_w_sh_1_iter_m	.476
183	inception_v3-tf	.476
184	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.476
185	resnet50-vitoimagevidnet8	.476
186	inception_v3	.476
187	resnet50_imagenet_full	.476
188	0.5x_resnet-18_LC_w_sh_1_iter	.475
189	imagenet_l2_3_0	.475
190	mobilenet_v2_1_4_224	.475
191	inception_v1	.474
192	yudixie_resnet18_imagenet1kpret_0_240719	.474
193	densenet-201-keras	.473
194	mobilenet_v2_1.3_224-tf	.473
195	efficientnet_b0_imagenet_full	.473
196	grcnn_v2_text_noise	.473
197	resnet50_ecoset_full	.473
198	ReAlnet01_cornet_fov12	.472
199	mobilenet_v2_0.75_224-tf	.472
200	resnet34_ecoset_full	.472
201	grcnn	.472
202	mobilenet_v2_1.4_224-tf	.471
203	mobilenet_v2_0_75_192	.471
204	mobilenet_v1_0.5_160	.471
205	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.471
206	resnet-18-LC_d_w_sh_1x1_conv_init_m	.470
207	resnet-18	.470
208	mobilenet_v1_0.5_224	.470
209	resnet50_simclr	.470
210	efficientnet_b1_imagenet_full	.469
211	grcnn_robust_v1	.469
212	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.469
213	mobilenet_v2_0_75_224	.469
214	mobilenet_v2_1.0_224-tf	.469
215	resnet-152_v2-tf	.469
216	mobilenet_v2_1_0_160	.467
217	convnext_small_imagenet_100_seed-0	.465
218	vit_large_patch14_clip_224:openai_ft_in1k	.465
219	mobilenet_v1_0.75_192	.465
220	mobilenet_v2_0_75_160	.464
221	mobilenet_v1_0.25_224	.464
222	shufflenet_v2_x1_0	.463
223	alexnet_random_linf8_perturb	.463
224	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.463
225	vit_large_patch14_clip_224:laion2b_ft_in1k	.463
226	mobilenet_v2_1_0_128	.462
227	alexnet	.462
228	mobilenet_v2_0_5_224	.462
229	RN50	.462
230	alexnet_random_l2_3_perturb	.461
231	mobilenet_v1_0.25_192	.461
232	BiT-M-R101x3	.461
233	blt_vs	.460
234	mobilenet_v2_0_5_192	.459
235	ReAlnet08	.459
236	convnext_base:clip_laiona_augreg_ft_in1k_384	.458
237	vgg_16	.458
238	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.458
239	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.458
240	resnet18-supervised	.457
241	BiT-M-R50x1	.457
242	mobilenet_v2_0.5_224-tf	.456
243	deit_base_imagenet_full_seed-0	.456
244	densenet-121	.456
245	BiT-S-R50x3	.456
246	0.5x_resnet-18	.455
247	vgg_19	.455
248	resnet-18_test_m	.455
249	konkle_alexnetgn_ipcl_ref01_primary_model	.455
250	efficientnet-b7	.455
251	ReAlnet10	.455
252	mobilenet_v2_0.75_160-tf	.455
253	vit_large_patch14_clip_336:laion2b_ft_in1k	.454
254	ReAlnet02	.453
255	BiT-M-R101x1	.453
256	ReAlnet06	.452
257	vgg-16-keras	.451
258	ReAlnet05	.451
259	0.5x_resnet-18_LC_w_sh_100_iter	.451
260	mobilenet_v2_1.0_96	.451
261	mobilenet_v2_1.0_128-tf	.450
262	ReAlnet09	.450
263	ReAlnet07	.450
264	ReAlnet01	.449
265	RN50	.448
266	ReAlnet04	.448
267	resnet-50-pytorch	.447
268	resnet-50-robust	.447
269	ReAlnet03	.447
270	mobilenet_v2_0.75_96	.447
271	resnet-18-LC_w_sh_10_iter_m	.447
272	resnet18-instance_recognition	.446
273	CORnet-Z	.446
