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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Sanghavi2020.V4-pls")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet01_cornet_fov4	.701
2	alexnet_fov4	.689
3	compact_resnet50_robust_V4	.675
4	custom_model_cv_18_dagger_408	.669
5	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.664
6	AdvProp_efficientnet-b4	.663
7	densenet-169-keras	.663
8	densenet-121-keras	.662
9	resnet50	.662
10	ReAlnet07_cornet	.661
11	ReAlnet08_cornet	.661
12	ReAlnet02_cornet	.660
13	compact_resnet50_V4	.659
14	ReAlnet10_cornet	.658
15	yudixie_resnet50_imagenet1kpret_0_240908	.656
16	resnet_50_v1	.656
17	resnet50-sup	.656
18	resnet_50_v1_spiking	.656
19	resnet_50_v1_spiking_l4	.656
20	resnet_50_v1_spiking_l4a	.656
21	resnet50_tutorial	.656
22	resnet_50_v1_spiking_l3	.656
23	yudixie_resnet50_imagenet1kpret_0_240312	.656
24	ReAlnet03_cornet	.656
25	ReAlnet05_cornet	.656
26	efficientnet_b1_imagenet_full	.656
27	ReAlnet04_cornet	.655
28	densenet-201-keras	.655
29	ReAlnet09_cornet	.655
30	antialiased-r50	.655
31	antialiased-rnext101_32x8d	.654
32	resnet50_random_linf8_perturb	.653
33	efficientnet_b2_imagenet_full	.653
34	SWSL_resnet50	.653
35	resnet34_ecoset_full	.653
36	focalnet_tiny_lrf_in1k	.653
37	ReAlnet06_cornet	.652
38	resnet50_random_l2_perturb	.652
39	resnet50-SIN_IN_IN	.651
40	resnet_SIN_IN_FT_IN	.651
41	resnet18-colorization	.651
42	ReAlnet01_cornet	.651
43	resnet-50_v1-tf	.650
44	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.650
45	inception_v1-tf	.649
46	resnet50-SIN_IN	.648
47	resnet-101_v1-tf	.648
48	resnet50_linf_4_robust	.648
49	AdvProp_efficientnet-b8	.647
50	effnetv2m_custom384	.647
51	xception-keras	.647
52	ReAlnet02_ALL	.647
53	resnext101_32x8d_wsl	.646
54	ReAlnet01_ALL	.646
55	ReAlnet08_ALL	.646
56	ReAlnet09_ALL	.646
57	resnet-152_v1-tf	.646
58	vgg_16	.646
59	vonegrcnn_62e_nobn	.646
60	ReAlnet07_ALL	.646
61	resnet-18-LC_w_sh_100_iter_conv_init_m	.646
62	ReAlnet06_ALL	.646
63	vgg-16-keras	.646
64	vit_base_patch16_clip_224:openai_ft_in1k	.646
65	vonegrcnn_52e_full	.645
66	resnext101_32x16d_wsl	.645
67	resnet152_imagenet_full	.645
68	vision_transformer_vit_large_patch16_224	.645
69	ReAlnet04_ALL	.645
70	deit_large_imagenet_full_seed-0	.645
71	ReAlnet03_ALL	.645
72	BiT-M-R101x3	.645
73	vonegrcnn_47e	.645
74	ReAlnet10_ALL	.644
75	resnet-50_v2-tf	.644
76	ReAlnet05_ALL	.644
77	resnet50_ewconplus_wzc	.644
78	resnet152_ecoset_full	.644
79	resnet-18-LC_d_w_sh_1x1_conv_init_m	.643
80	effnetb1_272x240	.643
81	AdvProp_efficientnet-b6	.643
82	resnet50_imagenet_100_seed-0	.643
83	BiT-S-R101x3	.643
84	omnivore_swinB	.642
85	Res2Net50_26w_4s	.642
86	resnet101_ecoset_full	.642
87	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.642
88	AT_efficientnet-b7	.642
89	resnet18_imagenet_full	.641
90	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.641
91	voneresnet-50	.641
92	resnet50_l2_3_robust	.641
93	0.5x_resnet-18_LC_w_sh_10_iter	.640
