Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50-barlow	.986
2	resnet-101_v2-tf	.984
3	resnet50-vicreg	.972
4	nasnet_mobile	.972
5	mobilenet_v2_1_3_224	.970
6	mobilenet_v2_1.0_192-tf	.968
7	inception_v3-tf	.970
8	yudixie_resnet18_imagenet1kpret_0_240719	.965
9	convnext_femto_ols:d1_in1k	.964
10	resnet50-SIN	.963
11	mobilenet_v2_0.35_224	.940
12	resnet-18-LC_w_sh_100_iter_conv_init_m	.939
13	mobilenet_v2_0.5_224-tf	.938
14	resnet-50_v2-tf	.939
15	mobilenet_v2_0_75_160	.932
16	resnet50-SIN_IN_IN	.931
17	resnet50-sup	.927
18	yudixie_resnet50_imagenet1kpret_0_240908	.926
19	resnet50_tutorial	.926
20	resnet50_FractalDB	.924
21	resnet_50_v1	.925
22	mobilenet_v2_0_75_224	.925
23	resnet50_robust_l2_eps1	.916
24	FrankRobWobv0	.914
25	texture_shape_resnet50_trained_on_SIN	.914
26	alexnet_l2_3_robust	.911
27	imagenet_l2_3_0	.908
28	mobilenet_v2_0_5_192	.905
29	resnet_SIN_IN_FT_IN	.903
30	BiT-M-R152x4	.900
31	resnet50-moclr8deg	.897
32	AdvProp_efficientnet-b6	.895
33	AdvProp_efficientnet-b8	.887
34	texture_shape_alexnet_trained_on_SIN	.882
35	effnetb1_272x240	.877
36	efficientnet_b0	.878
37	inception_v2-tf	.877
38	inception_v3	.874
39	RN50	.872
40	resnet-18-LC_conv_init_m	.870
41	resnet50_l2_3_robust	.870
42	alexnet	.868
43	resnet50-VITO-8deg-cc	.869
44	nasnet_mobile-tf	.869
45	yudixie_resnet18_object_class_0_240719	.868
46	resnet50_byol	.865
47	convnext_large:fb_in22k_ft_in1k	.863
48	resnet50-vicregl0p75	.859
49	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.855
50	inception_resnet_v2	.854
51	konkle_alexnetgn_ipcl_ref01_primary_model	.852
52	resnet-152_v1-tf	.851
53	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.852
54	efficientnet-b7	.849
55	mobilenet_v2_0.75_224-tf	.847
56	resnet-50_v1-tf	.845
57	mobilenet_v2_1_0_224	.844
58	resnet50_linf_4_robust	.842
59	mobilenet_v2_0.75_192-tf	.841
60	convnext_small_imagenet_full_seed-0	.841
61	yudixie_resnet18_rotation_reg_0_240719	.836
62	convnext_tiny_sup	.837
63	resnet-18-LC_w_sh_1_iter_conv_init_m	.834
64	efficientnet_b2_imagenet_full	.833
65	resnet-50-robust	.831
66	BiT-S-R50x1	.830
67	imagenet_l2_10_0	.830
68	nasnet_large-tf	.828
69	efficientnet_b0_imagenet_full	.827
70	CLIP_resnet50	.826
71	AdvProp_efficientnet-b7	.826
72	CLIP_resnet50_float32	.825
73	resnet50	.823
74	BiT-S-R101x3	.821
75	convnext_tiny:in12k_ft_in1k	.820
76	resnet18_imagenet21kP	.818
77	BiT-S-R101x1	.815
78	mobilenet_v2_0_5_224	.815
79	0.5x_resnet-18_LC_w_sh_100_iter	.815
80	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.815
81	AdvProp_efficientnet-b0	.814
82	resnet-18-LC_w_sh_10_iter_conv_init_m	.812
83	vgg_16	.813
84	alexnet_linf_8_robust	.807
85	0.5x_resnet-18_LC	.807
86	BiT-S-R152x4	.803
87	alexnet_random_l2_3_perturb	.801
88	convnext_small_imagenet_100_seed-0	.801
89	BiT-S-R50x3	.799
90	cvt_cvt-13-384-in1k_4_LucyV4	.800
91	yudixie_resnet18_distance_reg_0_240719	.796
92	efficientnet_b1_imagenet_full	.796
93	resnet50_robust_l2_eps3	.792
94	vgg-16-keras	.792
95	SWSL_resnet50	.793
96	resnet50_linf_8_robust	.792
