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	BiT-M-R50x3	.961
10	xception	.962
11	yudixie_resnet50_distance_translation_rotation_0_240908	.958
12	yudixie_resnet50_category_class_0_240908	.958
13	yudixie_resnet18_category_class_0_240719	.955
14	mobilenet_v2_1.0_224-tf	.951
15	resnet-152_v2-tf	.949
16	yudixie_resnet18_translation_rotation_0_240719	.946
17	efficientnet_b1_imagenet_full	.947
18	resnet-18-LC_d_w_sh_1x1_conv_init_m	.946
19	texture_shape_alexnet_trained_on_SIN	.944
20	convnext_large:fb_in22k_ft_in1k	.942
21	vgg_16	.942
22	resnet50_imagenet_100_seed-0	.942
23	inception_v1	.938
24	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.936
25	yudixie_resnet50_translation_rotation_0_240908	.935
26	efficientnet-b7	.935
27	alexnet_random_l2_3_perturb	.934
28	AdvProp_efficientnet-b6	.930
29	vgg_19	.929
30	alexnet	.926
31	yudixie_resnet50_distance_rotation_0_240908	.925
32	yudixie_resnet50_distance_reg_0_240908	.924
33	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.923
34	0.5x_resnet-18_LC	.923
35	resnet-18-LC_m	.922
36	alexnet_random_linf8_perturb	.921
37	grcnn_robust_v1	.921
38	resnet-18-LC_w_sh_1_iter_m	.919
39	resnet18_ecoset_full	.918
40	cvt_cvt-21-384-in1k_4_LucyV4	.917
41	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.916
42	AdvProp_efficientnet-b8	.915
43	vgg-16-keras	.916
44	yudixie_resnet50_distance_translation_0_240908	.912
45	resnet-18-LC_w_sh_10_iter_conv_init_m	.912
46	inception_v3-tf	.912
47	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.911
48	convnext_base:clip_laiona_augreg_ft_in1k_384	.909
49	resnet-50_v1-tf	.909
50	grcnn	.908
51	mobilenet_v2_1.0_96	.906
52	blt_vs	.907
53	alexnet_robust_correct	.905
54	resnext101_32x8d_wsl	.903
55	grcnn_109	.902
56	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.900
57	BiT-S-R50x3	.897
58	alexnet_early_checkpoint	.899
59	grcnn_v2_text_noise	.898
60	BiT-S-R101x3	.895
61	nasnet_large-tf	.896
62	resnet18_imagenet21kP	.896
63	densenet-201-keras	.893
64	mobilenet_v2_1.3_224-tf	.893
65	mobilenet_v2_1.0_160-tf	.892
66	yudixie_resnet50_translation_reg_0_240908	.892
67	resnet50_byol	.892
68	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.890
69	0.5x_resnet-18_LC_w_sh_10_iter	.890
70	yudixie_resnet50_object_class_0_240908	.889
71	alexnet_reduced_aliasing_early_checkpoint	.889
72	mobilenet_v2_0_75_192	.887
73	resnet50_imagenet_10_seed-0	.884
74	convnext_tiny_sup	.885
75	AdvProp_efficientnet-b7	.884
76	resnet-18-LC_w_sh_100_iter_conv_init_m	.882
77	alexnet_l2_3_robust	.882
78	yudixie_resnet18_distance_rotation_0_240719	.882
79	resnet-18-LC_w_sh_1_iter_conv_init_m	.879
80	resnet101	.878
81	yudixie_resnet18_object_class_0_240719	.878
82	squeezenet1_1	.877
83	densenet-201	.876
84	mobilenet_v1_1.0_128	.875
85	focalnet_tiny_lrf_in1k	.875
86	alexnet_linf_8_robust	.873
87	efficientnet_b0	.874
88	antialias-resnet152	.873
89	resnet_50_v2	.872
90	ReAlnet02	.872
91	ReAlnet02_cornet	.871
92	yudixie_resnet18_distance_translation_0_240719	.871
93	ReAlnet04	.871
94	ReAlnet03	.870
95	ReAlnet03_cornet	.867
96	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.868
