Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("ImageNet-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	fixres_resnext101_32x48d_wsl	.863
2	resnext101_32x32d_wsl	.854
3	convnext_xlarge:fb_in22k_ft_in1k	X
4	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	X
5	convnext_large:fb_in22k_ft_in1k	X
6	resnext101_32x16d_wsl	.851
7	vit_large_patch14_clip_224:openai_ft_in1k	X
8	vit_large_patch14_clip_224:laion2b_ft_in1k	X
9	vit_large_patch14_clip_224:openai_ft_in12k_in1k	X
10	resnext101_32x8d_wsl	.842
11	convnext_base:clip_laiona_augreg_ft_in1k_384	X
12	cvt_cvt-w24-384-in22k_finetuned-in1k_4	X
13	pnasnet_large-tf	.829
14	vit_base_patch16_clip_224:openai_ft_in12k_in1k	X
15	vit_base_patch16_clip_224:openai_ft_in1k	X
16	nasnet_large-tf	.827
17	SWSL_resnext101_32x8d	X
18	resnext101_32x48d_wsl	.822
19	swin_small_patch4_window7_224:ms_in22k_ft_in1k	X
20	convnext_tiny:in12k_ft_in1k	X
21	inception_resnet_v2	.804
22	inception_v4-tf	.802
23	convnext_large_imagenet_full_seed-0	X
24	convnext_base_imagenet_full_seed-0	X
25	deit_large_imagenet_full_seed-0	X
26	convnext_small_imagenet_full_seed-0	X
27	nasnet_large	X
28	vit_relpos_base_patch16_clsgap_224:sw_in1k	X
29	xception-keras	.790
30	focalnet_tiny_lrf_in1k	X
31	inception_v3-tf	.780
32	convnext_tiny_sup	X
33	resnet-152_v2-tf	.778
34	deit_base_imagenet_full_seed-0	X
35	convnext_tiny_imagenet_full_seed-0	X
36	effnetb1_272x240	X
37	resnet_101_v2	X
38	resnet-152_v2_pytorch	X
39	resnet-101_v2-tf	.774
40	cvt_cvt-13-384-in1k_4_LucyV4	X
41	resnet_152_v2	X
42	densenet-201-keras	.772
43	resnet-152_v1-tf	.768
44	resnet50-SIN_IN_IN	.767
45	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	X
46	cvt_cvt-21-224-in1k_4_LucyV4	X
47	cvt_cvt-13-224-in1k_4_LucyV4	X
48	resnet-101_v1-tf	.764
49	cvt_cvt-21-384-in1k_4_LucyV4	X
50	inception_v4	X
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	X
52	densenet-169-keras	.759
53	antialias-resnet152	X
54	resnet_50_v2	X
55	antialiased-rnext101_32x8d	X
56	resnet-50_v2-tf	.756
57	resnet-50-pytorch	.752
58	resnet-50_v1-tf	.752
59	AdvProp_efficientnet-b7	X
60	mobilenet_v2_1.4_224-tf	.750
61	deit_small_imagenet_full_seed-0	X
62	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	X
63	resnet_152_v1	X
64	Res2Net50_26w_4s	X
65	resnet50-SIN_IN	.746
66	grcnn_109	X
67	densenet-121-keras	.745
68	resnet101	X
69	mobilenet_v2_1.3_224-tf	.744
70	ViT_L_32_imagenet1k	X
71	AdvProp_efficientnet-b6	X
72	nasnet_mobile-tf	.740
73	cv_18_dagger_408_pretrained	X
74	AdvProp_efficientnet-b2	X
75	AT_efficientnet-b2	X
76	inception_v2-tf	.739
77	resnet_101_v1	X
78	inception_v3	X
79	BiT-S-R152x4	X
80	resnet-34	.733
81	efficientnet_b0	X
82	antialiased-r50	X
83	grcnn	X
84	inception_v3_pytorch	X
85	resnet_SIN_IN_FT_IN	X
86	BiT-S-R101x3	X
87	resnet50	X
88	convnext_femto_ols:d1_in1k	X
89	BiT-S-R152x2	X
90	efficientnet-b7	X
91	AdvProp_efficientnet-b0	X
92	BiT-S-R50x3	X
93	densenet_201_pytorch	X
94	densenet-201	X
95	resnet50-sup	X
96	BiT-S-R101x1	X
97	AT_efficientnet-b4	X
98	AdvProp_efficientnet-b4	X
99	resnet50_tutorial	X
100	resnet_50_v1	X
101	mobilenet_v2_1.0_224-tf	.718
102	densenet-169	X
103	vgg-16-keras	.715
104	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	X
105	vgg-19-keras	.711
106	mobilenet_v1_1.0_224	.709
107	BiT-S-R50x1	X
108	mobilenet_v2_1.0_192-tf	.707
109	nasnet_mobile	X
110	vonegrcnn_52e_full	X
111	vonegrcnn_52e_full	X
112	vonegrcnn_62e_nobn	X
113	vonegrcnn_62e_nobn	X
114	voneresnet-50-non_stochastic	.702
115	mobilenet_v1_1.0_192	.700
116	vonegrcnn_47e	X
117	inception_v1-tf	.698
