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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	vit_base_patch14_reg4_dinov2-lvd142m	.992
12	resnet50_linf_4_robust	.992
13	resnet34_imagenet_full	.990
14	resnet50_byol	.990
15	vit_base_patch14_dinov2-lvd142m	.991
16	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.990
17	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.990
18	resnet50_moco_v2	.990
19	yudixie_resnet50_object_class_0_240908	.989
20	briaai_rmbg_1_4	.989
21	resnet18_imagenet_full	.989
22	yudixie_resnet50_distance_translation_0_240908	.989
23	resnet-152_v2-tf	.989
24	antialiased-rnext101_32x8d	.988
25	vit_relpos_base_patch16_clsgap_224:sw_in1k	.987
26	resnet-18-LC_w_sh_10_iter_conv_init_m	.987
27	nasnet_mobile-tf	.988
28	texture_shape_alexnet_trained_on_SIN	.988
29	vision_transformer_vit_large_patch16_224	.988
30	nasnet_large-tf	.987
31	resnet-18-LC_d_w_sh_1x1_conv_init_m	.987
32	AT_efficientnet-b2	.987
33	voneresnet-50-robust	.987
34	mobilevit_small	.986
35	resnet-50-robust	.986
36	vgg_19	.986
37	resnet50-vitoimagevidnet8	.987
38	resnet50_robust_l2_eps1	.986
39	yudixie_resnet18_object_class_0_240719	.987
40	resnet-18-LC_w_sh_1_iter_conv_init_m	.986
41	resnet50_random_linf8_perturb	.986
42	resnet50_l2_3_robust	.986
43	AdvProp_efficientnet-b2	.985
44	vit_large_patch14_clip_224:openai_ft_in1k	.985
45	resnet50_random_l2_perturb	.985
46	yudixie_resnet50_distance_translation_rotation_0_240908	.985
47	yudixie_resnet50_translation_reg_0_240908	.984
48	yudixie_resnet50_random_0_240908	.983
49	voneresnet-50	.984
50	antialiased-r50	.983
51	resnet50-simclr-vissl	.982
52	resnet50_FractalDB	.984
53	alexnet_training_seed_01_fov16	.982
54	resnet50-moclr8deg	.982
55	cv_18_dagger_408_pretrained	.983
56	r3m_resnet34	.982
57	BiT-S-R50x3	.982
58	alexnet_training_seed_01_fov12	.981
59	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.981
60	densenet-121-keras	.980
61	resnet101_ecoset_full	.980
62	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.981
63	xception	.980
64	omnivore_swinT	.980
65	resnet152_imagenet_full	.979
66	grcnn_v2_text_noise	.979
67	SWSL_resnet50	.979
68	alexnet_training_seed_09	.979
69	yudixie_resnet50_translation_rotation_0_240908	.979
70	yudixie_resnet18_distance_rotation_0_240719	.978
71	resnet50_primary_visual_cortex	.978
72	resnet-50_untrained	.978
73	alexnet_training_seed_04	.977
74	resnet50-vicregl0p9	.977
75	yudixie_resnet18_rotation_reg_0_240719	.976
76	BiT-S-R50x1	.976
77	resnet-18_untrained	.977
78	resnet50-SIN_IN_IN	.976
79	yudixie_resnet50_distance_reg_0_240908	.976
80	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.976
81	resnet50_simclr	.976
82	vonegrcnn_62e_nobn	.976
83	barlow-twins-resnet50	.976
84	effnetb1_272x240	.976
85	grcnn_robust_v1	.975
86	ReAlnet01_cornet_fov4	.975
87	imagenet_l2_3_0	.975
88	convnext_large_imagenet_full_seed-0	.974
89	resnet-50_v1-tf	.975
90	densenet-121	.974
91	inception_v4-tf	.974
92	antialias-resnet152	.975
93	resnet34_ecoset_full	.974
94	vgg-16-keras	.973
95	resnet50_linf_8_robust	.974
96	yudixie_resnet18_translation_reg_0_240719	.974
97	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.974
98	alexnet_linf_8_robust	.973
