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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	vit_base_patch14_dinov2-lvd142m	.969
23	omnivore_swinS	.969
24	yudixie_resnet50_distance_rotation_0_240908	.968
25	resnet50_primary_visual_cortex	.968
26	yudixie_resnet50_category_class_0_240908	.966
27	yudixie_resnet18_category_class_0_240719	.965
28	BiT-M-R101x1	.966
29	AdvProp_efficientnet-b8	.965
30	grcnn_109	.964
31	ReAlnet01_cornet_fov12	.964
32	focalnet_tiny_lrf_in1k	.964
33	compact_resnet50_V4	.963
34	omnivore_swinB	.963
35	yudixie_resnet50_distance_translation_rotation_0_240908	.962
36	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.962
37	ReAlnet01_cornet_fov16	.961
38	alexnet_training_seed_04	.960
39	yudixie_resnet50_distance_translation_0_240908	.959
40	vonegrcnn_47e	.958
41	resnet-18_untrained	.958
42	resnet-50_untrained	.957
43	yudixie_resnet18_distance_rotation_0_240719	.954
44	compact_cornets_IT	.953
45	alexnet_training_seed_01_fov12	.951
46	yudixie_resnet50_translation_reg_0_240908	.952
47	r3m_resnet34_nocrop	.950
48	alexnet_training_seed_01_fov16	.949
49	mobilenet_v2_1-4_224_pytorch	.949
50	convnext_xlarge:fb_in22k_ft_in1k	.948
51	resnet-152_v2-tf	.948
52	ReAlnet01_cornet_fov4	.946
53	alexnet_fov4	.946
54	ReAlnet08_cornet	.945
55	ReAlnet08	.944
56	convnext_large:fb_in22k_ft_in1k	.944
57	vonegrcnn_52e_full	.943
58	yudixie_resnet50_distance_reg_0_240908	.942
59	mobilenet_v2_1.3_224-tf	.942
60	compact_resnet50_robust_IT	.941
61	0.5x_resnet-18_LC_w_sh_10_iter	.941
62	alexnet	.941
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	vit_base_patch14_reg4_dinov2-lvd142m	.930
84	ReAlnet01	.930
85	BiT-M-R50x1	.930
86	xception	.929
87	alexnet_training_seed_01	.928
88	ReAlnet04_ALL	.927
89	mobilenet_v2_1.4_224-tf	.927
90	ReAlnet06_cornet	.926
91	mobilenet_v2_1.0_96	.926
92	0.5x_resnet-18_LC_w_sh_100_iter	.926
93	ReAlnet06	.926
94	ReAlnet09	.926
95	ReAlnet09_cornet	.924
96	AlexNet_SIN	.924
97	yudixie_resnet18_distance_translation_rotation_0_240719	.924
98	alexnet_early_checkpoint	.924
99	inception_v1	.924
100	ReAlnet08_ALL	.924
101	briaai_rmbg_1_4	.923
102	ReAlnet03_ALL	.923
103	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.922
104	ReAlnet07_ALL	.922
105	ReAlnet07_cornet	.921
106	ReAlnet07	.921
107	0.5x_resnet-18	.920
108	vgg_19	.918
109	ReAlnet10_ALL	.919
110	ReAlnet02_ALL	.919
111	ReAlnet01_ALL	.919
112	cvt_cvt-13-224-in1k_4_LucyV4	.920
113	yudixie_resnet50_object_class_0_240908	.918
114	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.919
115	resnet-50_v1-tf	.919
116	BiT-S-R101x1	.919
117	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.919
118	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.918
119	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.919
120	grcnn_v2_text_noise	.918
121	ReAlnet06_ALL	.918
122	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.918
123	ReAlnet09_ALL	.918
124	ReAlnet10_cornet	.917
