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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	mobilenet_v2_0.75_192-tf	.991
2	yudixie_resnet50_translation_rotation_0_240908	.988
3	alexnet_training_seed_09	.989
4	convnext_base:clip_laiona_augreg_ft_in1k_384	.989
5	alexnet_training_seed_02	.987
6	yudixie_resnet50_rotation_reg_0_240908	.987
7	yudixie_resnet18_translation_reg_0_240719	.987
8	resnet-50x2_untrained	.984
9	resnet-18-LC_d_w_sh_1x1_conv_init_m	.981
10	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.980
11	yudixie_resnet18_object_class_0_240719	.978
12	blt_vs	.977
13	cvt_cvt-21-384-in1k_4_LucyV4	.978
14	resnet-18-LC_w_sh_10_iter_conv_init_m	.976
15	yudixie_resnet18_distance_reg_0_240719	.976
16	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.976
17	efficientnet_b1_imagenet_full	.973
18	yudixie_resnet18_distance_translation_0_240719	.973
19	efficientnet-b7	.972
20	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.972
21	yudixie_resnet18_rotation_reg_0_240719	.971
22	omnivore_swinS	.969
23	yudixie_resnet50_distance_rotation_0_240908	.968
24	resnet50_primary_visual_cortex	.968
25	yudixie_resnet50_category_class_0_240908	.966
26	yudixie_resnet18_category_class_0_240719	.965
27	BiT-M-R101x1	.966
28	AdvProp_efficientnet-b8	.965
29	grcnn_109	.964
30	ReAlnet01_cornet_fov12	.964
31	focalnet_tiny_lrf_in1k	.964
32	compact_resnet50_V4	.963
33	omnivore_swinB	.963
34	yudixie_resnet50_distance_translation_rotation_0_240908	.962
35	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.962
36	ReAlnet01_cornet_fov16	.961
37	alexnet_training_seed_04	.960
38	yudixie_resnet50_distance_translation_0_240908	.959
39	vonegrcnn_47e	.958
40	resnet-18_untrained	.958
41	resnet-50_untrained	.957
42	yudixie_resnet18_distance_rotation_0_240719	.954
43	compact_cornets_IT	.953
44	alexnet_training_seed_01_fov12	.951
45	yudixie_resnet50_translation_reg_0_240908	.952
46	r3m_resnet34_nocrop	.950
47	alexnet_training_seed_01_fov16	.949
48	mobilenet_v2_1-4_224_pytorch	.949
49	convnext_xlarge:fb_in22k_ft_in1k	.948
50	resnet-152_v2-tf	.948
51	ReAlnet01_cornet_fov4	.946
52	alexnet_fov4	.946
53	ReAlnet08_cornet	.945
54	ReAlnet08	.944
55	convnext_large:fb_in22k_ft_in1k	.944
56	vonegrcnn_52e_full	.943
57	yudixie_resnet50_distance_reg_0_240908	.942
58	mobilenet_v2_1.3_224-tf	.942
59	alexnet	.941
60	compact_resnet50_robust_IT	.941
61	alexnet	.941
62	0.5x_resnet-18_LC_w_sh_10_iter	.941
63	alexnet	.940
64	alexnet	.941
65	alexnet	.941
66	alexnet	.940
67	alexnet	.940
68	alexnet-baseline	.940
69	alexnet	.940
70	BiT-M-R50x3	.939
71	compact_resnet50_robust_V4	.939
72	deit_small_imagenet_100_seed-0	.939
73	alexnet_training_seed_07	.937
74	ReAlnet03_cornet	.936
75	resnet50-vicregl0p75	.935
76	vonegrcnn_47e	.934
77	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.935
78	inception_v3	.935
79	resnet50_byol	.934
80	resnet-18-LC_w_sh_1_iter_conv_init_m	.934
81	ReAlnet03	.934
82	yudixie_resnet50_random_0_240908	.933
83	CORnet-S	.933
84	ReAlnet04	.934
85	ReAlnet04_cornet	.933
86	convnext_femto_ols:d1_in1k	.933
87	alexnet_random_linf8_perturb	.932
88	yudixie_resnet18_translation_rotation_0_240719	.931
89	ReAlnet01_cornet	.930
90	ReAlnet01	.930
91	BiT-M-R50x1	.930
92	xception	.929
93	alexnet_training_seed_01	.928
94	ReAlnet04_ALL	.927
