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	ReAlnet_incorrect	1.000
16	yudixie_resnet18_distance_rotation_0_240719	.999
17	ReAlnet10	.999
18	ReAlnet01	1.000
19	ReAlnet03	.999
20	ReAlnet07_cornet	.999
21	ReAlnet03_cornet	.999
22	ReAlnet10_cornet	.999
23	voneresnet-50-non_stochastic	.999
24	ReAlnet07	.999
25	ReAlnet04	.999
26	ReAlnet04_cornet	.999
27	ReAlnet02	.999
28	resnet50_ecoset_full	.999
29	ReAlnet01_cornet	.999
30	convnext_tiny_imagenet_full_seed-0	.999
31	yudixie_resnet50_distance_reg_0_240908	.999
32	ReAlnet02_cornet	.999
33	CORnet-S	.999
34	ReAlnet06	.998
35	alexnet_reduced_aliasing_early_checkpoint	.998
36	ReAlnet06_cornet	.999
37	ReAlnet05	.998
38	ReAlnet05_cornet	.999
39	efficientnet-b7	.998
40	yudixie_resnet50_distance_translation_0_240908	.998
41	resnet-18-LC_1st_conv_m	.998
42	yudixie_resnet18_distance_reg_0_240719	.997
43	yudixie_resnet50_rotation_reg_0_240908	.997
44	resnet18_imagenet_full	.997
45	resnet34_ecoset_full	.996
46	convnext_small_imagenet_100_seed-0	.996
47	alexnet	.996
48	resnet-18-LC_w_sh_100_iter_m	.996
49	vgg_19	.996
50	0.5x_resnet-18	.996
51	omnivore_swinB	.996
52	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.996
53	yudixie_resnet18_category_class_0_240719	.996
54	resnet152_imagenet_full	.995
55	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.996
56	yudixie_resnet50_translation_rotation_0_240908	.995
57	ReAlnet09_cornet	.995
58	ReAlnet09	.995
59	resnet-18_test_m	.995
60	resnet-50x2_untrained	.995
61	mobilenet_v2_1.0_224-tf	.995
62	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.994
63	yudixie_resnet50_distance_translation_rotation_0_240908	.995
64	AdvProp_efficientnet-b6	.994
65	Res2Net50_26w_4s	.994
66	resnet-18-LC_w_sh_1_iter_conv_init_m	.994
67	alexnet_training_seed_01	.993
68	focalnet_tiny_lrf_in1k	.993
69	yudixie_resnet50_translation_reg_0_240908	.992
70	resnet-18_untrained	.992
71	CORnet-Z	.991
72	yudixie_resnet18_rotation_reg_0_240719	.991
73	yudixie_resnet50_distance_rotation_0_240908	.991
74	vonegrcnn_62e_nobn	.990
75	convnext_large:fb_in22k_ft_in1k	.990
76	AdvProp_efficientnet-b8	.989
77	resnet-50_untrained	.988
78	0.5x_resnet-18_LC_w_sh_1_iter	.988
79	cvt_cvt-21-384-in1k_4_LucyV4	.986
80	omnivore_swinS	.986
81	AdvProp_efficientnet-b0	.986
82	hmax	.986
83	mobilenet_v2_1.3_224-tf	.986
84	mobilenet_v2_0.75_192-tf	.986
85	mobilenet_v2_0.75_160-tf	.986
86	mobilenet_v2_1.0_192-tf	.986
87	mobilenet_v2_1.0_160-tf	.986
88	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.986
89	mobilenet_v2_0.5_224-tf	.986
90	mobilenet_v2_0.75_128	.986
91	mobilenet_v2_0.5_160	.986
92	mobilenet_v2_1.0_128-tf	.986
93	mobilenet_v2_0.5_128	.986
94	mobilenet_v2_0.75_96	.986
95	inception_v1-tf	.986
96	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.986
97	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.986
98	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.986
99	convnext_tiny_sup	.986
100	mobilenet_v1_0.25_128	.986
101	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.986
102	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.986
103	mobilenet_v2_1_0_128	.986
104	mobilenet_v2_1_0_224	.986
105	deit_small_imagenet_10_seed-0	.986
106	deit_small_imagenet_full_seed-0	.986
