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	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.221
112	voneresnet-50	.221
113	mobilenet_v2_0.5_192-tf	.221
114	antialiased-rnext101_32x8d	.221
115	AdvProp_efficientnet-b0	.220
116	VOneCORnet-S	.219
117	resnext101_32x48d_wsl	.219
118	BiT-M-R50x1	.219
119	inception_v3	.219
120	vonegrcnn_47e	.219
121	resnet-18-LC_w_sh_100_iter_m	.218
122	resnet34_ecoset_full	.218
123	resnet18-instance_recognition	.218
124	cv_18_dagger_408_pretrained	.218
125	resnet50-vitoimagevidnet8	.218
126	AdvProp_efficientnet-b7	.218
127	mobilenet_v2_0.5_224-tf	.217
128	convnext_base_imagenet_full_seed-0	.217
129	resnet-18-LC_w_sh_100_iter_conv_init_m	.216
130	CLIP-RN50	.216
131	mobilenet_v2_1.0_224-tf	.216
132	0.5x_resnet-18_LC	.216
133	resnet50-moclr8deg	.215
134	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.215
135	resnet50-vicreg	.215
136	AT_efficientnet-b7	.215
137	resnet50-simclr-vissl	.214
138	densenet-121-keras	.214
139	resnet50-barlow	.213
140	mobilenet_v2_1_0_224	.213
141	BiT-M-R101x1	.213
142	antialiased-r50	.212
143	pnasnet_large-tf	.212
144	AdvProp_efficientnet-b4	.212
145	0.5x_resnet-18_LC_w_sh_100_iter	.212
146	mobilenet_v1_0.75_224	.212
147	mobilenet_v2_0.75_160-tf	.212
148	resnext101_32x8d_wsl	.212
149	resnet-50-robust	.211
150	resnet-50-pytorch	.211
151	BiT-M-R50x3	.211
152	convnext_small_imagenet_full_seed-0	.211
153	resnet50_imagenet_100_seed-0	.210
154	resnext101_32x16d_wsl	.210
155	resnet-18-LC_w_sh_1_iter_conv_init_m	.210
156	0.5x_resnet-18_LC_w_sh_1_iter	.209
157	deit_base_imagenet_full_seed-0	.209
158	resnet18-local_aggregation	.209
159	CORnet-Z	.209
160	mobilenet_v2_0.35_224	.209
161	resnet50-vicregl0p75	.209
162	densenet-201	.208
163	vgg_16	.208
164	resnext101_32x32d_wsl	.208
165	resnet18-relative_position	.208
166	resnet-50_v1-tf	.208
167	resnet18-deepcluster	.208
168	resnet50_random_linf8_perturb	.208
169	resnet-34	.207
170	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.207
171	resnet-18-LC_d_w_sh_1x1_conv_init_m	.207
172	convnext_small_imagenet_100_seed-0	.207
173	resnet-18-LC_w_sh_1_iter_m	.207
174	0.5x_resnet-18_LC_w_sh_10_iter	.206
175	nasnet_mobile	.206
176	resnet50	.206
177	BiT-S-R50x1	.206
178	fixres_resnext101_32x48d_wsl	.206
179	resnet-18-LC_w_sh_10_iter_conv_init_m	.206
180	mobilenet_v1_0.75_128	.205
181	BiT-M-R152x2	.205
182	BiT-M-R101x3	.205
183	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.205
184	resnet18_imagenet21kP	.204
185	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.204
186	mobilenet_v2_0.5_128	.204
187	resnet50_linf_4_robust	.204
188	convnext_tiny_imagenet_full_seed-0	.204
189	mobilenet_v1_1.0_192	.204
190	resnet50-SIN_IN	.204
191	BiT-S-R101x3	.203
192	0.5x_resnet-18	.203
193	CLIP_resnet50	.203
194	CLIP_resnet50_float32	.203
195	mobilenet_v1_1.0_224	.203
196	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.202
197	resnet_SIN_IN_FT_IN	.202
198	resnet50-SIN_IN_IN	.202
199	mobilenet_v1_0.75_192	.202
200	BiT-S-R50x3	.202
201	densenet-169	.202
202	AdvProp_efficientnet-b8	.202
203	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.201
204	resnet-18	.201
205	resnet50_tutorial	.200
206	resnet50-sup	.200
207	resnet_50_v1	.200
208	yudixie_resnet50_imagenet1kpret_0_240908	.200
