Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Marques2020_Ringach2002-max_dc")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet-18-LC_1st_conv_m	.998
2	resnet50_imagenet_1_seed-0	.997
3	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.996
4	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.995
5	BiT-M-R50x3	.993
6	VOneCORnet-S	.993
7	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.992
8	resnet-101_v2-tf	.992
9	yudixie_resnet50_distance_rotation_0_240908	.993
10	convnext_large:fb_in22k_ft_in1k	.992
11	resnet50_linf_4_robust	.992
12	resnet34_imagenet_full	.990
13	resnet50_byol	.990
14	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.990
15	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.990
16	resnet50_moco_v2	.990
17	yudixie_resnet50_object_class_0_240908	.989
18	briaai_rmbg_1_4	.989
19	resnet18_imagenet_full	.989
20	yudixie_resnet50_distance_translation_0_240908	.989
21	resnet-152_v2-tf	.989
22	antialiased-rnext101_32x8d	.988
23	vit_relpos_base_patch16_clsgap_224:sw_in1k	.987
24	resnet-18-LC_w_sh_10_iter_conv_init_m	.987
25	nasnet_mobile-tf	.988
26	texture_shape_alexnet_trained_on_SIN	.988
27	vision_transformer_vit_large_patch16_224	.988
28	nasnet_large-tf	.987
29	resnet-18-LC_d_w_sh_1x1_conv_init_m	.987
30	AT_efficientnet-b2	.987
31	voneresnet-50-robust	.987
32	mobilevit_small	.986
33	resnet-50-robust	.986
34	vgg_19	.986
35	resnet50-vitoimagevidnet8	.987
36	resnet50_robust_l2_eps1	.986
37	yudixie_resnet18_object_class_0_240719	.987
38	resnet-18-LC_w_sh_1_iter_conv_init_m	.986
39	resnet50_random_linf8_perturb	.986
40	resnet50_l2_3_robust	.986
41	AdvProp_efficientnet-b2	.985
42	vit_large_patch14_clip_224:openai_ft_in1k	.985
43	resnet50_random_l2_perturb	.985
44	yudixie_resnet50_distance_translation_rotation_0_240908	.985
45	yudixie_resnet50_translation_reg_0_240908	.984
46	yudixie_resnet50_random_0_240908	.983
47	voneresnet-50	.984
48	antialiased-r50	.983
49	resnet50-simclr-vissl	.982
50	resnet50_FractalDB	.984
51	resnet50-moclr8deg	.982
52	cv_18_dagger_408_pretrained	.983
53	r3m_resnet34	.982
54	BiT-S-R50x3	.982
55	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.981
56	densenet-121-keras	.980
57	resnet101_ecoset_full	.980
58	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.981
59	xception	.980
60	omnivore_swinT	.980
61	resnet152_imagenet_full	.979
62	grcnn_v2_text_noise	.979
63	SWSL_resnet50	.979
64	alexnet_training_seed_09	.979
65	yudixie_resnet50_translation_rotation_0_240908	.979
66	yudixie_resnet18_distance_rotation_0_240719	.978
67	resnet-50_untrained	.978
68	alexnet_training_seed_04	.977
69	resnet50-vicregl0p9	.977
70	yudixie_resnet18_rotation_reg_0_240719	.976
71	BiT-S-R50x1	.976
72	resnet-18_untrained	.977
73	resnet50-SIN_IN_IN	.976
74	yudixie_resnet50_distance_reg_0_240908	.976
75	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.976
76	resnet50_simclr	.976
77	vonegrcnn_62e_nobn	.976
78	barlow-twins-resnet50	.976
79	effnetb1_272x240	.976
80	grcnn_robust_v1	.975
81	alexnet	.974
82	imagenet_l2_3_0	.975