97	inception_v4-tf	.780
98	yudixie_resnet18_distance_translation_rotation_0_240719	.777
99	resnet-101_v1-tf	.772
100	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.772
101	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.771
102	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.771
103	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.770
104	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.770
105	SWSL_resnext101_32x8d	.770
106	mobilenet_v2_1_0_160	.769
107	BiT-S-R152x2	.765
108	v1-pyr-nodown	.764
109	CLIP-RN50	.763
110	my-model	.761
111	resnet50_random_l2_perturb	.761
112	yudixie_resnet18_translation_rotation_0_240719	.760
113	mobilenet_v2_0.75_96	.760
114	BiT-M-R101x3	.758
115	r3m_resnet34	.757
116	vggface	.757
117	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.756
118	grcnn	.756
119	yudixie_resnet18_translation_reg_0_240719	.755
120	resnet50_random_linf8_perturb	.756
121	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.755
122	convnext_xlarge:fb_in22k_ft_in1k	.752
123	densenet-121	.752
124	yudixie_resnet18_distance_rotation_0_240719	.751
125	deit_large_imagenet_full_seed-0	.752
126	resnet-18-LC_w_sh_10_iter_m	.747
127	resnet-152_v2-tf	.746
128	yudixie_resnet18_distance_translation_0_240719	.747
129	pnasnet_large-tf	.746
130	xception	.746
131	effnetv2m_custom384	.744
132	convnext_base_imagenet_full_seed-0	.742
133	yudixie_resnet18_category_class_0_240719	.738
134	densenet-121-keras	.733
135	grcnn_109	.731
136	antialiased-r50	.731
137	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.730
138	mobilenet_v2_1-4_224_pytorch	.730
139	r3m_resnet18	.729
140	BiT-M-R152x2	.729
141	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.729
142	resnet-18-LC_w_sh_100_iter_m	.727
143	resnet-18_untrained	.726
144	antialias-resnet152	.724
145	blt_vs	.719
146	vgg-19-keras	.712
147	BiT-M-R101x1	.712
148	r3m_resnet50_nocrop	.712
149	mobilenet_v2_1_0_192	.707
150	resnet-50_untrained	.708
151	resnet50_moco_v2	.708
152	omnivore_swinB	.706
153	grcnn_robust_v1	.704
154	resnet-50x2_untrained	.704
155	mobilenet_v2_1.3_224-tf	.701
156	yudixie_resnet50_translation_reg_0_240908	.701
157	omnivore_swinS	.700
158	yudixie_resnet18_random_0_240719	.697
159	mobilenet_v2_1_4_224	.697
160	convnext_tiny_imagenet_full_seed-0	.694
161	mobilenet_v2_0_75_192	.694
162	0.5x_resnet-18	.691
163	densenet-201	.687
164	resnet-18-LC_m	.687
165	densenet-169	.685
166	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.685
167	yudixie_resnet50_random_0_240908	.683
168	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.680
169	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.681
170	densenet-169-keras	.678
171	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.675
172	grcnn_v2_text_noise	.672
173	yudixie_resnet50_object_class_0_240908	.670
174	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.668
175	yudixie_resnet50_rotation_reg_0_240908	.667
176	vonealexnet_gaussian_noise_std4_fixed	.665
177	mobilenet_v2_1.0_128-tf	.664
178	AlexNet_SIN	.664
