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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	.360
2	FrankRobWobv0	.342
3	compact_vgg19_V4	.328
4	compact_resnet50_V4	.306
5	compact_cornets_V4	.304
6	ReAlnet01_cornet_fov4	.283
7	ReAlnet10_cornet	.267
8	efficientnet_b2_imagenet_full	.266
9	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.265
10	cvt_cvt-21-384-in1k_4_LucyV4	.265
11	mobilenet_v2_0_75_224	.264
12	ReAlnet08_cornet	.263
13	konkle_alexnetgn_ipcl_ref01_primary_model	.261
14	effnetb1_272x240	.254
15	ReAlnet05_cornet	.254
16	mobilenet_v2_1_0_192	.253
17	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.253
18	resnet_101_v1	.253
19	compact_cornets_IT	.252
20	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.252
21	inception_v1	.252
22	mobilenet_v2_1_3_224	.252
23	mobilenet_v2_1.0_192-tf	.252
24	resnet101_imagenet_full	.252
25	resnet152_imagenet_full	.251
26	ReAlnet02_cornet	.250
27	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.250
28	ReAlnet03_cornet	.250
29	ReAlnet07_cornet	.250
30	focalnet_tiny_lrf_in1k	.249
31	resnet152_ecoset_full	.249
32	resnet-101_v2-tf	.248
33	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.248
34	ReAlnet01_cornet	.248
35	convnext_large:fb_in22k_ft_in1k	.248
36	resnet-152_v1-tf	.247
37	ReAlnet04_cornet	.247
38	vit_large_patch14_clip_224:laion2b_ft_in1k	.247
39	resnet-152_v2-tf	.247
40	vision_transformer_vit_large_patch16_224	.247
41	RN50	.247
42	ReAlnet06_cornet	.246
43	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.246
44	grcnn_robust_v1	.246
45	ReAlnet09_cornet	.245
46	omnivore_swinB	.245
47	omnivore_swinS	.245
48	cvt_cvt-21-224-in1k_4_LucyV4	.245
49	cvt_cvt-13-384-in1k_4_LucyV4	.244
50	custom_model_cv_18_dagger_408	.243
51	convnext_tiny:in12k_ft_in1k	.243
52	resnet101_ecoset_full	.243
53	grcnn_v2_text_noise	.243
54	ReAlnet10_ALL	.241
55	mobilenet_v2_1.0_160-tf	.241
56	ReAlnet09_ALL	.241
57	ReAlnet07_ALL	.241
58	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.240
59	vit_base_patch16_clip_224:openai_ft_in1k	.240
60	convnext_femto_ols:d1_in1k	.240
61	convnext_xlarge:fb_in22k_ft_in1k	.240
62	tv_efficientnet-b1	.240
63	ReAlnet04_ALL	.240
64	mobilenet_v2_0.75_224-tf	.239
65	vit_large_patch14_clip_224:openai_ft_in1k	.239
66	grcnn	.239
67	ReAlnet05_ALL	.239
68	ReAlnet08_ALL	.239
69	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.239
70	ReAlnet01_ALL	.238
71	mobilenet_v2_0.75_192-tf	.238
72	vit_large_patch14_clip_336:laion2b_ft_in1k	.238
73	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.238
74	ReAlnet02_ALL	.238
75	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.238
76	convnext_tiny_sup	.237
77	ReAlnet03_ALL	.237
78	resnet50_ewconplus_wzc	.237
79	mobilenet_v2_1_0_160	.237
80	omnivore_swinT	.237
81	ReAlnet06_ALL	.236
82	Res2Net50_26w_4s	.236
83	CORnet-S	.236
84	mobilenet_v2_0_75_192	.236
85	cvt_cvt-13-224-in1k_4_LucyV4	.236
86	resnet50_robust_l2_eps1	.235
87	convnext_large_imagenet_full_seed-0	.235
88	mobilenet_v2_0_75_160	.234
89	resnet-50_v2-tf	.233
90	voneresnet-50-non_stochastic	.233
91	xception-keras	.233
92	vonegrcnn_47e	.233
93	mobilenet_v2_1.3_224-tf	.233
94	efficientnet_b0	.233
95	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.232
96	vit_relpos_base_patch16_clsgap_224:sw_in1k	.231
97	inception_v1-tf	.231
98	densenet-121	.231
99	densenet-169-keras	.231
100	resnet50_hvs_wzc	.231
101	mobilenet_v2_0.5_160	.230
102	ViT-B/32	.230
103	AdvProp_efficientnet-b6	.230
104	barlow-twins-resnet50	.230
105	resnet50-VITO-8deg-cc	.230
106	mobilenet_v2_0_5_192	.230
