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

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