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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	yudixie_resnet18_distance_rotation_0_240719	.999
18	ReAlnet10	.999
19	ReAlnet01	1.000
20	ReAlnet03	.999
21	ReAlnet07_cornet	.999
22	ReAlnet03_cornet	.999
23	ReAlnet10_cornet	.999
24	voneresnet-50-non_stochastic	.999
25	ReAlnet07	.999
26	ReAlnet04	.999
27	ReAlnet04_cornet	.999
28	ReAlnet02	.999
29	resnet50_ecoset_full	.999
30	ReAlnet01_cornet	.999
31	convnext_tiny_imagenet_full_seed-0	.999
32	ReAlnet01_ALL	.999
33	yudixie_resnet50_distance_reg_0_240908	.999
34	ReAlnet09_ALL	.999
35	ReAlnet02_cornet	.999
36	CORnet-S	.999
37	ReAlnet06	.998
38	ReAlnet02_ALL	.998
39	alexnet_reduced_aliasing_early_checkpoint	.998
40	ReAlnet06_cornet	.999
41	ReAlnet05	.998
42	ReAlnet05_cornet	.999
43	compact_resnet50_robust_IT	.998
44	efficientnet-b7	.998
45	compact_cornets_IT	.998
46	yudixie_resnet50_distance_translation_0_240908	.998
47	resnet-18-LC_1st_conv_m	.998
48	yudixie_resnet18_distance_reg_0_240719	.997
49	yudixie_resnet50_rotation_reg_0_240908	.997
50	resnet18_imagenet_full	.997
51	compact_cornets_V4	.997
52	resnet34_ecoset_full	.996
53	convnext_small_imagenet_100_seed-0	.996
54	alexnet	.996
55	resnet-18-LC_w_sh_100_iter_m	.996
56	vgg_19	.996
57	0.5x_resnet-18	.996
58	omnivore_swinB	.996
59	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.996
60	yudixie_resnet18_category_class_0_240719	.996
61	ReAlnet08_ALL	.995
62	resnet152_imagenet_full	.995
63	yudixie_resnet18_cat_obj_class_all_latents_0_240719	.996
64	yudixie_resnet50_translation_rotation_0_240908	.995
65	ReAlnet09_cornet	.995
66	ReAlnet09	.995
67	resnet-18_test_m	.995
68	resnet-50x2_untrained	.995
69	mobilenet_v2_1.0_224-tf	.995
70	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.994
71	yudixie_resnet50_distance_translation_rotation_0_240908	.995
72	AdvProp_efficientnet-b6	.994
73	Res2Net50_26w_4s	.994
74	resnet-18-LC_w_sh_1_iter_conv_init_m	.994
75	alexnet_training_seed_01	.993
76	focalnet_tiny_lrf_in1k	.993
77	ReAlnet03_ALL	.992
78	ReAlnet05_ALL	.992
79	yudixie_resnet50_translation_reg_0_240908	.992
80	ReAlnet10_ALL	.992
81	ReAlnet06_ALL	.991
82	resnet-18_untrained	.992
83	CORnet-Z	.991
84	ReAlnet07_ALL	.991
85	yudixie_resnet18_rotation_reg_0_240719	.991
86	yudixie_resnet50_distance_rotation_0_240908	.991
87	vonegrcnn_62e_nobn	.990
88	convnext_large:fb_in22k_ft_in1k	.990
89	AdvProp_efficientnet-b8	.989
90	resnet-50_untrained	.988
91	0.5x_resnet-18_LC_w_sh_1_iter	.988
92	cvt_cvt-21-384-in1k_4_LucyV4	.986
93	AdvProp_efficientnet-b0	.986
94	hmax	.986
95	mobilenet_v2_1.3_224-tf	.986
96	mobilenet_v2_0.75_192-tf	.986
97	mobilenet_v2_0.75_160-tf	.986
98	mobilenet_v2_1.0_192-tf	.986
99	mobilenet_v2_1.0_160-tf	.986
100	mobilenet_v2_0.5_224-tf	.986
101	mobilenet_v2_0.75_128	.986
102	mobilenet_v2_0.5_160	.986
103	mobilenet_v2_1.0_128-tf	.986
104	mobilenet_v2_0.5_128	.986
105	mobilenet_v2_0.75_96	.986
106	inception_v1-tf	.986
107	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.986
108	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.986