107	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.229
108	mobilenet_v2_1_4_224	.229
109	CLIP_ViT-B_32	.229
110	resnet-101_v1-tf	.229
111	grcnn_109	.229
112	vonegrcnn_52e_full	.228
113	antialias-resnet152	.227
114	mobilenet_v2_1.4_224-tf	.227
115	resnet-18-LC_1st_conv_m	.227
116	inception_v3-tf	.227
117	inception_v2-tf	.227
118	effnetv2m_custom384	.227
119	resnet50_st_wzc_2	.226
120	vonegrcnn_62e_nobn	.226
121	inception_resnet_v2	.226
122	mobilenet_v2_0_5_224	.224
123	convnext_base:clip_laiona_augreg_ft_in1k_384	.224
124	densenet-201-keras	.224
125	resnet50-vicregl0p9	.224
126	deit_small_imagenet_full_seed-0	.224
127	vgg-19-keras	.224
128	deit_large_imagenet_full_seed-0	.223
129	vgg-16-keras	.222
130	resnet50_primary_visual_cortex	.222
131	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.221
132	voneresnet-50	.221
133	mobilenet_v2_0.5_192-tf	.221
134	antialiased-rnext101_32x8d	.221
135	AdvProp_efficientnet-b0	.220
136	VOneCORnet-S	.219
137	alexnet_fov4	.219
138	resnext101_32x48d_wsl	.219
139	BiT-M-R50x1	.219
140	inception_v3	.219
141	vonegrcnn_47e	.219
142	resnet-18-LC_w_sh_100_iter_m	.218
143	resnet34_ecoset_full	.218
144	resnet18-instance_recognition	.218
145	cv_18_dagger_408_pretrained	.218
146	resnet50-vitoimagevidnet8	.218
147	AdvProp_efficientnet-b7	.218
148	mobilenet_v2_0.5_224-tf	.217
149	convnext_base_imagenet_full_seed-0	.217
150	resnet-18-LC_w_sh_100_iter_conv_init_m	.216
151	CLIP-RN50	.216
152	mobilenet_v2_1.0_224-tf	.216
153	0.5x_resnet-18_LC	.216
154	yolos_tiny	.215
155	resnet50-moclr8deg	.215
156	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.215
157	resnet50-vicreg	.215
158	AT_efficientnet-b7	.215
159	resnet50-simclr-vissl	.214
160	densenet-121-keras	.214
161	resnet50-barlow	.213
162	mobilenet_v2_1_0_224	.213
163	BiT-M-R101x1	.213
164	antialiased-r50	.212
165	pnasnet_large-tf	.212
166	AdvProp_efficientnet-b4	.212
167	0.5x_resnet-18_LC_w_sh_100_iter	.212
168	mobilenet_v1_0.75_224	.212
169	mobilenet_v2_0.75_160-tf	.212
170	ReAlnet01_cornet_fov12	.212
171	resnext101_32x8d_wsl	.212
172	resnet-50-pytorch	.211
173	resnet-50-robust	.211
174	BiT-M-R50x3	.211
175	convnext_small_imagenet_full_seed-0	.211
176	resnet50_imagenet_100_seed-0	.210
177	compact_vgg19_IT	.210
178	resnext101_32x16d_wsl	.210
179	resnet-18-LC_w_sh_1_iter_conv_init_m	.210
180	AlexNet_SIN_fov4	.209
181	0.5x_resnet-18_LC_w_sh_1_iter	.209
182	deit_base_imagenet_full_seed-0	.209
183	resnet18-local_aggregation	.209
184	CORnet-Z	.209
185	mobilenet_v2_0.35_224	.209
186	resnet50-vicregl0p75	.209
187	densenet-201	.208
188	vgg_16	.208
189	resnext101_32x32d_wsl	.208
190	resnet18-relative_position	.208
191	resnet-50_v1-tf	.208
192	resnet18-deepcluster	.208
193	resnet50_random_linf8_perturb	.208
194	resnet-34	.207
195	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.207
196	resnet-18-LC_d_w_sh_1x1_conv_init_m	.207
197	convnext_small_imagenet_100_seed-0	.207
198	resnet-18-LC_w_sh_1_iter_m	.207
199	0.5x_resnet-18_LC_w_sh_10_iter	.206
200	nasnet_mobile	.206
201	resnet50	.206
202	BiT-S-R50x1	.206
203	fixres_resnext101_32x48d_wsl	.206
204	resnet-18-LC_w_sh_10_iter_conv_init_m	.206
205	mobilenet_v1_0.75_128	.205
206	BiT-M-R152x2	.205
207	BiT-M-R101x3	.205
208	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.205
209	resnet18_imagenet21kP	.204
210	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.204
211	mobilenet_v2_0.5_128	.204
212	resnet50_linf_4_robust	.204
213	convnext_tiny_imagenet_full_seed-0	.204
214	mobilenet_v1_1.0_192	.204
215	resnet50-SIN_IN	.204
216	BiT-S-R101x3	.203
217	0.5x_resnet-18	.203
218	CLIP_resnet50	.203