97	ReAlnet04_cornet	.868
98	alexnet	.866
99	alexnet-baseline	.864
100	alexnet	.865
101	alexnet	.865
102	alexnet	.864
103	alexnet	.865
104	alexnet	.865
105	alexnet	.865
106	yudixie_resnet18_distance_translation_rotation_0_240719	.864
107	0.5x_resnet-18_LC_w_sh_100_iter	.865
108	inception_v3	.863
109	AlexNet_SIN	.863
110	resnet50-vitoimagevidnet8	.863
111	ReAlnet05	.863
112	ReAlnet05_cornet	.862
113	convnext_xlarge:fb_in22k_ft_in1k	.862
114	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.862
115	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.861
116	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.861
117	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.862
118	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.861
119	omnivore_swinS	.862
120	resnet50-simclr-vissl	.861
121	ReAlnet01	.861
122	ReAlnet_incorrect	.860
123	squeezenet1_0	.859
124	ReAlnet01_cornet	.858
125	AdvProp_efficientnet-b4	.858
126	mobilenet_v2_0_5_224	.858
127	mobilenet_v1_0.5_128	.857
128	ReAlnet08	.857
129	ReAlnet08_cornet	.857
130	ReAlnet07_cornet	.857
131	ReAlnet07	.857
132	ReAlnet09	.856
133	resnet_50_v1	.856
134	resnet50-sup	.856
135	yudixie_resnet50_imagenet1kpret_0_240908	.855
136	ReAlnet09_cornet	.855
137	resnet50_tutorial	.855
138	imagenet_l2_3_0	.855
139	ReAlnet10_cornet	.854
140	ReAlnet10	.852
141	resnet50-moclr8deg	.850
142	resnet50-vicregl0p9	.850
143	convnext_base_imagenet_full_seed-0	.848
144	convnext_tiny_imagenet_full_seed-0	.847
145	mobilenet_v2_1_0_128	.847
146	ReAlnet06_cornet	.845
147	ReAlnet06	.845
148	mobilenet_v2_0.35_192	.843
149	inception_v4-tf	.843
150	inception_v2-tf	.843
151	CLIP-RN50	.840
152	vonegrcnn_62e_nobn	.840
153	resnet50_random_linf8_perturb	.838
154	resnet-50-robust	.838
155	resnet50_robust_l2_eps1	.837
156	0.5x_resnet-18	.836
157	mobilenet_v1_0.5_160	.835
158	resnet50-vicregl0p75	.835
159	BiT-M-R101x1	.835
160	convnext_small_imagenet_100_seed-0	.835
161	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.833
162	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.833
163	resnet_SIN_IN_FT_IN	.832
164	mobilenet_v2_1_3_224	.832
165	mobilenet_v2_0.75_192-tf	.830
166	mobilenet_v2_1_4_224	.831
167	imagenet_l2_10_0	.831
168	resnet-18-LC_w_sh_10_iter_m	.830
169	Res2Net50_26w_4s	.830
170	CORnet-Z	.828
171	resnet101_imagenet_full	.826
172	omnivore_swinB	.825
173	resnet50_l2_3_robust	.825
174	resnet50	.822
175	antialiased-r50	.820
176	resnet18-supervised	.819
177	vgg-19-keras	.818
178	convnext_large_imagenet_full_seed-0	.818
179	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.817
180	resnet50_linf_8_robust	.817
181	mobilenet_v2_0.5_96	.814
182	0.5x_resnet-18_LC_w_sh_1_iter	.813
183	resnet50-SIN_IN	.814
184	vit_base_patch16_clip_224:openai_ft_in1k	.813
185	resnet50-SIN_IN_IN	.813
186	resnet50_robust_l2_eps3	.812
187	mobilenet_v2_1.4_224-tf	.811
188	RN50	.812
189	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.809
190	resnet50_random_l2_perturb	.810
191	cornet_s	.808
192	CORnet-S	.806
193	vonealexnet_gaussian_noise_std4_fixed	.806
194	BiT-S-R152x2	.803
195	resnet50_linf_4_robust	.800
196	densenet-169	.799
