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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet08_cornet	1.0
2	v1-pyr-nodown	1.0
3	ReAlnet04_ALL	1.0
4	efficientnet_b1_imagenet_full	1.0
5	cornet_s	1.0
6	convnext_femto_ols:d1_in1k	1.0
7	ReAlnet08	1.0
8	compact_resnet50_V4	1.0
9	0.5x_resnet-18_LC_w_sh_10_iter	1.0
10	resnet34_imagenet_full	1.0
11	yudixie_resnet18_translation_reg_0_240719	1.0
12	CORnetZ_CIFAR10_bs32_20_04	1.0
13	vonealexnet_gaussian_noise_std4_fixed	1.0
14	resnet-18-LC_w_sh_100_iter_conv_init_m	1.0
15	resnet-18-LC_w_sh_10_iter_conv_init_m	1.0
16	yudixie_resnet18_translation_rotation_0_240719	1.0
17	ReAlnet01_cornet_fov12	.999
18	yudixie_resnet18_distance_rotation_0_240719	.999
19	ReAlnet10	.999
20	ReAlnet01	1.0
21	ReAlnet03	.999
22	ReAlnet07_cornet	.999
23	ReAlnet03_cornet	.999
24	ReAlnet10_cornet	.999
25	voneresnet-50-non_stochastic	.999
26	ReAlnet07	.999
27	ReAlnet04	.999
28	ReAlnet04_cornet	.999
29	ReAlnet02	.999
30	resnet50_ecoset_full	.999
31	ReAlnet01_cornet	.999
32	convnext_tiny_imagenet_full_seed-0	.999
33	ReAlnet01_ALL	.999
34	yudixie_resnet50_distance_reg_0_240908	.999
35	ReAlnet09_ALL	.999
36	ReAlnet02_cornet	.999
37	CORnet-S	.999
38	ReAlnet06	.998
39	ReAlnet02_ALL	.998
40	alexnet_reduced_aliasing_early_checkpoint	.998
41	ReAlnet06_cornet	.999
42	ReAlnet05	.998
43	ReAlnet05_cornet	.999
44	compact_resnet50_robust_IT	.998
45	efficientnet-b7	.998
46	compact_cornets_IT	.998
47	yudixie_resnet50_distance_translation_0_240908	.998
48	resnet-18-LC_1st_conv_m	.998
49	yudixie_resnet18_distance_reg_0_240719	.997
50	yudixie_resnet50_rotation_reg_0_240908	.997
51	resnet18_imagenet_full	.997
52	alexnet_training_seed_01_fov12	.997
53	compact_cornets_V4	.997
54	resnet34_ecoset_full	.996
55	convnext_small_imagenet_100_seed-0	.996
56	resnet-18-LC_w_sh_100_iter_m	.996
57	vgg_19	.996
58	0.5x_resnet-18	.996
59	omnivore_swinB	.996
60	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.996
61	yudixie_resnet18_category_class_0_240719	.996
62	ReAlnet08_ALL	.995
63	resnet152_imagenet_full	.995
64	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.996
65	yudixie_resnet50_translation_rotation_0_240908	.995
66	ReAlnet09_cornet	.995
67	ReAlnet09	.995
68	resnet-18_test_m	.995
69	resnet-50x2_untrained	.995
70	mobilenet_v2_1.0_224-tf	.995
71	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.994
72	yudixie_resnet50_distance_translation_rotation_0_240908	.995
73	AdvProp_efficientnet-b6	.994
74	Res2Net50_26w_4s	.994
75	resnet-18-LC_w_sh_1_iter_conv_init_m	.994
76	alexnet_training_seed_01	.993
77	AlexNet_SIN_fov12	.993
78	focalnet_tiny_lrf_in1k	.993
79	alexnet_fov12	.993
80	ReAlnet03_ALL	.992
81	ReAlnet05_ALL	.992
82	yudixie_resnet50_translation_reg_0_240908	.992
83	ReAlnet10_ALL	.992
84	ReAlnet06_ALL	.991
85	resnet-18_untrained	.992
86	CORnet-Z	.991
87	ReAlnet07_ALL	.991
88	yudixie_resnet18_rotation_reg_0_240719	.991
89	yudixie_resnet50_distance_rotation_0_240908	.991
90	vonegrcnn_62e_nobn	.990
91	convnext_large:fb_in22k_ft_in1k	.990
92	AdvProp_efficientnet-b8	.989
93	resnet-50_untrained	.988
94	0.5x_resnet-18_LC_w_sh_1_iter	.988
95	ReAlnet01_cornet_fov4	.987
96	cvt_cvt-21-384-in1k_4_LucyV4	.986
97	AdvProp_efficientnet-b0	.986
98	hmax	.986
99	mobilenet_v2_1.3_224-tf	.986