109	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.986
110	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.986
111	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.986
112	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.986
113	omnivore_swinS	.986
114	mobilenet_v1_0.25_128	.986
115	convnext_tiny_sup	.986
116	mobilenet_v2_1_0_128	.986
117	deit_small_imagenet_10_seed-0	.986
118	deit_small_imagenet_full_seed-0	.986
119	convnext_small_imagenet_full_seed-0	.986
120	compact_resnet50_robust_V4	.986
121	mobilenet_v2_1_0_224	.986
122	compact_vgg19_IT	.986
123	0.5x_resnet-18_LC_w_sh_100_iter	.986
124	resnet101	.985
125	resnet-18-LC_w_sh_10_iter_m	.984
126	resnet50-vicregl0p9	.984
127	mobilenet_v2_1.4_224-tf	.984
128	alexnet_random_l2_3_perturb	.984
129	barlow-twins-resnet50	.983
130	BiT-M-R50x3	.982
131	yudixie_resnet18_distance_translation_0_240719	.982
132	compact_resnet50_IT	.982
133	yudixie_resnet18_distance_translation_rotation_0_240719	.981
134	resnet152_ecoset_full	.981
135	texture_shape_alexnet_trained_on_SIN	.981
136	BiT-M-R101x1	.981
137	mobilenet_v2_1-4_224_pytorch	.980
138	compact_vgg19_V4	.979
139	resnet50_primary_visual_cortex	.979
140	mobilenet_v2_1_4_224	.978
141	resnet-18-LC_w_sh_1_iter_m	.977
142	yudixie_resnet18_object_class_0_240719	.975
143	resnet50-simclr-vissl	.975
144	resnet50-vitoimagevidnet8	.975
145	resnet-18-LC_m	.975
146	yudixie_resnet50_cat_obj_class_all_latents_0_240908	.974
147	vggface	.974
148	cvt_cvt-13-224-in1k_4_LucyV4	.971
149	alexnet_training_seed_09	.971
150	alexnet_early_checkpoint	.970
151	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	.970
152	resnet-18-LC_d_w_sh_1x1_conv_init_m	.968
153	vgg_16	.966
154	resnet50_ewconplus_wzc	.966
155	convnext_base:clip_laiona_augreg_ft_in1k_384	.966
156	yudixie_resnet50_random_0_240908	.965
157	alexnet_training_seed_07	.965
158	blt_vs	.963
159	resnet-152_v2-tf	.964
160	resnet101_ecoset_full	.964
161	cvt_cvt-21-224-in1k_4_LucyV4	.963
162	yudixie_resnet50_category_class_0_240908	.962
163	yudixie_resnet18_random_0_240719	.958
164	resnet50_imagenet_100_seed-0	.958
165	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.956
166	resnext101_32x8d_wsl	.954
167	alexnet_training_seed_04	.953
168	alexnet_robust_correct	.952
169	convnext_tiny:in12k_ft_in1k	.951
170	BiT-S-R50x1	.949
171	vgg-16-keras	.948
172	yudixie_resnet18_imagenet1kpret_0_240719	.946
173	xception	.946
174	inception_v3-tf	.945
175	resnet50_imagenet_10_seed-0	.945
176	resnet18_ecoset_full	.942
177	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.941
178	resnet101_imagenet_full	.941
179	resnet50-SIN_IN	.940
180	AlexNet_SIN	.939
181	convnext_large_imagenet_full_seed-0	.939
182	0.5x_resnet-18_LC	.939
183	mobilenet_v2_0_75_192	.939
184	inception_v3	.937
185	resnet-152_v2_pytorch	.937
186	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.936
187	CLIP-RN50	.935
188	resnet50_byol	.935
189	alexnet_training_seed_10	.933
190	resnet50-cifar	.933
191	mobilenet_v2_0.5_192-tf	.933
192	mobilenet_v2_0_5_224	.931
193	convnext_base_imagenet_full_seed-0	.931
