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

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