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

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: max_dc