99	0.5x_resnet-18_LC_w_sh_100_iter	.973
100	convnext_base:clip_laiona_augreg_ft_in1k_384	.973
101	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.972
102	resnet-18_test_m	.973
103	BiT-S-R152x2	.972
104	pnasnet_large-tf	.973
105	pnasnet_large_halfPrecision	.972
106	yudixie_resnet18_translation_rotation_0_240719	.972
107	resnet50-vicreg	.973
108	custom_model_cv_18_dagger_408	.972
109	resnet-50x2_untrained	.971
110	resnet-18-LC_w_sh_10_iter_m	.971
111	cvt_cvt-21-224-in1k_4_LucyV4	.972
112	SWSL_resnext101_32x8d	.971
113	resnet50	.971
114	vit_large_patch14_clip_336:laion2b_ft_in1k	.970
115	resnet18_ecoset_full	.970
116	vgg_16	.971
117	resnet-18-LC_conv_init_m	.969
118	r3m_resnet50_nocrop	.968
119	resnet_50_v1_spiking_l4a	.968
120	convnext_femto_ols:d1_in1k	.969
121	resnet50_tutorial	.968
122	imagenet_l2_10_0	.969
123	resnet_50_v1_spiking	.968
124	yudixie_resnet50_imagenet1kpret_0_240908	.969
125	resnet_50_v1	.969
126	resnet50-sup	.968
127	yudixie_resnet50_rotation_reg_0_240908	.969
128	resnet_50_v1_spiking_l3	.969
129	resnet_50_v1_spiking_l4	.968
130	resnet50_robust_l2_eps3	.968
131	resnet50-SIN	.968
132	deit_base_imagenet_full_seed-0	.967
133	CLIP_resnet50	.967
134	CORnetZ_CIFAR10_bs32_20_04	.968
135	texture_shape_resnet50_trained_on_SIN	.967
136	resnet-152_v1-tf	.968
137	0.5x_resnet-18_LC	.967
138	CLIP_resnet50_float32	.967
139	cvt_cvt-13-384-in1k_4_LucyV4	.967
140	vonealexnet_gaussian_noise_std4_fixed	.966
141	0.5x_resnet-18_LC_w_sh_1_iter	.965
142	inception_v2-tf	.965
143	grcnn_109	.964
144	resnet50-barlow	.963
145	vonegrcnn_47e	.963
146	resnet-18-LC_w_sh_100_iter_conv_init_m	.963
147	efficientnet_b1_imagenet_full	.963
148	nasnet_mobile	.964
149	resnet50-vicregl0p75	.962
150	alexnet_random_l2_3_perturb	.962
151	focalnet_tiny_lrf_in1k	.962
152	resnet50_ecoset_full	.961
153	convnext_base_imagenet_full_seed-0	.961
154	yudixie_resnet18_random_0_240719	.960
155	AdvProp_efficientnet-b8	.960
156	yudixie_resnet18_imagenet1kpret_0_240719	.960
157	yudixie_resnet18_distance_translation_rotation_0_240719	.960
158	resnet18_imagenet21kP	.960
159	yudixie_resnet18_category_class_0_240719	.959
160	ReAlnet01_cornet_fov12	.958
161	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.957
162	mobilenet_v2_0.75_192-tf	.958
163	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.957
164	AlexNet_SIN_fov4	.956
165	resnet50_imagenet_10_seed-0	.955
166	effnetv2m_custom384	.954
167	resnet-18-LC_w_sh_100_iter_m	.954
168	eBarlow_lmda_02_1000ep	.954
169	mobilenet_v2_0_5_224	.954
170	convnext_xlarge:fb_in22k_ft_in1k	.952
171	CLIP_ViT-B_32	.953
172	RN50	.953
173	mobilenet_v1_0.25_128	.952
174	resnet_50_v2	.952
175	resnet-18-LC_untrained	.952
176	blt_vs	.952
177	0.5x_resnet-18	.951
178	densenet-201	.951
179	vonegrcnn_52e_full	.951
180	resnet50-VITO-8deg-cc	.950
181	resnet50-cifar	.950
182	inception_v3-tf	.950
183	BiT-M-R50x1	.949
184	ReAlnet01_cornet_fov16	.948
185	ReAlnet06	.948
186	ReAlnet06_cornet	.947
187	vggface	.948
188	CORnetZ_CIFAR10	.947
189	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.947
190	ReAlnet09_cornet	.948
191	convnext_small_imagenet_100_seed-0	.947
192	vit_base_patch16_clip_224:openai_ft_in1k	.947
193	ReAlnet09	.946