107	convnext_small_imagenet_full_seed-0	.986
108	0.5x_resnet-18_LC_w_sh_100_iter	.986
109	resnet101	.985
110	resnet-18-LC_w_sh_10_iter_m	.984
111	resnet50-vicregl0p9	.984
112	mobilenet_v2_1.4_224-tf	.984
113	alexnet_random_l2_3_perturb	.984
114	barlow-twins-resnet50	.983
115	BiT-M-R50x3	.982
116	yudixie_resnet18_distance_translation_0_240719	.982
117	yudixie_resnet18_distance_translation_rotation_0_240719	.981
118	resnet152_ecoset_full	.981
119	texture_shape_alexnet_trained_on_SIN	.981
120	BiT-M-R101x1	.981
121	mobilenet_v2_1-4_224_pytorch	.980
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	CLIP_resnet50_float32	.925
181	CLIP_resnet50	.925
182	resnet50-vicregl0p75	.925
183	RN50	.925
184	VOneCORnet-S	.924
185	voneresnet-50-robust	.923
186	grcnn	.921
187	grcnn_robust_v1	.919
188	mobilenet_v2_1.0_96	.919
189	BiT-S-R101x3	.917
190	alexnet_training_seed_02	.915
191	yudixie_resnet50_object_class_0_240908	.914
192	grcnn_109	.913
193	grcnn_v2_text_noise	.912
194	r3m_resnet34_nocrop	.911
195	inception_v2-tf	.911
196	BiT-S-R50x3	.909
197	cvt_cvt-13-384-in1k_4_LucyV4	.909
198	resnet50_robust_l2_eps1	.908
199	alexnet_l2_3_robust	.907
200	resnet-50-robust	.907
201	resnet50-moclr8deg	.906
202	BiT-S-R152x4	.905
203	alexnet_random_linf8_perturb	.905
204	nasnet_large-tf	.905
205	alexnet	.901
206	resnet50_random_l2_perturb	.901
207	alexnet	.901
208	alexnet	.900
209	alexnet-baseline	.900
210	alexnet	.900
211	resnet50_random_linf8_perturb	.900
212	alexnet	.900
213	alexnet	.900
214	alexnet	.900
215	resnet-50_v2-tf	.898
216	resnet50_tutorial	.898
217	resnet50-sup	.897
218	resnet_SIN_IN_FT_IN	.897
219	yudixie_resnet50_imagenet1kpret_0_240908	.897
220	resnet_50_v1	.897
221	resnet50-SIN	.896
222	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.895
223	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.895
224	BiT-S-R101x1	.894
225	inception_v1	.893
226	antialias-resnet152	.893
227	densenet-201-keras	.892
228	my-model	.891
229	imagenet_l2_10_0	.890
230	alexnet_linf_8_robust	.889
231	efficientnet_b2_imagenet_full	.889
232	mobilenet_v2_1_3_224	.888
233	densenet-201	.886
234	resnet-18-LC_conv_init_m	.885
235	SWSL_resnet50	.884
236	efficientnet_b0	.884
237	densenet-169	.883
238	SWSL_resnext101_32x8d	.883
239	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.880
240	AdvProp_efficientnet-b4	.880
241	resnet50-vicreg	.879
242	resnet50-SIN_IN_IN	.878
243	ViT_L_32_imagenet1k	.877
244	resnet50	.877
245	resnet_50_v2	.876
246	resnet-152_v1-tf	.876
247	vgg-19-keras	.873
248	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
249	texture_shape_resnet50_trained_on_SIN	.871
250	imagenet_l2_3_0	.871
251	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.870
252	antialiased-r50	.868
253	alexnet_training_seed_03	.868
254	inception_v4-tf	.865
255	AdvProp_efficientnet-b7	.862
256	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.860
257	effnetv2m_custom384	.860
258	BiT-M-R152x4	.857
259	mobilevit_small	.855
260	shufflenet_v2_x1_0	.848
261	resnet50-VITO-8deg-cc	.846
262	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.846
263	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.846
264	densenet-121-keras	.845
265	densenet-121	.844
266	resnet50_robust_l2_eps3	.839