179	hmax	.666
180	densenet-201-keras	.664
181	mobilenet_v2_1.0_224-tf	.663
182	alexnet_training_seed_09	.664
183	barlow-twins-resnet50	.665
184	yudixie_resnet50_distance_translation_0_240908	.660
185	artResNet18_1	.658
186	resnet50-vitoimagevidnet8	.658
187	focalnet_tiny_lrf_in1k	.656
188	resnet34_imagenet_full	.654
189	resnet-18_test_m	.652
190	ViT_L_32_imagenet1k	.653
191	resnet-18-LC_d_w_sh_1x1_conv_init_m	.650
192	mobilenet_v2_1.4_224-tf	.648
193	voneresnet-50-non_stochastic	.647
194	yudixie_resnet50_distance_rotation_0_240908	.641
195	resnet_101_v1	.638
196	cvt_cvt-13-224-in1k_4_LucyV4	.638
197	0.5x_resnet-18_LC_w_sh_10_iter	.636
198	yudixie_resnet50_distance_translation_rotation_0_240908	.637
199	resnet50-simclr-vissl	.636
200	cvt_cvt-21-224-in1k_4_LucyV4	.635
201	AdvProp_efficientnet-b4	.635
202	resnet50-vicregl0p9	.633
203	alexnet_training_seed_01	.632
204	alexnet	.631
205	alexnet-baseline	.629
206	alexnet	.629
207	alexnet	.627
208	alexnet	.628
209	alexnet	.628
210	alexnet	.627
211	yudixie_resnet50_translation_rotation_0_240908	.626
212	alexnet	.625
213	mobilevit_small	.625
214	antialiased-rnext101_32x8d	.625
215	resnet50-SIN_IN	.623
216	ReAlnet04	.622
217	ReAlnet04_cornet	.618
218	resnet-18-LC_w_sh_1_iter_m	.616
219	ReAlnet02	.616
220	ReAlnet_incorrect	.616
221	ReAlnet01	.616
222	ReAlnet02_cornet	.616
223	ReAlnet01_cornet	.615
224	ReAlnet05_cornet	.615
225	ReAlnet05	.614
226	r3m_resnet50	.612
227	ReAlnet07	.610
228	ReAlnet10_cornet	.610
229	ReAlnet07_cornet	.609
230	ReAlnet08_cornet	.608
231	resnet-152_v2_pytorch	.608
232	ReAlnet10	.607
233	alexnet_training_seed_07	.606
234	ReAlnet08	.604
235	resnet18_imagenet_full	.602
236	ReAlnet06	.598
237	ReAlnet06_cornet	.596
238	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.595
239	convnext_large_imagenet_full_seed-0	.592
240	alexnet_training_seed_10	.591
241	ReAlnet03	.592
242	ReAlnet03_cornet	.592
243	yudixie_resnet50_distance_reg_0_240908	.591
244	convnext_base:clip_laiona_augreg_ft_in1k_384	.590
245	resnet-18-LC_1st_conv_m	.586
246	0.5x_resnet-18_LC_w_sh_1_iter	.585
247	yudixie_resnet50_category_class_0_240908	.584
248	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.584
249	CORnet-S	.585
250	resnet101	.579
251	resnet34_ecoset_full	.578
252	ReAlnet09	.570
253	BiT-M-R50x3	.567
254	ReAlnet09_cornet	.565
255	alexnet_robust_correct	.566
256	deit_base_imagenet_full_seed-0	.552
257	cv_18_dagger_408_pretrained	.542
258	vit_large_patch14_clip_224:laion2b_ft_in1k	.541
259	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.532
260	Res2Net50_26w_4s	.529
261	cvt_cvt-21-384-in1k_4_LucyV4	.528
262	cornet_s	.524
263	resnext101_32x8d_wsl	.523
264	omnivore_swinT	.518
265	resnet50_imagenet_1_seed-0	.513
266	AT_efficientnet-b2	.513
267	AdvProp_efficientnet-b2	.512
268	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
269	alexnet_training_seed_04	.508
270	resnet101_imagenet_full	.496
271	CORnetZ_CIFAR10	.494
272	inception_v1	.489
273	resnet50_imagenet_10_seed-0	.481