274	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.445
275	yudixie_resnet50_object_class_0_240908	.445
276	resnet18-local_aggregation	.444
277	mobilenet_v1_0.25_160	.443
278	vonealexnet_gaussian_noise_std4_fixed	.443
279	imagenet_l2_10_0	.443
280	BiT-S-R101x3	.443
281	mobilenet_v1_0.25_128	.442
282	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.442
283	mobilenet_v2_0.5_128	.441
284	squeezenet1_0	.441
285	mobilenet_v2_1.0_160-tf	.440
286	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.440
287	mobilenet_v2_1.0_192-tf	.439
288	mobilenet_v2_0.75_128	.438
289	BiT-S-R50x1	.438
290	alexnet	.438
291	alexnet	.438
292	alexnet	.438
293	alexnet	.438
294	alexnet	.438
295	alexnet	.438
296	alexnet	.438
297	alexnet	.438
298	alexnet-baseline	.438
299	alexnet	.438
300	alexnet_ks_torevert	.438
301	BiT-M-R152x4	.438
302	squeezenet1_1	.437
303	mobilenet_v2_0.35_224	.437
304	BiT-M-R50x3	.437
305	resnet18-colorization	.436
306	yudixie_resnet50_category_class_0_240908	.435
307	alexnet_l2_3_robust	.434
308	vgg-19-keras	.433
309	BiT-S-R101x1	.432
310	yolos_tiny	.432
311	mobilenet_v2_0.75_192-tf	.431
312	ReAlnet01_cornet_fov16	.431
313	resnet18-relative_position	.430
314	mobilenet_v2_0.5_96	.430
315	yudixie_resnet50_distance_translation_rotation_0_240908	.430
316	BiT-M-R152x2	.430
317	yudixie_resnet50_translation_rotation_0_240908	.429
318	texture_shape_alexnet_trained_on_SIN	.429
319	yudixie_resnet50_distance_rotation_0_240908	.429
320	deit_small_imagenet_100_seed-0	.427
321	yudixie_resnet50_distance_translation_0_240908	.427
322	yudixie_resnet50_translation_reg_0_240908	.425
323	resnet18-contrastive_multiview	.424
324	resnet-34	.424
325	mobilenet_v2_0.5_160	.423
326	mobilenet_v2_0.35_192	.421
327	resnet-18-LC_m	.421
328	yudixie_resnet18_object_class_0_240719	.420
329	mobilenet_v2_0.35_96	.419
330	mobilenet_v2_0.35_160	.419
331	mobilenet_v2_0.5_192-tf	.418
332	AlexNet_SIN	.417
333	alexnet_reduced_aliasing_early_checkpoint	.415
334	0.5x_resnet-18_LC	.415
335	BiT-S-R152x2	.414
336	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.414
337	yudixie_resnet50_distance_reg_0_240908	.414
338	artResNet18_1	.411
339	alexnet_early_checkpoint	.411
340	r3m_resnet18	.408
341	yudixie_resnet50_rotation_reg_0_240908	.406
342	r3m_resnet50	.406
343	resnet50	.406
344	resnet18-deepcluster	.405
345	r3m_resnet34	.404
346	BiT-S-R152x4	.403
347	compact_resnet50_IT	.401
348	yudixie_resnet18_category_class_0_240719	.401
349	ViT_L_32_imagenet1k	.395
350	mobilenet_v2_0.35_128	.394
351	resnet50-cifar	.392
352	yudixie_resnet18_distance_rotation_0_240719	.392
353	alexnet_training_seed_01_fov4	.391
354	compact_resnet50_robust_V4	.390
355	mobilenet_v2_1-4_224_pytorch	.384
356	yudixie_resnet18_distance_translation_0_240719	.382
357	yudixie_resnet18_distance_translation_rotation_0_240719	.380
358	resnet18-depth_prediction	.380
359	yudixie_resnet18_distance_reg_0_240719	.379
360	compact_resnet50_robust_IT	.375
