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	efficientnet_b1_imagenet_full	1.0
4	cornet_s	1.000
5	convnext_femto_ols:d1_in1k	1.0
6	ReAlnet08	1.000
7	0.5x_resnet-18_LC_w_sh_10_iter	1.0
8	resnet34_imagenet_full	1.0
9	yudixie_resnet18_translation_reg_0_240719	1.000
10	CORnetZ_CIFAR10_bs32_20_04	1.0
11	vonealexnet_gaussian_noise_std4_fixed	1.000
12	resnet-18-LC_w_sh_100_iter_conv_init_m	1.000
13	resnet-18-LC_w_sh_10_iter_conv_init_m	1.000
14	yudixie_resnet18_translation_rotation_0_240719	1.000
15	yudixie_resnet18_distance_rotation_0_240719	.999
16	ReAlnet10	.999
17	ReAlnet01	1.000
18	ReAlnet03	.999
19	ReAlnet07_cornet	.999
20	ReAlnet03_cornet	.999
21	ReAlnet10_cornet	.999
22	voneresnet-50-non_stochastic	.999
23	ReAlnet07	.999
24	ReAlnet04	.999
25	ReAlnet04_cornet	.999
26	ReAlnet02	.999
27	resnet50_ecoset_full	.999
28	ReAlnet01_cornet	.999
29	convnext_tiny_imagenet_full_seed-0	.999
30	yudixie_resnet50_distance_reg_0_240908	.999
31	ReAlnet02_cornet	.999
32	CORnet-S	.999
33	ReAlnet06	.998
34	alexnet_reduced_aliasing_early_checkpoint	.998
35	ReAlnet06_cornet	.999
36	ReAlnet05_cornet	.999
37	ReAlnet05	.998
38	efficientnet-b7	.998
39	yudixie_resnet50_distance_translation_0_240908	.998
40	resnet-18-LC_1st_conv_m	.998
41	yudixie_resnet18_distance_reg_0_240719	.997
42	yudixie_resnet50_rotation_reg_0_240908	.997
43	resnet18_imagenet_full	.997
44	resnet34_ecoset_full	.996
45	convnext_small_imagenet_100_seed-0	.996
46	alexnet	.996
47	resnet-18-LC_w_sh_100_iter_m	.996
48	vgg_19	.996
49	0.5x_resnet-18	.996
50	omnivore_swinB	.996
51	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.996
52	yudixie_resnet18_category_class_0_240719	.996
53	resnet152_imagenet_full	.995
54	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.996
55	yudixie_resnet50_translation_rotation_0_240908	.995
56	ReAlnet09_cornet	.995
57	ReAlnet09	.995
58	resnet-18_test_m	.995
59	resnet-50x2_untrained	.995
60	mobilenet_v2_1.0_224-tf	.995
61	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.994
62	yudixie_resnet50_distance_translation_rotation_0_240908	.995
63	AdvProp_efficientnet-b6	.994
64	Res2Net50_26w_4s	.994
65	resnet-18-LC_w_sh_1_iter_conv_init_m	.994
66	alexnet_training_seed_01	.993
67	focalnet_tiny_lrf_in1k	.993
68	yudixie_resnet50_translation_reg_0_240908	.992
69	resnet-18_untrained	.992
70	CORnet-Z	.991
71	yudixie_resnet18_rotation_reg_0_240719	.991
72	yudixie_resnet50_distance_rotation_0_240908	.991
73	vonegrcnn_62e_nobn	.990
74	convnext_large:fb_in22k_ft_in1k	.990
75	AdvProp_efficientnet-b8	.989
76	resnet-50_untrained	.988
77	0.5x_resnet-18_LC_w_sh_1_iter	.988
78	cvt_cvt-21-384-in1k_4_LucyV4	.986
79	mobilenet_v2_0.75_192-tf	.986
80	AdvProp_efficientnet-b0	.986
81	hmax	.986
82	mobilenet_v2_1.3_224-tf	.986
83	mobilenet_v2_0.75_160-tf	.986
84	mobilenet_v2_1.0_192-tf	.986
85	mobilenet_v2_1.0_160-tf	.986
86	mobilenet_v2_0.5_224-tf	.986
87	mobilenet_v2_0.75_128	.986
88	mobilenet_v2_0.5_160	.986
89	mobilenet_v2_1.0_128-tf	.986
90	mobilenet_v2_0.5_128	.986
91	mobilenet_v2_0.75_96	.986
92	inception_v1-tf	.986
