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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	.401
2	resnet50_robust_l2_eps1	.398
3	custom_model_cv_18_dagger_408	.397
4	resnet18-instance_recognition	.397
5	resnet-18-LC_w_sh_10_iter_conv_init_m	.393
6	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.393
7	resnet50-simclr-vissl	.385
8	resnet-18-LC_1st_conv_m	.383
9	ReAlnet08_cornet	.383
10	konkle_alexnetgn_ipcl_ref01_primary_model	.383
11	barlow-twins-resnet50	.382
12	voneresnet-50-robust	.381
13	resnet50-vicreg	.379
14	resnet50-barlow	.377
15	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.376
16	resnext101_32x8d_wsl	.375
17	ReAlnet07_cornet	.374
18	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.373
19	deit_base_imagenet_full_seed-0	.372
20	ReAlnet10_cornet	.372
21	convnext_small_imagenet_full_seed-0	.372
22	resnet-18-LC_w_sh_100_iter_m	.372
23	resnet50-vicregl0p9	.371
24	convnext_tiny_imagenet_full_seed-0	.371
25	resnet50_robust_l2_eps3	.370
26	eBarlow_lmda_02_1000ep	.369
27	resnet-18-LC_m	.369
28	resnet50-vicregl0p75	.368
29	vonegrcnn_62e_nobn	.367
30	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.367
31	vision_transformer_vit_large_patch16_224	.367
32	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.367
33	ReAlnet01_cornet	.367
34	voneresnet-50	.366
35	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.366
36	convnext_xlarge:fb_in22k_ft_in1k	.365
37	ReAlnet03_cornet	.365
38	convnext_large_imagenet_full_seed-0	.365
39	resnet50-moclr8deg	.365
40	vonegrcnn_52e_full	.364
41	vit_relpos_base_patch16_clsgap_224:sw_in1k	.364
42	ReAlnet02_cornet	.364
43	resnet-18-LC_w_sh_100_iter_conv_init_m	.363
44	inception_v1-tf	.362
45	resnet50-SIN_IN_IN	.360
46	resnet50_imagenet_100_seed-0	.360
47	nasnet_mobile	.359
48	convnext_base_imagenet_full_seed-0	.359
49	ReAlnet04_cornet	.359
50	ReAlnet05_cornet	.358
51	resnet18-local_aggregation	.358
52	densenet-201	.358
53	compact_resnet50_IT	.357
54	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.357
55	ReAlnet06_cornet	.357
56	ReAlnet09_cornet	.356
57	mobilenet_v2_1-4_224_pytorch	.356
58	resnet-50_v1-tf	.356
59	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.356
60	compact_cornets_IT	.355
61	cvt_cvt-13-384-in1k_4_LucyV4	.354
62	0.5x_resnet-18_LC_w_sh_1_iter	.352
63	mobilenet_v2_1.4_224-tf	.352
64	resnet-18-LC_w_sh_1_iter_m	.351
65	antialiased-r50	.351
66	grcnn	.351
67	ViT_L_32_imagenet1k	.351
68	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.350
69	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.350
70	deit3_base_patch16_224_fb_in1k	.350
71	efficientnet_b1_imagenet_full	.350
72	resnet101_ecoset_full	.350
73	densenet-201-keras	.349
74	0.5x_resnet-18_LC_w_sh_10_iter	.349
75	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.348
76	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.347
77	mobilenet_v2_1_4_224	.347
78	inception_v1	.346
79	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.346
80	resnet50_ecoset_full	.345
81	voneresnet-50-non_stochastic	.345
82	BiT-S-R152x4	.344
83	shufflenet_v2_x1_0	.343
84	resnet-18-LC_w_sh_1_iter_conv_init_m	.343