219	CLIP_resnet50_float32	.203
220	mobilenet_v1_1.0_224	.203
221	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.202
222	resnet50-SIN_IN_IN	.202
223	resnet_SIN_IN_FT_IN	.202
224	mobilenet_v1_0.75_192	.202
225	BiT-S-R50x3	.202
226	densenet-169	.202
227	AdvProp_efficientnet-b8	.202
228	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.201
229	resnet-18	.201
230	resnet_50_v1_spiking_l4	.200
231	resnet_50_v1_spiking	.200
232	resnet_50_v1_spiking_l4a	.200
233	resnet50_tutorial	.200
234	resnet_50_v1_spiking_l3	.200
235	resnet50-sup	.200
236	resnet_50_v1	.200
237	yudixie_resnet50_imagenet1kpret_0_240908	.200
238	resnet50_random_l2_perturb	.200
239	resnet50-SIN	.200
240	mobilenet_v1_0.5_128	.199
241	mobilenet_v2_0.35_160	.199
242	BiT-S-R152x2	.199
243	mobilenet_v2_1_0_128	.199
244	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
245	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.198
246	resnet-18-LC_m	.198
247	inception_v4-tf	.198
248	nasnet_large-tf	.198
249	mobilenet_v2_0.75_128	.197
250	my-model	.197
251	mobilenet_v1_0.25_224	.196
252	BiT-S-R101x1	.196
253	mobilenet_v1_1.0_160	.195
254	resnet18_ecoset_full	.195
255	nasnet_mobile-tf	.194
256	resnet34_imagenet_full	.194
257	mobilenet_v1_1.0_128	.193
258	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.192
259	resnet18-contrastive_multiview	.192
260	resnet50_imagenet_full	.192
261	alexnet_l2_3_robust	.192
262	resnet-18-LC_w_sh_10_iter_m	.192
263	efficientnet_b1_imagenet_full	.191
264	resnet50_robust_l2_eps3	.190
265	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.190
266	mobilenet_v1_0.25_192	.190
267	mobilenet_v1_0.75_160	.189
268	mobilenet_v1_0.5_224	.189
269	shufflenet_v2_x1_0	.189
270	resnet50_l2_3_robust	.189
271	BiT-M-R152x4	.189
272	alexnet_random_linf8_perturb	.187
273	squeezenet1_1	.187
274	voneresnet-50-robust	.187
275	resnet-18-LC_conv_init_m	.187
276	mobilenet_v1_0.5_192	.187
277	squeezenet1_0	.186
278	resnet18-supervised	.185
279	resnet18-colorization	.185
280	resnet18_imagenet_full	.185
281	texture_shape_resnet50_trained_on_SIN	.185
282	mobilenet_v2_0.35_192	.185
283	imagenet_l2_3_0	.184
284	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.184
285	yudixie_resnet50_object_class_0_240908	.184
286	SWSL_resnet50	.184
287	AdvProp_efficientnet-b2	.183
288	resnet50_linf_8_robust	.183
289	BiT-S-R152x4	.181
290	yudixie_resnet18_distance_translation_rotation_0_240719	.181
291	resnet101	.180
292	regnet_y_400mf	.180
293	alexnet_random_l2_3_perturb	.179
294	resnet50_ecoset_full	.179
295	r3m_resnet50	.179
296	resnet50_moco_v2	.178
297	AT_efficientnet-b4	.178
298	mobilenet_v1_0.25_160	.178
299	ReAlnet10	.177
300	RN50	.177
301	resnet50_simclr	.177
302	mobilenet_v2_1.0_128-tf	.176
303	r3m_resnet50_nocrop	.176
304	yudixie_resnet18_imagenet1kpret_0_240719	.176
305	resnet_50_v2	.175
306	mobilenet_v1_0.5_160	.175
307	yudixie_resnet18_distance_translation_0_240719	.175
308	alexnet_training_seed_01_fov4	.174
309	texture_shape_alexnet_trained_on_SIN	.174
310	prednet	.173
311	resnet-18_test_m	.173
312	deit_small_imagenet_100_seed-0	.172
313	yudixie_resnet18_translation_reg_0_240719	.172
314	vgg_19	.171
315	r3m_resnet18	.171
316	ReAlnet01_cornet_fov16	.171
317	ViT_L_32_imagenet1k	.171
318	yudixie_resnet50_category_class_0_240908	.171
319	alexnet_ks_torevert	.170
320	alexnet	.170
321	yudixie_resnet18_distance_reg_0_240719	.169
322	mobilenet_v2_0.75_96	.169
323	mobilenet_v2_1.0_96	.169
324	ReAlnet08	.169
325	mobilenet_v2_0.35_128	.169
326	AT_efficientnet-b2	.169
327	mobilenet_v1_0.25_128	.168
328	mobilenet_v2_1-4_224_pytorch	.168