95	mobilenet_v2_1.4_224-tf	.927
96	ReAlnet06_cornet	.926
97	mobilenet_v2_1.0_96	.926
98	0.5x_resnet-18_LC_w_sh_100_iter	.926
99	ReAlnet06	.926
100	ReAlnet09	.926
101	ReAlnet09_cornet	.924
102	AlexNet_SIN	.924
103	yudixie_resnet18_distance_translation_rotation_0_240719	.924
104	alexnet_early_checkpoint	.924
105	inception_v1	.924
106	ReAlnet08_ALL	.924
107	briaai_rmbg_1_4	.923
108	ReAlnet03_ALL	.923
109	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.922
110	ReAlnet07_ALL	.922
111	ReAlnet07_cornet	.921
112	ReAlnet07	.921
113	0.5x_resnet-18	.920
114	vgg_19	.918
115	ReAlnet10_ALL	.919
116	ReAlnet02_ALL	.919
117	ReAlnet01_ALL	.919
118	cvt_cvt-13-224-in1k_4_LucyV4	.920
119	yudixie_resnet50_object_class_0_240908	.918
120	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.919
121	resnet-50_v1-tf	.919
122	BiT-S-R101x1	.919
123	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.919
124	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.918
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.919
126	grcnn_v2_text_noise	.918
127	ReAlnet06_ALL	.918
128	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.918
129	ReAlnet09_ALL	.918
130	ReAlnet10_cornet	.917
131	grcnn	.916
132	ReAlnet05_cornet	.916
133	ReAlnet05	.916
134	ReAlnet05_ALL	.915
135	resnet-18-LC_1st_conv_m	.915
136	convnext_tiny_sup	.915
137	ReAlnet10	.915
138	resnet_SIN_IN_FT_IN	.913
139	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.911
140	convnext_tiny:in12k_ft_in1k	.911
141	ReAlnet02_cornet	.910
142	ReAlnet02	.909
143	resnet-18-LC_m	.909
144	compact_cornets_V4	.908
145	resnet50-sup	.908
146	yudixie_resnet50_imagenet1kpret_0_240908	.908
147	resnet50_tutorial	.908
148	alexnet_fov12	.908
149	resnet_50_v1	.908
150	resnet_50_v1_spiking_l4a	.907
151	resnet_50_v1_spiking	.908
152	resnet50	.907
153	resnet_50_v1_spiking_l3	.907
154	mobilenet_v2_1.0_160-tf	.907
155	resnet_50_v1_spiking_l4	.905
156	alexnet_robust_correct	.906
157	mobilenet_v2_1.0_128-tf	.904
158	BiT-S-R152x2	.902
159	resnet-18-LC_w_sh_100_iter_conv_init_m	.901
160	cvt_cvt-21-224-in1k_4_LucyV4	.900
161	mobilenet_v2_0.5_192-tf	.900
162	alexnet_fov16	.899
163	resnet18_ecoset_full	.897
164	deit_large_imagenet_full_seed-0	.898
165	texture_shape_alexnet_trained_on_SIN	.897
166	alexnet_training_seed_10	.896
167	SWSL_resnext101_32x8d	.896
168	vgg-16-keras	.895
169	alexnet_random_l2_3_perturb	.893
170	convnext_small_imagenet_full_seed-0	.894
171	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.894
172	densenet-201-keras	.892
173	AlexNet_SIN_fov12	.892
174	mobilenet_v2_1_4_224	.891
175	resnet50-SIN_IN_IN	.891
176	SWSL_resnet50	.891
177	yudixie_resnet18_imagenet1kpret_0_240719	.889
178	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.889
179	resnet-50-robust	.888
180	vgg-19-keras	.889
181	convnext_small_imagenet_10_seed-0	.886
182	AlexNet_SIN_fov4	.885
183	BiT-S-R152x4	.884
184	konkle_alexnetgn_ipcl_ref01_primary_model	.884
185	RN50	.883
186	CLIP-RN50	.882
187	compact_resnet50_IT	.879
188	resnet-18-LC_w_sh_10_iter_m	.880
189	AdvProp_efficientnet-b6	.878
190	cvt_cvt-13-384-in1k_4_LucyV4	.878
191	voneresnet-50-non_stochastic	.878
192	alexnet	.875
193	convnext_small_imagenet_100_seed-0	.875
194	alexnet_training_seed_03	.874
195	my-model	.874
196	resnet50_random_l2_perturb	.873
