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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	yudixie_resnet50_rotation_reg_0_240908	.984
2	cvt_cvt-13-224-in1k_4_LucyV4	.982
3	resnet-18-LC_1st_conv_m	.975
4	BiT-S-R50x1	.973
5	convnext_femto_ols:d1_in1k	.973
6	cvt_cvt-21-224-in1k_4_LucyV4	.971
7	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.969
8	yudixie_resnet18_translation_reg_0_240719	.966
9	ReAlnet01_cornet_fov4	.962
10	BiT-M-R50x3	.961
11	xception	.962
12	yudixie_resnet50_distance_translation_rotation_0_240908	.958
13	yudixie_resnet50_category_class_0_240908	.958
14	compact_vgg19_V4	.958
15	compact_cornets_IT	.957
16	yudixie_resnet18_category_class_0_240719	.955
17	mobilenet_v2_1.0_224-tf	.951
18	resnet-152_v2-tf	.949
19	compact_resnet50_V4	.949
20	yudixie_resnet18_translation_rotation_0_240719	.946
21	efficientnet_b1_imagenet_full	.947
22	resnet-18-LC_d_w_sh_1x1_conv_init_m	.946
23	texture_shape_alexnet_trained_on_SIN	.944
24	convnext_large:fb_in22k_ft_in1k	.942
25	vgg_16	.942
26	resnet50_imagenet_100_seed-0	.942
27	briaai_rmbg_1_4	.941
28	inception_v1	.938
29	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.936
30	yudixie_resnet50_translation_rotation_0_240908	.935
31	efficientnet-b7	.935
32	alexnet_random_l2_3_perturb	.934
33	AdvProp_efficientnet-b6	.930
34	vgg_19	.929
35	yudixie_resnet50_distance_rotation_0_240908	.925
36	yudixie_resnet50_distance_reg_0_240908	.924
37	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.923
38	0.5x_resnet-18_LC	.923
39	resnet-18-LC_m	.922
40	alexnet_random_linf8_perturb	.921
41	grcnn_robust_v1	.921
42	resnet-18-LC_w_sh_1_iter_m	.919
43	resnet50_primary_visual_cortex	.919
44	resnet18_ecoset_full	.918
45	cvt_cvt-21-384-in1k_4_LucyV4	.917
46	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.916
47	AdvProp_efficientnet-b8	.915
48	vgg-16-keras	.916
49	yudixie_resnet50_distance_translation_0_240908	.912
50	resnet-18-LC_w_sh_10_iter_conv_init_m	.912
51	inception_v3-tf	.912
52	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.911
53	convnext_base:clip_laiona_augreg_ft_in1k_384	.909
54	compact_cornets_V4	.909
55	resnet-50_v1-tf	.909
56	grcnn	.908
57	mobilenet_v2_1.0_96	.906
58	blt_vs	.907
59	alexnet_robust_correct	.905
60	resnext101_32x8d_wsl	.903
61	grcnn_109	.902
62	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.900
63	BiT-S-R50x3	.897
64	alexnet_early_checkpoint	.899
65	grcnn_v2_text_noise	.898
66	BiT-S-R101x3	.895
67	nasnet_large-tf	.896
68	compact_resnet50_IT	.895
69	resnet18_imagenet21kP	.896
70	densenet-201-keras	.893
71	mobilenet_v2_1.3_224-tf	.893
72	mobilenet_v2_1.0_160-tf	.892
73	yudixie_resnet50_translation_reg_0_240908	.892
74	resnet50_byol	.892
75	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.890
76	0.5x_resnet-18_LC_w_sh_10_iter	.890
77	yudixie_resnet50_object_class_0_240908	.889
78	alexnet_reduced_aliasing_early_checkpoint	.889
79	resnet50_ewconplus_wzc	.889
80	mobilenet_v2_0_75_192	.887
81	resnet50_imagenet_10_seed-0	.884
82	convnext_tiny_sup	.885
83	AdvProp_efficientnet-b7	.884
84	compact_resnet50_robust_V4	.883
85	resnet-18-LC_w_sh_100_iter_conv_init_m	.882
86	alexnet_l2_3_robust	.882
87	ReAlnet01_cornet_fov16	.882
88	yudixie_resnet18_distance_rotation_0_240719	.882
89	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
90	compact_resnet50_robust_IT	.879
91	resnet101	.878
92	yudixie_resnet18_object_class_0_240719	.878
93	AlexNet_SIN_fov4	.877
94	squeezenet1_1	.877
95	densenet-201	.876
