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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	vit_base_patch14_dinov2:lvd142m	.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	alexnet	.941
61	compact_resnet50_robust_IT	.941
62	alexnet	.941
63	0.5x_resnet-18_LC_w_sh_10_iter	.941
64	alexnet	.940
65	alexnet	.941
66	alexnet	.941
67	alexnet_workflow_trigger	.940
68	alexnet	.940
69	alexnet	.940
70	alexnet-baseline	.940
71	alexnet	.940
72	BiT-M-R50x3	.939
73	compact_resnet50_robust_V4	.939
74	deit_small_imagenet_100_seed-0	.939
75	alexnet_training_seed_07	.937
76	ReAlnet03_cornet	.936
77	resnet50-vicregl0p75	.935
78	vonegrcnn_47e	.934
79	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.935
80	inception_v3	.935
81	resnet50_byol	.934
82	resnet-18-LC_w_sh_1_iter_conv_init_m	.934
83	ReAlnet03	.934
84	yudixie_resnet50_random_0_240908	.933
85	CORnet-S	.933
86	ReAlnet04	.934
87	ReAlnet04_cornet	.933
88	convnext_femto_ols:d1_in1k	.933
89	alexnet_random_linf8_perturb	.932
90	yudixie_resnet18_translation_rotation_0_240719	.931
91	ReAlnet01_cornet	.930
92	ReAlnet01	.930
93	vit_base_patch14_reg4_dinov2:lvd142m	.930
94	BiT-M-R50x1	.930
95	xception	.929
96	alexnet_training_seed_01	.928
97	ReAlnet04_ALL	.927
98	mobilenet_v2_1.4_224-tf	.927
99	ReAlnet06_cornet	.926
100	mobilenet_v2_1.0_96	.926
101	0.5x_resnet-18_LC_w_sh_100_iter	.926
102	ReAlnet06	.926
103	ReAlnet09	.926
104	ReAlnet09_cornet	.924
105	AlexNet_SIN	.924
106	yudixie_resnet18_distance_translation_rotation_0_240719	.924
107	alexnet_early_checkpoint	.924
108	inception_v1	.924
109	ReAlnet08_ALL	.924
110	briaai_rmbg_1_4	.923
111	ReAlnet03_ALL	.923
112	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.922
113	ReAlnet07_ALL	.922
114	ReAlnet07_cornet	.921
115	ReAlnet07	.921
116	0.5x_resnet-18	.920
117	vgg_19	.918
118	ReAlnet10_ALL	.919
119	ReAlnet02_ALL	.919
120	ReAlnet01_ALL	.919
121	cvt_cvt-13-224-in1k_4_LucyV4	.920
122	yudixie_resnet50_object_class_0_240908	.918
123	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.919
124	resnet-50_v1-tf	.919
125	BiT-S-R101x1	.919
126	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.919
127	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.918
128	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.919
129	grcnn_v2_text_noise	.918
130	ReAlnet06_ALL	.918
131	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.918
132	ReAlnet09_ALL	.918
133	ReAlnet10_cornet	.917
134	grcnn	.916
135	ReAlnet05_cornet	.916
136	ReAlnet05	.916
137	ReAlnet05_ALL	.915
138	resnet-18-LC_1st_conv_m	.915
139	convnext_tiny_sup	.915
140	ReAlnet10	.915
141	resnet_SIN_IN_FT_IN	.913
142	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.911
143	convnext_tiny:in12k_ft_in1k	.911
144	ReAlnet02_cornet	.910
145	ReAlnet02	.909
146	resnet-18-LC_m	.909
147	compact_cornets_V4	.908
148	resnet50-sup	.908
149	yudixie_resnet50_imagenet1kpret_0_240908	.908
150	resnet50_tutorial	.908
151	alexnet_fov12	.908
152	resnet_50_v1	.908
153	resnet_50_v1_spiking_l4a	.907
154	resnet_50_v1_spiking	.908
155	resnet50	.907
156	resnet_50_v1_spiking_l3	.907
157	mobilenet_v2_1.0_160-tf	.907
158	resnet_50_v1_spiking_l4	.905
159	alexnet_robust_correct	.906
160	mobilenet_v2_1.0_128-tf	.904
161	BiT-S-R152x2	.902
162	resnet-18-LC_w_sh_100_iter_conv_init_m	.901
163	cvt_cvt-21-224-in1k_4_LucyV4	.900
164	mobilenet_v2_0.5_192-tf	.900
165	alexnet_fov16	.899
166	resnet18_ecoset_full	.897
167	deit_large_imagenet_full_seed-0	.898
168	texture_shape_alexnet_trained_on_SIN	.897
169	alexnet_training_seed_10	.896
170	SWSL_resnext101_32x8d	.896
171	vgg-16-keras	.895
172	alexnet_random_l2_3_perturb	.893
173	convnext_small_imagenet_full_seed-0	.894
174	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.894