209	resnet50_random_l2_perturb	.200
210	resnet50-SIN	.200
211	mobilenet_v1_0.5_128	.199
212	mobilenet_v2_0.35_160	.199
213	BiT-S-R152x2	.199
214	mobilenet_v2_1_0_128	.199
215	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.198
216	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.198
217	resnet-18-LC_m	.198
218	inception_v4-tf	.198
219	nasnet_large-tf	.198
220	mobilenet_v2_0.75_128	.197
221	my-model	.197
222	mobilenet_v1_0.25_224	.196
223	BiT-S-R101x1	.196
224	mobilenet_v1_1.0_160	.195
225	resnet18_ecoset_full	.195
226	nasnet_mobile-tf	.194
227	resnet34_imagenet_full	.194
228	mobilenet_v1_1.0_128	.193
229	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.192
230	resnet18-contrastive_multiview	.192
231	resnet50_imagenet_full	.192
232	alexnet_l2_3_robust	.192
233	resnet-18-LC_w_sh_10_iter_m	.192
234	efficientnet_b1_imagenet_full	.191
235	resnet50_robust_l2_eps3	.190
236	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.190
237	mobilenet_v1_0.25_192	.190
238	mobilenet_v1_0.75_160	.189
239	mobilenet_v1_0.5_224	.189
240	shufflenet_v2_x1_0	.189
241	resnet50_l2_3_robust	.189
242	BiT-M-R152x4	.189
243	alexnet_random_linf8_perturb	.187
244	squeezenet1_1	.187
245	voneresnet-50-robust	.187
246	resnet-18-LC_conv_init_m	.187
247	mobilenet_v1_0.5_192	.187
248	squeezenet1_0	.186
249	resnet18-supervised	.185
250	resnet18-colorization	.185
251	resnet18_imagenet_full	.185
252	texture_shape_resnet50_trained_on_SIN	.185
253	mobilenet_v2_0.35_192	.185
254	imagenet_l2_3_0	.184
255	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.184
256	yudixie_resnet50_object_class_0_240908	.184
257	SWSL_resnet50	.184
258	AdvProp_efficientnet-b2	.183
259	resnet50_linf_8_robust	.183
260	BiT-S-R152x4	.181
261	yudixie_resnet18_distance_translation_rotation_0_240719	.181
262	resnet101	.180
263	regnet_y_400mf	.180
264	alexnet_random_l2_3_perturb	.179
265	resnet50_ecoset_full	.179
266	r3m_resnet50	.179
267	resnet50_moco_v2	.178
268	AT_efficientnet-b4	.178
269	mobilenet_v1_0.25_160	.178
270	ReAlnet10	.177
271	RN50	.177
272	ReAlnet_incorrect	.177
273	resnet50_simclr	.177
274	mobilenet_v2_1.0_128-tf	.176
275	r3m_resnet50_nocrop	.176
276	yudixie_resnet18_imagenet1kpret_0_240719	.176
277	resnet_50_v2	.175
278	mobilenet_v1_0.5_160	.175
279	yudixie_resnet18_distance_translation_0_240719	.175
280	texture_shape_alexnet_trained_on_SIN	.174
281	alexnet	.174
282	prednet	.173
283	resnet-18_test_m	.173
284	deit_small_imagenet_100_seed-0	.172
285	yudixie_resnet18_translation_reg_0_240719	.172
286	vgg_19	.171
287	r3m_resnet18	.171
288	ViT_L_32_imagenet1k	.171
289	yudixie_resnet50_category_class_0_240908	.171
290	alexnet_ks_torevert	.170
291	alexnet	.170
292	alexnet-baseline	.170
293	alexnet	.170
294	alexnet	.170
295	alexnet	.170
296	alexnet	.170
297	alexnet	.170
298	alexnet	.170
299	alexnet	.170
300	yudixie_resnet18_distance_reg_0_240719	.169
301	mobilenet_v2_0.75_96	.169
302	mobilenet_v2_1.0_96	.169
303	ReAlnet08	.169
304	mobilenet_v2_0.35_128	.169
305	AT_efficientnet-b2	.169
306	mobilenet_v1_0.25_128	.168
307	mobilenet_v2_1-4_224_pytorch	.168
308	yudixie_resnet18_object_class_0_240719	.167
309	efficientnet_b0_imagenet_full	.166
310	cornet_s	.166
311	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.165