197	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.870
198	AdvProp_efficientnet-b7	.870
199	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.870
200	BiT-M-R152x4	.870
201	cornet_s	.869
202	vgg_16	.869
203	compact_vgg19_V4	.867
204	ViT_L_32_imagenet1k	.864
205	BiT-S-R101x3	.865
206	resnet50_robust_l2_eps3	.865
207	resnet50_imagenet_full	.865
208	resnet-152_v1-tf	.865
209	AlexNet_SIN_fov16	.863
210	alexnet_l2_3_robust	.862
211	densenet-201	.862
212	mobilenet_v2_1_0_128	.861
213	mobilenet_v2_1_0_224	.861
214	grcnn_robust_v1	.861
215	r3m_resnet34	.860
216	CLIP_resnet50	.860
217	resnet50-SIN	.859
218	resnet50-barlow	.859
219	CLIP_resnet50_float32	.858
220	resnet-152_v2_pytorch	.856
221	inception_v3-tf	.856
222	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.855
223	convnext_large_imagenet_full_seed-0	.855
224	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.855
225	resnet-50_v2-tf	.855
226	resnet50-VITO-8deg-cc	.854
227	texture_shape_resnet50_trained_on_SIN	.852
228	resnet-18-LC_w_sh_1_iter_m	.852
229	BiT-S-R50x3	.850
230	r3m_resnet18	.850
231	convnext_base_imagenet_full_seed-0	.850
232	resnet50_imagenet_100_seed-0	.849
233	resnet50-vicreg	.848
234	resnet101	.849
235	resnet-18-LC_w_sh_100_iter_m	.848
236	vision_transformer_vit_large_patch16_224	.845
237	vonealexnet_gaussian_noise_std4_fixed	.845
238	mobilenet_v2_1.0_192-tf	.844
239	BiT-S-R50x1	.842
240	BiT-M-R101x3	.842
241	resnet34_ecoset_full	.840
242	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.840
243	resnet50_linf_8_robust	.839
244	0.5x_resnet-18_LC	.839
245	resnet50_robust_l2_eps1	.836
246	antialiased-r50	.836
247	yudixie_resnet18_random_0_240719	.835
248	efficientnet_b2_imagenet_full	.835
249	convnext_tiny_imagenet_full_seed-0	.834
250	shufflenet_v2_x1_0	.833
251	mobilenet_v2_0.75_160-tf	.833
252	resnet-18_test_m	.833
253	vggface	.831
254	nasnet_mobile	.831
255	inception_v4-tf	.831
256	mobilenet_v2_1.0_224-tf	.830
257	vit_base_patch16_clip_224:openai_ft_in1k	.830
258	resnet18_imagenet_full	.828
259	resnet50_moco_v2	.826
260	BiT-M-R152x2	.826
261	resnet50_random_linf8_perturb	.825
262	mobilenet_v2_0_75_224	.825
263	AdvProp_efficientnet-b4	.822
264	nasnet_mobile-tf	.821
265	nasnet_large-tf	.820
266	convnext_small_imagenet_1_seed-0	.821
267	resnet50-vicregl0p9	.820
268	VOneCORnet-S	.816
269	alexnet_training_seed_01_fov4	.816
270	voneresnet-50	.813
271	Res2Net50_26w_4s	.815
272	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.814
273	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.813
274	mobilenet_v2_0.5_224-tf	.812
275	resnet_101_v1	.812
276	resnet50_linf_4_robust	.811
277	resnext101_32x8d_wsl	.810
278	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.810
279	pnasnet_large-tf	.809
280	voneresnet-50-robust	.808
281	artResNet18_1	.809
282	deit_small_imagenet_full_seed-0	.808
283	resnet50-moclr8deg	.808
284	mobilenet_v2_0.75_96	.808
285	antialias-resnet152	.806
286	CORnet-Z	.806
287	effnetv2m_custom384	.805
288	antialiased-rnext101_32x8d	.803
289	imagenet_l2_10_0	.802
290	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.802
291	resnet50-simclr-vissl	.802
292	resnet50_imagenet_10_seed-0	.800
293	resnet18_imagenet21kP	.801
294	0.5x_resnet-18_LC_w_sh_1_iter	.801
295	imagenet_l2_3_0	.799
296	r3m_resnet50	.797
