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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("SanghaviMurty2020.IT-pls")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet-18-LC_w_sh_10_iter_m	.458
2	resnet50_robust_l2_eps1	.455
3	custom_model_cv_18_dagger_408	.453
4	resnet18-instance_recognition	.453
5	resnet-18-LC_w_sh_10_iter_conv_init_m	.449
6	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.449
7	resnet50-simclr-vissl	.440
8	resnet-18-LC_1st_conv_m	.437
9	ReAlnet08_cornet	.437
10	konkle_alexnetgn_ipcl_ref01_primary_model	.437
11	barlow-twins-resnet50	.436
12	voneresnet-50-robust	.436
13	resnet50-vicreg	.433
14	resnet50-barlow	.430
15	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.429
16	resnext101_32x8d_wsl	.428
17	ReAlnet07_cornet	.428
18	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.426
19	deit_base_imagenet_full_seed-0	.425
20	ReAlnet10_cornet	.425
21	convnext_small_imagenet_full_seed-0	.425
22	resnet-18-LC_w_sh_100_iter_m	.425
23	resnet50-vicregl0p9	.424
24	convnext_tiny_imagenet_full_seed-0	.424
25	resnet50_robust_l2_eps3	.423
26	eBarlow_lmda_02_1000ep	.422
27	resnet-18-LC_m	.421
28	resnet50-vicregl0p75	.421
29	vonegrcnn_62e_nobn	.420
30	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.420
31	vision_transformer_vit_large_patch16_224	.419
32	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.419
33	ReAlnet01_cornet	.419
34	voneresnet-50	.418
35	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.418
36	convnext_xlarge:fb_in22k_ft_in1k	.417
37	ReAlnet03_cornet	.417
38	convnext_large_imagenet_full_seed-0	.416
39	resnet50-moclr8deg	.416
40	vonegrcnn_52e_full	.416
41	vit_relpos_base_patch16_clsgap_224:sw_in1k	.416
42	ReAlnet02_cornet	.415
43	resnet-18-LC_w_sh_100_iter_conv_init_m	.415
44	inception_v1-tf	.413
45	resnet50-SIN_IN_IN	.411
46	resnet50_imagenet_100_seed-0	.411
47	nasnet_mobile	.410
48	convnext_base_imagenet_full_seed-0	.410
49	ReAlnet04_cornet	.410
50	ReAlnet05_cornet	.409
51	resnet18-local_aggregation	.409
52	densenet-201	.408
53	compact_resnet50_IT	.408
54	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.408
55	ReAlnet06_cornet	.408
56	ReAlnet09_cornet	.407
57	mobilenet_v2_1-4_224_pytorch	.407
58	resnet-50_v1-tf	.406
59	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.406
60	compact_cornets_IT	.405
61	cvt_cvt-13-384-in1k_4_LucyV4	.405
62	0.5x_resnet-18_LC_w_sh_1_iter	.402
63	mobilenet_v2_1.4_224-tf	.402
64	resnet-18-LC_w_sh_1_iter_m	.401
65	antialiased-r50	.401
66	grcnn	.400
67	ViT_L_32_imagenet1k	.400
68	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.400
69	efficientnet_b1_imagenet_full	.399
70	resnet101_ecoset_full	.399
71	densenet-201-keras	.398
72	0.5x_resnet-18_LC_w_sh_10_iter	.398
73	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.398
74	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.397
75	mobilenet_v2_1_4_224	.396
76	inception_v1	.395
77	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.395
