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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	1.000
4	efficientnet_b1_imagenet_full	1.0
5	cornet_s	1.000
6	convnext_femto_ols:d1_in1k	1.0
7	ReAlnet08	1.000
8	compact_resnet50_V4	1.000
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	1.000
12	CORnetZ_CIFAR10_bs32_20_04	1.0
13	vonealexnet_gaussian_noise_std4_fixed	1.000
14	resnet-18-LC_w_sh_100_iter_conv_init_m	1.000
15	resnet-18-LC_w_sh_10_iter_conv_init_m	1.000
16	yudixie_resnet18_translation_rotation_0_240719	1.000
17	ReAlnet01_cornet_fov12	.999
18	yudixie_resnet18_distance_rotation_0_240719	.999
19	ReAlnet10	.999
20	ReAlnet01	1.000
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	alexnet	.996
57	resnet-18-LC_w_sh_100_iter_m	.996
58	vgg_19	.996
59	0.5x_resnet-18	.996
60	omnivore_swinB	.996
61	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.996
62	yudixie_resnet18_category_class_0_240719	.996
63	ReAlnet08_ALL	.995
64	resnet152_imagenet_full	.995
65	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.996
66	yudixie_resnet50_translation_rotation_0_240908	.995
67	ReAlnet09_cornet	.995
68	ReAlnet09	.995
69	resnet-18_test_m	.995
70	resnet-50x2_untrained	.995
71	mobilenet_v2_1.0_224-tf	.995
72	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.994
73	yudixie_resnet50_distance_translation_rotation_0_240908	.995
74	AdvProp_efficientnet-b6	.994
75	Res2Net50_26w_4s	.994
76	resnet-18-LC_w_sh_1_iter_conv_init_m	.994
77	alexnet_training_seed_01	.993
78	AlexNet_SIN_fov12	.993
79	focalnet_tiny_lrf_in1k	.993
80	alexnet_fov12	.993
81	ReAlnet03_ALL	.992
82	ReAlnet05_ALL	.992
83	yudixie_resnet50_translation_reg_0_240908	.992
84	ReAlnet10_ALL	.992
85	ReAlnet06_ALL	.991
86	resnet-18_untrained	.992
87	CORnet-Z	.991
88	ReAlnet07_ALL	.991
89	yudixie_resnet18_rotation_reg_0_240719	.991
90	yudixie_resnet50_distance_rotation_0_240908	.991
91	vonegrcnn_62e_nobn	.990
92	convnext_large:fb_in22k_ft_in1k	.990
93	AdvProp_efficientnet-b8	.989
94	resnet-50_untrained	.988
95	0.5x_resnet-18_LC_w_sh_1_iter	.988
96	ReAlnet01_cornet_fov4	.987
97	cvt_cvt-21-384-in1k_4_LucyV4	.986
98	AdvProp_efficientnet-b0	.986
99	hmax	.986
100	mobilenet_v2_1.3_224-tf	.986
101	mobilenet_v2_0.75_192-tf	.986
102	mobilenet_v2_0.75_160-tf	.986
103	mobilenet_v2_1.0_192-tf	.986
104	mobilenet_v2_1.0_160-tf	.986
105	mobilenet_v2_0.5_224-tf	.986
106	mobilenet_v2_0.75_128	.986
107	mobilenet_v2_0.5_160	.986
108	mobilenet_v2_1.0_128-tf	.986
109	mobilenet_v2_0.5_128	.986
110	mobilenet_v2_0.75_96	.986
111	inception_v1-tf	.986
112	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.986
113	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.986
114	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.986
115	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.986
116	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.986
117	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.986
118	omnivore_swinS	.986
119	mobilenet_v1_0.25_128	.986
120	convnext_tiny_sup	.986
121	mobilenet_v2_1_0_128	.986
122	deit_small_imagenet_10_seed-0	.986