93	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.986
94	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.986
95	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.986
96	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.986
97	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.986
98	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.986
99	omnivore_swinS	.986
100	mobilenet_v1_0.25_128	.986
101	convnext_tiny_sup	.986
102	mobilenet_v2_1_0_128	.986
103	mobilenet_v2_1_0_224	.986
104	deit_small_imagenet_10_seed-0	.986
105	convnext_small_imagenet_full_seed-0	.986
106	deit_small_imagenet_full_seed-0	.986
107	0.5x_resnet-18_LC_w_sh_100_iter	.986
108	resnet101	.985
109	resnet-18-LC_w_sh_10_iter_m	.984
110	resnet50-vicregl0p9	.984
111	mobilenet_v2_1.4_224-tf	.984
112	alexnet_random_l2_3_perturb	.984
113	barlow-twins-resnet50	.983
114	BiT-M-R50x3	.982
115	yudixie_resnet18_distance_translation_0_240719	.982
116	yudixie_resnet18_distance_translation_rotation_0_240719	.981
117	resnet152_ecoset_full	.981
118	texture_shape_alexnet_trained_on_SIN	.981
119	BiT-M-R101x1	.981
120	mobilenet_v2_1-4_224_pytorch	.980
121	resnet50_primary_visual_cortex	.979
122	mobilenet_v2_1_4_224	.978
123	resnet-18-LC_w_sh_1_iter_m	.977
124	yudixie_resnet18_object_class_0_240719	.975
125	resnet50-simclr-vissl	.975
126	resnet50-vitoimagevidnet8	.975
127	resnet-18-LC_m	.975
128	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.974
129	vggface	.974
130	cvt_cvt-13-224-in1k_4_LucyV4	.971
131	alexnet_training_seed_09	.971
132	alexnet_early_checkpoint	.970
133	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.970
134	resnet-18-LC_d_w_sh_1x1_conv_init_m	.968
135	vgg_16	.966
136	convnext_base:clip_laiona_augreg_ft_in1k_384	.966
137	yudixie_resnet50_random_0_240908	.965
138	alexnet_training_seed_07	.965
139	blt_vs	.963
140	resnet-152_v2-tf	.964
141	resnet101_ecoset_full	.964
142	cvt_cvt-21-224-in1k_4_LucyV4	.963
143	yudixie_resnet50_category_class_0_240908	.962
144	yudixie_resnet18_random_0_240719	.958
145	resnet50_imagenet_100_seed-0	.958
146	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.956
147	resnext101_32x8d_wsl	.954
148	alexnet_training_seed_04	.953
149	alexnet_robust_correct	.952
150	convnext_tiny:in12k_ft_in1k	.951
151	BiT-S-R50x1	.949
152	vgg-16-keras	.948
153	yudixie_resnet18_imagenet1kpret_0_240719	.946
154	xception	.946
155	inception_v3-tf	.945
156	resnet50_imagenet_10_seed-0	.945
157	resnet18_ecoset_full	.942
158	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.941
159	resnet101_imagenet_full	.941
160	resnet50-SIN_IN	.940
161	AlexNet_SIN	.939
162	convnext_large_imagenet_full_seed-0	.939
163	0.5x_resnet-18_LC	.939
164	mobilenet_v2_0_75_192	.939
165	inception_v3	.937
166	resnet-152_v2_pytorch	.937
167	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.936
168	CLIP-RN50	.935
169	resnet50_byol	.935
170	alexnet_training_seed_10	.933
171	resnet50-cifar	.933
172	mobilenet_v2_0.5_192-tf	.933
173	mobilenet_v2_0_5_224	.931
174	convnext_base_imagenet_full_seed-0	.931