194	resnet18_imagenet21kP	.931
195	voneresnet-50	.929
196	resnet-50_v1-tf	.928
197	BiT-S-R152x2	.927
198	convnext_xlarge:fb_in22k_ft_in1k	.927
199	briaai_rmbg_1_4	.927
200	CLIP_resnet50_float32	.925
201	CLIP_resnet50	.925
202	resnet50-vicregl0p75	.925
203	RN50	.925
204	VOneCORnet-S	.924
205	voneresnet-50-robust	.923
206	grcnn	.921
207	grcnn_robust_v1	.919
208	mobilenet_v2_1.0_96	.919
209	BiT-S-R101x3	.917
210	alexnet_training_seed_02	.915
211	yudixie_resnet50_object_class_0_240908	.914
212	grcnn_109	.913
213	grcnn_v2_text_noise	.912
214	r3m_resnet34_nocrop	.911
215	inception_v2-tf	.911
216	BiT-S-R50x3	.909
217	cvt_cvt-13-384-in1k_4_LucyV4	.909
218	resnet50_robust_l2_eps1	.908
219	alexnet_l2_3_robust	.907
220	resnet-50-robust	.907
221	resnet50-moclr8deg	.906
222	BiT-S-R152x4	.905
223	alexnet_random_linf8_perturb	.905
224	nasnet_large-tf	.905
225	alexnet	.901
226	resnet50_random_l2_perturb	.901
227	alexnet	.901
228	alexnet	.900
229	alexnet-baseline	.900
230	alexnet	.900
231	resnet50_random_linf8_perturb	.900
232	alexnet	.900
233	alexnet	.900
234	alexnet	.900
235	resnet-50_v2-tf	.898
236	resnet50_tutorial	.898
237	resnet50-sup	.897
238	resnet_50_v1_spiking	.897
239	resnet_SIN_IN_FT_IN	.897
240	yudixie_resnet50_imagenet1kpret_0_240908	.897
241	resnet_50_v1	.897
242	resnet_50_v1_spiking_l4a	.896
243	resnet50-SIN	.896
244	resnet_50_v1_spiking_l3	.896
245	resnet_50_v1_spiking_l4	.896
246	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.895
247	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.895
248	BiT-S-R101x1	.894
249	inception_v1	.893
250	antialias-resnet152	.893
251	densenet-201-keras	.892
252	my-model	.891
253	imagenet_l2_10_0	.890
254	alexnet_linf_8_robust	.889
255	efficientnet_b2_imagenet_full	.889
256	mobilenet_v2_1_3_224	.888
257	densenet-201	.886
258	resnet-18-LC_conv_init_m	.885
259	SWSL_resnet50	.884
260	efficientnet_b0	.884
261	densenet-169	.883
262	SWSL_resnext101_32x8d	.883
263	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.880
264	AdvProp_efficientnet-b4	.880
265	resnet50-vicreg	.879
266	resnet50-SIN_IN_IN	.878
267	ViT_L_32_imagenet1k	.877
268	resnet50	.877
269	resnet_50_v2	.876
270	resnet-152_v1-tf	.876
271	vgg-19-keras	.873
272	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.873
273	texture_shape_resnet50_trained_on_SIN	.871
274	imagenet_l2_3_0	.871
275	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.870
276	antialiased-r50	.868
277	alexnet_training_seed_03	.868
278	inception_v4-tf	.865
279	AdvProp_efficientnet-b7	.862
280	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.860
281	effnetv2m_custom384	.860
282	BiT-M-R152x4	.857
283	mobilevit_small	.855
284	shufflenet_v2_x1_0	.848
285	resnet50-VITO-8deg-cc	.846
286	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.846
287	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.846
288	densenet-121-keras	.845
289	densenet-121	.844
290	resnet50_robust_l2_eps3	.839
291	nasnet_mobile	.839
292	resnet-101_v1-tf	.838
293	mobilenet_v2_0_75_160	.838
294	r3m_resnet50	.838
295	resnet50_l2_3_robust	.835
296	artResNet18_1	.834