274	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.479
275	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.479
276	resnet18_ecoset_full	.478
277	alexnet_training_seed_02	.476
278	vit_large_patch14_clip_336:laion2b_ft_in1k	.470
279	resnet_50_v2	.462
280	resnet152_imagenet_full	.460
281	resnet50_ecoset_full	.457
282	resnet_101_v2	.451
283	resnet50_simclr	.451
284	mobilenet_v2_0.5_192-tf	.449
285	deit_small_imagenet_10_seed-0	.450
286	resnet101_ecoset_full	.437
287	custom_model_cv_18_dagger_408	.433
288	alexnet_early_checkpoint	.419
289	deit_small_imagenet_100_seed-0	.412
290	alexnet_random_linf8_perturb	.404
291	BiT-M-R50x1	.398
292	vit_relpos_base_patch16_clsgap_224:sw_in1k	.392
293	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.387
294	mobilenet_v2_0.5_160	.381
295	mobilenet_v1_0.25_128	.382
296	vit_large_patch14_clip_224:openai_ft_in1k	.370
297	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.363
298	shufflenet_v2_x1_0	.361
299	vgg_19	.361
300	resnet152_ecoset_full	.357
301	resnet50_imagenet_100_seed-0	.355
302	vit_base_patch16_clip_224:openai_ft_in1k	.344
303	alexnet_training_seed_03	.342
304	deit_small_imagenet_full_seed-0	.341
305	resnet50_imagenet_full	.331
306	vision_transformer_vit_large_patch16_224	.328
307	convnext_small_imagenet_10_seed-0	.316
308	CLIP_ViT-B_32	.306
309	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.305
310	resnet50-meshes-lt-100-original-pretrained	.276
311	alexnet_reduced_aliasing_early_checkpoint	.276
312	CORnet-Z	.276
313	inception_v1-tf	.276
314	mobilenet_v2_1.0_160-tf	.275
315	alexnet_training_seed_08	.268
316	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.260
317	VOneCORnet-S	.172
318	voneresnet-50	.170
319	vonegrcnn_52e_full	.163
320	vonegrcnn_52e_full	.162
321	vonegrcnn_47e	.159
322	voneresnet-50-robust	.156
323	ViT-B/32	.151
324	resnet-18-LC_untrained	.140
325	vonegrcnn_62e_nobn	.140
326	vonegrcnn_47e	.128
327	alexnet_training_seed_06	.085
328	convnext_small_imagenet_1_seed-0	.085
329	alexnet_training_seed_05	.085
330	mobilenet_v2_1.0_96	.064
331	mobilenet_v2_0.5_128	.000
332	resnet50_pretrained_with_retinal_waves	.000
333	mobilenet_v2_1_0_128	.000
334	resnet50-meshes-lt-100-original-scratch	.000
335	CORnetZ_CIFAR10_bs32_20_04	.000
336	my-model	.000
337	resnet50	.000
338	resnet50-cifar	.000
339	mobilenet_v2_0.75_160-tf	.000
340	mobilenet_v2_0.75_128	.000
341	resnext101_32x16d_wsl	X
342	deit_small_imagenet_1_seed-0	X
343	resnext101_32x48d_wsl	X
344	resnext101_32x32d_wsl	X
345	nasnet_large	X
346	pnasnet_large	X
347	fitvid_trained_on_physion	X
348	resnet_152_v1	X
349	inception_v3_pytorch	X
350	pnasnet_large_pytorch	X
351	vonegrcnn_62e_nobn	X
352	densenet_201_pytorch	X
353	resnet50_ImageNet	X
354	mobilenet_v2_0.35_160	X
355	openclip	X
356	regnet_y_400mf	X
357	resnet_152_v2	X
358	tv_efficientnet-b1	X
359	inception_v4	X
360	resnet18-supervised	X
361	alexnet_ks_torevert	X
362	yudixie_resnet50_imagenet1kpret_0_240312	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: grating_summation_field