96	mobilenet_v1_1.0_128	.875
97	focalnet_tiny_lrf_in1k	.875
98	alexnet_linf_8_robust	.873
99	efficientnet_b0	.874
100	antialias-resnet152	.873
101	resnet_50_v2	.872
102	ReAlnet02	.872
103	ReAlnet02_cornet	.871
104	yudixie_resnet18_distance_translation_0_240719	.871
105	ReAlnet04	.871
106	ReAlnet03	.870
107	ReAlnet03_cornet	.867
108	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.868
109	ReAlnet04_cornet	.868
110	alexnet	.865
111	yudixie_resnet18_distance_translation_rotation_0_240719	.864
112	0.5x_resnet-18_LC_w_sh_100_iter	.865
113	inception_v3	.863
114	AlexNet_SIN	.863
115	resnet50-vitoimagevidnet8	.863
116	ReAlnet05	.863
117	ReAlnet05_cornet	.862
118	convnext_xlarge:fb_in22k_ft_in1k	.862
119	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.862
120	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.861
121	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.861
122	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.862
123	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.861
124	omnivore_swinS	.862
125	resnet50-simclr-vissl	.861
126	ReAlnet01	.861
127	squeezenet1_0	.859
128	alexnet_fov16	.858
129	ReAlnet01_cornet	.858
130	AdvProp_efficientnet-b4	.858
131	mobilenet_v2_0_5_224	.858
132	mobilenet_v1_0.5_128	.857
133	ReAlnet08	.857
134	ReAlnet08_cornet	.857
135	ReAlnet07_cornet	.857
136	ReAlnet07	.857
137	resnet_50_v1_spiking_l4a	.855
138	ReAlnet09	.856
139	resnet_50_v1	.856
140	resnet_50_v1_spiking_l4	.856
141	resnet50-sup	.856
142	yudixie_resnet50_imagenet1kpret_0_240908	.855
143	ReAlnet09_cornet	.855
144	resnet50_tutorial	.855
145	resnet_50_v1_spiking_l3	.854
146	imagenet_l2_3_0	.855
147	ReAlnet10_cornet	.854
148	resnet_50_v1_spiking	.854
149	ReAlnet10	.852
150	alexnet_fov4	.852
151	resnet50-moclr8deg	.850
152	resnet50-vicregl0p9	.850
153	convnext_base_imagenet_full_seed-0	.848
154	convnext_tiny_imagenet_full_seed-0	.847
155	mobilenet_v2_1_0_128	.847
156	ReAlnet06_cornet	.845
157	ReAlnet06	.845
158	mobilenet_v2_0.35_192	.843
159	inception_v4-tf	.843
160	inception_v2-tf	.843
161	CLIP-RN50	.840
162	vonegrcnn_62e_nobn	.840
163	resnet50_random_linf8_perturb	.838
164	resnet-50-robust	.838
165	resnet50_robust_l2_eps1	.837
166	0.5x_resnet-18	.836
167	mobilenet_v1_0.5_160	.835
168	resnet50-vicregl0p75	.835
169	BiT-M-R101x1	.835
170	convnext_small_imagenet_100_seed-0	.835
171	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.833
172	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.833
173	resnet_SIN_IN_FT_IN	.832
174	mobilenet_v2_1_3_224	.832
175	mobilenet_v2_0.75_192-tf	.830
176	mobilenet_v2_1_4_224	.831
177	imagenet_l2_10_0	.831
178	resnet-18-LC_w_sh_10_iter_m	.830
179	Res2Net50_26w_4s	.830
180	CORnet-Z	.828
181	resnet101_imagenet_full	.826
182	omnivore_swinB	.825
183	resnet50_l2_3_robust	.825
184	resnet50	.822
185	antialiased-r50	.820
186	resnet18-supervised	.819
187	vgg-19-keras	.818
188	convnext_large_imagenet_full_seed-0	.818
189	alexnet_fov12	.818
190	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.817
191	resnet50_linf_8_robust	.817
192	AlexNet_SIN_fov12	.816
193	mobilenet_v2_0.5_96	.814
194	0.5x_resnet-18_LC_w_sh_1_iter	.813
195	resnet50-SIN_IN	.814
196	vit_base_patch16_clip_224:openai_ft_in1k	.813
197	resnet50-SIN_IN_IN	.813
198	resnet50_robust_l2_eps3	.812
199	mobilenet_v2_1.4_224-tf	.811
200	RN50	.812
201	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.809
202	resnet50_random_l2_perturb	.810
203	cornet_s	.808
204	CORnet-S	.806