78	resnet50_ecoset_full	.394
79	voneresnet-50-non_stochastic	.394
80	BiT-S-R152x4	.393
81	shufflenet_v2_x1_0	.392
82	resnet-18-LC_w_sh_1_iter_conv_init_m	.391
83	compact_resnet50_robust_IT	.391
84	cvt_cvt-21-224-in1k_4_LucyV4	.391
85	0.5x_resnet-18_LC	.391
86	mobilenet_v2_1.0_224-tf	.390
87	ReAlnet02_ALL	.390
88	resnet-101_v1-tf	.390
89	cvt_cvt-21-384-in1k_4_LucyV4	.389
90	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.389
91	CORnet-Z	.389
92	resnet50_hvs_wzc	.388
93	CORnet-S	.388
94	mobilenet_v2_1_0_224	.388
95	ReAlnet10_ALL	.387
96	ReAlnet01_ALL	.387
97	compact_vgg19_IT	.386
98	ReAlnet01_cornet_fov12	.386
99	cvt_cvt-13-224-in1k_4_LucyV4	.386
100	resnet50-SIN	.386
101	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.385
102	vit_large_patch14_clip_224:openai_ft_in1k	.384
103	ReAlnet03_ALL	.384
104	deit_large_imagenet_full_seed-0	.384
105	resnet50_imagenet_full	.384
106	cv_18_dagger_408_pretrained	.384
107	omnivore_swinB	.383
108	resnet-101_v2-tf	.383
109	ReAlnet06_ALL	.383
110	AT_efficientnet-b2	.383
111	ReAlnet05_ALL	.383
112	resnet-18-LC_d_w_sh_1x1_conv_init_m	.383
113	ReAlnet04_ALL	.382
114	resnet50_simclr	.382
115	grcnn_robust_v1	.382
116	resnet18-colorization	.381
117	omnivore_swinS	.381
118	ReAlnet09_ALL	.380
119	grcnn_v2_text_noise	.380
120	mobilenet_v2_0.75_160-tf	.379
121	resnet-50-pytorch	.379
122	resnet-50-robust	.379
123	convnext_tiny:in12k_ft_in1k	.379
124	resnet50-SIN_IN	.379
125	resnet-50_v2-tf	.378
126	resnet_50_v1_spiking	.378
127	resnet50-sup	.378
128	resnet_50_v1	.378
129	convnext_base:clip_laiona_augreg_ft_in1k_384	.378
130	antialiased-rnext101_32x8d	.378
131	vonegrcnn_47e	.377
132	ReAlnet08_ALL	.377
133	resnet50-VITO-8deg-cc	.377
134	mobilenet_v2_0_75_160	.376
135	Res2Net50_26w_4s	.376
136	resnet_SIN_IN_FT_IN	.376
137	vit_large_patch14_clip_224:laion2b_ft_in1k	.375
138	resnet50_l2_3_robust	.375
139	CLIP-RN50	.375
140	regnet_y_400mf	.375
141	omnivore_swinT	.374
142	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.374
143	alexnet_fov4	.373
144	resnet152_ecoset_full	.373
145	VOneCORnet-S	.373
146	mobilenet_v2_1_0_192	.373
147	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.372
148	grcnn_109	.372
149	nasnet_mobile-tf	.371
150	BiT-S-R50x3	.371
151	mobilenet_v2_1.3_224-tf	.371
152	nasnet_large-tf	.370
153	ReAlnet07_ALL	.370
154	resnet-152_v2-tf	.370
155	mobilenet_v2_1_0_128	.370
156	resnet18-contrastive_predictive	.370
157	densenet-121	.369
158	resnet50_linf_4_robust	.369
159	0.5x_resnet-18_LC_w_sh_100_iter	.368
160	resnet50_random_linf8_perturb	.368
161	resnet50-vitoimagevidnet8	.367
162	mobilenet_v1_0.75_160	.367
163	resnet-18-LC_conv_init_m	.366
164	densenet-121-keras	.366
165	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.366
166	mobilenet_v1_1.0_224	.365
167	mobilenet_v2_1_0_160	.365
168	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.364
169	deit_small_imagenet_full_seed-0	.364
170	AT_efficientnet-b4	.364