175	resnet18_imagenet21kP	.931
176	voneresnet-50	.929
177	resnet-50_v1-tf	.928
178	BiT-S-R152x2	.927
179	convnext_xlarge:fb_in22k_ft_in1k	.927
180	briaai_rmbg_1_4	.927
181	CLIP_resnet50_float32	.925
182	CLIP_resnet50	.925
183	resnet50-vicregl0p75	.925
184	RN50	.925
185	VOneCORnet-S	.924
186	voneresnet-50-robust	.923
187	grcnn	.921
188	grcnn_robust_v1	.919
189	mobilenet_v2_1.0_96	.919
190	BiT-S-R101x3	.917
191	alexnet_training_seed_02	.915
192	yudixie_resnet50_object_class_0_240908	.914
193	grcnn_109	.913
194	grcnn_v2_text_noise	.912
195	r3m_resnet34_nocrop	.911
196	inception_v2-tf	.911
197	BiT-S-R50x3	.909
198	cvt_cvt-13-384-in1k_4_LucyV4	.909
199	resnet50_robust_l2_eps1	.908
200	alexnet_l2_3_robust	.907
201	resnet-50-robust	.907
202	resnet50-moclr8deg	.906
203	BiT-S-R152x4	.905
204	alexnet_random_linf8_perturb	.905
205	nasnet_large-tf	.905
206	alexnet	.901
207	resnet50_random_l2_perturb	.901
208	alexnet	.901
209	alexnet	.900
210	alexnet-baseline	.900
211	alexnet	.900
212	resnet50_random_linf8_perturb	.900
213	alexnet	.900
214	alexnet	.900
215	alexnet	.900
216	resnet-50_v2-tf	.898
217	resnet50_tutorial	.898
218	resnet50-sup	.897
219	resnet_50_v1_spiking	.897
220	resnet_SIN_IN_FT_IN	.897
221	yudixie_resnet50_imagenet1kpret_0_240908	.897
222	resnet_50_v1	.897
223	resnet_50_v1_spiking_l4a	.896
224	resnet50-SIN	.896
225	resnet_50_v1_spiking_l3	.896
226	resnet_50_v1_spiking_l4	.896
227	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.895
228	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.895
229	BiT-S-R101x1	.894
230	inception_v1	.893
231	antialias-resnet152	.893
232	densenet-201-keras	.892
233	my-model	.891
234	imagenet_l2_10_0	.890
235	alexnet_linf_8_robust	.889
236	efficientnet_b2_imagenet_full	.889
237	mobilenet_v2_1_3_224	.888
238	densenet-201	.886
239	resnet-18-LC_conv_init_m	.885
240	SWSL_resnet50	.884
241	efficientnet_b0	.884
242	densenet-169	.883
243	SWSL_resnext101_32x8d	.883
244	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.880
245	AdvProp_efficientnet-b4	.880
246	resnet50-vicreg	.879
247	resnet50-SIN_IN_IN	.878
248	ViT_L_32_imagenet1k	.877
249	resnet50	.877
250	resnet_50_v2	.876
251	resnet-152_v1-tf	.876
252	vgg-19-keras	.873
253	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
254	texture_shape_resnet50_trained_on_SIN	.871
255	imagenet_l2_3_0	.871
256	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.870
257	antialiased-r50	.868
258	alexnet_training_seed_03	.868
259	inception_v4-tf	.865
260	AdvProp_efficientnet-b7	.862
261	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.860
262	effnetv2m_custom384	.860
263	BiT-M-R152x4	.857
264	mobilevit_small	.855
265	shufflenet_v2_x1_0	.848
266	resnet50-VITO-8deg-cc	.846
267	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.846
268	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.846
269	densenet-121-keras	.845
270	densenet-121	.844
271	resnet50_robust_l2_eps3	.839
272	nasnet_mobile	.839
273	resnet-101_v1-tf	.838
274	mobilenet_v2_0_75_160	.838
275	r3m_resnet50	.838
276	resnet50_l2_3_robust	.835
