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