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

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: opr_cv_diff