171	mobilenet_v2_0.75_192-tf	.363
172	BiT-M-R50x3	.363
173	mobilenet_v2_1_3_224	.363
174	CLIP_ViT-B_32	.362
175	yudixie_resnet50_imagenet1kpret_0_240908	.362
176	yudixie_resnet50_imagenet1kpret_0_240312	.362
177	resnet_50_v1_spiking_l4	.362
178	resnet_50_v1_spiking_l4a	.362
179	resnet50_tutorial	.362
180	mobilenet_v2_0.5_160	.362
181	FrankRobWobv0	.362
182	ViT-B/32	.361
183	texture_shape_resnet50_trained_on_SIN	.361
184	resnet152_imagenet_full	.361
185	AdvProp_efficientnet-b2	.360
186	resnet50	.360
187	0.5x_resnet-18	.360
188	imagenet_l2_3_0	.360
189	mobilenet_v2_1.0_128-tf	.360
190	vgg-19-keras	.360
191	resnet50_st_wzc_2	.360
192	inception_resnet_v2	.360
193	resnet50_imagenet_10_seed-0	.360
194	efficientnet_b2_imagenet_full	.359
195	alexnet_random_l2_3_perturb	.359
196	resnet50_random_l2_perturb	.358
197	resnet50_moco_v2	.358
198	resnet50_ewconplus_wzc	.357
199	effnetv2m_custom384	.357
200	alexnet_l2_3_robust	.357
201	mobilenet_v1_0.25_160	.356
202	resnet-18_test_m	.356
203	resnet-18	.356
204	AT_efficientnet-b7	.356
205	mobilenet_v1_0.75_192	.356
206	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.356
207	mobilenet_v2_0.75_128	.355
208	mobilenet_v1_1.0_160	.355
209	BiT-S-R152x2	.355
210	AdvProp_efficientnet-b4	.355
211	resnet34_ecoset_full	.354
212	vgg-16-keras	.354
213	efficientnet_b0	.354
214	mobilenet_v1_1.0_128	.354
215	resnet-152_v1-tf	.354
216	efficientnet_b0_imagenet_full	.354
217	yudixie_resnet18_imagenet1kpret_0_240719	.354
218	resnet18_ecoset_full	.353
219	mobilenet_v2_0.75_224-tf	.352
220	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.352
221	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.351
222	AdvProp_efficientnet-b7	.351
223	mobilenet_v2_0.35_96	.351
224	inception_v3-tf	.351
225	mobilenet_v1_0.75_224	.350
226	resnet50_linf_8_robust	.349
227	ReAlnet01_cornet_fov16	.349
228	mobilenet_v1_0.5_224	.349
229	mobilenet_v2_1.0_192-tf	.349
230	convnext_small_imagenet_100_seed-0	.348
231	convnext_large:fb_in22k_ft_in1k	.348
232	mobilenet_v2_0_75_224	.348
233	mobilenet_v1_1.0_192	.347
234	ReAlnet01_cornet_fov4	.347
235	RN50	.346
236	antialias-resnet152	.346
237	xception-keras	.346
238	vit_base_patch16_clip_224:openai_ft_in1k	.345
239	resnet_50_v2	.344
240	alexnet_random_linf8_perturb	.344
241	resnet34_imagenet_full	.344
242	alexnet_reduced_aliasing_early_checkpoint	.344
243	AdvProp_efficientnet-b6	.343
244	mobilenet_v1_0.5_160	.343
245	mobilenet_v2_0.5_128	.343
246	mobilenet_v2_0.5_192-tf	.342
247	resnet101	.341
248	resnet101_imagenet_full	.341
249	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.341
250	mobilenet_v2_0.35_160	.341
251	resnet_101_v2	.341
252	efficientnet-b7	.340
253	AdvProp_efficientnet-b0	.340
254	BiT-S-R50x1	.339
255	vonealexnet_gaussian_noise_std4_fixed	.339
256	mobilenet_v2_0_5_192	.339
257	vgg_16	.338
258	mobilenet_v2_1.0_160-tf	.338
259	densenet-169	.338
260	focalnet_tiny_lrf_in1k	.336
261	effnetb1_272x240	.336