85	compact_resnet50_robust_IT	.342
86	cvt_cvt-21-224-in1k_4_LucyV4	.342
87	0.5x_resnet-18_LC	.342
88	resnet50_ewconplus_wzc_seed2_2__323	.342
89	mobilenet_v2_1.0_224-tf	.342
90	ReAlnet02_ALL	.342
91	resnet-101_v1-tf	.341
92	cvt_cvt-21-384-in1k_4_LucyV4	.341
93	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.341
94	CORnet-Z	.340
95	CORnet-S	.340
96	mobilenet_v2_1_0_224	.339
97	ReAlnet10_ALL	.339
98	ReAlnet01_ALL	.339
99	compact_vgg19_IT	.338
100	ReAlnet01_cornet_fov12	.338
101	cvt_cvt-13-224-in1k_4_LucyV4	.338
102	resnet50-SIN	.338
103	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.337
104	vit_large_patch14_clip_224:openai_ft_in1k	.337
105	ReAlnet03_ALL	.336
106	deit_large_imagenet_full_seed-0	.336
107	resnet50_imagenet_full	.336
108	cv_18_dagger_408_pretrained	.336
109	omnivore_swinB	.336
110	resnet-101_v2-tf	.336
111	resnet50_ewconplus_noseed_2_323	.335
112	ReAlnet06_ALL	.335
113	AT_efficientnet-b2	.335
114	ReAlnet05_ALL	.335
115	resnet-18-LC_d_w_sh_1x1_conv_init_m	.335
116	ReAlnet04_ALL	.335
117	resnet50_simclr	.335
118	grcnn_robust_v1	.334
119	resnet50_ewconplus_wzc_seed5_2__323	.334
120	resnet18-colorization	.334
121	omnivore_swinS	.334
122	resnet50_ewconplus_wzc_seed3_2_323	.333
123	ReAlnet09_ALL	.333
124	grcnn_v2_text_noise	.332
125	mobilenet_v2_0.75_160-tf	.332
126	resnet-50-pytorch	.332
127	resnet-50-robust	.332
128	convnext_tiny:in12k_ft_in1k	.332
129	resnet50-SIN_IN	.332
130	resnet-50_v2-tf	.331
131	resnet_50_v1_spiking	.331
132	resnet50-sup	.331
133	resnet_50_v1	.331
134	convnext_base:clip_laiona_augreg_ft_in1k_384	.331
135	antialiased-rnext101_32x8d	.331
136	vonegrcnn_47e	.330
137	ReAlnet08_ALL	.330
138	resnet50_ewconplus_wzc_seed4_2_323	.330
139	resnet50-VITO-8deg-cc	.330
140	mobilenet_v2_0_75_160	.329
141	Res2Net50_26w_4s	.329
142	resnet_SIN_IN_FT_IN	.329
143	resnet50_st_wzc_2_323	.329
144	vit_large_patch14_clip_224:laion2b_ft_in1k	.329
145	resnet50_l2_3_robust	.329
146	CLIP-RN50	.328
147	regnet_y_400mf	.328
148	omnivore_swinT	.328
149	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.328
150	resnet50_ewconplus_wzc_seed7_2_323	.327
151	alexnet_fov4	.327
152	resnet152_ecoset_full	.326
153	VOneCORnet-S	.326
154	deit3_base_patch16_224_fb_in22k_ft_in1k	.326
155	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.326
156	mobilenet_v2_1_0_192	.326
157	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.326
158	grcnn_109	.326
159	nasnet_mobile-tf	.325
160	BiT-S-R50x3	.325
161	mobilenet_v2_1.3_224-tf	.325
162	nasnet_large-tf	.324
163	ReAlnet07_ALL	.324
164	resnet-152_v2-tf	.324
165	mobilenet_v2_1_0_128	.324
166	resnet18-contrastive_predictive	.324
167	densenet-121	.324
168	resnet50_linf_4_robust	.323
169	0.5x_resnet-18_LC_w_sh_100_iter	.322
170	resnet50_random_linf8_perturb	.322
171	resnet50-vitoimagevidnet8	.321
172	mobilenet_v1_0.75_160	.321
173	resnet-18-LC_conv_init_m	.321
174	densenet-121-keras	.321
175	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.320
176	resnet50_ewconplus_wzc_seed6_2_323	.320
177	mobilenet_v1_1.0_224	.320
178	mobilenet_v2_1_0_160	.319
