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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	vit_large_clip336_openai	.372
22	convnext_small_imagenet_full_seed-0	.372
23	resnet-18-LC_w_sh_100_iter_m	.372
24	resnet50-vicregl0p9	.371
25	convnext_tiny_imagenet_full_seed-0	.371
26	resnet50_robust_l2_eps3	.370
27	eBarlow_lmda_02_1000ep	.369
28	resnet-18-LC_m	.369
29	resnet50-vicregl0p75	.368
30	vonegrcnn_62e_nobn	.367
31	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.367
32	vision_transformer_vit_large_patch16_224	.367
33	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.367
34	ReAlnet01_cornet	.367
35	voneresnet-50	.366
36	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.366
37	convnext_xlarge:fb_in22k_ft_in1k	.365
38	ReAlnet03_cornet	.365
39	convnext_large_imagenet_full_seed-0	.365
40	resnet50-moclr8deg	.365
41	vonegrcnn_52e_full	.364
42	vit_relpos_base_patch16_clsgap_224:sw_in1k	.364
43	ReAlnet02_cornet	.364
44	resnet-18-LC_w_sh_100_iter_conv_init_m	.363
45	inception_v1-tf	.362
46	resnet50-SIN_IN_IN	.360
47	resnet50_imagenet_100_seed-0	.360
48	nasnet_mobile	.359
49	convnext_base_imagenet_full_seed-0	.359
50	ReAlnet04_cornet	.359
51	ReAlnet05_cornet	.358
52	resnet18-local_aggregation	.358
53	densenet-201	.358
54	compact_resnet50_IT	.357
55	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.357
56	ReAlnet06_cornet	.357
57	ReAlnet09_cornet	.356
58	mobilenet_v2_1-4_224_pytorch	.356
59	resnet-50_v1-tf	.356
60	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.356
61	vit_huge_clip336_laion	.355
62	compact_cornets_IT	.355
63	cvt_cvt-13-384-in1k_4_LucyV4	.354
64	0.5x_resnet-18_LC_w_sh_1_iter	.352
65	mobilenet_v2_1.4_224-tf	.352
66	resnet-18-LC_w_sh_1_iter_m	.351
67	antialiased-r50	.351
68	grcnn	.351
69	ViT_L_32_imagenet1k	.351
70	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.350
71	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.350
72	deit3_base_patch16_224_fb_in1k	.350
73	efficientnet_b1_imagenet_full	.350
74	resnet101_ecoset_full	.350
75	densenet-201-keras	.349
76	0.5x_resnet-18_LC_w_sh_10_iter	.349
77	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.348
78	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.347
79	mobilenet_v2_1_4_224	.347
80	inception_v1	.346
81	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.346
82	resnet50_ecoset_full	.345
83	voneresnet-50-non_stochastic	.345
84	BiT-S-R152x4	.344
85	shufflenet_v2_x1_0	.343
86	resnet-18-LC_w_sh_1_iter_conv_init_m	.343
87	compact_resnet50_robust_IT	.342
88	dinov2_vitg14_lc	.342
89	cvt_cvt-21-224-in1k_4_LucyV4	.342
90	0.5x_resnet-18_LC	.342
91	resnet50_ewconplus_wzc_seed2_2__323	.342
92	convnext_large_clip_laion2b_finelayers	.342
93	mobilenet_v2_1.0_224-tf	.342
94	ReAlnet02_ALL	.342
95	resnet-101_v1-tf	.341
96	cvt_cvt-21-384-in1k_4_LucyV4	.341
97	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.341
