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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("SanghaviMurty2020.V4-pls")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	compact_resnet50_robust_V4	.348
2	FrankRobWobv0	.330
3	compact_vgg19_V4	.317
4	compact_resnet50_V4	.295
5	compact_cornets_V4	.293
6	ReAlnet01_cornet_fov4	.273
7	ReAlnet10_cornet	.258
8	efficientnet_b2_imagenet_full	.257
9	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.256
10	cvt_cvt-21-384-in1k_4_LucyV4	.256
11	mobilenet_v2_0_75_224	.256
12	ReAlnet08_cornet	.254
13	konkle_alexnetgn_ipcl_ref01_primary_model	.253
14	effnetb1_272x240	.246
15	ReAlnet05_cornet	.246
16	mobilenet_v2_1_0_192	.245
17	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.245
18	resnet_101_v1	.245
19	compact_cornets_IT	.244
20	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.244
21	inception_v1	.244
22	mobilenet_v2_1_3_224	.243
23	mobilenet_v2_1.0_192-tf	.243
24	resnet101_imagenet_full	.243
25	resnet152_imagenet_full	.243
26	ReAlnet02_cornet	.242
27	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.242
28	ReAlnet03_cornet	.242
29	ReAlnet07_cornet	.241
30	focalnet_tiny_lrf_in1k	.240
31	resnet152_ecoset_full	.240
32	resnet-101_v2-tf	.240
33	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.240
34	ReAlnet01_cornet	.240
35	convnext_large:fb_in22k_ft_in1k	.239
36	deit3_base_patch16_224_fb_in22k_ft_in1k	.239
37	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.239
38	resnet-152_v1-tf	.239
39	ReAlnet04_cornet	.239
40	vit_large_patch14_clip_224:laion2b_ft_in1k	.239
41	resnet-152_v2-tf	.239
42	vision_transformer_vit_large_patch16_224	.239
43	RN50	.239
44	ReAlnet06_cornet	.238
45	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.238
46	grcnn_robust_v1	.237
47	ReAlnet09_cornet	.237
48	omnivore_swinB	.237
49	omnivore_swinS	.237
50	cvt_cvt-21-224-in1k_4_LucyV4	.236
51	convnext_large_clip_laion2b_finelayers	.236
52	cvt_cvt-13-384-in1k_4_LucyV4	.235
53	custom_model_cv_18_dagger_408	.235
54	convnext_tiny:in12k_ft_in1k	.235
55	resnet101_ecoset_full	.235
56	grcnn_v2_text_noise	.235
57	dinov2_vitl14_reg_lc	.234
58	dinov2_vitl14_reg_lc_featread	.234
59	ReAlnet10_ALL	.233
60	mobilenet_v2_1.0_160-tf	.233
61	ReAlnet09_ALL	.233
62	ReAlnet07_ALL	.233
63	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.232
64	vit_base_patch16_clip_224:openai_ft_in1k	.232
65	convnext_femto_ols:d1_in1k	.232
66	convnext_xlarge:fb_in22k_ft_in1k	.232
67	tv_efficientnet-b1	.232
68	ReAlnet04_ALL	.232
69	vit_base_patch16_dinov3-lvd1689m	.231
70	mobilenet_v2_0.75_224-tf	.231
71	vit_large_clip336_openai	.231
72	vit_large_patch14_clip_224:openai_ft_in1k	.231
73	grcnn	.231
74	ReAlnet05_ALL	.231
75	ReAlnet08_ALL	.231
76	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.231
77	ReAlnet01_ALL	.230
78	mobilenet_v2_0.75_192-tf	.230
79	vit_large_patch14_clip_336:laion2b_ft_in1k	.230
80	dinov2_vitg14_lc	.230
81	vit_base_patch14_dinov2-lvd142m	.230
82	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.230
83	ReAlnet02_ALL	.230
84	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.230
85	convnext_tiny_sup	.229
86	ReAlnet03_ALL	.229