94	compact_vgg19_V4	.640
95	AdvProp_efficientnet-b7	.640
96	resnet18_imagenet21kP	.640
97	yudixie_resnet18_imagenet1kpret_0_240719	.640
98	inception_resnet_v2	.639
99	resnet18_ecoset_full	.639
100	konkle_alexnetgn_ipcl_ref01_primary_model	.639
101	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.639
102	inception_v2-tf	.639
103	voneresnet-50-robust	.638
104	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.638
105	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.638
106	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.638
107	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.638
108	AdvProp_efficientnet-b2	.638
109	mobilenet_v1_1.0_160	.638
110	convnext_tiny_imagenet_full_seed-0	.638
111	AdvProp_efficientnet-b0	.638
112	resnext101_32x48d_wsl	.637
113	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.637
114	efficientnet_b0_imagenet_full	.637
115	efficientnet_b0	.636
116	AT_efficientnet-b2	.636
117	mobilenet_v2_1_4_224	.636
118	resnet101_imagenet_full	.636
119	BiT-M-R152x2	.636
120	texture_shape_resnet50_trained_on_SIN	.636
121	resnet34_imagenet_full	.636
122	resnet-18-LC_w_sh_100_iter_m	.636
123	SWSL_resnext101_32x8d	.636
124	resnet-18-LC_w_sh_1_iter_m	.636
125	convnext_small_imagenet_full_seed-0	.636
126	omnivore_swinS	.635
127	resnet50_imagenet_full	.635
128	convnext_base_imagenet_full_seed-0	.635
129	resnet-152_v2-tf	.635
130	antialias-resnet152	.634
131	CORnet-S	.634
132	compact_resnet50_IT	.634
133	cv_18_dagger_408_pretrained	.634
134	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.634
135	BiT-S-R50x3	.634
136	omnivore_swinT	.634
137	resnet50_robust_l2_eps1	.633
138	BiT-M-R152x4	.633
139	resnet101	.633
140	vit_relpos_base_patch16_clsgap_224:sw_in1k	.633
141	resnet-101_v2-tf	.632
142	voneresnet-50-non_stochastic	.632
143	mobilenet_v1_1.0_224	.632
144	CLIP-RN50	.632
145	resnet-18	.632
146	resnet-18-LC_w_sh_1_iter_conv_init_m	.632
147	FrankRobWobv0	.632
148	resnet-18-LC_w_sh_10_iter_conv_init_m	.631
149	convnext_tiny_sup	.631
150	grcnn_v2_text_noise	.631
151	VOneCORnet-S	.631
152	resnet50_moco_v2	.631
153	mobilenet_v2_1.0_128-tf	.631
154	convnext_femto_ols:d1_in1k	.631
155	BiT-S-R152x2	.631
156	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.630
157	nasnet_mobile	.630
158	BiT-M-R101x1	.630
159	resnext101_32x32d_wsl	.629
160	mobilenet_v2_1_3_224	.629
161	AT_efficientnet-b4	.629
162	resnet50-vicreg	.629
163	alexnet	.629
164	densenet-169	.629
165	regnet_y_400mf	.628
166	AlexNet_SIN_fov4	.628
167	barlow-twins-resnet50	.627
168	inception_v3-tf	.627
169	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.627
170	deit_small_imagenet_full_seed-0	.627
171	nasnet_large-tf	.627
172	densenet-201	.627
173	grcnn_109	.626
174	resnet50_ecoset_full	.626
175	resnet50-vicregl0p9	.626
176	BiT-M-R50x1	.626
177	resnet-18_test_m	.625
178	resnet18-local_aggregation	.625
179	mobilenet_v2_1.3_224-tf	.625
180	cvt_cvt-21-384-in1k_4_LucyV4	.625
181	cvt_cvt-13-384-in1k_4_LucyV4	.623