175	densenet-201-keras	.892
176	AlexNet_SIN_fov12	.892
177	mobilenet_v2_1_4_224	.891
178	resnet50-SIN_IN_IN	.891
179	SWSL_resnet50	.891
180	yudixie_resnet18_imagenet1kpret_0_240719	.889
181	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.889
182	resnet-50-robust	.888
183	vgg-19-keras	.889
184	convnext_small_imagenet_10_seed-0	.886
185	AlexNet_SIN_fov4	.885
186	BiT-S-R152x4	.884
187	konkle_alexnetgn_ipcl_ref01_primary_model	.884
188	RN50	.883
189	CLIP-RN50	.882
190	compact_resnet50_IT	.879
191	resnet-18-LC_w_sh_10_iter_m	.880
192	AdvProp_efficientnet-b6	.878
193	cvt_cvt-13-384-in1k_4_LucyV4	.878
194	voneresnet-50-non_stochastic	.878
195	alexnet	.875
196	convnext_small_imagenet_100_seed-0	.875
197	alexnet_training_seed_03	.874
198	my-model	.874
199	resnet50_random_l2_perturb	.873
200	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.870
201	AdvProp_efficientnet-b7	.870
202	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.870
203	BiT-M-R152x4	.870
204	cornet_s	.869
205	vgg_16	.869
206	compact_vgg19_V4	.867
207	ViT_L_32_imagenet1k	.864
208	BiT-S-R101x3	.865
209	resnet50_robust_l2_eps3	.865
210	resnet50_imagenet_full	.865
211	resnet-152_v1-tf	.865
212	AlexNet_SIN_fov16	.863
213	alexnet_l2_3_robust	.862
214	densenet-201	.862
215	mobilenet_v2_1_0_128	.861
216	mobilenet_v2_1_0_224	.861
217	grcnn_robust_v1	.861
218	r3m_resnet34	.860
219	CLIP_resnet50	.860
220	resnet50-SIN	.859
221	resnet50-barlow	.859
222	CLIP_resnet50_float32	.858
223	resnet-152_v2_pytorch	.856
224	inception_v3-tf	.856
225	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.855
226	convnext_large_imagenet_full_seed-0	.855
227	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.855
228	resnet-50_v2-tf	.855
229	resnet50-VITO-8deg-cc	.854
230	texture_shape_resnet50_trained_on_SIN	.852
231	resnet-18-LC_w_sh_1_iter_m	.852
232	BiT-S-R50x3	.850
233	r3m_resnet18	.850
234	convnext_base_imagenet_full_seed-0	.850
235	resnet50_imagenet_100_seed-0	.849
236	resnet50-vicreg	.848
237	resnet101	.849
238	resnet-18-LC_w_sh_100_iter_m	.848
239	vision_transformer_vit_large_patch16_224	.845
240	vonealexnet_gaussian_noise_std4_fixed	.845
241	mobilenet_v2_1.0_192-tf	.844
242	BiT-S-R50x1	.842
243	BiT-M-R101x3	.842
244	resnet34_ecoset_full	.840
245	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.840
246	resnet50_linf_8_robust	.839
247	0.5x_resnet-18_LC	.839
248	resnet50_robust_l2_eps1	.836
249	antialiased-r50	.836
250	yudixie_resnet18_random_0_240719	.835
251	efficientnet_b2_imagenet_full	.835
252	convnext_tiny_imagenet_full_seed-0	.834
253	shufflenet_v2_x1_0	.833
254	mobilenet_v2_0.75_160-tf	.833
255	resnet-18_test_m	.833
256	vggface	.831
257	nasnet_mobile	.831
258	inception_v4-tf	.831
259	mobilenet_v2_1.0_224-tf	.830
260	vit_base_patch16_clip_224:openai_ft_in1k	.830
261	resnet18_imagenet_full	.828
262	resnet50_moco_v2	.826
263	BiT-M-R152x2	.826
264	resnet50_random_linf8_perturb	.825
265	mobilenet_v2_0_75_224	.825
266	AdvProp_efficientnet-b4	.822
267	nasnet_mobile-tf	.821
268	nasnet_large-tf	.820
269	convnext_small_imagenet_1_seed-0	.821
270	resnet50-vicregl0p9	.820
271	VOneCORnet-S	.816
272	alexnet_training_seed_01_fov4	.816
273	voneresnet-50	.813
274	Res2Net50_26w_4s	.815
275	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.814
276	vit_base_patch16_dinov3:lvd1689m	.813
277	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.813
278	mobilenet_v2_0.5_224-tf	.812
279	resnet_101_v1	.812
280	resnet50_linf_4_robust	.811
281	resnext101_32x8d_wsl	.810
282	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.810
283	pnasnet_large-tf	.809
284	voneresnet-50-robust	.808
285	artResNet18_1	.809
286	deit_small_imagenet_full_seed-0	.808
287	resnet50-moclr8deg	.808