83	convnext_large_imagenet_full_seed-0	.974
84	resnet-50_v1-tf	.975
85	densenet-121	.974
86	inception_v4-tf	.974
87	antialias-resnet152	.975
88	resnet34_ecoset_full	.974
89	vgg-16-keras	.973
90	resnet50_linf_8_robust	.974
91	yudixie_resnet18_translation_reg_0_240719	.974
92	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.974
93	alexnet_linf_8_robust	.973
94	0.5x_resnet-18_LC_w_sh_100_iter	.973
95	convnext_base:clip_laiona_augreg_ft_in1k_384	.973
96	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.972
97	resnet-18_test_m	.973
98	BiT-S-R152x2	.972
99	pnasnet_large-tf	.973
100	yudixie_resnet18_translation_rotation_0_240719	.972
101	resnet50-vicreg	.973
102	custom_model_cv_18_dagger_408	.972
103	resnet-50x2_untrained	.971
104	resnet-18-LC_w_sh_10_iter_m	.971
105	cvt_cvt-21-224-in1k_4_LucyV4	.972
106	SWSL_resnext101_32x8d	.971
107	resnet50	.971
108	vit_large_patch14_clip_336:laion2b_ft_in1k	.970
109	resnet18_ecoset_full	.970
110	vgg_16	.971
111	resnet-18-LC_conv_init_m	.969
112	r3m_resnet50_nocrop	.968
113	convnext_femto_ols:d1_in1k	.969
114	resnet50_tutorial	.968
115	imagenet_l2_10_0	.969
116	yudixie_resnet50_imagenet1kpret_0_240908	.969
117	resnet_50_v1	.969
118	resnet50-sup	.968
119	yudixie_resnet50_rotation_reg_0_240908	.969
120	resnet50_robust_l2_eps3	.968
121	resnet50-SIN	.968
122	deit_base_imagenet_full_seed-0	.967
123	CLIP_resnet50	.967
124	CORnetZ_CIFAR10_bs32_20_04	.968
125	texture_shape_resnet50_trained_on_SIN	.967
126	resnet-152_v1-tf	.968
127	0.5x_resnet-18_LC	.967
128	CLIP_resnet50_float32	.967
129	cvt_cvt-13-384-in1k_4_LucyV4	.967
130	vonealexnet_gaussian_noise_std4_fixed	.966
131	0.5x_resnet-18_LC_w_sh_1_iter	.965
132	inception_v2-tf	.965
133	grcnn_109	.964
134	resnet50-barlow	.963
135	vonegrcnn_47e	.963
136	resnet-18-LC_w_sh_100_iter_conv_init_m	.963
137	efficientnet_b1_imagenet_full	.963
138	nasnet_mobile	.964
139	resnet50-vicregl0p75	.962
140	alexnet_random_l2_3_perturb	.962
141	focalnet_tiny_lrf_in1k	.962
142	resnet50_ecoset_full	.961
143	convnext_base_imagenet_full_seed-0	.961
144	yudixie_resnet18_random_0_240719	.960
145	AdvProp_efficientnet-b8	.960
146	yudixie_resnet18_imagenet1kpret_0_240719	.960
147	yudixie_resnet18_distance_translation_rotation_0_240719	.960
148	resnet18_imagenet21kP	.960
149	yudixie_resnet18_category_class_0_240719	.959
150	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.957
151	mobilenet_v2_0.75_192-tf	.958
152	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.957
153	resnet50_imagenet_10_seed-0	.955
154	effnetv2m_custom384	.954
155	resnet-18-LC_w_sh_100_iter_m	.954
156	mobilenet_v2_0_5_224	.954
157	convnext_xlarge:fb_in22k_ft_in1k	.952
158	CLIP_ViT-B_32	.953
159	RN50	.953
160	mobilenet_v1_0.25_128	.952
161	resnet_50_v2	.952
162	resnet-18-LC_untrained	.952
163	blt_vs	.952
164	0.5x_resnet-18	.951
165	densenet-201	.951
166	vonegrcnn_52e_full	.951
167	resnet50-VITO-8deg-cc	.950
168	resnet50-cifar	.950
169	inception_v3-tf	.950
170	BiT-M-R50x1	.949
171	ReAlnet06	.948
172	ReAlnet06_cornet	.947
173	vggface	.948