179	resnet50_st_wzc_seed4_2_323	.319
180	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.319
181	deit_small_imagenet_full_seed-0	.319
182	AT_efficientnet-b4	.319
183	mobilenet_v2_0.75_192-tf	.318
184	resnet50_st_wzc_noseed_2_323	.318
185	BiT-M-R50x3	.318
186	mobilenet_v2_1_3_224	.318
187	CLIP_ViT-B_32	.317
188	yudixie_resnet50_imagenet1kpret_0_240908	.317
189	yudixie_resnet50_imagenet1kpret_0_240312	.317
190	resnet_50_v1_spiking_l4	.317
191	resnet_50_v1_spiking_l4a	.317
192	resnet50_tutorial	.317
193	mobilenet_v2_0.5_160	.317
194	FrankRobWobv0	.317
195	ViT-B/32	.317
196	resnet50_ewconplus_wzc_2_323	.317
197	texture_shape_resnet50_trained_on_SIN	.316
198	resnet152_imagenet_full	.316
199	AdvProp_efficientnet-b2	.316
200	resnet50	.315
201	0.5x_resnet-18	.315
202	imagenet_l2_3_0	.315
203	mobilenet_v2_1.0_128-tf	.315
204	vgg-19-keras	.315
205	resnet50_st_wzc_2	.315
206	inception_resnet_v2	.315
207	resnet50_imagenet_10_seed-0	.315
208	efficientnet_b2_imagenet_full	.315
209	alexnet_random_l2_3_perturb	.315
210	resnet50_random_l2_perturb	.313
211	resnet50_moco_v2	.313
212	resnet50_ewconplus_wzc	.313
213	effnetv2m_custom384	.312
214	alexnet_l2_3_robust	.312
215	mobilenet_v1_0.25_160	.312
216	resnet-18_test_m	.312
217	resnet-18	.312
218	AT_efficientnet-b7	.312
219	mobilenet_v1_0.75_192	.312
220	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.312
221	mobilenet_v2_0.75_128	.311
222	mobilenet_v1_1.0_160	.311
223	BiT-S-R152x2	.311
224	AdvProp_efficientnet-b4	.311
225	resnet34_ecoset_full	.310
226	vgg-16-keras	.310
227	efficientnet_b0	.310
228	mobilenet_v1_1.0_128	.310
229	resnet-152_v1-tf	.310
230	efficientnet_b0_imagenet_full	.310
231	yudixie_resnet18_imagenet1kpret_0_240719	.310
232	yudixie_wbh_object_class_scaling_0415_imagenet	.310
233	resnet50_st_wzc_seed3_2_323	.309
234	resnet18_ecoset_full	.309
235	mobilenet_v2_0.75_224-tf	.309
236	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.308
237	resnet50_st_wzc_seed5_2_323	.308
238	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.308
239	AdvProp_efficientnet-b7	.308
240	mobilenet_v2_0.35_96	.307
241	inception_v3-tf	.307
242	mobilenet_v1_0.75_224	.306
243	resnet50_linf_8_robust	.306
244	ReAlnet01_cornet_fov16	.306
245	mobilenet_v1_0.5_224	.305
246	mobilenet_v2_1.0_192-tf	.305
247	convnext_small_imagenet_100_seed-0	.305
248	convnext_large:fb_in22k_ft_in1k	.304
249	mobilenet_v2_0_75_224	.304
250	mobilenet_v1_1.0_192	.304
251	ReAlnet01_cornet_fov4	.303
252	RN50	.303
253	antialias-resnet152	.303
254	xception-keras	.303
255	vit_base_patch16_clip_224:openai_ft_in1k	.302
256	resnet_50_v2	.301
257	alexnet_random_linf8_perturb	.301
258	resnet34_imagenet_full	.301
259	alexnet_reduced_aliasing_early_checkpoint	.301
260	AdvProp_efficientnet-b6	.301
261	mobilenet_v1_0.5_160	.300
262	mobilenet_v2_0.5_128	.300
263	mobilenet_v2_0.5_192-tf	.300
264	resnet101	.299
265	resnet101_imagenet_full	.299
266	resnet50_st_wzc_seed7_2_323	.299
267	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.298
268	mobilenet_v2_0.35_160	.298
269	resnet_101_v2	.298
270	efficientnet-b7	.298
271	AdvProp_efficientnet-b0	.298