125	grcnn	.916
126	ReAlnet05_cornet	.916
127	ReAlnet05	.916
128	ReAlnet05_ALL	.915
129	resnet-18-LC_1st_conv_m	.915
130	convnext_tiny_sup	.915
131	ReAlnet10	.915
132	resnet_SIN_IN_FT_IN	.913
133	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.911
134	convnext_tiny:in12k_ft_in1k	.911
135	ReAlnet02_cornet	.910
136	ReAlnet02	.909
137	resnet-18-LC_m	.909
138	compact_cornets_V4	.908
139	resnet50-sup	.908
140	yudixie_resnet50_imagenet1kpret_0_240908	.908
141	resnet50_tutorial	.908
142	alexnet_fov12	.908
143	resnet_50_v1	.908
144	resnet_50_v1_spiking_l4a	.907
145	resnet_50_v1_spiking	.908
146	resnet50	.907
147	resnet_50_v1_spiking_l3	.907
148	mobilenet_v2_1.0_160-tf	.907
149	resnet_50_v1_spiking_l4	.905
150	alexnet_robust_correct	.906
151	mobilenet_v2_1.0_128-tf	.904
152	BiT-S-R152x2	.902
153	resnet-18-LC_w_sh_100_iter_conv_init_m	.901
154	cvt_cvt-21-224-in1k_4_LucyV4	.900
155	mobilenet_v2_0.5_192-tf	.900
156	alexnet_fov16	.899
157	resnet18_ecoset_full	.897
158	deit_large_imagenet_full_seed-0	.898
159	texture_shape_alexnet_trained_on_SIN	.897
160	alexnet_training_seed_10	.896
161	SWSL_resnext101_32x8d	.896
162	vgg-16-keras	.895
163	alexnet_random_l2_3_perturb	.893
164	convnext_small_imagenet_full_seed-0	.894
165	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.894
166	densenet-201-keras	.892
167	AlexNet_SIN_fov12	.892
168	mobilenet_v2_1_4_224	.891
169	resnet50-SIN_IN_IN	.891
170	SWSL_resnet50	.891
171	yudixie_resnet18_imagenet1kpret_0_240719	.889
172	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.889
173	resnet-50-robust	.888
174	vgg-19-keras	.889
175	convnext_small_imagenet_10_seed-0	.886
176	AlexNet_SIN_fov4	.885
177	BiT-S-R152x4	.884
178	konkle_alexnetgn_ipcl_ref01_primary_model	.884
179	RN50	.883
180	CLIP-RN50	.882
181	compact_resnet50_IT	.879
182	resnet-18-LC_w_sh_10_iter_m	.880
183	AdvProp_efficientnet-b6	.878
184	cvt_cvt-13-384-in1k_4_LucyV4	.878
185	voneresnet-50-non_stochastic	.878
186	convnext_small_imagenet_100_seed-0	.875
187	alexnet_training_seed_03	.874
188	my-model	.874
189	resnet50_random_l2_perturb	.873
190	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.870
191	AdvProp_efficientnet-b7	.870
192	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.870
193	BiT-M-R152x4	.870
194	cornet_s	.869
195	vgg_16	.869
196	compact_vgg19_V4	.867
197	aughost_fair_resnet18	.867
198	ViT_L_32_imagenet1k	.864
199	BiT-S-R101x3	.865
200	resnet50_robust_l2_eps3	.865
201	resnet50_imagenet_full	.865
202	resnet-152_v1-tf	.865
203	AlexNet_SIN_fov16	.863
204	alexnet_l2_3_robust	.862
205	densenet-201	.862
206	mobilenet_v2_1_0_128	.861
207	mobilenet_v2_1_0_224	.861
208	grcnn_robust_v1	.861
209	r3m_resnet34	.860
210	CLIP_resnet50	.860
211	resnet50-SIN	.859
212	resnet50-barlow	.859
213	CLIP_resnet50_float32	.858
214	resnet-152_v2_pytorch	.856
215	inception_v3-tf	.856
216	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.855
217	convnext_large_imagenet_full_seed-0	.855