87	resnet50_ewconplus_wzc	.229
88	mobilenet_v2_1_0_160	.229
89	omnivore_swinT	.229
90	ReAlnet06_ALL	.228
91	Res2Net50_26w_4s	.228
92	CORnet-S	.228
93	mobilenet_v2_0_75_192	.228
94	cvt_cvt-13-224-in1k_4_LucyV4	.228
95	resnet50_robust_l2_eps1	.227
96	convnext_large_imagenet_full_seed-0	.227
97	vit_base_patch14_reg4_dinov2-lvd142m	.227
98	eva02_large_in22k	.227
99	dinov2_vitl14_lc	.227
100	resnet50_ewconplus_wzc_seed2_2__323	.226
101	mobilenet_v2_0_75_160	.226
102	resnet50_st_wzc_seed7_2_323	.225
103	resnet-50_v2-tf	.225
104	voneresnet-50-non_stochastic	.225
105	xception-keras	.225
106	vonegrcnn_47e	.225
107	mobilenet_v2_1.3_224-tf	.225
108	efficientnet_b0	.225
109	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.224
110	vit_relpos_base_patch16_clsgap_224:sw_in1k	.224
111	inception_v1-tf	.223
112	densenet-121	.223
113	densenet-169-keras	.223
114	resnet50_hvs_wzc	.223
115	mobilenet_v2_0.5_160	.223
116	ViT-B/32	.222
117	AdvProp_efficientnet-b6	.222
118	barlow-twins-resnet50	.222
119	resnet50-VITO-8deg-cc	.222
120	mobilenet_v2_0_5_192	.222
121	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.222
122	mobilenet_v2_1_4_224	.222
123	CLIP_ViT-B_32	.222
124	resnet-101_v1-tf	.221
125	grcnn_109	.221
126	dinov2_vitg14_reg_lc	.220
127	convnext_xxlarge_clip	.220
128	vonegrcnn_52e_full	.220
129	antialias-resnet152	.220
130	mobilenet_v2_1.4_224-tf	.219
131	resnet-18-LC_1st_conv_m	.219
132	inception_v3-tf	.219
133	resnet50_ewconplus_wzc_seed5_2__323	.219
134	inception_v2-tf	.219
135	effnetv2m_custom384	.219
136	resnet50_st_wzc_seed3_2_323	.219
137	resnet50_st_wzc_2	.218
138	vonegrcnn_62e_nobn	.218
139	inception_resnet_v2	.218
140	mobilenet_v2_0_5_224	.217
141	convnext_base:clip_laiona_augreg_ft_in1k_384	.217
142	densenet-201-keras	.217
143	resnet50-vicregl0p9	.216
144	deit_small_imagenet_full_seed-0	.216
145	vgg-19-keras	.216
146	deit_large_imagenet_full_seed-0	.216
147	resnet50_ewconplus_wzc_seed6_2_323	.215
148	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.215
149	deit3_base_patch16_224_fb_in1k	.215
150	vgg-16-keras	.214
151	resnet50_primary_visual_cortex	.214
152	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.214
153	voneresnet-50	.214
154	mobilenet_v2_0.5_192-tf	.213
155	antialiased-rnext101_32x8d	.213
156	AdvProp_efficientnet-b0	.213
157	convnext_large_in22k_supervised	.212
158	VOneCORnet-S	.212
159	alexnet_fov4	.212
160	resnext101_32x48d_wsl	.212
161	BiT-M-R50x1	.211
162	inception_v3	.211
163	vonegrcnn_47e	.211
164	resnet-18-LC_w_sh_100_iter_m	.211
165	resnet34_ecoset_full	.211
166	resnet18-instance_recognition	.211
167	cv_18_dagger_408_pretrained	.210
168	resnet50-vitoimagevidnet8	.210
169	AdvProp_efficientnet-b7	.210
170	mobilenet_v2_0.5_224-tf	.210
171	convnext_base_imagenet_full_seed-0	.209
172	resnet-18-LC_w_sh_100_iter_conv_init_m	.209
173	CLIP-RN50	.209
174	mobilenet_v2_1.0_224-tf	.209
175	dinov2_vitl14_lc_res336	.209
176	0.5x_resnet-18_LC	.209
177	yolos_tiny	.208
178	resnet50-moclr8deg	.208
179	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.208
180	resnet50-vicreg	.208
181	AT_efficientnet-b7	.207
182	resnet50-simclr-vissl	.207
183	densenet-121-keras	.206