174	CORnetZ_CIFAR10	.947
175	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.947
176	ReAlnet09_cornet	.948
177	convnext_small_imagenet_100_seed-0	.947
178	vit_base_patch16_clip_224:openai_ft_in1k	.947
179	ReAlnet09	.946
180	AdvProp_efficientnet-b6	.946
181	resnet-18-LC_m	.946
182	vgg-19-keras	.944
183	CORnet-Z	.944
184	vonegrcnn_47e	.943
185	resnet50-SIN_IN	.943
186	resnext101_32x8d_wsl	.943
187	densenet-201-keras	.943
188	ViT_L_32_imagenet1k	.942
189	alexnet_training_seed_02	.943
190	Res2Net50_26w_4s	.943
191	CLIP-RN50	.942
192	yudixie_resnet50_category_class_0_240908	.942
193	alexnet_training_seed_07	.942
194	r3m_resnet18	.941
195	mobilenet_v2_0_75_192	.940
196	mobilenet_v2_0.75_224-tf	.940
197	resnet-50_v2-tf	.940
198	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.939
199	mobilenet_v2_0_75_160	.938
200	densenet-169	.939
201	inception_v3	.938
202	ReAlnet10	.938
203	ReAlnet10_cornet	.937
204	ReAlnet07	.936
205	deit_large_imagenet_full_seed-0	.937
206	ReAlnet07_cornet	.937
207	FrankRobWobv0	.936
208	alexnet_l2_3_robust	.935
209	resnet101_imagenet_full	.936
210	efficientnet_b0	.935
211	AlexNet_SIN	.935
212	yudixie_resnet18_distance_translation_0_240719	.935
213	resnet152_ecoset_full	.935
214	resnet-101_v1-tf	.935
215	resnet_SIN_IN_FT_IN	.935
216	cvt_cvt-13-224-in1k_4_LucyV4	.935
217	konkle_alexnetgn_ipcl_ref01_primary_model	.934
218	ReAlnet03	.933
219	ReAlnet03_cornet	.933
220	ReAlnet05_cornet	.933
221	convnext_tiny_sup	.933
222	alexnet-baseline	.933
223	alexnet	.933
224	alexnet	.933
225	AdvProp_efficientnet-b7	.933
226	ReAlnet05	.932
227	alexnet	.932
228	alexnet	.932
229	alexnet	.931
230	r3m_resnet50	.931
231	mobilenet_v2_1.0_128-tf	.931
232	alexnet	.931
233	alexnet	.930
234	mobilenet_v2_0_5_192	.929
235	BiT-S-R152x4	.929
236	resnet50_imagenet_full	.929
237	my-model	.929
238	BiT-S-R101x3	.928
239	mobilenet_v2_0_75_224	.927
240	BiT-M-R152x4	.927
241	resnet-18-LC_w_sh_1_iter_m	.926
242	alexnet_training_seed_01	.926
243	grcnn	.925
244	ReAlnet_incorrect	.926
245	alexnet_training_seed_10	.924
246	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.924
247	ReAlnet01	.924
248	convnext_tiny_imagenet_full_seed-0	.924
249	ReAlnet08	.924
250	ReAlnet01_cornet	.924
251	ReAlnet08_cornet	.923
252	mobilenet_v2_1.4_224-tf	.923
253	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.922
254	ReAlnet04	.922
255	efficientnet_b0_imagenet_full	.921
256	resnet-152_v2_pytorch	.922
257	resnet101	.921
258	ReAlnet04_cornet	.921
259	ReAlnet02	.919
260	ReAlnet02_cornet	.919
261	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.917
262	alexnet_reduced_aliasing_early_checkpoint	.917
263	resnet50_pretrained_with_retinal_waves	.918
264	alexnet_robust_correct	.918
265	mobilenet_v2_1_0_128	.916
266	mobilenet_v2_1_0_160	.915
267	inception_v1	.914
268	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.914
269	BiT-M-R101x1	.914
270	alexnet_early_checkpoint	.913
271	inception_resnet_v2	.914
272	BiT-M-R101x3	.911
273	deit_small_imagenet_100_seed-0	.911
274	cornet_s	.911
275	convnext_small_imagenet_full_seed-0	.910