288	mobilenet_v2_0.75_96	.808
289	antialias-resnet152	.806
290	CORnet-Z	.806
291	effnetv2m_custom384	.805
292	antialiased-rnext101_32x8d	.803
293	imagenet_l2_10_0	.802
294	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.802
295	resnet50-simclr-vissl	.802
296	resnet50_imagenet_10_seed-0	.800
297	resnet18_imagenet21kP	.801
298	0.5x_resnet-18_LC_w_sh_1_iter	.801
299	imagenet_l2_3_0	.799
300	r3m_resnet50	.797
301	vit_large_patch14_clip_224:laion2b_ft_in1k	.795
302	vit_large_patch14_clip_336:laion2b_ft_in1k	.796
303	resnet50_l2_3_robust	.794
304	resnet152_ecoset_full	.792
305	cv_18_dagger_408_pretrained	.792
306	r3m_resnet50_nocrop	.791
307	resnet34_imagenet_full	.791
308	custom_model_cv_18_dagger_408	.789
309	resnet50_ewconplus_wzc	.789
310	mobilenet_v2_0_75_192	.789
311	resnet50_FractalDB	.787
312	resnet-101_v1-tf	.785
313	resnet-101_v2-tf	.783
314	mobilenet_v2_1_3_224	.775
315	alexnet_linf_8_robust	.775
316	barlow-twins-resnet50	.774
317	FrankRobWobv0	.774
318	resnet152_imagenet_full	.773
319	alexnet_training_seed_08	.772
320	resnet50_simclr	.770
321	resnet50-vitoimagevidnet8	.769
322	inception_v2-tf	.768
323	resnet101_ecoset_full	.764
324	densenet-121	.762
325	densenet-169	.760
326	AdvProp_efficientnet-b0	.758
327	vonegrcnn_62e_nobn	.757
328	resnet_50_v2	.755
329	compact_vgg19_IT	.751
330	mobilenet_v2_0_75_160	.751
331	resnet50_ecoset_full	.750
332	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.745
333	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.746
334	mobilenet_v2_0.35_224	.742
335	alexnet_reduced_aliasing_early_checkpoint	.738
336	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.736
337	deit_base_imagenet_full_seed-0	.735
338	mobilenet_v2_0_5_192	.734
339	resnet101_imagenet_full	.730
340	effnetb1_272x240	.727
341	resnet50-cifar	.722
342	omnivore_swinT	.721
343	vit_large_patch14_clip_224:openai_ft_in1k	.720
344	densenet-121-keras	.720
345	resnet50-SIN_IN	.719
346	densenet-169-keras	.715
347	resnet_101_v2	.714
348	CLIP_ViT-B_32	.713
349	mobilenet_v2_1_0_160	.712
350	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.710
351	inception_v1-tf	.708
352	CORnetZ_CIFAR10	.703
353	resnet50	.702
354	mobilenet_v2_1_0_192	.700
355	mobilenet_v2_0.5_160	.694
356	efficientnet_b0_imagenet_full	.693
357	inception_resnet_v2	.683
358	vit_relpos_base_patch16_clsgap_224:sw_in1k	.680
359	resnet-18-LC_conv_init_m	.676
360	yolos_tiny	.668
361	mobilenet_v2_0_5_224	.663
362	resnet50_pretrained_with_retinal_waves	.661
363	AdvProp_efficientnet-b2	.635
364	AT_efficientnet-b2	.633
365	my-model	.629
366	efficientnet_b0	.604
367	alexnet_training_seed_06	.563
368	CORnetZ_CIFAR10_bs32_20_04	.547
369	alexnet_training_seed_05	.546
370	ViT-B/32	.532
371	mobilenet_v2_0.75_128	.521
372	resnet50_imagenet_1_seed-0	.492
373	resnet-18-LC_untrained	.487
374	mobilenet_v2_0.75_224-tf	.437
375	v1-pyr-nodown	.419
376	hmax	.405
377	mobilenet_v2_0.5_128	.398
378	resnet50-meshes-lt-100-original-pretrained	.367
379	mobilenet_v1_0.25_128	.349
380	deit_small_imagenet_10_seed-0	.320
381	resnet50-meshes-lt-100-original-scratch	.265
382	mobilenet_v2_0.35_160
383	resnet18-supervised
384	fitvid_trained_on_physion
385	openclip
386	resnext101_32x32d_wsl
387	resnext101_32x48d_wsl
388	resnet50_ImageNet
389	regnet_y_400mf
390	tv_efficientnet-b1
391	alexnet_ks_torevert
392	yudixie_resnet50_imagenet1kpret_0_240312
393	resnet_152_v1
394	resnext101_32x16d_wsl
395	densenet_201_pytorch
396	pnasnet_large_pytorch
397	inception_v3_pytorch
398	mobilevit_small
399	inception_v4
400	nasnet_large
401	deit_small_imagenet_1_seed-0
402	pnasnet_large
403	vonegrcnn_62e_nobn
404	vonegrcnn_52e_full
405	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: opr_cv_diff