100	mobilenet_v2_0.75_192-tf	.986
101	mobilenet_v2_0.75_160-tf	.986
102	mobilenet_v2_1.0_192-tf	.986
103	mobilenet_v2_1.0_160-tf	.986
104	mobilenet_v2_0.5_224-tf	.986
105	mobilenet_v2_0.75_128	.986
106	mobilenet_v2_0.5_160	.986
107	mobilenet_v2_1.0_128-tf	.986
108	mobilenet_v2_0.5_128	.986
109	mobilenet_v2_0.75_96	.986
110	inception_v1-tf	.986
111	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.986
112	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.986
113	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.986
114	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.986
115	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.986
116	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.986
117	omnivore_swinS	.986
118	mobilenet_v1_0.25_128	.986
119	convnext_tiny_sup	.986
120	mobilenet_v2_1_0_128	.986
121	deit_small_imagenet_10_seed-0	.986
122	deit_small_imagenet_full_seed-0	.986
123	convnext_small_imagenet_full_seed-0	.986
124	compact_resnet50_robust_V4	.986
125	ReAlnet01_cornet_fov16	.986
126	mobilenet_v2_1_0_224	.986
127	compact_vgg19_IT	.986
128	0.5x_resnet-18_LC_w_sh_100_iter	.986
129	resnet101	.985
130	resnet-18-LC_w_sh_10_iter_m	.984
131	resnet50-vicregl0p9	.984
132	mobilenet_v2_1.4_224-tf	.984
133	alexnet_random_l2_3_perturb	.984
134	barlow-twins-resnet50	.983
135	BiT-M-R50x3	.982
136	yudixie_resnet18_distance_translation_0_240719	.982
137	compact_resnet50_IT	.982
138	yudixie_resnet18_distance_translation_rotation_0_240719	.981
139	resnet152_ecoset_full	.981
140	texture_shape_alexnet_trained_on_SIN	.981
141	BiT-M-R101x1	.981
142	mobilenet_v2_1-4_224_pytorch	.980
143	compact_vgg19_V4	.979
144	resnet50_primary_visual_cortex	.979
145	alexnet_fov16	.979
146	mobilenet_v2_1_4_224	.978
147	resnet-18-LC_w_sh_1_iter_m	.977
148	yudixie_resnet18_object_class_0_240719	.975
149	resnet50-simclr-vissl	.975
150	resnet50-vitoimagevidnet8	.975
151	resnet-18-LC_m	.975
152	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.974
153	vggface	.974
154	alexnet_training_seed_01_fov16	.972
155	cvt_cvt-13-224-in1k_4_LucyV4	.971
156	alexnet_training_seed_09	.971
157	alexnet_early_checkpoint	.970
158	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.970
159	resnet-18-LC_d_w_sh_1x1_conv_init_m	.968
160	vgg_16	.966
161	resnet50_ewconplus_wzc	.966
162	convnext_base:clip_laiona_augreg_ft_in1k_384	.966
163	yudixie_resnet50_random_0_240908	.965
164	alexnet_training_seed_07	.965
165	blt_vs	.963
166	resnet-152_v2-tf	.964
167	resnet101_ecoset_full	.964
168	cvt_cvt-21-224-in1k_4_LucyV4	.963
169	yudixie_resnet50_category_class_0_240908	.962
170	yudixie_resnet18_random_0_240719	.958
171	resnet50_imagenet_100_seed-0	.958
172	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.956
173	resnext101_32x8d_wsl	.954
174	alexnet_training_seed_04	.953
175	alexnet_robust_correct	.952
176	convnext_tiny:in12k_ft_in1k	.951
177	BiT-S-R50x1	.949
178	vgg-16-keras	.948
179	yudixie_resnet18_imagenet1kpret_0_240719	.946
180	xception	.946
181	inception_v3-tf	.945
182	resnet50_imagenet_10_seed-0	.945
183	resnet18_ecoset_full	.942
184	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.941
185	resnet101_imagenet_full	.941
186	resnet50-SIN_IN	.940
187	AlexNet_SIN	.939
188	convnext_large_imagenet_full_seed-0	.939
189	0.5x_resnet-18_LC	.939
