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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	resnet50_st_wzc_seed5_2_323	.237
51	cvt_cvt-21-224-in1k_4_LucyV4	.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	resnet50_ewconplus_wzc_seed7_2_323	.233
58	resnet50_ewconplus_wzc_2_323	.233
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	resnet50_ewconplus_noseed_2_323	.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	vit_base_patch14_dinov2-lvd142m	.230
81	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.230
82	ReAlnet02_ALL	.230
83	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.230
84	convnext_tiny_sup	.229
85	ReAlnet03_ALL	.229
86	resnet50_ewconplus_wzc	.229
87	mobilenet_v2_1_0_160	.229
88	omnivore_swinT	.229
89	ReAlnet06_ALL	.228
90	Res2Net50_26w_4s	.228
91	CORnet-S	.228
92	mobilenet_v2_0_75_192	.228
93	cvt_cvt-13-224-in1k_4_LucyV4	.228
94	resnet50_robust_l2_eps1	.227
95	convnext_large_imagenet_full_seed-0	.227
96	vit_base_patch14_reg4_dinov2-lvd142m	.227
97	resnet50_ewconplus_wzc_seed2_2__323	.226
98	resnet50_ewconplus_wzc_seed4_2_323	.226
99	mobilenet_v2_0_75_160	.226
100	resnet50_st_wzc_seed7_2_323	.225
101	resnet-50_v2-tf	.225
102	voneresnet-50-non_stochastic	.225
103	xception-keras	.225
104	vonegrcnn_47e	.225
105	mobilenet_v2_1.3_224-tf	.225
106	efficientnet_b0	.225
107	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.224
108	vit_relpos_base_patch16_clsgap_224:sw_in1k	.224
109	inception_v1-tf	.223
110	densenet-121	.223
111	densenet-169-keras	.223
112	mobilenet_v2_0.5_160	.223
113	ViT-B/32	.222
114	AdvProp_efficientnet-b6	.222
115	barlow-twins-resnet50	.222
116	resnet50-VITO-8deg-cc	.222
117	mobilenet_v2_0_5_192	.222
118	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.222
119	mobilenet_v2_1_4_224	.222
120	CLIP_ViT-B_32	.222
121	resnet-101_v1-tf	.221
122	grcnn_109	.221
123	resnet50_ewconplus_wzc_seed3_2_323	.221
124	resnet50_st_wzc_2_323	.221
125	vonegrcnn_52e_full	.220
126	antialias-resnet152	.220
127	mobilenet_v2_1.4_224-tf	.219
128	resnet-18-LC_1st_conv_m	.219
129	inception_v3-tf	.219
130	resnet50_ewconplus_wzc_seed5_2__323	.219
131	inception_v2-tf	.219
132	effnetv2m_custom384	.219
133	resnet50_st_wzc_seed3_2_323	.219
134	resnet50_st_wzc_2	.218
135	vonegrcnn_62e_nobn	.218
136	inception_resnet_v2	.218
137	mobilenet_v2_0_5_224	.217
138	convnext_base:clip_laiona_augreg_ft_in1k_384	.217
139	densenet-201-keras	.217
140	resnet50-vicregl0p9	.216
141	deit_small_imagenet_full_seed-0	.216
142	vgg-19-keras	.216
143	deit_large_imagenet_full_seed-0	.216
144	resnet50_ewconplus_wzc_seed6_2_323	.215
145	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.215
146	deit3_base_patch16_224_fb_in1k	.215
147	vgg-16-keras	.214
148	resnet50_primary_visual_cortex	.214
149	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.214
150	voneresnet-50	.214
151	mobilenet_v2_0.5_192-tf	.213
152	antialiased-rnext101_32x8d	.213
153	AdvProp_efficientnet-b0	.213
154	VOneCORnet-S	.212
155	alexnet_fov4	.212
156	resnext101_32x48d_wsl	.212
157	BiT-M-R50x1	.211
158	inception_v3	.211
159	vonegrcnn_47e	.211
160	resnet-18-LC_w_sh_100_iter_m	.211
161	resnet34_ecoset_full	.211
162	resnet18-instance_recognition	.211
163	cv_18_dagger_408_pretrained	.210
164	resnet50-vitoimagevidnet8	.210
165	AdvProp_efficientnet-b7	.210