218	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.855
219	resnet-50_v2-tf	.855
220	resnet50-VITO-8deg-cc	.854
221	texture_shape_resnet50_trained_on_SIN	.852
222	resnet-18-LC_w_sh_1_iter_m	.852
223	BiT-S-R50x3	.850
224	r3m_resnet18	.850
225	convnext_base_imagenet_full_seed-0	.850
226	resnet50_imagenet_100_seed-0	.849
227	resnet50-vicreg	.848
228	resnet101	.849
229	resnet-18-LC_w_sh_100_iter_m	.848
230	vision_transformer_vit_large_patch16_224	.845
231	vonealexnet_gaussian_noise_std4_fixed	.845
232	mobilenet_v2_1.0_192-tf	.844
233	BiT-S-R50x1	.842
234	BiT-M-R101x3	.842
235	resnet34_ecoset_full	.840
236	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.840
237	resnet50_linf_8_robust	.839
238	0.5x_resnet-18_LC	.839
239	resnet50_robust_l2_eps1	.836
240	antialiased-r50	.836
241	yudixie_resnet18_random_0_240719	.835
242	efficientnet_b2_imagenet_full	.835
243	convnext_tiny_imagenet_full_seed-0	.834
244	shufflenet_v2_x1_0	.833
245	mobilenet_v2_0.75_160-tf	.833
246	resnet-18_test_m	.833
247	vggface	.831
248	nasnet_mobile	.831
249	inception_v4-tf	.831
250	mobilenet_v2_1.0_224-tf	.830
251	vit_base_patch16_clip_224:openai_ft_in1k	.830
252	resnet18_imagenet_full	.828
253	resnet50_moco_v2	.826
254	BiT-M-R152x2	.826
255	resnet50_random_linf8_perturb	.825
256	mobilenet_v2_0_75_224	.825
257	AdvProp_efficientnet-b4	.822
258	nasnet_mobile-tf	.821
259	nasnet_large-tf	.820
260	convnext_small_imagenet_1_seed-0	.821
261	resnet50-vicregl0p9	.820
262	VOneCORnet-S	.816
263	alexnet_training_seed_01_fov4	.816
264	voneresnet-50	.813
265	Res2Net50_26w_4s	.815
266	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.814
267	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.813
268	vit_base_patch16_dinov3-lvd1689m	.813
269	mobilenet_v2_0.5_224-tf	.812
270	resnet_101_v1	.812
271	resnet50_linf_4_robust	.811
272	resnext101_32x8d_wsl	.810
273	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.810
274	pnasnet_large-tf	.809
275	voneresnet-50-robust	.808
276	artResNet18_1	.809
277	deit_small_imagenet_full_seed-0	.808
278	resnet50-moclr8deg	.808
279	mobilenet_v2_0.75_96	.808
280	antialias-resnet152	.806
281	CORnet-Z	.806
282	effnetv2m_custom384	.805
283	antialiased-rnext101_32x8d	.803
284	imagenet_l2_10_0	.802
285	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.802
286	resnet50-simclr-vissl	.802
287	resnet50_imagenet_10_seed-0	.800
288	resnet18_imagenet21kP	.801
289	0.5x_resnet-18_LC_w_sh_1_iter	.801
290	imagenet_l2_3_0	.799
291	pnasnet_large_halfPrecision	.797
292	r3m_resnet50	.797
293	vit_large_patch14_clip_224:laion2b_ft_in1k	.795
294	vit_large_patch14_clip_336:laion2b_ft_in1k	.796
295	resnet50_l2_3_robust	.794
296	resnet152_ecoset_full	.792
297	cv_18_dagger_408_pretrained	.792
298	r3m_resnet50_nocrop	.791
299	resnet34_imagenet_full	.791
300	custom_model_cv_18_dagger_408	.789
301	resnet50_ewconplus_wzc	.789
302	mobilenet_v2_0_75_192	.789
303	resnet50_FractalDB	.787