194	AdvProp_efficientnet-b6	.946
195	resnet-18-LC_m	.946
196	vgg-19-keras	.944
197	CORnet-Z	.944
198	alexnet_fov4	.944
199	vonegrcnn_47e	.943
200	resnet50-SIN_IN	.943
201	resnext101_32x8d_wsl	.943
202	densenet-201-keras	.943
203	ReAlnet04_ALL	.944
204	ViT_L_32_imagenet1k	.942
205	alexnet_training_seed_02	.943
206	Res2Net50_26w_4s	.943
207	compact_resnet50_robust_V4	.942
208	CLIP-RN50	.942
209	yudixie_resnet50_category_class_0_240908	.942
210	alexnet_training_seed_07	.942
211	r3m_resnet18	.941
212	mobilenet_v2_0_75_192	.940
213	mobilenet_v2_0.75_224-tf	.940
214	resnet-50_v2-tf	.940
215	ReAlnet09_ALL	.939
216	compact_cornets_IT	.938
217	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.939
218	mobilenet_v2_0_75_160	.938
219	densenet-169	.939
220	inception_v3	.938
221	ReAlnet10	.938
222	ReAlnet10_cornet	.937
223	ReAlnet07	.936
224	deit_large_imagenet_full_seed-0	.937
225	ReAlnet07_cornet	.937
226	ReAlnet08_ALL	.936
227	FrankRobWobv0	.936
228	alexnet_l2_3_robust	.935
229	resnet101_imagenet_full	.936
230	efficientnet_b0	.935
231	AlexNet_SIN	.935
232	yudixie_resnet18_distance_translation_0_240719	.935
233	resnet152_ecoset_full	.935
234	resnet-101_v1-tf	.935
235	resnet_SIN_IN_FT_IN	.935
236	cvt_cvt-13-224-in1k_4_LucyV4	.935
237	compact_vgg19_IT	.934
238	konkle_alexnetgn_ipcl_ref01_primary_model	.934
239	ReAlnet03	.933
240	ReAlnet03_cornet	.933
241	ReAlnet05_cornet	.933
242	convnext_tiny_sup	.933
243	alexnet	.933
244	AdvProp_efficientnet-b7	.933
245	ReAlnet05	.932
246	ReAlnet02_ALL	.933
247	r3m_resnet50	.931
248	mobilenet_v2_1.0_128-tf	.931
249	ReAlnet06_ALL	.931
250	ReAlnet01_ALL	.931
251	ReAlnet10_ALL	.930
252	mobilenet_v2_0_5_192	.929
253	BiT-S-R152x4	.929
254	resnet50_imagenet_full	.929
255	my-model	.929
256	BiT-S-R101x3	.928
257	mobilenet_v2_0_75_224	.927
258	BiT-M-R152x4	.927
259	resnet-18-LC_w_sh_1_iter_m	.926
260	alexnet_training_seed_01	.926
261	grcnn	.925
262	alexnet_training_seed_10	.924
263	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.924
264	ReAlnet01	.924
265	convnext_tiny_imagenet_full_seed-0	.924
266	ReAlnet08	.924
267	ReAlnet01_cornet	.924
268	ReAlnet08_cornet	.923
269	ReAlnet03_ALL	.923
270	compact_resnet50_V4	.923
271	mobilenet_v2_1.4_224-tf	.923
272	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.922
273	ReAlnet04	.922
274	efficientnet_b0_imagenet_full	.921
275	resnet-152_v2_pytorch	.922
276	resnet101	.921
277	ReAlnet05_ALL	.920
278	ReAlnet04_cornet	.921
279	ReAlnet02	.919
280	alexnet_fov16	.919
281	ReAlnet02_cornet	.919
282	compact_cornets_V4	.917
283	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.917
284	alexnet_reduced_aliasing_early_checkpoint	.917
285	resnet50_pretrained_with_retinal_waves	.918
286	compact_vgg19_V4	.916
287	alexnet_robust_correct	.918
288	ReAlnet07_ALL	.915
289	mobilenet_v2_1_0_128	.916
290	mobilenet_v2_1_0_160	.915
291	inception_v1	.914
292	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.914
293	BiT-M-R101x1	.914
294	alexnet_early_checkpoint	.913
295	inception_resnet_v2	.914
296	BiT-M-R101x3	.911
297	deit_small_imagenet_100_seed-0	.911
298	cornet_s	.911
299	convnext_small_imagenet_full_seed-0	.910
300	cvt_cvt-21-384-in1k_4_LucyV4	.909
301	densenet-169-keras	.907