205	vonealexnet_gaussian_noise_std4_fixed	.806
206	BiT-S-R152x2	.803
207	ReAlnet07_ALL	.802
208	aughost_fair_resnet18	.800
209	resnet50_linf_4_robust	.800
210	ReAlnet08_ALL	.800
211	densenet-169	.799
212	ReAlnet04_ALL	.798
213	convnext_small_imagenet_full_seed-0	.797
214	resnet-50x2_untrained	.797
215	AdvProp_efficientnet-b0	.796
216	ReAlnet06_ALL	.793
217	resnet50-SIN	.792
218	ReAlnet03_ALL	.792
219	ReAlnet02_ALL	.791
220	resnet-50_untrained	.791
221	ReAlnet10_ALL	.791
222	texture_shape_resnet50_trained_on_SIN	.791
223	resnet-101_v2-tf	.790
224	ReAlnet05_ALL	.789
225	ReAlnet01_ALL	.788
226	ReAlnet01_cornet_fov12	.787
227	resnet-18_untrained	.788
228	CLIP_resnet50_float32	.788
229	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.787
230	r3m_resnet34_nocrop	.787
231	CLIP_resnet50	.785
232	resnet-50_v2-tf	.786
233	compact_vgg19_IT	.786
234	ReAlnet09_ALL	.785
235	resnet34_imagenet_full	.777
236	yudixie_resnet18_imagenet1kpret_0_240719	.777
237	SWSL_resnet50	.773
238	resnet50-vicreg	.773
239	resnet-101_v1-tf	.771
240	mobilenet_v2_0.35_96	.769
241	resnet-18_test_m	.766
242	densenet-121	.765
243	convnext_tiny:in12k_ft_in1k	.763
244	resnet152_imagenet_full	.761
245	resnet50_imagenet_full	.760
246	alexnet_training_seed_01_fov16	.760
247	vit_large_patch14_clip_224:laion2b_ft_in1k	.759
248	voneresnet-50-non_stochastic	.759
249	resnet18_imagenet_full	.758
250	deit_small_imagenet_full_seed-0	.757
251	pnasnet_large-tf	.757
252	pnasnet_large_halfPrecision	.757
253	efficientnet_b0_imagenet_full	.756
254	mobilenet_v2_1.0_192-tf	.754
255	mobilenet_v2_1_0_192	.754
256	resnet34_ecoset_full	.754
257	resnet101_ecoset_full	.749
258	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.747
259	resnet-152_v1-tf	.745
260	mobilenet_v1_0.5_224	.744
261	alexnet_training_seed_01_fov12	.744
262	alexnet_training_seed_07	.743
263	yudixie_resnet18_distance_reg_0_240719	.742
264	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.741
265	mobilenet_v1_0.75_192	.739
266	shufflenet_v2_x1_0	.740
267	cvt_cvt-13-384-in1k_4_LucyV4	.739
268	mobilenet_v2_0_75_224	.738
269	BiT-S-R101x1	.736
270	BiT-M-R101x3	.732
271	AlexNet_SIN_fov16	.732
272	alexnet_training_seed_02	.730
273	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.727
274	FrankRobWobv0	.727
275	voneresnet-50-robust	.727
276	vggface	.727
277	mobilenet_v2_0.5_192-tf	.726
278	yudixie_resnet18_rotation_reg_0_240719	.725
279	resnet50-cifar	.722
280	BiT-M-R152x4	.721
281	antialiased-rnext101_32x8d	.717
282	BiT-S-R152x4	.716
283	vit_base_patch14_reg4_dinov2-lvd142m	.716
284	resnet50-VITO-8deg-cc	.715
285	voneresnet-50	.714
286	resnet152_ecoset_full	.709
287	mobilenet_v1_0.25_224	.709
288	VOneCORnet-S	.708
289	yolos_tiny	.707
290	densenet-121-keras	.703
291	resnet50_FractalDB	.703
292	mobilenet_v1_0.75_224	.701
293	vit_large_patch14_clip_336:laion2b_ft_in1k	.702
294	alexnet_training_seed_04	.700
295	mobilenet_v2_0.75_128	.700
296	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.700
297	mobilenet_v1_0.25_160	.696
298	SWSL_resnext101_32x8d	.695
299	v1-pyr-nodown	.695
300	resnet-18-LC_w_sh_100_iter_m	.694
301	CLIP_ViT-B_32	.693
302	alexnet_training_seed_01	.693
303	mobilenet_v1_1.0_224	.691
304	resnet50_ecoset_full	.691
305	omnivore_swinT	.690
306	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.689
307	densenet-169-keras	.687
308	my-model	.687
309	konkle_alexnetgn_ipcl_ref01_primary_model	.686
310	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.685