123	deit_small_imagenet_full_seed-0	.986
124	convnext_small_imagenet_full_seed-0	.986
125	compact_resnet50_robust_V4	.986
126	ReAlnet01_cornet_fov16	.986
127	mobilenet_v2_1_0_224	.986
128	compact_vgg19_IT	.986
129	0.5x_resnet-18_LC_w_sh_100_iter	.986
130	resnet101	.985
131	resnet-18-LC_w_sh_10_iter_m	.984
132	resnet50-vicregl0p9	.984
133	mobilenet_v2_1.4_224-tf	.984
134	alexnet_random_l2_3_perturb	.984
135	barlow-twins-resnet50	.983
136	BiT-M-R50x3	.982
137	yudixie_resnet18_distance_translation_0_240719	.982
138	compact_resnet50_IT	.982
139	yudixie_resnet18_distance_translation_rotation_0_240719	.981
140	resnet152_ecoset_full	.981
141	texture_shape_alexnet_trained_on_SIN	.981
142	BiT-M-R101x1	.981
143	mobilenet_v2_1-4_224_pytorch	.980
144	compact_vgg19_V4	.979
145	resnet50_primary_visual_cortex	.979
146	alexnet_fov16	.979
147	mobilenet_v2_1_4_224	.978
148	resnet-18-LC_w_sh_1_iter_m	.977
149	yudixie_resnet18_object_class_0_240719	.975
150	resnet50-simclr-vissl	.975
151	resnet50-vitoimagevidnet8	.975
152	resnet-18-LC_m	.975
153	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.974
154	vggface	.974
155	alexnet_training_seed_01_fov16	.972
156	cvt_cvt-13-224-in1k_4_LucyV4	.971
157	alexnet_training_seed_09	.971
158	alexnet_early_checkpoint	.970
159	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.970
160	resnet-18-LC_d_w_sh_1x1_conv_init_m	.968
161	vgg_16	.966
162	resnet50_ewconplus_wzc	.966
163	convnext_base:clip_laiona_augreg_ft_in1k_384	.966
164	yudixie_resnet50_random_0_240908	.965
165	alexnet_training_seed_07	.965
166	blt_vs	.963
167	resnet-152_v2-tf	.964
168	resnet101_ecoset_full	.964
169	cvt_cvt-21-224-in1k_4_LucyV4	.963
170	yudixie_resnet50_category_class_0_240908	.962
171	yudixie_resnet18_random_0_240719	.958
172	resnet50_imagenet_100_seed-0	.958
173	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.956
174	resnext101_32x8d_wsl	.954
175	alexnet_training_seed_04	.953
176	alexnet_robust_correct	.952
177	convnext_tiny:in12k_ft_in1k	.951
178	BiT-S-R50x1	.949
179	vgg-16-keras	.948
180	yudixie_resnet18_imagenet1kpret_0_240719	.946
181	xception	.946
182	inception_v3-tf	.945
183	resnet50_imagenet_10_seed-0	.945
184	resnet18_ecoset_full	.942
185	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.941
186	resnet101_imagenet_full	.941
187	resnet50-SIN_IN	.940
188	AlexNet_SIN	.939
189	convnext_large_imagenet_full_seed-0	.939
190	0.5x_resnet-18_LC	.939
191	mobilenet_v2_0_75_192	.939
192	inception_v3	.937
193	resnet-152_v2_pytorch	.937
194	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.936
195	CLIP-RN50	.935
196	resnet50_byol	.935
197	alexnet_training_seed_10	.933
198	resnet50-cifar	.933
199	mobilenet_v2_0.5_192-tf	.933
200	mobilenet_v2_0_5_224	.931
201	convnext_base_imagenet_full_seed-0	.931
202	resnet18_imagenet21kP	.931
203	voneresnet-50	.929
204	resnet-50_v1-tf	.928
205	BiT-S-R152x2	.927
206	convnext_xlarge:fb_in22k_ft_in1k	.927
207	vit_base_patch14_reg4_dinov2:lvd142m	.927
208	briaai_rmbg_1_4	.927
209	alexnet_fov4	.926
210	CLIP_resnet50_float32	.925
211	CLIP_resnet50	.925