166	mobilenet_v2_0.5_224-tf	.210
167	convnext_base_imagenet_full_seed-0	.209
168	resnet-18-LC_w_sh_100_iter_conv_init_m	.209
169	CLIP-RN50	.209
170	mobilenet_v2_1.0_224-tf	.209
171	0.5x_resnet-18_LC	.209
172	yolos_tiny	.208
173	resnet50-moclr8deg	.208
174	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.208
175	resnet50-vicreg	.208
176	AT_efficientnet-b7	.207
177	resnet50-simclr-vissl	.207
178	densenet-121-keras	.206
179	pnasnet_large_halfPrecision	.206
180	resnet50-barlow	.206
181	mobilenet_v2_1_0_224	.206
182	BiT-M-R101x1	.205
183	antialiased-r50	.205
184	pnasnet_large-tf	.205
185	AdvProp_efficientnet-b4	.205
186	0.5x_resnet-18_LC_w_sh_100_iter	.205
187	mobilenet_v1_0.75_224	.205
188	mobilenet_v2_0.75_160-tf	.205
189	ReAlnet01_cornet_fov12	.205
190	resnext101_32x8d_wsl	.205
191	resnet-50-pytorch	.204
192	resnet-50-robust	.204
193	BiT-M-R50x3	.204
194	convnext_small_imagenet_full_seed-0	.204
195	resnet50_imagenet_100_seed-0	.203
196	compact_vgg19_IT	.203
197	resnext101_32x16d_wsl	.203
198	resnet-18-LC_w_sh_1_iter_conv_init_m	.203
199	AlexNet_SIN_fov4	.202
200	0.5x_resnet-18_LC_w_sh_1_iter	.202
201	deit_base_imagenet_full_seed-0	.202
202	resnet18-local_aggregation	.202
203	CORnet-Z	.202
204	mobilenet_v2_0.35_224	.202
205	resnet50-vicregl0p75	.202
206	densenet-201	.202
207	vgg_16	.201
208	resnext101_32x32d_wsl	.201
209	resnet18-relative_position	.201
210	resnet-50_v1-tf	.201
211	resnet18-deepcluster	.201
212	resnet50_random_linf8_perturb	.201
213	resnet-34	.200
214	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.200
215	resnet-18-LC_d_w_sh_1x1_conv_init_m	.200
216	convnext_small_imagenet_100_seed-0	.200
217	resnet-18-LC_w_sh_1_iter_m	.200
218	0.5x_resnet-18_LC_w_sh_10_iter	.199
219	nasnet_mobile	.199
220	resnet50	.199
221	BiT-S-R50x1	.199
222	fixres_resnext101_32x48d_wsl	.199
223	resnet-18-LC_w_sh_10_iter_conv_init_m	.199
224	mobilenet_v1_0.75_128	.198
225	BiT-M-R152x2	.198
226	BiT-M-R101x3	.198
227	resnet50_st_wzc_seed6_2_323	.198
228	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.198
229	resnet18_imagenet21kP	.198
230	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.198
231	mobilenet_v2_0.5_128	.198
232	resnet50_linf_4_robust	.197
233	convnext_tiny_imagenet_full_seed-0	.197
234	mobilenet_v1_1.0_192	.197
235	resnet50-SIN_IN	.197
236	BiT-S-R101x3	.196
237	0.5x_resnet-18	.196
238	CLIP_resnet50	.196
239	CLIP_resnet50_float32	.196
240	mobilenet_v1_1.0_224	.196
241	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.196
242	resnet_SIN_IN_FT_IN	.195
243	resnet50-SIN_IN_IN	.195
244	mobilenet_v1_0.75_192	.195
245	BiT-S-R50x3	.195
246	densenet-169	.195
247	AdvProp_efficientnet-b8	.195
248	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.194
249	resnet-18	.194
250	resnet_50_v1_spiking_l4	.194
251	resnet_50_v1_spiking	.194
252	resnet_50_v1_spiking_l4a	.194
253	resnet50_tutorial	.194
254	resnet_50_v1_spiking_l3	.194
255	resnet50-sup	.194
256	resnet_50_v1	.194
257	yudixie_resnet50_imagenet1kpret_0_240908	.194
258	resnet50_random_l2_perturb	.193
259	resnet50-SIN	.193
260	mobilenet_v1_0.5_128	.193
261	mobilenet_v2_0.35_160	.193
262	BiT-S-R152x2	.192
263	mobilenet_v2_1_0_128	.192
264	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.192
265	resnet50_st_wzc_noseed_2_323	.192