304	resnet-101_v1-tf	.785
305	resnet-101_v2-tf	.783
306	mobilenet_v2_1_3_224	.775
307	alexnet_linf_8_robust	.775
308	barlow-twins-resnet50	.774
309	FrankRobWobv0	.774
310	resnet152_imagenet_full	.773
311	alexnet_training_seed_08	.772
312	resnet50_simclr	.770
313	resnet50-vitoimagevidnet8	.769
314	inception_v2-tf	.768
315	resnet101_ecoset_full	.764
316	densenet-121	.762
317	eBarlow_lmda_02_1000ep	.760
318	densenet-169	.760
319	AdvProp_efficientnet-b0	.758
320	vonegrcnn_62e_nobn	.757
321	resnet_50_v2	.755
322	compact_vgg19_IT	.751
323	mobilenet_v2_0_75_160	.751
324	resnet50_ecoset_full	.750
325	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.745
326	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.746
327	mobilenet_v2_0.35_224	.742
328	alexnet_reduced_aliasing_early_checkpoint	.738
329	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.736
330	deit_base_imagenet_full_seed-0	.735
331	mobilenet_v2_0_5_192	.734
332	resnet101_imagenet_full	.730
333	effnetb1_272x240	.727
334	resnet50-cifar	.722
335	omnivore_swinT	.721
336	vit_large_patch14_clip_224:openai_ft_in1k	.720
337	densenet-121-keras	.720
338	resnet50-SIN_IN	.719
339	densenet-169-keras	.715
340	resnet_101_v2	.714
341	CLIP_ViT-B_32	.713
342	mobilenet_v2_1_0_160	.712
343	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.710
344	inception_v1-tf	.708
345	CORnetZ_CIFAR10	.703
346	resnet50	.702
347	mobilenet_v2_1_0_192	.700
348	mobilenet_v2_0.5_160	.694
349	efficientnet_b0_imagenet_full	.693
350	inception_resnet_v2	.683
351	vit_relpos_base_patch16_clsgap_224:sw_in1k	.680
352	resnet-18-LC_conv_init_m	.676
353	yolos_tiny	.668
354	mobilenet_v2_0_5_224	.663
355	resnet50_pretrained_with_retinal_waves	.661
356	AdvProp_efficientnet-b2	.635
357	AT_efficientnet-b2	.633
358	my-model	.629
359	efficientnet_b0	.604
360	alexnet_training_seed_06	.563
361	CORnetZ_CIFAR10_bs32_20_04	.547
362	alexnet_training_seed_05	.546
363	ViT-B/32	.532
364	mobilenet_v2_0.75_128	.521
365	resnet50_imagenet_1_seed-0	.492
366	resnet-18-LC_untrained	.487
367	mobilenet_v2_0.75_224-tf	.437
368	v1-pyr-nodown	.419
369	hmax	.405
370	mobilenet_v2_0.5_128	.398
371	resnet50-meshes-lt-100-original-pretrained	.367
372	mobilenet_v1_0.25_128	.349
373	deit_small_imagenet_10_seed-0	.320
374	resnet50-meshes-lt-100-original-scratch	.265
375	mobilenet_v2_0.35_160
376	resnet18-supervised
377	fitvid_trained_on_physion
378	openclip
379	resnext101_32x32d_wsl
380	resnext101_32x48d_wsl
381	resnet50_ImageNet
382	regnet_y_400mf
383	tv_efficientnet-b1
384	alexnet_ks_torevert
385	yudixie_resnet50_imagenet1kpret_0_240312
386	resnet_152_v1
387	resnext101_32x16d_wsl
388	densenet_201_pytorch
389	pnasnet_large_pytorch
390	inception_v3_pytorch
391	mobilevit_small
392	inception_v4
393	nasnet_large
394	deit_small_imagenet_1_seed-0
395	pnasnet_large
396	vonegrcnn_62e_nobn
397	vonegrcnn_52e_full
398	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: opr_cv_diff