311	mobilenet_v2_1-4_224_pytorch	.684
312	alexnet_training_seed_09	.682
313	nasnet_mobile-tf	.681
314	mobilenet_v2_0.35_224	.680
315	mobilenet_v2_0_75_160	.680
316	effnetv2m_custom384	.676
317	eBarlow_lmda_02_1000ep	.676
318	resnet50-barlow	.675
319	mobilenet_v2_1.0_128-tf	.674
320	vision_transformer_vit_large_patch16_224	.670
321	mobilenet_v1_0.25_128	.668
322	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.667
323	vit_base_patch14_dinov2-lvd142m	.666
324	mobilenet_v2_1_0_160	.667
325	cv_18_dagger_408_pretrained	.665
326	deit_small_imagenet_100_seed-0	.662
327	mobilenet_v1_0.5_192	.660
328	resnet50	.658
329	resnet50_moco_v2	.658
330	resnet50_simclr	.657
331	custom_model_cv_18_dagger_408	.656
332	artResNet18_1	.655
333	nasnet_mobile	.654
334	mobilenet_v2_1_0_224	.649
335	mobilenet_v1_1.0_192	.648
336	yudixie_resnet50_random_0_240908	.646
337	effnetb1_272x240	.645
338	resnet-18-LC_conv_init_m	.644
339	alexnet_training_seed_10	.645
340	BiT-M-R152x2	.641
341	AdvProp_efficientnet-b2	.627
342	mobilenet_v2_0.75_160-tf	.624
343	AT_efficientnet-b2	.625
344	vonegrcnn_52e_full	.623
345	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.621
346	vit_large_patch14_clip_224:openai_ft_in1k	.621
347	mobilenet_v2_0_5_192	.619
348	vit_relpos_base_patch16_clsgap_224:sw_in1k	.616
349	resnet-152_v2_pytorch	.615
350	convnext_small_imagenet_10_seed-0	.615
351	BiT-M-R50x1	.613
352	yudixie_resnet18_random_0_240719	.610
353	CORnetZ_CIFAR10_bs32_20_04	.605
354	vonegrcnn_47e	.604
355	inception_v1-tf	.600
356	mobilenet_v2_0.5_224-tf	.597
357	mobilenet_v1_1.0_160	.597
358	alexnet_training_seed_03	.596
359	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.591
360	deit_base_imagenet_full_seed-0	.589
361	r3m_resnet34	.590
362	vonegrcnn_47e	.581
363	deit_large_imagenet_full_seed-0	.577
364	mobilenet_v1_0.75_128	.570
365	mobilenet_v1_0.75_160	.565
366	mobilenet_v1_0.25_192	.560
367	mobilenet_v2_0.75_96	.557
368	inception_resnet_v2	.554
369	alexnet_training_seed_01_fov4	.554
370	efficientnet_b2_imagenet_full	.550
371	r3m_resnet50_nocrop	.550
372	hmax	.535
373	r3m_resnet50	.519
374	r3m_resnet18	.516
375	resnet50_pretrained_with_retinal_waves	.515
376	my-model	.493
377	mobilenet_v2_0.75_224-tf	.490
378	alexnet_training_seed_08	.462
379	convnext_small_imagenet_1_seed-0	.459
380	CORnetZ_CIFAR10	.447
381	vit_base_patch16_dinov3-lvd1689m	.445
382	ViT-B/32	.385
383	mobilenet_v2_0.35_128	.375
384	deit_small_imagenet_10_seed-0	.375
385	resnet50_imagenet_1_seed-0	.355
386	alexnet_training_seed_05	.271
387	mobilenet_v2_0.5_160	.271
388	alexnet_training_seed_06	.215
389	resnet-18-LC_untrained	.170
390	resnet50-meshes-lt-100-original-pretrained	.169
391	resnet50-meshes-lt-100-original-scratch	.081
392	mobilenet_v2_0.5_128	.028
393	unet_entire
394	mobilenet_v2_0.35_160
395	fitvid_trained_on_physion
396	openclip
397	resnext101_32x32d_wsl
398	resnext101_32x48d_wsl
399	resnet50_ImageNet
400	resnet_101_v1
401	resnet_152_v2
402	regnet_y_400mf
403	tv_efficientnet-b1
404	alexnet_ks_torevert
405	yudixie_resnet50_imagenet1kpret_0_240312
406	resnet_152_v1
407	ViT_L_32_imagenet1k
408	resnext101_32x16d_wsl
409	densenet_201_pytorch
410	inception_v3_pytorch
411	pnasnet_large_pytorch
412	mobilevit_small
413	inception_v4
414	nasnet_large
415	deit_small_imagenet_1_seed-0
416	pnasnet_large
417	vonegrcnn_62e_nobn
418	resnet_101_v2
419	vonegrcnn_52e_full

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: circular_variance