272	BiT-S-R50x1	.297
273	vonealexnet_gaussian_noise_std4_fixed	.297
274	mobilenet_v2_0_5_192	.296
275	resnet50_st_wzc_seed6_2_323	.296
276	vgg_16	.296
277	mobilenet_v2_1.0_160-tf	.296
278	densenet-169	.296
279	focalnet_tiny_lrf_in1k	.294
280	effnetb1_272x240	.294
281	squeezenet1_1	.293
282	resnet18_imagenet_full	.293
283	deepeisnn_ei_imagenet_resnet18	.293
284	BiT-S-R101x3	.293
285	CLIP_resnet50	.292
286	CLIP_resnet50_float32	.292
287	inception_v3	.292
288	AlexNet_SIN	.291
289	inception_v2-tf	.290
290	r3m_resnet34	.290
291	densenet-169-keras	.290
292	mobilenet_v1_0.75_128	.290
293	resnet_50_v1_spiking_l3	.289
294	mobilenet_v2_0_75_192	.289
295	mobilenet_v2_0.35_128	.289
296	yolos_tiny	.288
297	resnext101_32x16d_wsl	.288
298	r3m_resnet18	.287
299	resnet50_byol	.287
300	mobilenet_v2_0.5_224-tf	.287
301	SWSL_resnet50	.287
302	mobilenet_v2_0.35_224	.286
303	resnet18-contrastive_multiview	.286
304	resnext101_32x48d_wsl	.285
305	pnasnet_large-tf	.285
306	resnet18-supervised	.282
307	convnext_femto_ols:d1_in1k	.282
308	mobilenet_v1_0.5_192	.282
309	mobilenet_v2_0.5_96	.281
310	mobilenet_v2_0.75_96	.281
311	fixres_resnext101_32x48d_wsl	.280
312	resnet18-deepcluster	.280
313	BiT-M-R50x1	.279
314	BiT-M-R152x4	.278
315	resnet18-relative_position	.277
316	convnext_tiny_sup	.276
317	BiT-S-R101x1	.276
318	AdvProp_efficientnet-b8	.276
319	resnext101_32x32d_wsl	.275
320	blt_vs	.275
321	mobilenet_v1_0.5_128	.275
322	resnet18_imagenet21kP	.275
323	alexnet_ks_torevert	.274
324	alexnet	.274
325	vit_large_patch14_clip_336:laion2b_ft_in1k	.273
326	yudixie_resnet50_object_class_0_240908	.273
327	imagenet_l2_10_0	.273
328	BiT-M-R101x3	.273
329	BiT-M-R152x2	.272
330	resnet18_fair	.271
331	mobilenet_v1_0.25_192	.271
332	mobilenet_v2_0.35_192	.271
333	r3m_resnet50_nocrop	.270
334	r3m_resnet50	.269
335	RN50	.269
336	AlexNet_SIN_fov4	.269
337	mobilenet_v2_0_5_224	.268
338	texture_shape_alexnet_trained_on_SIN	.268
339	alexnet_linf_8_robust	.268
340	ReAlnet06	.268
341	pr1_stl10_disentangled_alpha04	.267
342	resnet-34	.266
343	vit_base_patch14_dinov2-lvd142m	.266
344	alexnet_fov12	.265
345	artResNet18_1	.265
346	deit_small_imagenet_100_seed-0	.264
347	ReAlnet09	.264
348	ReAlnet08	.263
349	alexnet_early_checkpoint	.263
350	mobilenet_v1_0.25_224	.261
351	pnasnet_large_halfPrecision	.261
352	ReAlnet01	.260
353	SWSL_resnext101_32x8d	.258
354	ReAlnet02	.258
355	briaai_rmbg_1_4	.258
356	ReAlnet05	.254
357	mobilenet_v2_1.0_96	.252
358	vgg_19	.252
359	ReAlnet07	.251
360	alexnet_robust_correct	.249
361	BiT-M-R101x1	.249
362	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.248
363	ReAlnet10	.247
364	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.246
365	ReAlnet04	.246
366	alexnet_fov16	.246
367	inception_v4-tf	.244
368	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.244
369	AlexNet_SIN_fov16	.241
370	ReAlnet03	.236
371	my-model	.235
372	yudixie_resnet50_distance_reg_0_240908	.234
373	squeezenet1_0	.232
374	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.229
375	AlexNet_SIN_fov12	.229
376	yudixie_resnet50_translation_rotation_0_240908	.229
377	cornet_s	.225