277	artResNet18_1	.834
278	resnet50-barlow	.834
279	efficientnet_b0_imagenet_full	.828
280	resnet50_linf_8_robust	.828
281	resnet50_FractalDB	.828
282	resnet-101_v2-tf	.827
283	resnet50_imagenet_full	.826
284	vision_transformer_vit_large_patch16_224	.826
285	vonegrcnn_47e	.823
286	densenet-169-keras	.821
287	BiT-M-R50x1	.820
288	deit_small_imagenet_100_seed-0	.820
289	resnet50_linf_4_robust	.813
290	mobilenet_v2_0_5_192	.813
291	nasnet_mobile-tf	.812
292	konkle_alexnetgn_ipcl_ref01_primary_model	.805
293	pnasnet_large-tf	.800
294	r3m_resnet50_nocrop	.797
295	vit_base_patch16_clip_224:openai_ft_in1k	.797
296	mobilenet_v2_0_75_224	.797
297	AT_efficientnet-b2	.795
298	mobilenet_v2_1_0_192	.795
299	vonegrcnn_52e_full	.795
300	vit_large_patch14_clip_336:laion2b_ft_in1k	.795
301	AdvProp_efficientnet-b2	.795
302	convnext_small_imagenet_1_seed-0	.793
303	vonegrcnn_47e	.793
304	effnetb1_272x240	.787
305	BiT-M-R101x3	.786
306	resnet50_moco_v2	.782
307	vonegrcnn_52e_full	.781
308	antialiased-rnext101_32x8d	.780
309	resnet50_simclr	.774
310	mobilenet_v2_0.35_224	.771
311	mobilenet_v2_1_0_160	.765
312	r3m_resnet18	.754
313	cv_18_dagger_408_pretrained	.754
314	deit_large_imagenet_full_seed-0	.748
315	alexnet_training_seed_08	.748
316	custom_model_cv_18_dagger_408	.747
317	vit_large_patch14_clip_224:laion2b_ft_in1k	.742
318	omnivore_swinT	.736
319	FrankRobWobv0	.735
320	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.734
321	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.733
322	r3m_resnet34	.718
323	convnext_small_imagenet_10_seed-0	.718
324	BiT-M-R152x2	.711
325	vit_relpos_base_patch16_clsgap_224:sw_in1k	.694
326	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.692
327	CLIP_ViT-B_32	.686
328	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.681
329	vit_large_patch14_clip_224:openai_ft_in1k	.676
330	yolos_tiny	.672
331	inception_resnet_v2	.662
332	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.648
333	mobilenet_v2_0.75_224-tf	.643
334	deit_base_imagenet_full_seed-0	.628
335	resnet50	.607
336	CORnetZ_CIFAR10	.601
337	my-model	.557
338	alexnet_training_seed_05	.513
339	resnet50_pretrained_with_retinal_waves	.481
340	ViT-B/32	.370
341	resnet50_imagenet_1_seed-0	.322
342	alexnet_training_seed_06	.321
343	resnet-18-LC_untrained	.274
344	resnet50-meshes-lt-100-original-pretrained	.138
345	resnet50-meshes-lt-100-original-scratch	.052
346	resnet_101_v1
347	tv_efficientnet-b1
348	regnet_y_400mf
349	nasnet_large
350	resnet_152_v2
351	deit_small_imagenet_1_seed-0
352	resnet18-supervised
353	inception_v4
354	pnasnet_large
355	fitvid_trained_on_physion
356	pnasnet_large_pytorch
357	inception_v3_pytorch
358	densenet_201_pytorch
359	resnext101_32x16d_wsl
360	mobilenet_v2_0.35_160
361	resnet_101_v2
362	openclip
363	vonegrcnn_62e_nobn
364	resnext101_32x32d_wsl
365	resnext101_32x48d_wsl
366	resnet_152_v1
367	resnet50_ImageNet
368	yudixie_resnet50_imagenet1kpret_0_240312
369	alexnet_ks_torevert

Benchmark bibtex

None

Ceiling

0.99.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_selective