302	convnext_small_imagenet_10_seed-0	.901
303	AlexNet_SIN_fov16	.897
304	alexnet_training_seed_03	.897
305	mobilenet_v2_1.3_224-tf	.894
306	CORnet-S	.894
307	omnivore_swinS	.893
308	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.893
309	0.5x_resnet-18_LC_w_sh_10_iter	.893
310	mobilenet_v2_1_0_192	.893
311	efficientnet-b7	.892
312	efficientnet_b2_imagenet_full	.891
313	compact_resnet50_IT	.891
314	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.890
315	mobilenet_v2_1.0_160-tf	.889
316	mobilenet_v2_1_3_224	.883
317	convnext_tiny:in12k_ft_in1k	.882
318	yudixie_resnet18_distance_reg_0_240719	.882
319	resnet50_imagenet_100_seed-0	.882
320	compact_resnet50_robust_IT	.882
321	mobilenet_v2_1.0_224-tf	.881
322	shufflenet_v2_x1_0	.877
323	alexnet_training_seed_01_fov4	.877
324	mobilenet_v2_1_4_224	.877
325	mobilenet_v2_1-4_224_pytorch	.876
326	mobilenet_v2_0.5_192-tf	.875
327	v1-pyr-nodown	.872
328	mobilenet_v2_1.0_192-tf	.872
329	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.867
330	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.868
331	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.867
332	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.867
333	alexnet_random_linf8_perturb	.867
334	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
335	mobilenet_v2_0.5_224-tf	.864
336	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.863
337	mobilenet_v2_0.35_224	.860
338	convnext_small_imagenet_1_seed-0	.859
339	omnivore_swinB	.854
340	BiT-S-R101x1	.848
341	vit_large_patch14_clip_224:laion2b_ft_in1k	.847
342	yolos_tiny	.845
343	BiT-M-R152x2	.843
344	mobilenet_v2_1.0_96	.838
345	resnet50	.837
346	resnet50_ewconplus_wzc	.827
347	alexnet_training_seed_08	.824
348	aughost_fair_resnet18	.820
349	voneresnet-50-non_stochastic	.819
350	alexnet_training_seed_06	.817
351	mobilenet_v2_0.75_160-tf	.811
352	mobilenet_v2_0.5_160	.810
353	artResNet18_1	.801
354	ViT-B/32	.796
355	inception_v1-tf	.795
356	mobilenet_v2_1_0_224	.794
357	AlexNet_SIN_fov12	.790
358	mobilenet_v2_0.75_96	.783
359	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.780
360	alexnet_training_seed_05	.781
361	AdvProp_efficientnet-b0	.775
362	vit_base_patch16_dinov3-lvd1689m	.756
363	alexnet_fov12	.756
364	mobilenet_v2_0.75_128	.730
365	hmax	.689
366	mobilenet_v2_0.5_128	.689
367	my-model	.690
368	deit_small_imagenet_full_seed-0	.686
369	AdvProp_efficientnet-b4	.654
370	deit_small_imagenet_10_seed-0	.343
371	resnet50-meshes-lt-100-original-pretrained	.172
372	resnet50-meshes-lt-100-original-scratch	.116
373	mobilenet_v2_0.35_160
374	resnet18-supervised
375	fitvid_trained_on_physion
376	openclip
377	resnext101_32x32d_wsl
378	resnext101_32x48d_wsl
379	resnet50_ImageNet
380	regnet_y_400mf
381	tv_efficientnet-b1
382	alexnet_ks_torevert
383	yudixie_resnet50_imagenet1kpret_0_240312
384	resnet_152_v1
385	resnext101_32x16d_wsl
386	densenet_201_pytorch
387	pnasnet_large_pytorch
388	inception_v3_pytorch
389	inception_v4
390	nasnet_large
391	deit_small_imagenet_1_seed-0
392	pnasnet_large
393	vonegrcnn_62e_nobn
394	resnet_101_v2
395	resnet_152_v2
396	vonegrcnn_52e_full
397	resnet_101_v1

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: max_dc