182	resnet50-vicregl0p75	.623
183	resnet50-VITO-8deg-cc	.623
184	pnasnet_large-tf	.623
185	0.5x_resnet-18_LC_w_sh_100_iter	.623
186	grcnn	.623
187	resnet_50_v2	.623
188	tv_efficientnet-b1	.623
189	resnet50-barlow	.622
190	mobilenet_v2_1_0_128	.622
191	alexnet_random_linf8_perturb	.622
192	densenet-121	.622
193	vgg_19	.622
194	inception_v3	.622
195	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.622
196	mobilenet_v1_0.75_128	.621
197	mobilenet_v2_0_75_224	.621
198	RN50	.621
199	convnext_tiny:in12k_ft_in1k	.621
200	alexnet_random_l2_3_perturb	.621
201	deit_base_imagenet_full_seed-0	.621
202	vonealexnet_gaussian_noise_std4_fixed	.621
203	ViT-B/32	.621
204	CLIP_resnet50	.620
205	CLIP_resnet50_float32	.620
206	CLIP_ViT-B_32	.620
207	cornet_s	.619
208	mobilenet_v2_1.4_224-tf	.619
209	mobilenet_v2_1_0_192	.619
210	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.619
211	mobilenet_v2_1.0_160-tf	.619
212	fixres_resnext101_32x48d_wsl	.619
213	cvt_cvt-13-224-in1k_4_LucyV4	.618
214	mobilenet_v1_1.0_192	.618
215	compact_resnet50_robust_IT	.618
216	mobilenet_v2_0.75_224-tf	.618
217	mobilenet_v1_0.75_160	.618
218	mobilenet_v2_1_0_224	.618
219	resnet50-moclr8deg	.618
220	compact_cornets_V4	.617
221	mobilenet_v2_0_75_192	.617
222	grcnn_robust_v1	.617
223	convnext_large_imagenet_full_seed-0	.617
224	resnet18-instance_recognition	.617
225	BiT-S-R101x1	.617
226	BiT-S-R50x1	.617
227	alexnet	.617
228	alexnet-baseline	.617
229	alexnet	.617
230	alexnet	.617
231	alexnet	.617
232	alexnet	.617
233	alexnet	.617
234	alexnet	.617
235	alexnet	.617
236	alexnet	.617
237	alexnet_ks_torevert	.617
238	convnext_xlarge:fb_in22k_ft_in1k	.617
239	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.616
240	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.616
241	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.616
242	convnext_base:clip_laiona_augreg_ft_in1k_384	.615
243	vgg-19-keras	.615
244	BiT-M-R50x3	.615
245	resnet50-simclr-vissl	.614
246	mobilenet_v2_1.0_192-tf	.614
247	mobilenet_v1_1.0_128	.614
248	0.5x_resnet-18_LC_w_sh_1_iter	.614
249	imagenet_l2_3_0	.614
250	0.5x_resnet-18	.614
251	vit_large_patch14_clip_224:openai_ft_in1k	.613
252	mobilenet_v1_0.75_192	.613
253	resnet50-vitoimagevidnet8	.613
254	resnet50_robust_l2_eps3	.612
255	yudixie_resnet50_object_class_0_240908	.612
256	mobilenet_v1_0.75_224	.612
257	resnet50_byol	.612
258	resnet50_byol	.612
259	resnet50-SIN	.612
260	mobilenet_v2_0.75_192-tf	.611
261	cvt_cvt-21-224-in1k_4_LucyV4	.609
262	nasnet_mobile-tf	.609
263	efficientnet-b7	.609
264	mobilenet_v2_0.75_160-tf	.609
265	mobilenet_v2_1.0_224-tf	.609
266	inception_v4-tf	.608
267	mobilenet_v2_1_0_160	.608
268	mobilenet_v2_0.75_128	.608
269	squeezenet1_0	.608
270	BiT-S-R152x4	.608
271	yudixie_resnet50_translation_reg_0_240908	.607
272	yudixie_resnet50_category_class_0_240908	.606
273	CORnet-Z	.606
274	resnet-18-LC_1st_conv_m	.605
275	mobilenet_v2_0_5_192	.605
276	ReAlnet01_cornet_fov12	.605
