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

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