276	cvt_cvt-21-384-in1k_4_LucyV4	.909
277	densenet-169-keras	.907
278	convnext_small_imagenet_10_seed-0	.901
279	alexnet_training_seed_03	.897
280	mobilenet_v2_1.3_224-tf	.894
281	CORnet-S	.894
282	omnivore_swinS	.893
283	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.893
284	0.5x_resnet-18_LC_w_sh_10_iter	.893
285	mobilenet_v2_1_0_192	.893
286	efficientnet-b7	.892
287	efficientnet_b2_imagenet_full	.891
288	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.890
289	mobilenet_v2_1.0_160-tf	.889
290	mobilenet_v2_1_3_224	.883
291	convnext_tiny:in12k_ft_in1k	.882
292	yudixie_resnet18_distance_reg_0_240719	.882
293	resnet50_imagenet_100_seed-0	.882
294	mobilenet_v2_1.0_224-tf	.881
295	shufflenet_v2_x1_0	.877
296	mobilenet_v2_1_4_224	.877
297	mobilenet_v2_1-4_224_pytorch	.876
298	mobilenet_v2_0.5_192-tf	.875
299	v1-pyr-nodown	.872
300	mobilenet_v2_1.0_192-tf	.872
301	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.867
302	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.868
303	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.867
304	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.867
305	alexnet_random_linf8_perturb	.867
306	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
307	mobilenet_v2_0.5_224-tf	.864
308	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.863
309	mobilenet_v2_0.35_224	.860
310	convnext_small_imagenet_1_seed-0	.859
311	omnivore_swinB	.854
312	BiT-S-R101x1	.848
313	vit_large_patch14_clip_224:laion2b_ft_in1k	.847
314	yolos_tiny	.845
315	BiT-M-R152x2	.843
316	mobilenet_v2_1.0_96	.838
317	resnet50	.837
318	alexnet_training_seed_08	.824
319	voneresnet-50-non_stochastic	.819
320	alexnet_training_seed_06	.817
321	mobilenet_v2_0.75_160-tf	.811
322	mobilenet_v2_0.5_160	.810
323	artResNet18_1	.801
324	ViT-B/32	.796
325	inception_v1-tf	.795
326	mobilenet_v2_1_0_224	.794
327	mobilenet_v2_0.75_96	.783
328	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.780
329	alexnet_training_seed_05	.781
330	AdvProp_efficientnet-b0	.775
331	mobilenet_v2_0.75_128	.730
332	hmax	.689
333	mobilenet_v2_0.5_128	.689
334	my-model	.690
335	deit_small_imagenet_full_seed-0	.686
336	AdvProp_efficientnet-b4	.654
337	deit_small_imagenet_10_seed-0	.343
338	resnet50-meshes-lt-100-original-pretrained	.172
339	resnet50-meshes-lt-100-original-scratch	.116
340	densenet_201_pytorch	X
341	yudixie_resnet50_imagenet1kpret_0_240312	X
342	nasnet_large	X
343	deit_small_imagenet_1_seed-0	X
344	alexnet_ks_torevert	X
345	pnasnet_large	X
346	tv_efficientnet-b1	X
347	regnet_y_400mf	X
348	mobilenet_v2_0.35_160	X
349	vonegrcnn_62e_nobn	X
350	resnet_101_v2	X
351	resnet50_ImageNet	X
352	resnext101_32x48d_wsl	X
353	inception_v4	X
354	resnext101_32x32d_wsl	X
355	resnet18-supervised	X
356	resnet_152_v2	X
357	resnext101_32x16d_wsl	X
358	resnet_152_v1	X
359	vonegrcnn_52e_full	X
360	pnasnet_large_pytorch	X
361	openclip	X
362	inception_v3_pytorch	X
363	resnet_101_v1	X
364	fitvid_trained_on_physion	X

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: max_dc