277	resnet18-relative_position	.604
278	resnet-18-LC_m	.604
279	mobilenet_v1_0.5_192	.604
280	resnet-34	.604
281	vit_large_patch14_clip_224:laion2b_ft_in1k	.603
282	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.603
283	mobilenet_v2_0_5_224	.603
284	shufflenet_v2_x1_0	.602
285	squeezenet1_1	.602
286	RN50	.602
287	inception_v1	.602
288	resnet-18-LC_w_sh_10_iter_m	.602
289	resnet18-contrastive_multiview	.601
290	mobilenet_v2_0.5_224-tf	.601
291	mobilenet_v2_0.5_128	.601
292	yudixie_resnet50_distance_translation_0_240908	.600
293	mobilenet_v1_0.25_224	.600
294	mobilenet_v1_0.5_224	.599
295	convnext_small_imagenet_100_seed-0	.599
296	resnet50_linf_8_robust	.598
297	mobilenet_v2_0_75_160	.598
298	yudixie_resnet50_translation_rotation_0_240908	.597
299	yudixie_resnet50_distance_reg_0_240908	.597
300	resnet18-supervised	.597
301	yudixie_resnet50_distance_translation_rotation_0_240908	.597
302	yudixie_resnet50_distance_rotation_0_240908	.596
303	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.596
304	mobilenet_v2_1.0_96	.595
305	vit_large_patch14_clip_336:laion2b_ft_in1k	.594
306	mobilenet_v2_0.5_192-tf	.594
307	convnext_large:fb_in22k_ft_in1k	.594
308	mobilenet_v1_0.5_160	.593
309	0.5x_resnet-18_LC	.593
310	resnet-18-LC_conv_init_m	.592
311	mobilenet_v1_0.5_128	.589
312	mobilenet_v2_0.35_224	.588
313	alexnet_reduced_aliasing_early_checkpoint	.588
314	resnet18-deepcluster	.587
315	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.585
316	resnet50_primary_visual_cortex	.585
317	mobilenet_v2_0.5_160	.585
318	resnet50_simclr	.585
319	r3m_resnet18	.584
320	ReAlnet10	.583
321	r3m_resnet34	.581
322	mobilenet_v2_0.35_192	.581
323	ReAlnet02	.579
324	yudixie_resnet18_category_class_0_240719	.579
325	ReAlnet08	.579
326	alexnet_early_checkpoint	.579
327	mobilenet_v2_0.75_96	.578
328	ReAlnet07	.577
329	texture_shape_alexnet_trained_on_SIN	.575
330	ReAlnet03	.574
331	yudixie_resnet50_rotation_reg_0_240908	.573
332	AlexNet_SIN	.573
333	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.573
334	ReAlnet06	.573
335	imagenet_l2_10_0	.572
336	ReAlnet01	.572
337	ReAlnet09	.570
338	ReAlnet04	.569
339	mobilenet_v1_0.25_192	.569
340	ReAlnet05	.568
341	mobilenet_v2_0.5_96	.566
342	yudixie_resnet18_object_class_0_240719	.565
343	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.562
344	alexnet_l2_3_robust	.562
345	yolos_tiny	.561
346	mobilenet_v1_0.25_160	.561
347	mobilenet_v2_0.35_160	.559
348	yudixie_resnet18_distance_translation_rotation_0_240719	.559
349	compact_cornets_IT	.556
350	ReAlnet01_cornet_fov16	.554
351	resnet50	.554
352	resnet-50-pytorch	.552
353	resnet-50-robust	.552
354	r3m_resnet50	.552
355	vggface	.550
356	yudixie_resnet18_translation_rotation_0_240719	.549
357	yudixie_resnet18_distance_reg_0_240719	.548
358	yudixie_resnet18_distance_rotation_0_240719	.545
359	yudixie_resnet18_translation_reg_0_240719	.545
360	artResNet18_1	.544
361	ViT_L_32_imagenet1k	.543
362	r3m_resnet50_nocrop	.539
