Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_Cavanaugh2002-surround_diameter")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50-VITO-8deg-cc	.983
2	mobilenet_v2_0.75_224-tf	.968
3	mobilenet_v2_0_75_224	.964
4	mobilenet_v2_1.0_192-tf	.953
5	resnet-101_v2-tf	.948
6	convnext_large:fb_in22k_ft_in1k	.940
7	nasnet_mobile	.938
8	mobilenet_v2_0_5_192	.932
9	mobilenet_v2_0_5_224	.926
10	mobilenet_v2_0_75_160	.922
11	resnet-50_v2-tf	.919
12	convnext_femto_ols:d1_in1k	.899
13	imagenet_l2_10_0	.887
14	inception_resnet_v2	.871
15	convnext_xlarge:fb_in22k_ft_in1k	.870
16	omnivore_swinB	.863
17	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.863
18	resnet-101_v1-tf	.857
19	cvt_cvt-21-384-in1k_4_LucyV4	.848
20	resnet50-SIN	.848
21	convnext_tiny_sup	.847
22	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.844
23	efficientnet_b0_imagenet_full	.839
24	imagenet_l2_3_0	.838
25	cvt_cvt-21-224-in1k_4_LucyV4	.834
26	convnext_small_imagenet_full_seed-0	.832
27	resnet-152_v1-tf	.829
28	nasnet_mobile-tf	.828
29	omnivore_swinS	.827
30	mobilenet_v2_0.5_224-tf	.824
31	resnet50-vicreg	.815
32	alexnet_linf_8_robust	.810
33	mobilenet_v2_1_0_224	.808
34	mobilenet_v2_1-4_224_pytorch	.807
35	convnext_base:clip_laiona_augreg_ft_in1k_384	.807
36	efficientnet_b1_imagenet_full	.805
37	convnext_small_imagenet_100_seed-0	.795
38	cvt_cvt-13-224-in1k_4_LucyV4	.794
39	BiT-M-R152x4	.794
40	nasnet_large-tf	.789
41	resnet50_robust_l2_eps1	.789
42	inception_v3	.785
43	0.5x_resnet-18_LC_w_sh_100_iter	.782
44	effnetb1_272x240	.781
45	FrankRobWobv0	.780
46	alexnet_robust_correct	.779
47	resnet50-barlow	.775
48	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.770
49	texture_shape_resnet50_trained_on_SIN	.768
50	resnet50_l2_3_robust	.767
51	resnet50-SIN_IN_IN	.765
52	resnet_SIN_IN_FT_IN	.765
53	RN50	.764
54	resnet50_moco_v2	.762
55	mobilenet_v2_1_3_224	.756
56	convnext_base_imagenet_full_seed-0	.756
57	resnet50-moclr8deg	.754
58	BiT-S-R50x3	.754
59	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.753
60	resnet-50-robust	.752
61	CLIP_resnet50_float32	.751
62	CLIP_resnet50	.752
63	inception_v3-tf	.752
64	deit_small_imagenet_100_seed-0	.751
65	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.749
66	yudixie_resnet18_imagenet1kpret_0_240719	.749
67	yudixie_resnet50_imagenet1kpret_0_240908	.747
68	resnet_50_v1	.747
69	resnet50-sup	.748
70	resnet50_tutorial	.747
71	resnet50_linf_8_robust	.746
72	alexnet_training_seed_09	.744
73	convnext_tiny:in12k_ft_in1k	.742
74	antialiased-rnext101_32x8d	.742
75	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.743
76	alexnet_training_seed_10	.742
77	SWSL_resnext101_32x8d	.742
78	resnet50_byol	.740
79	mobilevit_small	.740
80	resnet50	.738
81	resnet50_linf_4_robust	.737
82	mobilenet_v2_0.35_224	.736
83	alexnet_l2_3_robust	.736
84	resnet-18-LC_w_sh_100_iter_conv_init_m	.736
85	resnet50_robust_l2_eps3	.736
86	mobilenet_v2_1_0_160	.738
87	texture_shape_alexnet_trained_on_SIN	.736
88	resnet50-vicregl0p75	.735
89	SWSL_resnet50	.736
90	resnet50_FractalDB	.735
91	pnasnet_large-tf	.735
92	alexnet_training_seed_01	.734
93	resnet-50_v1-tf	.733
94	convnext_large_imagenet_full_seed-0	.732
95	resnet18_imagenet21kP	.729
96	efficientnet_b0	.728
97	BiT-S-R152x4	.727