297	vit_large_patch14_clip_224:laion2b_ft_in1k	.795
298	vit_large_patch14_clip_336:laion2b_ft_in1k	.796
299	resnet50_l2_3_robust	.794
300	resnet152_ecoset_full	.792
301	cv_18_dagger_408_pretrained	.792
302	r3m_resnet50_nocrop	.791
303	resnet34_imagenet_full	.791
304	custom_model_cv_18_dagger_408	.789
305	resnet50_ewconplus_wzc	.789
306	mobilenet_v2_0_75_192	.789
307	resnet50_FractalDB	.787
308	resnet-101_v1-tf	.785
309	resnet-101_v2-tf	.783
310	mobilenet_v2_1_3_224	.775
311	alexnet_linf_8_robust	.775
312	barlow-twins-resnet50	.774
313	FrankRobWobv0	.774
314	resnet152_imagenet_full	.773
315	alexnet_training_seed_08	.772
316	resnet50_simclr	.770
317	resnet50-vitoimagevidnet8	.769
318	inception_v2-tf	.768
319	resnet101_ecoset_full	.764
320	densenet-121	.762
321	densenet-169	.760
322	AdvProp_efficientnet-b0	.758
323	vonegrcnn_62e_nobn	.757
324	resnet_50_v2	.755
325	compact_vgg19_IT	.751
326	mobilenet_v2_0_75_160	.751
327	resnet50_ecoset_full	.750
328	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.745
329	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.746
330	mobilenet_v2_0.35_224	.742
331	alexnet_reduced_aliasing_early_checkpoint	.738
332	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.736
333	deit_base_imagenet_full_seed-0	.735
334	mobilenet_v2_0_5_192	.734
335	resnet101_imagenet_full	.730
336	effnetb1_272x240	.727
337	resnet50-cifar	.722
338	omnivore_swinT	.721
339	vit_large_patch14_clip_224:openai_ft_in1k	.720
340	densenet-121-keras	.720
341	resnet50-SIN_IN	.719
342	densenet-169-keras	.715
343	resnet_101_v2	.714
344	CLIP_ViT-B_32	.713
345	mobilenet_v2_1_0_160	.712
346	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.710
347	inception_v1-tf	.708
348	CORnetZ_CIFAR10	.703
349	resnet50	.702
350	mobilenet_v2_1_0_192	.700
351	mobilenet_v2_0.5_160	.694
352	efficientnet_b0_imagenet_full	.693
353	inception_resnet_v2	.683
354	vit_relpos_base_patch16_clsgap_224:sw_in1k	.680
355	resnet-18-LC_conv_init_m	.676
356	yolos_tiny	.668
357	mobilenet_v2_0_5_224	.663
358	resnet50_pretrained_with_retinal_waves	.661
359	AdvProp_efficientnet-b2	.635
360	AT_efficientnet-b2	.633
361	my-model	.629
362	efficientnet_b0	.604
363	alexnet_training_seed_06	.563
364	CORnetZ_CIFAR10_bs32_20_04	.547
365	alexnet_training_seed_05	.546
366	ViT-B/32	.532
367	mobilenet_v2_0.75_128	.521
368	resnet50_imagenet_1_seed-0	.492
369	resnet-18-LC_untrained	.487
370	mobilenet_v2_0.75_224-tf	.437
371	v1-pyr-nodown	.419
372	hmax	.405
373	mobilenet_v2_0.5_128	.398
374	resnet50-meshes-lt-100-original-pretrained	.367
375	mobilenet_v1_0.25_128	.349
376	deit_small_imagenet_10_seed-0	.320
377	resnet50-meshes-lt-100-original-scratch	.265
378	mobilenet_v2_0.35_160
379	resnet18-supervised
380	fitvid_trained_on_physion
381	openclip
382	resnext101_32x32d_wsl
383	resnext101_32x48d_wsl
384	resnet50_ImageNet
385	regnet_y_400mf
386	tv_efficientnet-b1
387	alexnet_ks_torevert
388	yudixie_resnet50_imagenet1kpret_0_240312
389	resnet_152_v1
390	resnext101_32x16d_wsl
391	densenet_201_pytorch
392	pnasnet_large_pytorch
393	inception_v3_pytorch
394	mobilevit_small
395	inception_v4
396	nasnet_large
397	deit_small_imagenet_1_seed-0
398	pnasnet_large
399	vonegrcnn_62e_nobn
400	vonegrcnn_52e_full
401	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: opr_cv_diff