266	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.192
267	resnet-18-LC_m	.191
268	inception_v4-tf	.191
269	nasnet_large-tf	.191
270	mobilenet_v2_0.75_128	.190
271	my-model	.190
272	mobilenet_v1_0.25_224	.190
273	BiT-S-R101x1	.190
274	resnet50_st_wzc_seed4_2_323	.190
275	mobilenet_v1_1.0_160	.189
276	resnet18_ecoset_full	.189
277	nasnet_mobile-tf	.187
278	resnet34_imagenet_full	.187
279	mobilenet_v1_1.0_128	.186
280	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.186
281	resnet18-contrastive_multiview	.186
282	resnet50_imagenet_full	.185
283	alexnet_l2_3_robust	.185
284	resnet-18-LC_w_sh_10_iter_m	.185
285	efficientnet_b1_imagenet_full	.184
286	resnet50_robust_l2_eps3	.184
287	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.183
288	mobilenet_v1_0.25_192	.183
289	mobilenet_v1_0.75_160	.183
290	mobilenet_v1_0.5_224	.183
291	shufflenet_v2_x1_0	.183
292	resnet50_l2_3_robust	.183
293	BiT-M-R152x4	.183
294	alexnet_random_linf8_perturb	.181
295	squeezenet1_1	.181
296	voneresnet-50-robust	.181
297	resnet-18-LC_conv_init_m	.181
298	mobilenet_v1_0.5_192	.181
299	deepeisnn_ei_imagenet_resnet18	.180
300	squeezenet1_0	.180
301	resnet18-supervised	.179
302	resnet18-colorization	.179
303	resnet18_imagenet_full	.179
304	texture_shape_resnet50_trained_on_SIN	.179
305	mobilenet_v2_0.35_192	.179
306	imagenet_l2_3_0	.178
307	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.178
308	yudixie_resnet50_object_class_0_240908	.178
309	SWSL_resnet50	.177
310	AdvProp_efficientnet-b2	.177
311	resnet50_linf_8_robust	.177
312	BiT-S-R152x4	.175
313	yudixie_resnet18_distance_translation_rotation_0_240719	.175
314	resnet101	.174
315	regnet_y_400mf	.174
316	resnet18_fair	.174
317	alexnet_random_l2_3_perturb	.173
318	resnet50_ecoset_full	.173
319	r3m_resnet50	.173
320	resnet50_moco_v2	.172
321	AT_efficientnet-b4	.172
322	mobilenet_v1_0.25_160	.172
323	ReAlnet10	.171
324	RN50	.171
325	resnet50_simclr	.171
326	mobilenet_v2_1.0_128-tf	.171
327	r3m_resnet50_nocrop	.170
328	yudixie_wbh_object_class_scaling_0415_imagenet	.170
329	yudixie_resnet18_imagenet1kpret_0_240719	.170
330	resnet_50_v2	.170
331	mobilenet_v1_0.5_160	.169
332	yudixie_resnet18_distance_translation_0_240719	.169
333	alexnet_training_seed_01_fov4	.169
334	texture_shape_alexnet_trained_on_SIN	.168
335	prednet	.167
336	convnext_base_fb_in22k_ft_in1k	.167
337	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.167
338	resnet-18_test_m	.167
339	deit_small_imagenet_100_seed-0	.166
340	yudixie_resnet18_translation_reg_0_240719	.166
341	vgg_19	.166
342	r3m_resnet18	.165
343	ReAlnet01_cornet_fov16	.165
344	ViT_L_32_imagenet1k	.165
345	yudixie_resnet50_category_class_0_240908	.165
346	alexnet_ks_torevert	.164
347	alexnet	.164
348	yudixie_resnet18_distance_reg_0_240719	.164
349	mobilenet_v2_0.75_96	.164
350	mobilenet_v2_1.0_96	.163
351	ReAlnet08	.163
352	mobilenet_v2_0.35_128	.163
353	AT_efficientnet-b2	.163
354	mobilenet_v1_0.25_128	.162
355	mobilenet_v2_1-4_224_pytorch	.162
356	yudixie_resnet18_object_class_0_240719	.162
357	efficientnet_b0_imagenet_full	.160
358	cornet_s	.160
359	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.160
360	ReAlnet07	.160
361	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.159
362	resnet50_imagenet_10_seed-0	.158
363	ReAlnet06	.158
364	resnet50	.158
