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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	briaai_rmbg_1_4	.927
208	alexnet_fov4	.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	alexnet	.901
237	resnet50_random_l2_perturb	.901
238	alexnet	.901
239	alexnet	.900
240	alexnet-baseline	.900
241	alexnet	.900
242	resnet50_random_linf8_perturb	.900
243	alexnet	.900
244	alexnet	.900
245	alexnet	.900
246	resnet-50_v2-tf	.898
247	resnet50_tutorial	.898
248	resnet50-sup	.897
249	resnet_50_v1_spiking	.897
250	resnet_SIN_IN_FT_IN	.897
251	yudixie_resnet50_imagenet1kpret_0_240908	.897
252	resnet_50_v1	.897
253	resnet_50_v1_spiking_l4a	.896
254	resnet50-SIN	.896
255	resnet_50_v1_spiking_l3	.896
256	resnet_50_v1_spiking_l4	.896
257	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.895
258	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.895
259	BiT-S-R101x1	.894
260	inception_v1	.893
261	antialias-resnet152	.893
262	densenet-201-keras	.892
263	my-model	.891
264	imagenet_l2_10_0	.890
265	alexnet_linf_8_robust	.889
266	efficientnet_b2_imagenet_full	.889
267	mobilenet_v2_1_3_224	.888
268	densenet-201	.886
269	resnet-18-LC_conv_init_m	.885
270	SWSL_resnet50	.884
271	efficientnet_b0	.884
272	densenet-169	.883
273	SWSL_resnext101_32x8d	.883
274	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.880
275	AdvProp_efficientnet-b4	.880
276	resnet50-vicreg	.879
277	resnet50-SIN_IN_IN	.878
278	ViT_L_32_imagenet1k	.877
279	resnet50	.877
280	resnet_50_v2	.876
281	resnet-152_v1-tf	.876
282	vgg-19-keras	.873
283	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
284	texture_shape_resnet50_trained_on_SIN	.871
285	imagenet_l2_3_0	.871
286	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.870
287	antialiased-r50	.868
288	alexnet_training_seed_03	.868
289	inception_v4-tf	.865
290	AdvProp_efficientnet-b7	.862
291	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.860
292	effnetv2m_custom384	.860
293	BiT-M-R152x4	.857
294	mobilevit_small	.855
295	shufflenet_v2_x1_0	.848
296	resnet50-VITO-8deg-cc	.846
297	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.846
298	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.846
299	densenet-121-keras	.845
300	densenet-121	.844
301	resnet50_robust_l2_eps3	.839
302	nasnet_mobile	.839
303	resnet-101_v1-tf	.838
304	mobilenet_v2_0_75_160	.838
305	r3m_resnet50	.838
306	resnet50_l2_3_robust	.835
307	artResNet18_1	.834
308	resnet50-barlow	.834
309	efficientnet_b0_imagenet_full	.828
310	resnet50_linf_8_robust	.828
311	resnet50_FractalDB	.828
312	resnet-101_v2-tf	.827
313	resnet50_imagenet_full	.826
314	vision_transformer_vit_large_patch16_224	.826
315	vonegrcnn_47e	.823
316	densenet-169-keras	.821
317	BiT-M-R50x1	.820
318	deit_small_imagenet_100_seed-0	.820
319	resnet50_linf_4_robust	.813
320	mobilenet_v2_0_5_192	.813
321	nasnet_mobile-tf	.812
322	konkle_alexnetgn_ipcl_ref01_primary_model	.805
323	pnasnet_large-tf	.800
324	r3m_resnet50_nocrop	.797
325	vit_base_patch16_clip_224:openai_ft_in1k	.797
326	mobilenet_v2_0_75_224	.797
327	AT_efficientnet-b2	.795
328	mobilenet_v2_1_0_192	.795
329	vonegrcnn_52e_full	.795
330	vit_large_patch14_clip_336:laion2b_ft_in1k	.795
331	AdvProp_efficientnet-b2	.795
332	convnext_small_imagenet_1_seed-0	.793
333	vonegrcnn_47e	.793
334	effnetb1_272x240	.787
335	BiT-M-R101x3	.786
336	resnet50_moco_v2	.782
337	vonegrcnn_52e_full	.781
338	antialiased-rnext101_32x8d	.780
339	resnet50_simclr	.774
340	mobilenet_v2_0.35_224	.771
341	mobilenet_v2_1_0_160	.765
342	r3m_resnet18	.754
343	cv_18_dagger_408_pretrained	.754
344	deit_large_imagenet_full_seed-0	.748
345	alexnet_training_seed_08	.748
346	custom_model_cv_18_dagger_408	.747
347	vit_large_patch14_clip_224:laion2b_ft_in1k	.742
348	omnivore_swinT	.736
349	FrankRobWobv0	.735
350	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.734
351	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.733
352	alexnet_training_seed_01_fov4	.732
353	r3m_resnet34	.718
354	convnext_small_imagenet_10_seed-0	.718
355	BiT-M-R152x2	.711
356	vit_relpos_base_patch16_clsgap_224:sw_in1k	.694
357	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.692
358	CLIP_ViT-B_32	.686
359	AlexNet_SIN_fov16	.684
360	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.681
361	vit_large_patch14_clip_224:openai_ft_in1k	.676
362	yolos_tiny	.672
363	inception_resnet_v2	.662
364	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.648
365	mobilenet_v2_0.75_224-tf	.643
366	deit_base_imagenet_full_seed-0	.628
367	resnet50	.607
368	CORnetZ_CIFAR10	.601
369	my-model	.557
370	alexnet_training_seed_05	.513
371	resnet50_pretrained_with_retinal_waves	.481
372	ViT-B/32	.370
373	resnet50_imagenet_1_seed-0	.322
374	alexnet_training_seed_06	.321
375	resnet-18-LC_untrained	.274
376	resnet50-meshes-lt-100-original-pretrained	.138
377	resnet50-meshes-lt-100-original-scratch	.052
378	mobilenet_v2_0.35_160
379	resnet18-supervised
380	fitvid_trained_on_physion
381	openclip
382	resnext101_32x32d_wsl
383	resnext101_32x48d_wsl
384	resnet50_ImageNet
385	regnet_y_400mf
386	tv_efficientnet-b1
387	resnet_101_v1
388	alexnet_ks_torevert
389	yudixie_resnet50_imagenet1kpret_0_240312
390	resnet_152_v1
391	resnext101_32x16d_wsl
392	densenet_201_pytorch
393	inception_v3_pytorch
394	pnasnet_large_pytorch
395	inception_v4
396	nasnet_large
397	deit_small_imagenet_1_seed-0
398	pnasnet_large
399	resnet_101_v2
400	vonegrcnn_62e_nobn
401	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.99.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_selective