312	ReAlnet07	.165
313	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.164
314	resnet50_imagenet_10_seed-0	.164
315	ReAlnet06	.163
316	resnet50	.163
317	vonealexnet_gaussian_noise_std4_fixed	.163
318	AlexNet_SIN	.163
319	resnet50_byol	.163
320	yudixie_resnet50_translation_rotation_0_240908	.163
321	vggface	.162
322	imagenet_l2_10_0	.162
323	efficientnet-b7	.162
324	artResNet18_1	.162
325	ReAlnet02	.160
326	resnet18-depth_prediction	.160
327	ReAlnet05	.159
328	alexnet_linf_8_robust	.159
329	yudixie_resnet50_distance_translation_0_240908	.159
330	mobilenet_v2_0.35_96	.159
331	yudixie_resnet50_distance_translation_rotation_0_240908	.158
332	mobilenet_v2_0.5_96	.158
333	yudixie_resnet50_translation_reg_0_240908	.158
334	ReAlnet04	.157
335	SWSL_resnext101_32x8d	.157
336	yudixie_resnet18_distance_rotation_0_240719	.157
337	ReAlnet03	.157
338	yudixie_resnet50_distance_rotation_0_240908	.156
339	ReAlnet09	.156
340	resnet18-contrastive_predictive	.155
341	yudixie_resnet18_translation_rotation_0_240719	.154
342	ReAlnet01	.154
343	yudixie_resnet18_rotation_reg_0_240719	.152
344	r3m_resnet34	.150
345	yudixie_resnet50_distance_reg_0_240908	.150
346	blt_vs	.149
347	resnet50-meshes-lt-100-original-pretrained	.148
348	alexnet_early_checkpoint	.148
349	yudixie_resnet18_category_class_0_240719	.148
350	yudixie_resnet50_rotation_reg_0_240908	.146
351	alexnet_reduced_aliasing_early_checkpoint	.144
352	alexnet_robust_correct	.143
353	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.140
354	resnet50-meshes-lt-100-original-scratch	.135
355	v1-pyr-nodown	.116
356	alexnet_training_seed_02	.116
357	resnet50-cifar	.114
358	alexnet_training_seed_01	.108
359	resnet18-autoencoder	.108
360	CORnetZ_CIFAR10	.108
361	alexnet_training_seed_03	.105
362	alexnet_training_seed_05	.105
363	alexnet_training_seed_08	.103
364	alexnet_training_seed_10	.103
365	alexnet_training_seed_06	.101
366	alexnet_training_seed_09	.101
367	alexnet_training_seed_07	.098
368	resnet-50_untrained	.095
369	resnet50_FractalDB	.094
370	resnet50_pretrained_with_retinal_waves	.094
371	resnet-50x2_untrained	.094
372	alexnet_training_seed_04	.092
373	resnet-18_untrained	.089
374	hmax	.083
375	CORnetZ_CIFAR10_bs32_20_04	.081
376	yudixie_resnet18_random_0_240719	.076
377	yudixie_resnet50_random_0_240908	.068
378	resnet50_imagenet_1_seed-0	.065
379	mobilevit_small	.062
380	resnet-18-LC_untrained	.051
381	convnext_small_imagenet_10_seed-0	.042
382	resnet-152_v2_pytorch	.037
383	convnext_small_imagenet_1_seed-0	.031
384	deit_small_imagenet_10_seed-0	.027
385	deit_small_imagenet_1_seed-0	.021
386	fitvid_trained_on_physion	.021
387	pixels	.015
388	my-model	.013
389	PredRNN	X
390	resnet18-simclr	X
391	vonegrcnn_62e_nobn	X
392	resnet_152_v2	X
393	SimVP	X
394	TAU	X
395	MIM	X
396	AT_efficientnet-b0	X
397	vonegrcnn_52e_full	X
398	pnasnet_large	X
399	nasnet_large	X
400	openclip	X
401	yudixie_resnet50_imagenet1kpret_0_240312	X
402	efficientnet-b4	X
403	efficientnet-b2	X
404	resnet_152_v1	X
405	xception	X
406	efficientnet-b6	X
407	inception_v4	X
408	densenet_201_pytorch	X
409	pnasnet_large_pytorch	X
410	efficientnet-b0	X
411	inception_v3_pytorch	X
412	ConvLSTM	X
413	resnet50_ImageNet	X
414	resnet_101_v2	X

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