98	alexnet_training_seed_07	.726
99	resnet50_random_l2_perturb	.725
100	alexnet_training_seed_02	.723
101	yudixie_resnet18_object_class_0_240719	.719
102	0.5x_resnet-18_LC	.713
103	alexnet_random_l2_3_perturb	.710
104	alexnet	.710
105	blt_vs	.691
106	BiT-S-R152x2	.688
107	CLIP-RN50	.689
108	konkle_alexnetgn_ipcl_ref01_primary_model	.686
109	inception_v2-tf	.681
110	BiT-S-R101x3	.680
111	resnet50_random_linf8_perturb	.679
112	convnext_tiny_imagenet_full_seed-0	.677
113	alexnet_training_seed_04	.674
114	inception_v4-tf	.669
115	ViT_L_32_imagenet1k	.664
116	cvt_cvt-13-384-in1k_4_LucyV4	.664
117	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.664
118	mobilenet_v1_0.25_128	.658
119	mobilenet_v2_0.75_96	.658
120	mobilenet_v2_0.5_160	.658
121	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.652
122	efficientnet_b2_imagenet_full	.635
123	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.632
124	resnet50_simclr	.627
125	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.628
126	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.626
127	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.624
128	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.624
129	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.622
130	BiT-S-R101x1	.621
131	omnivore_swinT	.620
132	resnet-18-LC_conv_init_m	.619
133	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.613
134	densenet-121-keras	.611
135	r3m_resnet34	.610
136	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.609
137	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.609
138	effnetv2m_custom384	.603
139	deit_large_imagenet_full_seed-0	.601
140	antialiased-r50	.601
141	resnet-152_v2-tf	.598
142	cv_18_dagger_408_pretrained	.597
143	vgg-16-keras	.592
144	CORnetZ_CIFAR10	.589
145	BiT-S-R50x1	.588
146	r3m_resnet18	.587
147	BiT-M-R101x3	.584
148	my-model	.582
149	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.580
150	densenet-169-keras	.578
151	deit_base_imagenet_full_seed-0	.567
152	briaai_rmbg_1_4	.562
153	densenet-169	.557
154	resnet-18-LC_w_sh_1_iter_conv_init_m	.556
155	vgg_16	.555
156	vggface	.555
157	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.554
158	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.551
159	convnext_small_imagenet_10_seed-0	.550
160	r3m_resnet50_nocrop	.545
161	yudixie_resnet18_random_0_240719	.543
162	BiT-M-R152x2	.541
163	BiT-M-R50x3	.537
164	vgg-19-keras	.534
165	vit_large_patch14_clip_224:openai_ft_in1k	.532
166	densenet-121	.531
167	vit_large_patch14_clip_224:laion2b_ft_in1k	.525
168	AdvProp_efficientnet-b2	.519
169	densenet-201-keras	.518
170	AT_efficientnet-b2	.517
171	vit_relpos_base_patch16_clsgap_224:sw_in1k	.516
172	BiT-M-R50x1	.512
173	focalnet_tiny_lrf_in1k	.509
174	resnet-18-LC_w_sh_10_iter_m	.507
175	resnet-152_v2_pytorch	.506
176	mobilenet_v2_0.75_192-tf	.505
177	shufflenet_v2_x1_0	.504
178	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.503
179	AdvProp_efficientnet-b4	.502
180	antialias-resnet152	.498
181	grcnn_robust_v1	.497
182	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.494
183	BiT-M-R101x1	.494
184	resnet-18-LC_w_sh_100_iter_m	.493
185	resnet-18-LC_w_sh_10_iter_conv_init_m	.491
186	custom_model_cv_18_dagger_408	.489
