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

0.86.

Note that scores are relative to this ceiling.

Data: SanghaviJozwik2020.IT

4916 stimuli recordings from 26 sites in IT

Metric: pls