190	mobilenet_v2_0_75_192	.939
191	inception_v3	.937
192	resnet-152_v2_pytorch	.937
193	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.936
194	CLIP-RN50	.935
195	resnet50_byol	.935
196	alexnet_training_seed_10	.933
197	resnet50-cifar	.933
198	mobilenet_v2_0.5_192-tf	.933
199	mobilenet_v2_0_5_224	.931
200	convnext_base_imagenet_full_seed-0	.931
201	resnet18_imagenet21kP	.931
202	voneresnet-50	.929
203	resnet-50_v1-tf	.928
204	BiT-S-R152x2	.927
205	convnext_xlarge:fb_in22k_ft_in1k	.927
206	briaai_rmbg_1_4	.927
207	alexnet_fov4	.926
208	vit_base_patch14_reg4_dinov2-lvd142m	.926
209	CLIP_resnet50_float32	.925
210	CLIP_resnet50	.925
211	resnet50-vicregl0p75	.925
212	RN50	.925
213	VOneCORnet-S	.924
214	voneresnet-50-robust	.923
215	grcnn	.921
216	grcnn_robust_v1	.919
217	mobilenet_v2_1.0_96	.919
218	BiT-S-R101x3	.917
219	alexnet_training_seed_02	.915
220	yudixie_resnet50_object_class_0_240908	.914
221	grcnn_109	.913
222	grcnn_v2_text_noise	.912
223	r3m_resnet34_nocrop	.911
224	inception_v2-tf	.911
225	BiT-S-R50x3	.909
226	AlexNet_SIN_fov4	.909
227	cvt_cvt-13-384-in1k_4_LucyV4	.909
228	resnet50_robust_l2_eps1	.908
229	alexnet_l2_3_robust	.907
230	resnet-50-robust	.907
231	resnet50-moclr8deg	.906
232	BiT-S-R152x4	.905
233	alexnet_random_linf8_perturb	.905
234	nasnet_large-tf	.905
235	alexnet	.901
236	resnet50_random_l2_perturb	.901
237	resnet50_random_linf8_perturb	.900
238	resnet-50_v2-tf	.898
239	resnet50_tutorial	.898
240	resnet50-sup	.897
241	resnet_50_v1_spiking	.897
242	resnet_SIN_IN_FT_IN	.897
243	yudixie_resnet50_imagenet1kpret_0_240908	.897
244	resnet_50_v1	.897
245	resnet_50_v1_spiking_l4a	.896
246	resnet50-SIN	.896
247	resnet_50_v1_spiking_l3	.896
248	resnet_50_v1_spiking_l4	.896
249	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.895
250	vit_base_patch14_dinov2-lvd142m	.895
251	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.895
252	BiT-S-R101x1	.894
253	inception_v1	.893
254	antialias-resnet152	.893
255	densenet-201-keras	.892
256	my-model	.891
257	imagenet_l2_10_0	.890
258	alexnet_linf_8_robust	.889
259	efficientnet_b2_imagenet_full	.889
260	mobilenet_v2_1_3_224	.888
261	densenet-201	.886
262	resnet-18-LC_conv_init_m	.885
263	SWSL_resnet50	.884
264	efficientnet_b0	.884
265	densenet-169	.883
266	SWSL_resnext101_32x8d	.883
267	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.880
268	AdvProp_efficientnet-b4	.880
269	resnet50-vicreg	.879
270	resnet50-SIN_IN_IN	.878
271	ViT_L_32_imagenet1k	.877
272	resnet50	.877
273	resnet_50_v2	.876
274	resnet-152_v1-tf	.876
275	vgg-19-keras	.873
276	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
277	texture_shape_resnet50_trained_on_SIN	.871
278	imagenet_l2_3_0	.871
279	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.870
280	antialiased-r50	.868
281	alexnet_training_seed_03	.868
282	inception_v4-tf	.865
283	AdvProp_efficientnet-b7	.862
284	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.860
285	effnetv2m_custom384	.860
286	BiT-M-R152x4	.857
287	mobilevit_small	.855
288	shufflenet_v2_x1_0	.848
289	resnet50-VITO-8deg-cc	.846
290	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.846
291	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.846
292	densenet-121-keras	.845
293	densenet-121	.844