212	resnet50-vicregl0p75	.925
213	RN50	.925
214	VOneCORnet-S	.924
215	voneresnet-50-robust	.923
216	grcnn	.921
217	grcnn_robust_v1	.919
218	mobilenet_v2_1.0_96	.919
219	BiT-S-R101x3	.917
220	alexnet_training_seed_02	.915
221	yudixie_resnet50_object_class_0_240908	.914
222	grcnn_109	.913
223	grcnn_v2_text_noise	.912
224	r3m_resnet34_nocrop	.911
225	inception_v2-tf	.911
226	BiT-S-R50x3	.909
227	AlexNet_SIN_fov4	.909
228	cvt_cvt-13-384-in1k_4_LucyV4	.909
229	resnet50_robust_l2_eps1	.908
230	alexnet_l2_3_robust	.907
231	resnet-50-robust	.907
232	resnet50-moclr8deg	.906
233	BiT-S-R152x4	.905
234	alexnet_random_linf8_perturb	.905
235	nasnet_large-tf	.905
236	alexnet	.901
237	alexnet	.901
238	resnet50_random_l2_perturb	.901
239	alexnet	.901
240	alexnet	.900
241	alexnet-baseline	.900
242	alexnet	.900
243	resnet50_random_linf8_perturb	.900
244	alexnet_workflow_trigger	.900
245	alexnet	.900
246	alexnet	.900
247	alexnet	.900
248	resnet-50_v2-tf	.898
249	resnet50_tutorial	.898
250	resnet50-sup	.897
251	resnet_50_v1_spiking	.897
252	resnet_SIN_IN_FT_IN	.897
253	yudixie_resnet50_imagenet1kpret_0_240908	.897
254	resnet_50_v1	.897
255	resnet_50_v1_spiking_l4a	.896
256	resnet50-SIN	.896
257	resnet_50_v1_spiking_l3	.896
258	resnet_50_v1_spiking_l4	.896
259	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.895
260	vit_base_patch14_dinov2:lvd142m	.895
261	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.895
262	BiT-S-R101x1	.894
263	inception_v1	.893
264	antialias-resnet152	.893
265	densenet-201-keras	.892
266	my-model	.891
267	imagenet_l2_10_0	.890
268	alexnet_linf_8_robust	.889
269	efficientnet_b2_imagenet_full	.889
270	mobilenet_v2_1_3_224	.888
271	densenet-201	.886
272	resnet-18-LC_conv_init_m	.885
273	SWSL_resnet50	.884
274	efficientnet_b0	.884
275	densenet-169	.883
276	SWSL_resnext101_32x8d	.883
277	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.880
278	AdvProp_efficientnet-b4	.880
279	resnet50-vicreg	.879
280	resnet50-SIN_IN_IN	.878
281	ViT_L_32_imagenet1k	.877
282	resnet50	.877
283	resnet_50_v2	.876
284	resnet-152_v1-tf	.876
285	vgg-19-keras	.873
286	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
287	texture_shape_resnet50_trained_on_SIN	.871
288	imagenet_l2_3_0	.871
289	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.870
290	antialiased-r50	.868
291	alexnet_training_seed_03	.868
292	inception_v4-tf	.865
293	AdvProp_efficientnet-b7	.862
294	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.860
295	effnetv2m_custom384	.860
296	BiT-M-R152x4	.857
297	mobilevit_small	.855
298	shufflenet_v2_x1_0	.848
299	resnet50-VITO-8deg-cc	.846
300	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.846
301	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.846
302	densenet-121-keras	.845
303	densenet-121	.844
304	resnet50_robust_l2_eps3	.839
305	nasnet_mobile	.839
306	resnet-101_v1-tf	.838
307	mobilenet_v2_0_75_160	.838
308	r3m_resnet50	.838
309	resnet50_l2_3_robust	.835
310	artResNet18_1	.834
311	resnet50-barlow	.834
312	efficientnet_b0_imagenet_full	.828
313	resnet50_linf_8_robust	.828