197	convnext_small_imagenet_full_seed-0	.797
198	resnet-50x2_untrained	.797
199	AdvProp_efficientnet-b0	.796
200	resnet50-SIN	.792
201	resnet-50_untrained	.791
202	texture_shape_resnet50_trained_on_SIN	.791
203	resnet-101_v2-tf	.790
204	resnet-18_untrained	.788
205	CLIP_resnet50_float32	.788
206	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.787
207	r3m_resnet34_nocrop	.787
208	CLIP_resnet50	.785
209	resnet-50_v2-tf	.786
210	resnet34_imagenet_full	.777
211	yudixie_resnet18_imagenet1kpret_0_240719	.777
212	SWSL_resnet50	.773
213	resnet50-vicreg	.773
214	resnet-101_v1-tf	.771
215	mobilenet_v2_0.35_96	.769
216	resnet-18_test_m	.766
217	densenet-121	.765
218	convnext_tiny:in12k_ft_in1k	.763
219	resnet152_imagenet_full	.761
220	resnet50_imagenet_full	.760
221	vit_large_patch14_clip_224:laion2b_ft_in1k	.759
222	voneresnet-50-non_stochastic	.759
223	resnet18_imagenet_full	.758
224	deit_small_imagenet_full_seed-0	.757
225	pnasnet_large-tf	.757
226	efficientnet_b0_imagenet_full	.756
227	mobilenet_v2_1.0_192-tf	.754
228	mobilenet_v2_1_0_192	.754
229	resnet34_ecoset_full	.754
230	resnet101_ecoset_full	.749
231	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.747
232	resnet-152_v1-tf	.745
233	mobilenet_v1_0.5_224	.744
234	alexnet_training_seed_07	.743
235	yudixie_resnet18_distance_reg_0_240719	.742
236	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.741
237	mobilenet_v1_0.75_192	.739
238	shufflenet_v2_x1_0	.740
239	cvt_cvt-13-384-in1k_4_LucyV4	.739
240	mobilenet_v2_0_75_224	.738
241	BiT-S-R101x1	.736
242	BiT-M-R101x3	.732
243	alexnet_training_seed_02	.730
244	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.727
245	FrankRobWobv0	.727
246	voneresnet-50-robust	.727
247	vggface	.727
248	mobilenet_v2_0.5_192-tf	.726
249	yudixie_resnet18_rotation_reg_0_240719	.725
250	resnet50-cifar	.722
251	BiT-M-R152x4	.721
252	antialiased-rnext101_32x8d	.717
253	BiT-S-R152x4	.716
254	resnet50-VITO-8deg-cc	.715
255	voneresnet-50	.714
256	resnet152_ecoset_full	.709
257	mobilenet_v1_0.25_224	.709
258	VOneCORnet-S	.708
259	densenet-121-keras	.703
260	resnet50_FractalDB	.703
261	mobilenet_v1_0.75_224	.701
262	vit_large_patch14_clip_336:laion2b_ft_in1k	.702
263	alexnet_training_seed_04	.700
264	mobilenet_v2_0.75_128	.700
265	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.700
266	mobilenet_v1_0.25_160	.696
267	SWSL_resnext101_32x8d	.695
268	v1-pyr-nodown	.695
269	resnet-18-LC_w_sh_100_iter_m	.694
270	CLIP_ViT-B_32	.693
271	alexnet_training_seed_01	.693
272	mobilenet_v1_1.0_224	.691
273	resnet50_ecoset_full	.691
274	omnivore_swinT	.690
275	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.689
276	densenet-169-keras	.687
277	my-model	.687
278	konkle_alexnetgn_ipcl_ref01_primary_model	.686
279	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.685
280	mobilenet_v2_1-4_224_pytorch	.684
281	alexnet_training_seed_09	.682
282	nasnet_mobile-tf	.681
283	mobilenet_v2_0.35_224	.680
284	mobilenet_v2_0_75_160	.680
285	effnetv2m_custom384	.676
286	resnet50-barlow	.675
287	mobilenet_v2_1.0_128-tf	.674
288	vision_transformer_vit_large_patch16_224	.670