262	squeezenet1_1	.335
263	resnet18_imagenet_full	.335
264	BiT-S-R101x3	.334
265	CLIP_resnet50	.334
266	CLIP_resnet50_float32	.334
267	inception_v3	.334
268	AlexNet_SIN	.332
269	inception_v2-tf	.332
270	r3m_resnet34	.331
271	densenet-169-keras	.331
272	mobilenet_v1_0.75_128	.331
273	resnet_50_v1_spiking_l3	.330
274	mobilenet_v2_0_75_192	.330
275	mobilenet_v2_0.35_128	.330
276	yolos_tiny	.329
277	resnext101_32x16d_wsl	.328
278	r3m_resnet18	.328
279	resnet50_byol	.328
280	mobilenet_v2_0.5_224-tf	.328
281	SWSL_resnet50	.328
282	mobilenet_v2_0.35_224	.327
283	resnet18-contrastive_multiview	.327
284	resnext101_32x48d_wsl	.326
285	pnasnet_large-tf	.325
286	resnet18-supervised	.322
287	convnext_femto_ols:d1_in1k	.322
288	mobilenet_v1_0.5_192	.322
289	mobilenet_v2_0.5_96	.321
290	mobilenet_v2_0.75_96	.321
291	fixres_resnext101_32x48d_wsl	.320
292	resnet18-deepcluster	.320
293	BiT-M-R50x1	.319
294	BiT-M-R152x4	.317
295	resnet18-relative_position	.316
296	convnext_tiny_sup	.315
297	BiT-S-R101x1	.315
298	AdvProp_efficientnet-b8	.315
299	resnext101_32x32d_wsl	.314
300	blt_vs	.314
301	mobilenet_v1_0.5_128	.314
302	resnet18_imagenet21kP	.314
303	alexnet_ks_torevert	.313
304	alexnet	.313
305	vit_large_patch14_clip_336:laion2b_ft_in1k	.312
306	yudixie_resnet50_object_class_0_240908	.312
307	imagenet_l2_10_0	.312
308	BiT-M-R101x3	.311
309	BiT-M-R152x2	.311
310	mobilenet_v1_0.25_192	.309
311	mobilenet_v2_0.35_192	.309
312	r3m_resnet50_nocrop	.308
313	r3m_resnet50	.308
314	RN50	.308
315	AlexNet_SIN_fov4	.307
316	mobilenet_v2_0_5_224	.306
317	texture_shape_alexnet_trained_on_SIN	.306
318	alexnet_linf_8_robust	.306
319	ReAlnet06	.305
320	resnet-34	.304
321	alexnet_fov12	.303
322	artResNet18_1	.302
323	deit_small_imagenet_100_seed-0	.302
324	ReAlnet09	.301
325	ReAlnet08	.301
326	alexnet_early_checkpoint	.300
327	mobilenet_v1_0.25_224	.298
328	ReAlnet01	.296
329	SWSL_resnext101_32x8d	.295
330	ReAlnet02	.295
331	briaai_rmbg_1_4	.294
332	ReAlnet05	.290
333	mobilenet_v2_1.0_96	.288
334	vgg_19	.288
335	ReAlnet07	.287
336	alexnet_robust_correct	.285
337	BiT-M-R101x1	.284
338	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.283
339	ReAlnet10	.282
340	ReAlnet04	.281
341	alexnet_fov16	.281
342	inception_v4-tf	.279
343	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.279
344	AlexNet_SIN_fov16	.275
345	ReAlnet03	.270
346	my-model	.268
347	yudixie_resnet50_distance_reg_0_240908	.267
348	squeezenet1_0	.265
349	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.262
350	AlexNet_SIN_fov12	.262
351	yudixie_resnet50_translation_rotation_0_240908	.261
352	cornet_s	.256
353	yudixie_resnet50_category_class_0_240908	.255
354	yudixie_resnet18_object_class_0_240719	.253
355	yudixie_resnet18_category_class_0_240719	.250
356	mobilenet_v1_0.25_128	.245
357	prednet	.241
358	yudixie_resnet50_distance_rotation_0_240908	.239
359	yudixie_resnet50_distance_translation_0_240908	.237
360	vggface	.235
