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

Benchmark bibtex

None

Ceiling

0.97.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: opr_cv_diff