98	CORnet-Z	.340
99	resnet50_hvs_wzc	.340
100	CORnet-S	.340
101	mobilenet_v2_1_0_224	.339
102	ReAlnet10_ALL	.339
103	ReAlnet01_ALL	.339
104	compact_vgg19_IT	.338
105	ReAlnet01_cornet_fov12	.338
106	cvt_cvt-13-224-in1k_4_LucyV4	.338
107	resnet50-SIN	.338
108	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.337
109	vit_large_patch14_clip_224:openai_ft_in1k	.337
110	ReAlnet03_ALL	.336
111	deit_large_imagenet_full_seed-0	.336
112	resnet50_imagenet_full	.336
113	cv_18_dagger_408_pretrained	.336
114	omnivore_swinB	.336
115	resnet-101_v2-tf	.336
116	convnext_xxlarge_clip	.336
117	ReAlnet06_ALL	.335
118	AT_efficientnet-b2	.335
119	ReAlnet05_ALL	.335
120	resnet-18-LC_d_w_sh_1x1_conv_init_m	.335
121	ReAlnet04_ALL	.335
122	resnet50_simclr	.335
123	grcnn_robust_v1	.334
124	resnet50_ewconplus_wzc_seed5_2__323	.334
125	resnet18-colorization	.334
126	dinov2_vitg14_reg_lc	.334
127	omnivore_swinS	.334
128	ReAlnet09_ALL	.333
129	grcnn_v2_text_noise	.332
130	mobilenet_v2_0.75_160-tf	.332
131	resnet-50-pytorch	.332
132	resnet-50-robust	.332
133	convnext_tiny:in12k_ft_in1k	.332
134	resnet50-SIN_IN	.332
135	resnet-50_v2-tf	.331
136	resnet_50_v1_spiking	.331
137	resnet50-sup	.331
138	resnet_50_v1	.331
139	convnext_base:clip_laiona_augreg_ft_in1k_384	.331
140	antialiased-rnext101_32x8d	.331
141	vonegrcnn_47e	.330
142	ReAlnet08_ALL	.330
143	resnet50-VITO-8deg-cc	.330
144	mobilenet_v2_0_75_160	.329
145	Res2Net50_26w_4s	.329
146	resnet_SIN_IN_FT_IN	.329
147	vit_large_patch14_clip_224:laion2b_ft_in1k	.329
148	resnet50_l2_3_robust	.329
149	CLIP-RN50	.328
150	regnet_y_400mf	.328
151	omnivore_swinT	.328
152	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.328
153	alexnet_fov4	.327
154	resnet152_ecoset_full	.326
155	VOneCORnet-S	.326
156	deit3_base_patch16_224_fb_in22k_ft_in1k	.326
157	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.326
158	mobilenet_v2_1_0_192	.326
159	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.326
160	grcnn_109	.326
161	nasnet_mobile-tf	.325
162	BiT-S-R50x3	.325
163	mobilenet_v2_1.3_224-tf	.325
164	nasnet_large-tf	.324
165	ReAlnet07_ALL	.324
166	resnet-152_v2-tf	.324
167	mobilenet_v2_1_0_128	.324
168	resnet18-contrastive_predictive	.324
169	densenet-121	.324
170	resnet50_linf_4_robust	.323
171	0.5x_resnet-18_LC_w_sh_100_iter	.322
172	resnet50_random_linf8_perturb	.322
173	resnet50-vitoimagevidnet8	.321
174	mobilenet_v1_0.75_160	.321
175	resnet-18-LC_conv_init_m	.321
176	densenet-121-keras	.321
177	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.320
178	resnet50_ewconplus_wzc_seed6_2_323	.320
179	dinov2_vitl14_reg_lc	.320
180	dinov2_vitl14_reg_lc_featread	.320
181	mobilenet_v1_1.0_224	.320
182	mobilenet_v2_1_0_160	.319
183	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.319
184	deit_small_imagenet_full_seed-0	.319
185	AT_efficientnet-b4	.319
186	mobilenet_v2_0.75_192-tf	.318
187	BiT-M-R50x3	.318