187	CLIP_ViT-B_32	.481
188	r3m_resnet50	.480
189	AdvProp_efficientnet-b8	.476
190	yolos_tiny	.471
191	resnet-18-LC_1st_conv_m	.470
192	grcnn	.467
193	xception	.466
194	grcnn_v2_text_noise	.462
195	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.451
196	AdvProp_efficientnet-b7	.448
197	yudixie_resnet50_random_0_240908	.447
198	mobilenet_v2_1_0_192	.442
199	alexnet_training_seed_03	.440
200	vit_large_patch14_clip_336:laion2b_ft_in1k	.440
201	efficientnet-b7	.433
202	grcnn_109	.427
203	yudixie_resnet18_translation_reg_0_240719	.426
204	mobilenet_v2_1.3_224-tf	.426
205	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.425
206	AdvProp_efficientnet-b0	.423
207	mobilenet_v2_1_4_224	.423
208	resnet-18-LC_d_w_sh_1x1_conv_init_m	.418
209	yudixie_resnet18_distance_translation_0_240719	.417
210	yudixie_resnet18_translation_rotation_0_240719	.417
211	yudixie_resnet18_distance_rotation_0_240719	.414
212	densenet-201	.414
213	resnet-18-LC_m	.411
214	yudixie_resnet18_distance_translation_rotation_0_240719	.409
215	mobilenet_v2_0_75_192	.408
216	vonealexnet_gaussian_noise_std4_fixed	.404
217	yudixie_resnet18_distance_reg_0_240719	.399
218	yudixie_resnet18_category_class_0_240719	.397
219	yudixie_resnet50_rotation_reg_0_240908	.396
220	yudixie_resnet50_distance_reg_0_240908	.394
221	yudixie_resnet50_translation_rotation_0_240908	.395
222	yudixie_resnet50_translation_reg_0_240908	.394
223	yudixie_resnet50_distance_rotation_0_240908	.394
224	yudixie_resnet50_distance_translation_rotation_0_240908	.392
225	yudixie_resnet50_distance_translation_0_240908	.392
226	resnet101	.390
227	mobilenet_v2_1.4_224-tf	.389
228	mobilenet_v2_1.0_224-tf	.389
229	mobilenet_v2_1.0_128-tf	.389
230	resnet50-vitoimagevidnet8	.390
231	mobilenet_v2_1.0_96	.389
232	resnet-50x2_untrained	.389
233	resnet-50_untrained	.389
234	hmax	.390
235	resnet-18-LC_w_sh_1_iter_m	.390
236	resnet-18_test_m	.390
237	resnet-18_untrained	.390
238	0.5x_resnet-18_LC_w_sh_10_iter	.390
239	0.5x_resnet-18_LC_w_sh_1_iter	.390
240	barlow-twins-resnet50	.389
241	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.390
242	yudixie_resnet50_category_class_0_240908	.389
243	yudixie_resnet18_rotation_reg_0_240719	.390
244	inception_v1	.389
245	yudixie_resnet50_object_class_0_240908	.389
246	artResNet18_1	.389
247	deit_small_imagenet_10_seed-0	.390
248	resnet_101_v1	.390
249	0.5x_resnet-18	.389
250	resnet50-simclr-vissl	.390
251	mobilenet_v2_0.5_192-tf	.389
252	resnet50-vicregl0p9	.389
253	ReAlnet02	.388
254	ReAlnet06	.389
255	ReAlnet10_cornet	.389
256	CORnet-S	.389
257	ReAlnet_incorrect	.389
258	resnet34_imagenet_full	.389
259	ReAlnet02_cornet	.389
260	ReAlnet08	.389
261	ReAlnet06_cornet	.388
262	ReAlnet01_cornet	.389
263	ReAlnet03_cornet	.389
264	resnet50-SIN_IN	.388
265	resnet18_ecoset_full	.388
266	ReAlnet10	.389
267	ReAlnet03	.388
268	ReAlnet01	.388
269	ReAlnet08_cornet	.388
270	ReAlnet07_cornet	.388
271	ReAlnet07	.387
272	ReAlnet04_cornet	.387
273	ReAlnet09	.387
274	ReAlnet09_cornet	.387
275	ReAlnet04	.387
276	alexnet	.386
277	ReAlnet05	.387
278	ReAlnet05_cornet	.386
279	alexnet	.386
280	alexnet	.385
281	alexnet	.385
282	alexnet	.385
283	alexnet	.385
284	resnet34_ecoset_full	.385
285	alexnet	.386
286	alexnet-baseline	.385
