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