365	vonealexnet_gaussian_noise_std4_fixed	.158
366	AlexNet_SIN	.157
367	resnet50_byol	.157
368	yudixie_resnet50_translation_rotation_0_240908	.157
369	vggface	.157
370	imagenet_l2_10_0	.156
371	efficientnet-b7	.156
372	artResNet18_1	.156
373	ReAlnet02	.155
374	resnet18-depth_prediction	.155
375	ReAlnet05	.154
376	alexnet_linf_8_robust	.154
377	yudixie_resnet50_distance_translation_0_240908	.154
378	mobilenet_v2_0.35_96	.154
379	yudixie_resnet50_distance_translation_rotation_0_240908	.153
380	mobilenet_v2_0.5_96	.153
381	yudixie_resnet50_translation_reg_0_240908	.152
382	ReAlnet04	.152
383	SWSL_resnext101_32x8d	.152
384	yudixie_resnet18_distance_rotation_0_240719	.152
385	ReAlnet03	.151
386	yudixie_resnet50_distance_rotation_0_240908	.151
387	ReAlnet09	.150
388	resnet18-contrastive_predictive	.150
389	compact_resnet50_IT	.150
390	alexnet_fov12	.149
391	yudixie_resnet18_translation_rotation_0_240719	.149
392	ReAlnet01	.148
393	AlexNet_SIN_fov12	.148
394	yudixie_resnet18_rotation_reg_0_240719	.147
395	convnext_base_fb_in1k	.145
396	convnext_base_fb_in1k_gram_aligned_dinov3	.145
397	r3m_resnet34	.145
398	yudixie_resnet50_distance_reg_0_240908	.145
399	blt_vs	.144
400	resnet50-meshes-lt-100-original-pretrained	.143
401	alexnet_early_checkpoint	.143
402	yudixie_resnet18_category_class_0_240719	.143
403	yudixie_resnet50_rotation_reg_0_240908	.141
404	alexnet_reduced_aliasing_early_checkpoint	.139
405	alexnet_robust_correct	.138
406	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.135
407	compact_resnet50_robust_IT	.132
408	briaai_rmbg_1_4	.132
409	resnet50-meshes-lt-100-original-scratch	.131
410	AlexNet_SIN_fov16	.130
411	deepeisnn_ei_cifar10_resnet18	.129
412	alexnet_fov16	.128
413	v1-pyr-nodown	.112
414	alexnet_training_seed_02	.112
415	deepeisnn_ei_cifar10_vgg19	.111
416	resnet50-cifar	.110
417	alexnet_training_seed_01	.105
418	resnet18-autoencoder	.105
419	CORnetZ_CIFAR10	.104
420	alexnet_training_seed_03	.102
421	alexnet_training_seed_05	.102
422	alexnet_training_seed_01_fov12	.101
423	alexnet_training_seed_08	.100
424	alexnet_training_seed_10	.100
425	aughost_fair_resnet18	.099
426	alexnet_training_seed_06	.098
427	alexnet_training_seed_09	.098
428	alexnet_training_seed_07	.095
429	yudixie_wbh_object_class_scaling_0415_run_2	.095
430	resnet-50_untrained	.092
431	resnet50_FractalDB	.091
432	resnet50_pretrained_with_retinal_waves	.091
433	resnet-50x2_untrained	.091
434	alexnet_training_seed_04	.089
435	yudixie_wbh_object_class_scaling_0415_run_1	.088
436	alexnet_training_seed_01_fov16	.087
437	resnet-18_untrained	.086
438	yudixie_wbh_object_class_scaling_0415_run_0	.084
439	hmax	.080
440	CORnetZ_CIFAR10_bs32_20_04	.079
441	yudixie_wbh_object_class_scaling_0415_run_3	.074
442	yudixie_resnet18_random_0_240719	.074
443	yudixie_resnet50_random_0_240908	.065
444	resnet50_imagenet_1_seed-0	.063
445	mobilevit_small	.060
446	predictive_coding_pc	.054
447	pr1_stl10_disentangled_alpha04	.050
448	resnet-18-LC_untrained	.049
449	convnext_small_imagenet_10_seed-0	.040
450	resnet-152_v2_pytorch	.036
451	convnext_small_imagenet_1_seed-0	.030
452	deit_small_imagenet_10_seed-0	.026
453	deit_small_imagenet_1_seed-0	.020
454	fitvid_trained_on_physion	.020
455	pixels	.014
456	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