329	yudixie_resnet18_object_class_0_240719	.167
330	efficientnet_b0_imagenet_full	.166
331	cornet_s	.166
332	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.165
333	ReAlnet07	.165
334	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.164
335	resnet50_imagenet_10_seed-0	.164
336	ReAlnet06	.163
337	vonealexnet_gaussian_noise_std4_fixed	.163
338	AlexNet_SIN	.163
339	resnet50_byol	.163
340	yudixie_resnet50_translation_rotation_0_240908	.163
341	vggface	.162
342	imagenet_l2_10_0	.162
343	efficientnet-b7	.162
344	artResNet18_1	.162
345	ReAlnet02	.160
346	resnet18-depth_prediction	.160
347	ReAlnet05	.159
348	alexnet_linf_8_robust	.159
349	yudixie_resnet50_distance_translation_0_240908	.159
350	mobilenet_v2_0.35_96	.159
351	yudixie_resnet50_distance_translation_rotation_0_240908	.158
352	mobilenet_v2_0.5_96	.158
353	yudixie_resnet50_translation_reg_0_240908	.158
354	ReAlnet04	.157
355	SWSL_resnext101_32x8d	.157
356	yudixie_resnet18_distance_rotation_0_240719	.157
357	ReAlnet03	.157
358	yudixie_resnet50_distance_rotation_0_240908	.156
359	ReAlnet09	.156
360	resnet18-contrastive_predictive	.155
361	compact_resnet50_IT	.155
362	alexnet_fov12	.154
363	yudixie_resnet18_translation_rotation_0_240719	.154
364	ReAlnet01	.154
365	AlexNet_SIN_fov12	.153
366	yudixie_resnet18_rotation_reg_0_240719	.152
367	r3m_resnet34	.150
368	yudixie_resnet50_distance_reg_0_240908	.150
369	blt_vs	.149
370	alexnet_early_checkpoint	.148
371	yudixie_resnet18_category_class_0_240719	.148
372	yudixie_resnet50_rotation_reg_0_240908	.146
373	alexnet_reduced_aliasing_early_checkpoint	.144
374	alexnet_robust_correct	.143
375	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.140
376	compact_resnet50_robust_IT	.137
377	briaai_rmbg_1_4	.136
378	AlexNet_SIN_fov16	.134
379	alexnet_fov16	.132
380	v1-pyr-nodown	.116
381	alexnet_training_seed_02	.116
382	resnet50-cifar	.114
383	alexnet_training_seed_01	.108
384	resnet18-autoencoder	.108
385	CORnetZ_CIFAR10	.108
386	alexnet_training_seed_03	.105
387	alexnet_training_seed_05	.105
388	alexnet_training_seed_01_fov12	.105
389	alexnet_training_seed_08	.103
390	alexnet_training_seed_10	.103
391	alexnet_training_seed_06	.101
392	alexnet_training_seed_09	.101
393	alexnet_training_seed_07	.098
394	resnet-50_untrained	.095
395	resnet50_FractalDB	.094
396	resnet50_pretrained_with_retinal_waves	.094
397	resnet-50x2_untrained	.094
398	alexnet_training_seed_04	.092
399	alexnet_training_seed_01_fov16	.090
400	resnet-18_untrained	.089
401	hmax	.083
402	CORnetZ_CIFAR10_bs32_20_04	.081
403	yudixie_resnet18_random_0_240719	.076
404	yudixie_resnet50_random_0_240908	.068
405	resnet50_imagenet_1_seed-0	.065
406	mobilevit_small	.062
407	resnet-18-LC_untrained	.051
408	convnext_small_imagenet_10_seed-0	.042
409	resnet-152_v2_pytorch	.037
410	convnext_small_imagenet_1_seed-0	.031
411	deit_small_imagenet_10_seed-0	.027
412	deit_small_imagenet_1_seed-0	.021
413	fitvid_trained_on_physion	.021
414	pixels	.015
415	my-model	.013
416	efficientnet-b0
417	efficientnet-b6
418	efficientnet-b2
419	efficientnet-b4
420	AT_efficientnet-b0
421	resnet18-simclr
422	openclip
423	resnet50_ImageNet
424	yudixie_resnet50_imagenet1kpret_0_240312
425	resnet_152_v1
426	ConvLSTM
427	PredRNN
428	SimVP
429	TAU
430	MIM
431	densenet_201_pytorch
432	pnasnet_large_pytorch
433	inception_v3_pytorch
434	eBarlow_lmda_02_1000ep
435	inception_v4
436	xception
437	nasnet_large
438	pnasnet_large
439	resnet_101_v2
440	vonegrcnn_62e_nobn
441	resnet_152_v2
442	vonegrcnn_52e_full

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