297	resnet50-barlow	.834
298	efficientnet_b0_imagenet_full	.828
299	resnet50_linf_8_robust	.828
300	resnet50_FractalDB	.828
301	resnet-101_v2-tf	.827
302	resnet50_imagenet_full	.826
303	vision_transformer_vit_large_patch16_224	.826
304	vonegrcnn_47e	.823
305	densenet-169-keras	.821
306	BiT-M-R50x1	.820
307	deit_small_imagenet_100_seed-0	.820
308	resnet50_linf_4_robust	.813
309	mobilenet_v2_0_5_192	.813
310	nasnet_mobile-tf	.812
311	konkle_alexnetgn_ipcl_ref01_primary_model	.805
312	pnasnet_large-tf	.800
313	r3m_resnet50_nocrop	.797
314	vit_base_patch16_clip_224:openai_ft_in1k	.797
315	mobilenet_v2_0_75_224	.797
316	AT_efficientnet-b2	.795
317	mobilenet_v2_1_0_192	.795
318	vonegrcnn_52e_full	.795
319	vit_large_patch14_clip_336:laion2b_ft_in1k	.795
320	AdvProp_efficientnet-b2	.795
321	convnext_small_imagenet_1_seed-0	.793
322	vonegrcnn_47e	.793
323	effnetb1_272x240	.787
324	BiT-M-R101x3	.786
325	resnet50_moco_v2	.782
326	vonegrcnn_52e_full	.781
327	antialiased-rnext101_32x8d	.780
328	resnet50_simclr	.774
329	mobilenet_v2_0.35_224	.771
330	mobilenet_v2_1_0_160	.765
331	r3m_resnet18	.754
332	cv_18_dagger_408_pretrained	.754
333	deit_large_imagenet_full_seed-0	.748
334	alexnet_training_seed_08	.748
335	custom_model_cv_18_dagger_408	.747
336	vit_large_patch14_clip_224:laion2b_ft_in1k	.742
337	omnivore_swinT	.736
338	FrankRobWobv0	.735
339	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.734
340	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.733
341	r3m_resnet34	.718
342	convnext_small_imagenet_10_seed-0	.718
343	BiT-M-R152x2	.711
344	vit_relpos_base_patch16_clsgap_224:sw_in1k	.694
345	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.692
346	CLIP_ViT-B_32	.686
347	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.681
348	vit_large_patch14_clip_224:openai_ft_in1k	.676
349	yolos_tiny	.672
350	inception_resnet_v2	.662
351	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.648
352	mobilenet_v2_0.75_224-tf	.643
353	deit_base_imagenet_full_seed-0	.628
354	resnet50	.607
355	CORnetZ_CIFAR10	.601
356	my-model	.557
357	alexnet_training_seed_05	.513
358	resnet50_pretrained_with_retinal_waves	.481
359	ViT-B/32	.370
360	resnet50_imagenet_1_seed-0	.322
361	alexnet_training_seed_06	.321
362	resnet-18-LC_untrained	.274
363	resnet50-meshes-lt-100-original-pretrained	.138
364	resnet50-meshes-lt-100-original-scratch	.052
365	mobilenet_v2_0.35_160
366	resnet18-supervised
367	fitvid_trained_on_physion
368	openclip
369	resnext101_32x32d_wsl
370	resnext101_32x48d_wsl
371	resnet50_ImageNet
372	regnet_y_400mf
373	tv_efficientnet-b1
374	resnet_101_v1
375	alexnet_ks_torevert
376	yudixie_resnet50_imagenet1kpret_0_240312
377	resnet_152_v1
378	resnext101_32x16d_wsl
379	densenet_201_pytorch
380	inception_v3_pytorch
381	pnasnet_large_pytorch
382	inception_v4
383	nasnet_large
384	deit_small_imagenet_1_seed-0
385	pnasnet_large
386	resnet_101_v2
387	vonegrcnn_62e_nobn
388	resnet_152_v2

Benchmark bibtex

None

Ceiling

0.99.

Note that scores are relative to this ceiling.

Data: Marques2020_Ringach2002

Metric: or_selective