184	pnasnet_large_halfPrecision	.206
185	resnet50-barlow	.206
186	mobilenet_v2_1_0_224	.206
187	BiT-M-R101x1	.205
188	antialiased-r50	.205
189	pnasnet_large-tf	.205
190	AdvProp_efficientnet-b4	.205
191	0.5x_resnet-18_LC_w_sh_100_iter	.205
192	mobilenet_v1_0.75_224	.205
193	mobilenet_v2_0.75_160-tf	.205
194	ReAlnet01_cornet_fov12	.205
195	resnext101_32x8d_wsl	.205
196	resnet-50-pytorch	.204
197	resnet-50-robust	.204
198	BiT-M-R50x3	.204
199	convnext_small_imagenet_full_seed-0	.204
200	resnet50_imagenet_100_seed-0	.203
201	compact_vgg19_IT	.203
202	resnext101_32x16d_wsl	.203
203	resnet-18-LC_w_sh_1_iter_conv_init_m	.203
204	AlexNet_SIN_fov4	.202
205	0.5x_resnet-18_LC_w_sh_1_iter	.202
206	deit_base_imagenet_full_seed-0	.202
207	resnet18-local_aggregation	.202
208	CORnet-Z	.202
209	mobilenet_v2_0.35_224	.202
210	resnet50-vicregl0p75	.202
211	densenet-201	.202
212	vgg_16	.201
213	resnext101_32x32d_wsl	.201
214	resnet18-relative_position	.201
215	vit_huge_clip336_laion	.201
216	resnet-50_v1-tf	.201
217	resnet18-deepcluster	.201
218	resnet50_random_linf8_perturb	.201
219	resnet-34	.200
220	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.200
221	resnet-18-LC_d_w_sh_1x1_conv_init_m	.200
222	convnext_small_imagenet_100_seed-0	.200
223	resnet-18-LC_w_sh_1_iter_m	.200
224	0.5x_resnet-18_LC_w_sh_10_iter	.199
225	nasnet_mobile	.199
226	resnet50	.199
227	BiT-S-R50x1	.199
228	fixres_resnext101_32x48d_wsl	.199
229	resnet-18-LC_w_sh_10_iter_conv_init_m	.199
230	mobilenet_v1_0.75_128	.198
231	BiT-M-R152x2	.198
232	BiT-M-R101x3	.198
233	resnet50_st_wzc_seed6_2_323	.198
234	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.198
235	resnet18_imagenet21kP	.198
236	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.198
237	mobilenet_v2_0.5_128	.198
238	resnet50_linf_4_robust	.197
239	convnext_tiny_imagenet_full_seed-0	.197
240	mobilenet_v1_1.0_192	.197
241	resnet50-SIN_IN	.197
242	BiT-S-R101x3	.196
243	0.5x_resnet-18	.196
244	CLIP_resnet50	.196
245	CLIP_resnet50_float32	.196
246	mobilenet_v1_1.0_224	.196
247	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.196
248	resnet_SIN_IN_FT_IN	.195
249	resnet50-SIN_IN_IN	.195
250	mobilenet_v1_0.75_192	.195
251	BiT-S-R50x3	.195
252	densenet-169	.195
253	AdvProp_efficientnet-b8	.195
254	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.194
255	resnet-18	.194
256	resnet_50_v1_spiking_l4	.194
257	resnet_50_v1_spiking	.194
258	resnet_50_v1_spiking_l4a	.194
259	resnet50_tutorial	.194
260	resnet_50_v1_spiking_l3	.194
261	resnet50-sup	.194
262	resnet_50_v1	.194
263	yudixie_resnet50_imagenet1kpret_0_240908	.194
264	resnet50_random_l2_perturb	.193
265	resnet50-SIN	.193
266	mobilenet_v1_0.5_128	.193
267	mobilenet_v2_0.35_160	.193
268	BiT-S-R152x2	.192
269	mobilenet_v2_1_0_128	.192
270	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.192
271	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.192
272	resnet-18-LC_m	.191
273	inception_v4-tf	.191
274	nasnet_large-tf	.191
275	mobilenet_v2_0.75_128	.190
276	my-model	.190
277	mobilenet_v1_0.25_224	.190
278	BiT-S-R101x1	.190
279	mobilenet_v1_1.0_160	.189
280	resnet18_ecoset_full	.189
