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