361	resnet-18-LC_1st_conv_m	.375
362	prednet	.374
363	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.373
364	yudixie_resnet18_translation_rotation_0_240719	.372
365	alexnet_linf_8_robust	.371
366	yudixie_resnet18_translation_reg_0_240719	.370
367	vggface	.367
368	AlexNet_SIN_fov12	.361
369	yudixie_resnet18_rotation_reg_0_240719	.360
370	resnet-18-LC_conv_init_m	.360
371	alexnet_fov12	.351
372	resnet50_imagenet_10_seed-0	.349
373	compact_vgg19_V4	.348
374	my-model	.347
375	compact_resnet50_V4	.344
376	alexnet_robust_correct	.337
377	AlexNet_SIN_fov16	.318
378	alexnet_fov16	.318
379	resnet50-meshes-lt-100-original-pretrained	.315
380	resnet50-meshes-lt-100-original-scratch	.308
381	compact_cornets_IT	.307
382	compact_cornets_V4	.299
383	resnet18-contrastive_predictive	.296
384	briaai_rmbg_1_4	.291
385	compact_vgg19_IT	.289
386	alexnet_training_seed_02	.282
387	alexnet_training_seed_06	.281
388	CORnetZ_CIFAR10	.277
389	alexnet_training_seed_01	.275
390	resnet18-autoencoder	.275
391	alexnet_training_seed_09	.275
392	alexnet_training_seed_10	.274
393	alexnet_training_seed_07	.273
394	alexnet_training_seed_05	.270
395	resnet-50x2_untrained	.270
396	alexnet_training_seed_04	.268
397	alexnet_training_seed_08	.268
398	alexnet_training_seed_03	.267
399	resnet-50_untrained	.250
400	CORnetZ_CIFAR10_bs32_20_04	.249
401	alexnet_training_seed_01_fov12	.247
402	yudixie_resnet50_random_0_240908	.246
403	resnet-18_untrained	.245
404	yudixie_resnet18_random_0_240719	.244
405	v1-pyr-nodown	.244
406	resnet50_pretrained_with_retinal_waves	.236
407	hmax	.224
408	alexnet_training_seed_01_fov16	.218
409	resnet-18-LC_untrained	.201
410	resnet50_imagenet_1_seed-0	.198
411	resnet50_FractalDB	.193
412	resnet-152_v2_pytorch	.191
413	convnext_small_imagenet_10_seed-0	.069
414	deit_small_imagenet_10_seed-0	.055
415	deit_small_imagenet_1_seed-0	.038
416	convnext_small_imagenet_1_seed-0	.036
417	pixels	.025
418	my-model	.021
419	efficientnet-b0
420	efficientnet-b6
421	efficientnet-b2
422	efficientnet-b4
423	AT_efficientnet-b0
424	fixres_resnext101_32x48d_wsl
425	resnet18-simclr
426	r3m_resnet50_nocrop
427	fitvid_trained_on_physion
428	openclip
429	resnet50_ImageNet
430	resnet_152_v1
431	ConvLSTM
432	PredRNN
433	SimVP
434	TAU
435	MIM
436	densenet_201_pytorch
437	pnasnet_large_pytorch
438	inception_v3_pytorch
439	mobilevit_small
440	inception_v4
441	xception
442	nasnet_large
443	pnasnet_large
444	resnet_101_v2
445	convnext_xxlarge:clip_laion2b_soup_ft_in1k
446	vonegrcnn_62e_nobn
447	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
448	resnet_152_v2
449	vonegrcnn_47e
450	vonegrcnn_52e_full
451	resnet_101_v1
452	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k

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

0.96.

Note that scores are relative to this ceiling.

Data: SanghaviJozwik2020.V4

4916 stimuli recordings from 50 sites in V4

Metric: pls