378	yudixie_resnet50_category_class_0_240908	.223
379	convnext_base_fb_in1k_gram_aligned_dinov3	.222
380	yudixie_resnet18_object_class_0_240719	.221
381	vit_base_patch14_reg4_dinov2-lvd142m	.219
382	yudixie_resnet18_category_class_0_240719	.219
383	vit_base_patch16_dinov3-lvd1689m	.215
384	mobilenet_v1_0.25_128	.214
385	prednet	.211
386	yudixie_resnet50_distance_rotation_0_240908	.209
387	yudixie_resnet50_distance_translation_0_240908	.207
388	vggface	.206
389	yudixie_resnet50_distance_translation_rotation_0_240908	.205
390	resnet-152_v2_pytorch	.200
391	yudixie_resnet50_translation_reg_0_240908	.199
392	compact_resnet50_V4	.195
393	resnet18-depth_prediction	.195
394	resnet50_primary_visual_cortex	.192
395	resnet50	.192
396	yudixie_resnet50_rotation_reg_0_240908	.190
397	compact_resnet50_robust_V4	.188
398	aughost_fair_resnet18	.188
399	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.186
400	CORnetZ_CIFAR10	.184
401	compact_vgg19_V4	.183
402	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.182
403	yudixie_resnet18_distance_translation_0_240719	.181
404	deepeisnn_ei_cifar10_resnet18	.181
405	compact_cornets_V4	.175
406	deepeisnn_ei_cifar10_vgg19	.173
407	yudixie_resnet18_distance_translation_rotation_0_240719	.166
408	alexnet_training_seed_08	.164
409	resnet50_imagenet_1_seed-0	.163
410	alexnet_training_seed_01_fov4	.163
411	alexnet_training_seed_02	.162
412	v1-pyr-nodown	.162
413	alexnet_training_seed_09	.157
414	alexnet_training_seed_07	.157
415	yudixie_resnet18_distance_rotation_0_240719	.156
416	resnet50-cifar	.154
417	alexnet_training_seed_10	.153
418	yudixie_resnet18_translation_rotation_0_240719	.152
419	alexnet_training_seed_01	.150
420	yudixie_resnet18_distance_reg_0_240719	.150
421	alexnet_training_seed_05	.148
422	yudixie_resnet18_rotation_reg_0_240719	.146
423	predictive_coding_pc	.146
424	resnet50-meshes-lt-100-original-scratch	.140
425	alexnet_training_seed_01_fov12	.139
426	resnet-50_untrained	.139
427	mobilevit_small	.137
428	yudixie_wbh_object_class_scaling_0415_run_0	.136
429	alexnet_training_seed_04	.134
430	resnet50_FractalDB	.133
431	alexnet_training_seed_06	.133
432	alexnet_training_seed_01_fov16	.133
433	yudixie_resnet18_translation_reg_0_240719	.132
434	resnet-18_untrained	.131
435	resnet-50x2_untrained	.129
436	yudixie_resnet18_random_0_240719	.126
437	convnext_base_fb_in22k_ft_in1k	.124
438	yudixie_wbh_object_class_scaling_0415_run_2	.123
439	resnet50_pretrained_with_retinal_waves	.122
440	resnet18-autoencoder	.118
441	yudixie_resnet50_random_0_240908	.117
442	convnext_small_imagenet_10_seed-0	.116
443	yudixie_wbh_object_class_scaling_0415_run_3	.102
444	convnext_base_fb_in1k	.100
445	resnet50-meshes-lt-100-original-pretrained	.090
446	yudixie_wbh_object_class_scaling_0415_run_1	.090
447	CORnetZ_CIFAR10_bs32_20_04	.084
448	fitvid_trained_on_physion	.066
449	resnet-18-LC_untrained	.063
450	convnext_small_imagenet_1_seed-0	.047
451	deit_small_imagenet_10_seed-0	.027
452	deit_small_imagenet_1_seed-0	.027
453	my-model	.013
454	pixels	.012

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