281	nasnet_mobile-tf	.187
282	resnet34_imagenet_full	.187
283	mobilenet_v1_1.0_128	.186
284	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.186
285	resnet18-contrastive_multiview	.186
286	resnet50_imagenet_full	.185
287	alexnet_l2_3_robust	.185
288	resnet-18-LC_w_sh_10_iter_m	.185
289	efficientnet_b1_imagenet_full	.184
290	resnet50_robust_l2_eps3	.184
291	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.183
292	mobilenet_v1_0.25_192	.183
293	mobilenet_v1_0.75_160	.183
294	mobilenet_v1_0.5_224	.183
295	shufflenet_v2_x1_0	.183
296	resnet50_l2_3_robust	.183
297	BiT-M-R152x4	.183
298	alexnet_random_linf8_perturb	.181
299	squeezenet1_1	.181
300	voneresnet-50-robust	.181
301	resnet-18-LC_conv_init_m	.181
302	mobilenet_v1_0.5_192	.181
303	deepeisnn_ei_imagenet_resnet18	.180
304	squeezenet1_0	.180
305	resnet18-supervised	.179
306	resnet18-colorization	.179
307	resnet18_imagenet_full	.179
308	texture_shape_resnet50_trained_on_SIN	.179
309	mobilenet_v2_0.35_192	.179
310	imagenet_l2_3_0	.178
311	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.178
312	yudixie_resnet50_object_class_0_240908	.178
313	SWSL_resnet50	.177
314	AdvProp_efficientnet-b2	.177
315	resnet50_linf_8_robust	.177
316	BiT-S-R152x4	.175
317	yudixie_resnet18_distance_translation_rotation_0_240719	.175
318	resnet101	.174
319	regnet_y_400mf	.174
320	resnet18_fair	.174
321	alexnet_random_l2_3_perturb	.173
322	resnet50_ecoset_full	.173
323	r3m_resnet50	.173
324	resnet50_moco_v2	.172
325	AT_efficientnet-b4	.172
326	mobilenet_v1_0.25_160	.172
327	ReAlnet10	.171
328	RN50	.171
329	resnet50_simclr	.171
330	mobilenet_v2_1.0_128-tf	.171
331	r3m_resnet50_nocrop	.170
332	yudixie_wbh_object_class_scaling_0415_imagenet	.170
333	yudixie_resnet18_imagenet1kpret_0_240719	.170
334	resnet_50_v2	.170
335	mobilenet_v1_0.5_160	.169
336	yudixie_resnet18_distance_translation_0_240719	.169
337	alexnet_training_seed_01_fov4	.169
338	texture_shape_alexnet_trained_on_SIN	.168
339	prednet	.167
340	convnext_base_fb_in22k_ft_in1k	.167
341	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.167
342	resnet-18_test_m	.167
343	deit_small_imagenet_100_seed-0	.166
344	yudixie_resnet18_translation_reg_0_240719	.166
345	vgg_19	.166
346	r3m_resnet18	.165
347	ReAlnet01_cornet_fov16	.165
348	ViT_L_32_imagenet1k	.165
349	yudixie_resnet50_category_class_0_240908	.165
350	alexnet_ks_torevert	.164
351	alexnet	.164
352	yudixie_resnet18_distance_reg_0_240719	.164
353	mobilenet_v2_0.75_96	.164
354	mobilenet_v2_1.0_96	.163
355	ReAlnet08	.163
356	mobilenet_v2_0.35_128	.163
357	AT_efficientnet-b2	.163
358	mobilenet_v1_0.25_128	.162
359	mobilenet_v2_1-4_224_pytorch	.162
360	yudixie_resnet18_object_class_0_240719	.162
361	efficientnet_b0_imagenet_full	.160
362	cornet_s	.160
363	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.160
364	ReAlnet07	.160
365	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.159
366	resnet50_imagenet_10_seed-0	.158
367	ReAlnet06	.158
368	vonealexnet_gaussian_noise_std4_fixed	.158
369	AlexNet_SIN	.157
370	resnet50_byol	.157
371	yudixie_resnet50_translation_rotation_0_240908	.157
372	vggface	.157
373	imagenet_l2_10_0	.156