267	nasnet_mobile	.839
268	resnet-101_v1-tf	.838
269	mobilenet_v2_0_75_160	.838
270	r3m_resnet50	.838
271	resnet50_l2_3_robust	.835
272	artResNet18_1	.834
273	resnet50-barlow	.834
274	efficientnet_b0_imagenet_full	.828
275	resnet50_linf_8_robust	.828
276	resnet50_FractalDB	.828
277	resnet-101_v2-tf	.827
278	resnet50_imagenet_full	.826
279	vision_transformer_vit_large_patch16_224	.826
280	vonegrcnn_47e	.823
281	densenet-169-keras	.821
282	BiT-M-R50x1	.820
283	deit_small_imagenet_100_seed-0	.820
284	resnet50_linf_4_robust	.813
285	mobilenet_v2_0_5_192	.813
286	nasnet_mobile-tf	.812
287	konkle_alexnetgn_ipcl_ref01_primary_model	.805
288	pnasnet_large-tf	.800
289	r3m_resnet50_nocrop	.797
290	vit_base_patch16_clip_224:openai_ft_in1k	.797
291	mobilenet_v2_0_75_224	.797
292	AT_efficientnet-b2	.795
293	mobilenet_v2_1_0_192	.795
294	vonegrcnn_52e_full	.795
295	vit_large_patch14_clip_336:laion2b_ft_in1k	.795
296	AdvProp_efficientnet-b2	.795
297	convnext_small_imagenet_1_seed-0	.793
298	vonegrcnn_47e	.793
299	effnetb1_272x240	.787
300	BiT-M-R101x3	.786
301	resnet50_moco_v2	.782
302	vonegrcnn_52e_full	.781
303	antialiased-rnext101_32x8d	.780
304	resnet50_simclr	.774
305	mobilenet_v2_0.35_224	.771
306	mobilenet_v2_1_0_160	.765
307	r3m_resnet18	.754
308	cv_18_dagger_408_pretrained	.754
309	deit_large_imagenet_full_seed-0	.748
310	alexnet_training_seed_08	.748
311	custom_model_cv_18_dagger_408	.747
312	vit_large_patch14_clip_224:laion2b_ft_in1k	.742
313	omnivore_swinT	.736
314	FrankRobWobv0	.735
315	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.734
316	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.733
317	r3m_resnet34	.718
318	convnext_small_imagenet_10_seed-0	.718
319	BiT-M-R152x2	.711
320	vit_relpos_base_patch16_clsgap_224:sw_in1k	.694
321	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.692
322	CLIP_ViT-B_32	.686
323	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.681
324	vit_large_patch14_clip_224:openai_ft_in1k	.676
325	inception_resnet_v2	.662
326	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.648
327	mobilenet_v2_0.75_224-tf	.643
328	deit_base_imagenet_full_seed-0	.628
329	resnet50	.607
330	CORnetZ_CIFAR10	.601
331	my-model	.557
332	alexnet_training_seed_05	.513
333	resnet50_pretrained_with_retinal_waves	.481
334	ViT-B/32	.370
335	resnet50_imagenet_1_seed-0	.322
336	alexnet_training_seed_06	.321
337	resnet-18-LC_untrained	.274
338	resnet50-meshes-lt-100-original-pretrained	.138
339	resnet50-meshes-lt-100-original-scratch	.052
340	inception_v4	X
341	pnasnet_large_pytorch	X
342	inception_v3_pytorch	X
343	densenet_201_pytorch	X
344	resnet18-supervised	X
345	resnext101_32x16d_wsl	X
346	resnext101_32x32d_wsl	X
347	resnext101_32x48d_wsl	X
348	fitvid_trained_on_physion	X
349	resnet50_ImageNet	X
350	regnet_y_400mf	X
351	tv_efficientnet-b1	X
352	resnet_101_v1	X
353	resnet_152_v1	X
354	alexnet_ks_torevert	X
355	mobilenet_v2_0.35_160	X
356	resnet_152_v2	X
357	yudixie_resnet50_imagenet1kpret_0_240312	X
358	pnasnet_large	X
359	deit_small_imagenet_1_seed-0	X
360	openclip	X
361	nasnet_large	X
362	resnet_101_v2	X
363	vonegrcnn_62e_nobn	X

Benchmark bibtex

None

Ceiling

0.99.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_selective