314	resnet50_FractalDB	.828
315	resnet-101_v2-tf	.827
316	resnet50_imagenet_full	.826
317	vision_transformer_vit_large_patch16_224	.826
318	vonegrcnn_47e	.823
319	densenet-169-keras	.821
320	BiT-M-R50x1	.820
321	deit_small_imagenet_100_seed-0	.820
322	resnet50_linf_4_robust	.813
323	mobilenet_v2_0_5_192	.813
324	nasnet_mobile-tf	.812
325	konkle_alexnetgn_ipcl_ref01_primary_model	.805
326	pnasnet_large-tf	.800
327	r3m_resnet50_nocrop	.797
328	vit_base_patch16_clip_224:openai_ft_in1k	.797
329	mobilenet_v2_0_75_224	.797
330	AT_efficientnet-b2	.795
331	mobilenet_v2_1_0_192	.795
332	vonegrcnn_52e_full	.795
333	vit_large_patch14_clip_336:laion2b_ft_in1k	.795
334	AdvProp_efficientnet-b2	.795
335	convnext_small_imagenet_1_seed-0	.793
336	vonegrcnn_47e	.793
337	effnetb1_272x240	.787
338	BiT-M-R101x3	.786
339	resnet50_moco_v2	.782
340	vonegrcnn_52e_full	.781
341	antialiased-rnext101_32x8d	.780
342	resnet50_simclr	.774
343	mobilenet_v2_0.35_224	.771
344	mobilenet_v2_1_0_160	.765
345	r3m_resnet18	.754
346	cv_18_dagger_408_pretrained	.754
347	deit_large_imagenet_full_seed-0	.748
348	alexnet_training_seed_08	.748
349	custom_model_cv_18_dagger_408	.747
350	vit_large_patch14_clip_224:laion2b_ft_in1k	.742
351	omnivore_swinT	.736
352	FrankRobWobv0	.735
353	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.734
354	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.733
355	alexnet_training_seed_01_fov4	.732
356	r3m_resnet34	.718
357	convnext_small_imagenet_10_seed-0	.718
358	BiT-M-R152x2	.711
359	vit_relpos_base_patch16_clsgap_224:sw_in1k	.694
360	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.692
361	CLIP_ViT-B_32	.686
362	AlexNet_SIN_fov16	.684
363	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.681
364	vit_large_patch14_clip_224:openai_ft_in1k	.676
365	vit_base_patch16_dinov3:lvd1689m	.674
366	yolos_tiny	.672
367	inception_resnet_v2	.662
368	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.648
369	mobilenet_v2_0.75_224-tf	.643
370	deit_base_imagenet_full_seed-0	.628
371	resnet50	.607
372	CORnetZ_CIFAR10	.601
373	my-model	.557
374	alexnet_training_seed_05	.513
375	resnet50_pretrained_with_retinal_waves	.481
376	ViT-B/32	.370
377	resnet50_imagenet_1_seed-0	.322
378	alexnet_training_seed_06	.321
379	resnet-18-LC_untrained	.274
380	resnet50-meshes-lt-100-original-pretrained	.138
381	resnet50-meshes-lt-100-original-scratch	.052
382	mobilenet_v2_0.35_160
383	resnet18-supervised
384	fitvid_trained_on_physion
385	openclip
386	resnext101_32x32d_wsl
387	resnext101_32x48d_wsl
388	resnet50_ImageNet
389	regnet_y_400mf
390	tv_efficientnet-b1
391	resnet_101_v1
392	alexnet_ks_torevert
393	yudixie_resnet50_imagenet1kpret_0_240312
394	resnet_152_v1
395	resnext101_32x16d_wsl
396	densenet_201_pytorch
397	inception_v3_pytorch
398	pnasnet_large_pytorch
399	inception_v4
400	nasnet_large
401	deit_small_imagenet_1_seed-0
402	pnasnet_large
403	resnet_101_v2
404	vonegrcnn_62e_nobn
405	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.99.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_selective