361	yudixie_resnet50_distance_translation_rotation_0_240908	.234
362	resnet-152_v2_pytorch	.229
363	yudixie_resnet50_translation_reg_0_240908	.227
364	compact_resnet50_V4	.223
365	resnet18-depth_prediction	.223
366	resnet50_primary_visual_cortex	.220
367	yudixie_resnet50_rotation_reg_0_240908	.216
368	compact_resnet50_robust_V4	.215
369	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.213
370	CORnetZ_CIFAR10	.211
371	compact_vgg19_V4	.209
372	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.208
373	yudixie_resnet18_distance_translation_0_240719	.207
374	compact_cornets_V4	.200
375	yudixie_resnet18_distance_translation_rotation_0_240719	.190
376	alexnet_training_seed_08	.187
377	resnet50_imagenet_1_seed-0	.186
378	alexnet_training_seed_01_fov4	.186
379	alexnet_training_seed_02	.185
380	v1-pyr-nodown	.185
381	alexnet_training_seed_09	.180
382	alexnet_training_seed_07	.179
383	yudixie_resnet18_distance_rotation_0_240719	.178
384	resnet50-cifar	.176
385	alexnet_training_seed_10	.175
386	yudixie_resnet18_translation_rotation_0_240719	.173
387	alexnet_training_seed_01	.171
388	yudixie_resnet18_distance_reg_0_240719	.171
389	alexnet_training_seed_05	.169
390	yudixie_resnet18_rotation_reg_0_240719	.167
391	alexnet_training_seed_01_fov12	.159
392	resnet-50_untrained	.158
393	mobilevit_small	.156
394	alexnet_training_seed_04	.153
395	resnet50_FractalDB	.152
396	alexnet_training_seed_06	.152
397	alexnet_training_seed_01_fov16	.151
398	yudixie_resnet18_translation_reg_0_240719	.150
399	resnet-18_untrained	.150
400	resnet-50x2_untrained	.148
401	yudixie_resnet18_random_0_240719	.144
402	resnet50_pretrained_with_retinal_waves	.140
403	resnet18-autoencoder	.134
404	yudixie_resnet50_random_0_240908	.133
405	convnext_small_imagenet_10_seed-0	.132
406	CORnetZ_CIFAR10_bs32_20_04	.095
407	fitvid_trained_on_physion	.076
408	resnet-18-LC_untrained	.072
409	convnext_small_imagenet_1_seed-0	.054
410	deit_small_imagenet_10_seed-0	.031
411	deit_small_imagenet_1_seed-0	.030
412	my-model	.015
413	pixels	.013
414	AT_efficientnet-b0
415	efficientnet-b0
416	efficientnet-b6
417	efficientnet-b2
418	efficientnet-b4
419	resnet18-simclr
420	openclip
421	resnet50_ImageNet
422	tv_efficientnet-b1
423	resnet_152_v1
424	ConvLSTM
425	PredRNN
426	SimVP
427	TAU
428	MIM
429	hmax
430	densenet_201_pytorch
431	pnasnet_large_pytorch
432	inception_v3_pytorch
433	inception_v4
434	xception
435	nasnet_large
436	pnasnet_large
437	vonegrcnn_62e_nobn
438	resnet_152_v2
439	vonegrcnn_47e
440	vonegrcnn_52e_full
441	resnet_101_v1
442	alexnet_training_seed_03

Benchmark bibtex

@misc{Sanghavi_Murty_DiCarlo_2021,
  title={SanghaviMurty2020},
  url={osf.io/fchme},
  DOI={10.17605/OSF.IO/FCHME},
  publisher={OSF},
  author={Sanghavi, Sachi and Murty, N A R and DiCarlo, James J},
  year={2021},
  month={Nov}
}

Ceiling

0.88.

Note that scores are relative to this ceiling.

Data: SanghaviMurty2020.IT

300 stimuli recordings from 29 sites in IT

Metric: pls