188	mobilenet_v2_1_3_224	.318
189	CLIP_ViT-B_32	.317
190	yudixie_resnet50_imagenet1kpret_0_240908	.317
191	yudixie_resnet50_imagenet1kpret_0_240312	.317
192	resnet_50_v1_spiking_l4	.317
193	resnet_50_v1_spiking_l4a	.317
194	resnet50_tutorial	.317
195	mobilenet_v2_0.5_160	.317
196	FrankRobWobv0	.317
197	ViT-B/32	.317
198	texture_shape_resnet50_trained_on_SIN	.316
199	resnet152_imagenet_full	.316
200	AdvProp_efficientnet-b2	.316
201	resnet50	.315
202	0.5x_resnet-18	.315
203	imagenet_l2_3_0	.315
204	mobilenet_v2_1.0_128-tf	.315
205	vgg-19-keras	.315
206	resnet50_st_wzc_2	.315
207	inception_resnet_v2	.315
208	resnet50_imagenet_10_seed-0	.315
209	efficientnet_b2_imagenet_full	.315
210	alexnet_random_l2_3_perturb	.315
211	resnet50_random_l2_perturb	.313
212	resnet50_moco_v2	.313
213	resnet50_ewconplus_wzc	.313
214	effnetv2m_custom384	.312
215	alexnet_l2_3_robust	.312
216	mobilenet_v1_0.25_160	.312
217	resnet-18_test_m	.312
218	resnet-18	.312
219	AT_efficientnet-b7	.312
220	mobilenet_v1_0.75_192	.312
221	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.312
222	mobilenet_v2_0.75_128	.311
223	mobilenet_v1_1.0_160	.311
224	BiT-S-R152x2	.311
225	AdvProp_efficientnet-b4	.311
226	resnet34_ecoset_full	.310
227	vgg-16-keras	.310
228	efficientnet_b0	.310
229	mobilenet_v1_1.0_128	.310
230	resnet-152_v1-tf	.310
231	efficientnet_b0_imagenet_full	.310
232	yudixie_resnet18_imagenet1kpret_0_240719	.310
233	yudixie_wbh_object_class_scaling_0415_imagenet	.310
234	resnet50_st_wzc_seed3_2_323	.309
235	resnet18_ecoset_full	.309
236	mobilenet_v2_0.75_224-tf	.309
237	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.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	dinov2_vitl14_lc_res336	.302
257	resnet_50_v2	.301
258	alexnet_random_linf8_perturb	.301
259	resnet34_imagenet_full	.301
260	alexnet_reduced_aliasing_early_checkpoint	.301
261	AdvProp_efficientnet-b6	.301
262	seresnext50_32x4d	.300
263	mobilenet_v1_0.5_160	.300
264	mobilenet_v2_0.5_128	.300
265	dinov2_vitl14_lc	.300
266	mobilenet_v2_0.5_192-tf	.300
267	resnet101	.299
268	resnet101_imagenet_full	.299
269	resnet50_st_wzc_seed7_2_323	.299
270	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.298
271	mobilenet_v2_0.35_160	.298
272	resnet_101_v2	.298
273	efficientnet-b7	.298
274	AdvProp_efficientnet-b0	.298
275	BiT-S-R50x1	.297
276	vonealexnet_gaussian_noise_std4_fixed	.297
277	mobilenet_v2_0_5_192	.296
278	resnet50_st_wzc_seed6_2_323	.296
279	vgg_16	.296
280	mobilenet_v2_1.0_160-tf	.296
281	densenet-169	.296
282	focalnet_tiny_lrf_in1k	.294
283	effnetb1_272x240	.294
284	squeezenet1_1	.293
285	resnet18_imagenet_full	.293
286	deepeisnn_ei_imagenet_resnet18	.293
287	BiT-S-R101x3	.293
288	CLIP_resnet50	.292
289	CLIP_resnet50_float32	.292
290	inception_v3	.292
291	AlexNet_SIN	.291
292	inception_v2-tf	.290
293	r3m_resnet34	.290
294	densenet-169-keras	.290
295	mobilenet_v1_0.75_128	.290