287	voneresnet-50-non_stochastic	.384
288	resnet18_imagenet_full	.384
289	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.379
290	v1-pyr-nodown	.374
291	Res2Net50_26w_4s	.369
292	cornet_s	.368
293	resnet-18-LC_untrained	.361
294	vonegrcnn_47e	.358
295	vision_transformer_vit_large_patch16_224	.355
296	VOneCORnet-S	.344
297	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.336
298	vonegrcnn_52e_full	.334
299	voneresnet-50	.329
300	resnext101_32x8d_wsl	.328
301	voneresnet-50-robust	.319
302	vonegrcnn_62e_nobn	.314
303	vonegrcnn_47e	.314
304	convnext_small_imagenet_1_seed-0	.312
305	alexnet_training_seed_05	.311
306	alexnet_training_seed_08	.305
307	vit_base_patch16_clip_224:openai_ft_in1k	.303
308	AdvProp_efficientnet-b6	.297
309	alexnet_training_seed_06	.281
310	resnet_50_v2	.280
311	resnet50_imagenet_1_seed-0	.269
312	alexnet_reduced_aliasing_early_checkpoint	.265
313	resnet50_imagenet_10_seed-0	.257
314	ViT-B/32	.255
315	resnet101_imagenet_full	.245
316	resnet50_ecoset_full	.222
317	resnet101_ecoset_full	.217
318	alexnet_random_linf8_perturb	.198
319	resnet152_ecoset_full	.197
320	alexnet_early_checkpoint	.190
321	vgg_19	.188
322	resnet152_imagenet_full	.177
323	resnet50-meshes-lt-100-original-pretrained	.156
324	mobilenet_v2_1.0_160-tf	.156
325	CORnet-Z	.156
326	inception_v1-tf	.156
327	resnet50_imagenet_full	.156
328	AlexNet_SIN	.156
329	resnet50_imagenet_100_seed-0	.156
330	deit_small_imagenet_full_seed-0	.028
331	mobilenet_v2_1_0_128	.000
332	mobilenet_v2_0.5_128	.000
333	resnet50-meshes-lt-100-original-scratch	.000
334	resnet50-cifar	.000
335	resnet50	.000
336	my-model	.000
337	CORnetZ_CIFAR10_bs32_20_04	.000
338	mobilenet_v2_0.75_160-tf	.000
339	resnet50_pretrained_with_retinal_waves	.000
340	mobilenet_v2_0.75_128	.000
341	yudixie_resnet50_imagenet1kpret_0_240312	X
342	resnet_152_v2	X
343	inception_v3_pytorch	X
344	pnasnet_large_pytorch	X
345	regnet_y_400mf	X
346	alexnet_ks_torevert	X
347	densenet_201_pytorch	X
348	deit_small_imagenet_1_seed-0	X
349	mobilenet_v2_0.35_160	X
350	nasnet_large	X
351	pnasnet_large	X
352	resnext101_32x32d_wsl	X
353	inception_v4	X
354	resnext101_32x48d_wsl	X
355	vonegrcnn_62e_nobn	X
356	resnet_101_v2	X
357	resnet18-supervised	X
358	tv_efficientnet-b1	X
359	resnet_152_v1	X
360	vonegrcnn_52e_full	X
361	openclip	X
362	resnet50_ImageNet	X
363	fitvid_trained_on_physion	X
364	resnext101_32x16d_wsl	X

Benchmark bibtex

@article{Cavanaugh2002,
            author = {Cavanaugh, James R. and Bair, Wyeth and Movshon, J. A.},
            doi = {10.1152/jn.00692.2001},
            isbn = {0022-3077 (Print) 0022-3077 (Linking)},
            issn = {0022-3077},
            journal = {Journal of Neurophysiology},
            mendeley-groups = {Benchmark effects/Done,Benchmark effects/*Surround Suppression},
            number = {5},
            pages = {2530--2546},
            pmid = {12424292},
            title = {{Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons}},
            url = {http://www.physiology.org/doi/10.1152/jn.00692.2001},
            volume = {88},
            year = {2002}
            }

Ceiling

0.95.

Note that scores are relative to this ceiling.

Data: Marques2020_Cavanaugh2002

Metric: surround_diameter