289	mobilenet_v1_0.25_128	.668
290	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.667
291	mobilenet_v2_1_0_160	.667
292	cv_18_dagger_408_pretrained	.665
293	deit_small_imagenet_100_seed-0	.662
294	mobilenet_v1_0.5_192	.660
295	resnet50	.658
296	resnet50_moco_v2	.658
297	resnet50_simclr	.657
298	custom_model_cv_18_dagger_408	.656
299	artResNet18_1	.655
300	nasnet_mobile	.654
301	mobilenet_v2_1_0_224	.649
302	mobilenet_v1_1.0_192	.648
303	yudixie_resnet50_random_0_240908	.646
304	effnetb1_272x240	.645
305	resnet-18-LC_conv_init_m	.644
306	alexnet_training_seed_10	.645
307	BiT-M-R152x2	.641
308	AdvProp_efficientnet-b2	.627
309	mobilenet_v2_0.75_160-tf	.624
310	AT_efficientnet-b2	.625
311	vonegrcnn_52e_full	.623
312	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.621
313	vit_large_patch14_clip_224:openai_ft_in1k	.621
314	mobilenet_v2_0_5_192	.619
315	vit_relpos_base_patch16_clsgap_224:sw_in1k	.616
316	resnet-152_v2_pytorch	.615
317	convnext_small_imagenet_10_seed-0	.615
318	BiT-M-R50x1	.613
319	yudixie_resnet18_random_0_240719	.610
320	CORnetZ_CIFAR10_bs32_20_04	.605
321	vonegrcnn_47e	.604
322	inception_v1-tf	.600
323	mobilenet_v2_0.5_224-tf	.597
324	mobilenet_v1_1.0_160	.597
325	alexnet_training_seed_03	.596
326	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.591
327	deit_base_imagenet_full_seed-0	.589
328	r3m_resnet34	.590
329	vonegrcnn_47e	.581
330	deit_large_imagenet_full_seed-0	.577
331	mobilenet_v1_0.75_128	.570
332	mobilenet_v1_0.75_160	.565
333	mobilenet_v1_0.25_192	.560
334	mobilenet_v2_0.75_96	.557
335	inception_resnet_v2	.554
336	efficientnet_b2_imagenet_full	.550
337	r3m_resnet50_nocrop	.550
338	hmax	.535
339	r3m_resnet50	.519
340	r3m_resnet18	.516
341	resnet50_pretrained_with_retinal_waves	.515
342	my-model	.493
343	mobilenet_v2_0.75_224-tf	.490
344	alexnet_training_seed_08	.462
345	convnext_small_imagenet_1_seed-0	.459
346	CORnetZ_CIFAR10	.447
347	ViT-B/32	.385
348	deit_small_imagenet_10_seed-0	.375
349	mobilenet_v2_0.35_128	.375
350	resnet50_imagenet_1_seed-0	.355
351	alexnet_training_seed_05	.271
352	mobilenet_v2_0.5_160	.271
353	alexnet_training_seed_06	.215
354	resnet-18-LC_untrained	.170
355	resnet50-meshes-lt-100-original-pretrained	.169
356	resnet50-meshes-lt-100-original-scratch	.081
357	mobilenet_v2_0.5_128	.028
358	nasnet_large	X
359	pnasnet_large	X
360	vonegrcnn_52e_full	X
361	ViT_L_32_imagenet1k	X
362	resnext101_32x16d_wsl	X
363	resnet50_ImageNet	X
364	mobilevit_small	X
365	densenet_201_pytorch	X
366	inception_v3_pytorch	X
367	resnext101_32x48d_wsl	X
368	resnext101_32x32d_wsl	X
369	pnasnet_large_pytorch	X
370	mobilenet_v2_0.35_160	X
371	fitvid_trained_on_physion	X
372	openclip	X
373	inception_v4	X
374	unet_entire	X
375	deit_small_imagenet_1_seed-0	X
376	yudixie_resnet50_imagenet1kpret_0_240312	X
377	alexnet_ks_torevert	X
378	resnet_152_v1	X
379	tv_efficientnet-b1	X
380	vonegrcnn_62e_nobn	X
381	regnet_y_400mf	X
382	resnet_152_v2	X
383	resnet_101_v2	X
384	resnet_101_v1	X

Benchmark bibtex

None

Ceiling

0.96.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: circular_variance