296	resnet_50_v1_spiking_l3	.289
297	mobilenet_v2_0_75_192	.289
298	mobilenet_v2_0.35_128	.289
299	yolos_tiny	.288
300	resnext101_32x16d_wsl	.288
301	r3m_resnet18	.287
302	resnet50_byol	.287
303	mobilenet_v2_0.5_224-tf	.287
304	SWSL_resnet50	.287
305	mobilenet_v2_0.35_224	.286
306	eva02_large_in22k	.286
307	resnet18-contrastive_multiview	.286
308	resnext101_32x48d_wsl	.285
309	pnasnet_large-tf	.285
310	eicircuit_resnet18_bn_20260919	.284
311	resnet18-supervised	.282
312	convnext_femto_ols:d1_in1k	.282
313	mobilenet_v1_0.5_192	.282
314	mobilenet_v2_0.5_96	.281
315	mobilenet_v2_0.75_96	.281
316	fixres_resnext101_32x48d_wsl	.280
317	resnet18-deepcluster	.280
318	BiT-M-R50x1	.279
319	BiT-M-R152x4	.278
320	resnet18-relative_position	.277
321	convnext_tiny_sup	.276
322	BiT-S-R101x1	.276
323	AdvProp_efficientnet-b8	.276
324	resnext101_32x32d_wsl	.275
325	blt_vs	.275
326	mobilenet_v1_0.5_128	.275
327	resnet18_imagenet21kP	.275
328	alexnet_ks_torevert	.274
329	alexnet	.274
330	vit_large_patch14_clip_336:laion2b_ft_in1k	.273
331	yudixie_resnet50_object_class_0_240908	.273
332	imagenet_l2_10_0	.273
333	BiT-M-R101x3	.273
334	BiT-M-R152x2	.272
335	resnet18_fair	.271
336	mobilenet_v1_0.25_192	.271
337	mobilenet_v2_0.35_192	.271
338	r3m_resnet50_nocrop	.270
339	r3m_resnet50	.269
340	RN50	.269
341	AlexNet_SIN_fov4	.269
342	mobilenet_v2_0_5_224	.268
343	texture_shape_alexnet_trained_on_SIN	.268
344	alexnet_linf_8_robust	.268
345	ReAlnet06	.268
346	pr1_stl10_disentangled_alpha04	.267
347	resnet-34	.266
348	vit_base_patch14_dinov2-lvd142m	.266
349	alexnet_fov12	.265
350	artResNet18_1	.265
351	deit_small_imagenet_100_seed-0	.264
352	ReAlnet09	.264
353	ReAlnet08	.263
354	alexnet_early_checkpoint	.263
355	regnety_032	.261
356	mobilenet_v1_0.25_224	.261
357	pnasnet_large_halfPrecision	.261
358	ReAlnet01	.260
359	SWSL_resnext101_32x8d	.258
360	ReAlnet02	.258
361	briaai_rmbg_1_4	.258
362	ReAlnet05	.254
363	mobilenet_v2_1.0_96	.252
364	vgg_19	.252
365	ReAlnet07	.251
366	alexnet_robust_correct	.249
367	BiT-M-R101x1	.249
368	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.248
369	ReAlnet10	.247
370	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.246
371	ReAlnet04	.246
372	alexnet_fov16	.246
373	inception_v4-tf	.244
374	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.244
375	AlexNet_SIN_fov16	.241
376	ReAlnet03	.236
377	my-model	.235
378	yudixie_resnet50_distance_reg_0_240908	.234
379	squeezenet1_0	.232
380	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.229
381	AlexNet_SIN_fov12	.229
382	yudixie_resnet50_translation_rotation_0_240908	.229
383	cornet_s	.225
384	yudixie_resnet50_category_class_0_240908	.223
385	convnext_base_fb_in1k_gram_aligned_dinov3	.222
386	yudixie_resnet18_object_class_0_240719	.221
387	vit_base_patch14_reg4_dinov2-lvd142m	.219
388	yudixie_resnet18_category_class_0_240719	.219