294	resnet50_robust_l2_eps3	.839
295	nasnet_mobile	.839
296	resnet-101_v1-tf	.838
297	mobilenet_v2_0_75_160	.838
298	r3m_resnet50	.838
299	resnet50_l2_3_robust	.835
300	artResNet18_1	.834
301	resnet50-barlow	.834
302	efficientnet_b0_imagenet_full	.828
303	resnet50_linf_8_robust	.828
304	resnet50_FractalDB	.828
305	resnet-101_v2-tf	.827
306	resnet50_imagenet_full	.826
307	vision_transformer_vit_large_patch16_224	.826
308	vonegrcnn_47e	.823
309	densenet-169-keras	.821
310	BiT-M-R50x1	.820
311	aughost_fair_resnet18	.820
312	deit_small_imagenet_100_seed-0	.820
313	resnet50_linf_4_robust	.813
314	mobilenet_v2_0_5_192	.813
315	nasnet_mobile-tf	.812
316	konkle_alexnetgn_ipcl_ref01_primary_model	.805
317	eBarlow_lmda_02_1000ep	.803
318	pnasnet_large-tf	.800
319	pnasnet_large_halfPrecision	.799
320	r3m_resnet50_nocrop	.797
321	vit_base_patch16_clip_224:openai_ft_in1k	.797
322	mobilenet_v2_0_75_224	.797
323	AT_efficientnet-b2	.795
324	mobilenet_v2_1_0_192	.795
325	vonegrcnn_52e_full	.795
326	vit_large_patch14_clip_336:laion2b_ft_in1k	.795
327	AdvProp_efficientnet-b2	.795
328	convnext_small_imagenet_1_seed-0	.793
329	vonegrcnn_47e	.793
330	effnetb1_272x240	.787
331	BiT-M-R101x3	.786
332	resnet50_moco_v2	.782
333	vonegrcnn_52e_full	.781
334	antialiased-rnext101_32x8d	.780
335	resnet50_simclr	.774
336	mobilenet_v2_0.35_224	.771
337	mobilenet_v2_1_0_160	.765
338	r3m_resnet18	.754
339	cv_18_dagger_408_pretrained	.754
340	deit_large_imagenet_full_seed-0	.748
341	alexnet_training_seed_08	.748
342	custom_model_cv_18_dagger_408	.747
343	vit_large_patch14_clip_224:laion2b_ft_in1k	.742
344	omnivore_swinT	.736
345	FrankRobWobv0	.735
346	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.734
347	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.733
348	alexnet_training_seed_01_fov4	.732
349	r3m_resnet34	.718
350	convnext_small_imagenet_10_seed-0	.718
351	BiT-M-R152x2	.711
352	vit_relpos_base_patch16_clsgap_224:sw_in1k	.694
353	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.692
354	CLIP_ViT-B_32	.686
355	AlexNet_SIN_fov16	.684
356	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.681
357	vit_large_patch14_clip_224:openai_ft_in1k	.676
358	vit_base_patch16_dinov3-lvd1689m	.673
359	yolos_tiny	.672
360	inception_resnet_v2	.662
361	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.648
362	mobilenet_v2_0.75_224-tf	.643
363	deit_base_imagenet_full_seed-0	.628
364	resnet50	.607
365	CORnetZ_CIFAR10	.601
366	my-model	.557
367	alexnet_training_seed_05	.513
368	resnet50_pretrained_with_retinal_waves	.481
369	ViT-B/32	.370
370	resnet50_imagenet_1_seed-0	.322
371	alexnet_training_seed_06	.321
372	resnet-18-LC_untrained	.274
373	resnet50-meshes-lt-100-original-pretrained	.138
374	resnet50-meshes-lt-100-original-scratch	.052
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	resnet_101_v1
385	alexnet_ks_torevert
386	yudixie_resnet50_imagenet1kpret_0_240312
387	resnet_152_v1
388	resnext101_32x16d_wsl
389	densenet_201_pytorch
390	inception_v3_pytorch
391	pnasnet_large_pytorch
392	inception_v4
393	nasnet_large
394	deit_small_imagenet_1_seed-0
395	pnasnet_large
396	resnet_101_v2
397	vonegrcnn_62e_nobn
398	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.99.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_selective