374	efficientnet-b7	.156
375	artResNet18_1	.156
376	ReAlnet02	.155
377	resnet18-depth_prediction	.155
378	ReAlnet05	.154
379	alexnet_linf_8_robust	.154
380	yudixie_resnet50_distance_translation_0_240908	.154
381	mobilenet_v2_0.35_96	.154
382	yudixie_resnet50_distance_translation_rotation_0_240908	.153
383	mobilenet_v2_0.5_96	.153
384	yudixie_resnet50_translation_reg_0_240908	.152
385	ReAlnet04	.152
386	SWSL_resnext101_32x8d	.152
387	yudixie_resnet18_distance_rotation_0_240719	.152
388	ReAlnet03	.151
389	yudixie_resnet50_distance_rotation_0_240908	.151
390	ReAlnet09	.150
391	resnet18-contrastive_predictive	.150
392	compact_resnet50_IT	.150
393	alexnet_fov12	.149
394	yudixie_resnet18_translation_rotation_0_240719	.149
395	ReAlnet01	.148
396	AlexNet_SIN_fov12	.148
397	yudixie_resnet18_rotation_reg_0_240719	.147
398	convnext_base_fb_in1k	.145
399	convnext_base_fb_in1k_gram_aligned_dinov3	.145
400	r3m_resnet34	.145
401	yudixie_resnet50_distance_reg_0_240908	.145
402	blt_vs	.144
403	alexnet_early_checkpoint	.143
404	yudixie_resnet18_category_class_0_240719	.143
405	yudixie_resnet50_rotation_reg_0_240908	.141
406	alexnet_reduced_aliasing_early_checkpoint	.139
407	alexnet_robust_correct	.138
408	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.135
409	compact_resnet50_robust_IT	.132
410	briaai_rmbg_1_4	.132
411	AlexNet_SIN_fov16	.130
412	deepeisnn_ei_cifar10_resnet18	.129
413	alexnet_fov16	.128
414	v1-pyr-nodown	.112
415	alexnet_training_seed_02	.112
416	deepeisnn_ei_cifar10_vgg19	.111
417	resnet50-cifar	.110
418	alexnet_training_seed_01	.105
419	resnet18-autoencoder	.105
420	CORnetZ_CIFAR10	.104
421	alexnet_training_seed_03	.102
422	alexnet_training_seed_05	.102
423	alexnet_training_seed_01_fov12	.101
424	alexnet_training_seed_08	.100
425	alexnet_training_seed_10	.100
426	aughost_fair_resnet18	.099
427	alexnet_training_seed_06	.098
428	alexnet_training_seed_09	.098
429	alexnet_training_seed_07	.095
430	yudixie_wbh_object_class_scaling_0415_run_2	.095
431	resnet-50_untrained	.092
432	resnet50_FractalDB	.091
433	resnet50_pretrained_with_retinal_waves	.091
434	resnet-50x2_untrained	.091
435	alexnet_training_seed_04	.089
436	yudixie_wbh_object_class_scaling_0415_run_1	.088
437	alexnet_training_seed_01_fov16	.087
438	resnet-18_untrained	.086
439	yudixie_wbh_object_class_scaling_0415_run_0	.084
440	hmax	.080
441	CORnetZ_CIFAR10_bs32_20_04	.079
442	yudixie_wbh_object_class_scaling_0415_run_3	.074
443	yudixie_resnet18_random_0_240719	.074
444	yudixie_resnet50_random_0_240908	.065
445	resnet50_imagenet_1_seed-0	.063
446	mobilevit_small	.060
447	predictive_coding_pc	.054
448	pr1_stl10_disentangled_alpha04	.050
449	resnet-18-LC_untrained	.049
450	convnext_small_imagenet_10_seed-0	.040
451	resnet-152_v2_pytorch	.036
452	convnext_small_imagenet_1_seed-0	.030
453	deit_small_imagenet_10_seed-0	.026
454	deit_small_imagenet_1_seed-0	.020
455	fitvid_trained_on_physion	.020
456	pixels	.014
457	my-model	.013

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

0.97.

Note that scores are relative to this ceiling.

Data: SanghaviMurty2020.V4

300 stimuli recordings from 46 sites in V4

Metric: pls