389	vit_base_patch16_dinov3-lvd1689m	.215
390	mobilenet_v1_0.25_128	.214
391	prednet	.211
392	yudixie_resnet50_distance_rotation_0_240908	.209
393	yudixie_resnet50_distance_translation_0_240908	.207
394	vggface	.206
395	yudixie_resnet50_distance_translation_rotation_0_240908	.205
396	resnet-152_v2_pytorch	.200
397	yudixie_resnet50_translation_reg_0_240908	.199
398	compact_resnet50_V4	.195
399	resnet18-depth_prediction	.195
400	resnet50_primary_visual_cortex	.192
401	yudixie_resnet50_rotation_reg_0_240908	.190
402	compact_resnet50_robust_V4	.188
403	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.186
404	CORnetZ_CIFAR10	.184
405	compact_vgg19_V4	.183
406	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.182
407	yudixie_resnet18_distance_translation_0_240719	.181
408	deepeisnn_ei_cifar10_resnet18	.181
409	compact_cornets_V4	.175
410	deepeisnn_ei_cifar10_vgg19	.173
411	yudixie_resnet18_distance_translation_rotation_0_240719	.166
412	alexnet_training_seed_08	.164
413	resnet50_imagenet_1_seed-0	.163
414	alexnet_training_seed_01_fov4	.163
415	alexnet_training_seed_02	.162
416	v1-pyr-nodown	.162
417	alexnet_training_seed_09	.157
418	alexnet_training_seed_07	.157
419	yudixie_resnet18_distance_rotation_0_240719	.156
420	resnet50-cifar	.154
421	alexnet_training_seed_10	.153
422	yudixie_resnet18_translation_rotation_0_240719	.152
423	alexnet_training_seed_01	.150
424	yudixie_resnet18_distance_reg_0_240719	.150
425	alexnet_training_seed_05	.148
426	yudixie_resnet18_rotation_reg_0_240719	.146
427	predictive_coding_pc	.146
428	gabor_filter_mean_abs_4x4_v2	.140
429	alexnet_training_seed_01_fov12	.139
430	resnet-50_untrained	.139
431	mobilevit_small	.137
432	yudixie_wbh_object_class_scaling_0415_run_0	.136
433	alexnet_training_seed_04	.134
434	resnet50_FractalDB	.133
435	alexnet_training_seed_06	.133
436	alexnet_training_seed_01_fov16	.133
437	yudixie_resnet18_translation_reg_0_240719	.132
438	convnext_large_in22k_supervised	.131
439	resnet-18_untrained	.131
440	resnet-50x2_untrained	.129
441	yudixie_resnet18_random_0_240719	.126
442	convnext_base_fb_in22k_ft_in1k	.124
443	yudixie_wbh_object_class_scaling_0415_run_2	.123
444	resnet50_pretrained_with_retinal_waves	.122
445	resnet18-autoencoder	.118
446	yudixie_resnet50_random_0_240908	.117
447	convnext_small_imagenet_10_seed-0	.116
448	gabor_filter_entropy_4x4_v2	.109
449	gabor_filter_entropy_4x4_v2_forcedCosine	.105
450	yudixie_wbh_object_class_scaling_0415_run_3	.102
451	gabor_filter_entropy_4x4	.101
452	convnext_base_fb_in1k	.100
453	yudixie_wbh_object_class_scaling_0415_run_1	.090
454	CORnetZ_CIFAR10_bs32_20_04	.084
455	fitvid_trained_on_physion	.066
456	resnet-18-LC_untrained	.063
457	convnext_small_imagenet_1_seed-0	.047
458	deit_small_imagenet_10_seed-0	.027
459	deit_small_imagenet_1_seed-0	.027
460	my-model	.013
461	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