363	compact_vgg19_IT	.539
364	mobilenet_v2_0.35_96	.536
365	yudixie_resnet18_rotation_reg_0_240719	.535
366	yudixie_resnet18_distance_translation_0_240719	.534
367	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.534
368	deit_small_imagenet_100_seed-0	.533
369	mobilenet_v1_0.25_128	.520
370	my-model	.519
371	mobilenet_v2_0.35_128	.514
372	resnet50_imagenet_10_seed-0	.514
373	mobilenet_v2_1-4_224_pytorch	.512
374	AlexNet_SIN_fov12	.503
375	prednet	.502
376	resnet18-depth_prediction	.498
377	alexnet_training_seed_01_fov4	.497
378	resnet50-cifar	.471
379	alexnet_linf_8_robust	.464
380	alexnet_fov12	.464
381	alexnet_robust_correct	.448
382	resnet18-contrastive_predictive	.443
383	alexnet_fov16	.426
384	AlexNet_SIN_fov16	.421
385	resnet18-autoencoder	.412
386	alexnet_training_seed_02	.411
387	briaai_rmbg_1_4	.409
388	v1-pyr-nodown	.407
389	resnet-152_v2_pytorch	.403
390	resnet50-meshes-lt-100-original-pretrained	.403
391	alexnet_training_seed_06	.400
392	alexnet_training_seed_09	.394
393	CORnetZ_CIFAR10	.392
394	alexnet_training_seed_01	.390
395	alexnet_training_seed_03	.388
396	resnet50-meshes-lt-100-original-scratch	.388
397	alexnet_training_seed_07	.387
398	alexnet_training_seed_08	.386
399	alexnet_training_seed_10	.385
400	alexnet_training_seed_05	.384
401	resnet50_pretrained_with_retinal_waves	.382
402	CORnetZ_CIFAR10_bs32_20_04	.382
403	alexnet_training_seed_04	.378
404	yudixie_resnet50_random_0_240908	.372
405	mobilevit_small	.371
406	resnet-18_untrained	.369
407	resnet50_FractalDB	.369
408	resnet-50_untrained	.368
409	alexnet_training_seed_01_fov12	.363
410	resnet-50x2_untrained	.361
411	yudixie_resnet18_random_0_240719	.356
412	alexnet_training_seed_01_fov16	.342
413	resnet50_imagenet_1_seed-0	.335
414	resnet-18-LC_untrained	.303
415	deit_small_imagenet_10_seed-0	.175
416	convnext_small_imagenet_10_seed-0	.136
417	fitvid_trained_on_physion	.132
418	deit_small_imagenet_1_seed-0	.114
419	convnext_small_imagenet_1_seed-0	.104
420	pixels	.085
421	my-model	.085
422	resnet18-simclr
423	openclip
424	resnet50_ImageNet
425	resnet_152_v1
426	ConvLSTM
427	PredRNN
428	SimVP
429	TAU
430	MIM
431	hmax
432	densenet_201_pytorch
433	pnasnet_large_pytorch
434	inception_v3_pytorch
435	inception_v4
436	xception
437	blt_vs
438	pnasnet_large
439	nasnet_large
440	resnet_101_v2
441	convnext_xxlarge:clip_laion2b_soup_ft_in1k
442	vonegrcnn_62e_nobn
443	resnet_152_v2
444	resnet_101_v1
445	vonegrcnn_47e
446	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k
447	vonegrcnn_52e_full
448	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k

Benchmark bibtex

@misc{Sanghavi_DiCarlo_2021,
  title={Sanghavi2020},
  url={osf.io/chwdk},
  DOI={10.17605/OSF.IO/CHWDK},
  publisher={OSF},
  author={Sanghavi, Sachi and DiCarlo, James J},
  year={2021},
  month={Nov}
}

Ceiling

0.89.

Note that scores are relative to this ceiling.

Data: Sanghavi2020.V4

5760 stimuli recordings from 47 sites in V4

Metric: pls