118	mobilenet_v2_0.75_224-tf	.698
119	resnet-18	.698
120	densenet-121	X
121	efficientnet_b2_imagenet_full	X
122	CORnet-S	X
123	mobilenet_v2_1.0_160-tf	.688
124	mobilenet_v2_0.75_192-tf	.687
125	regnet_y_400mf	X
126	mobilenet_v1_0.75_224	.684
127	efficientnet_b1_imagenet_full	X
128	efficientnet_b0_imagenet_full	X
129	mobilenet_v1_1.0_160	.680
130	vgg_19	X
131	mobilenet_v1_0.75_192	.672
132	resnet50_robust_l2_eps1	X
133	xception	X
134	vgg_16	X
135	mobilenet_v2_0.75_160-tf	.664
136	blt_vs	X
137	inception_v1	X
138	mobilenet_v2_0.5_224-tf	.654
139	voneresnet-50-robust	X
140	mobilenet_v2_1.0_128-tf	.653
141	mobilenet_v1_0.75_160	.653
142	mobilenet_v1_1.0_128	.652
143	mobilenet_v2_1_0_192	X
144	ReAlnet06	X
145	ReAlnet06_cornet	X
146	ReAlnet04_cornet	X
147	ReAlnet04	X
148	resnet50-moclr8deg	X
149	ReAlnet05_cornet	X
150	ReAlnet05	X
151	ReAlnet01_cornet	X
152	ReAlnet_incorrect	X
153	ReAlnet01	X
154	ReAlnet07_cornet	X
155	ReAlnet07	X
156	ReAlnet03_cornet	X
157	ReAlnet03	X
158	ReAlnet09_cornet	X
159	ReAlnet09	X
160	ReAlnet08	X
161	ReAlnet08_cornet	X
162	shufflenet_v2_x1_0	X
163	ReAlnet02	X
164	ReAlnet02_cornet	X
165	ReAlnet10	X
166	ReAlnet10_cornet	X
167	custom_model_cv_18_dagger_408	X
168	pnasnet_large_pytorch	X
169	mobilenet_v2_0.5_192-tf	.639
170	mobilenet_v1_0.5_224	.633
171	mobilenet_v2_0.75_128	.632
172	AdvProp_efficientnet-b8	X
173	mobilenet_v2_0_75_192	X
174	resnet50_moco_v2	X
175	mobilenet_v1_0.75_128	.621
176	mobilenet_v1_0.5_192	.617
177	resnet50-VITO-8deg-cc	X
178	mobilenet_v2_0_5_224	X
179	resnet50-vitoimagevidnet8	X
180	mobilenet_v2_0.5_160	.610
181	mobilenet_v2_0.35_224	.603
182	mobilenet_v2_1.0_96	.603
183	resnet50-SIN	.602
184	mobilenet_v2_0_5_192	X
185	mobilenet_v1_0.5_160	.591
186	mobilenet_v2_0.75_96	.588
187	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	X
188	mobilenet_v2_0.35_192	.582
189	alexnet	.577
190	mobilenet_v2_0.5_128	.577
191	squeezenet1_0	.575
192	squeezenet1_1	.575
193	resnet50_l2_3_robust	X
194	0.5x_resnet-18_LC_w_sh_1_iter	X
195	mobilenet_v1_0.5_128	.563
196	mobilenet_v2_0.35_160	.557
197	alexnet_random_linf8_perturb	X
198	mobilenet_v2_1-4_224_pytorch	X
199	alexnet	X
200	convnext_small_imagenet_100_seed-0	X
201	imagenet_l2_3_0	X
202	resnet-50-robust	X
203	resnet50_robust_l2_eps3	X
204	CLIP-RN50	X
205	alexnet	X
206	alexnet-baseline	X
207	alexnet	X
208	alexnet	X
209	alexnet_ks_torevert	X
210	mobilenet_v2_0.5_96	.512
211	mobilenet_v2_0.35_128	.508
212	resnet50_simclr	X
213	alexnet_early_checkpoint	X
214	mobilenet_v1_0.25_224	.498
215	mobilenet_v1_0.25_192	.477
216	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	X
217	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	X
218	CORnet-Z	.470
219	resnet50_linf_8_robust	X
220	mobilenet_v1_0.25_160	.455
221	mobilenet_v2_0.35_96	.455
222	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	X
223	resnet50_imagenet_100_seed-0	X
224	mobilenet_v1_0.25_128	.415
225	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	X
226	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	X
227	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	X
228	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	X
229	alexnet_l2_3_robust	X
230	imagenet_l2_10_0	X
231	alexnet_robust_correct	X
232	AlexNet_SIN	X
233	deit_small_imagenet_100_seed-0	X
234	alexnet_linf_8_robust	X
235	bagnet9	.260
236	resnet50_imagenet_10_seed-0	X
237	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	X
238	convnext_small_imagenet_10_seed-0	X
239	resnet50_imagenet_1_seed-0	X
240	deit_small_imagenet_10_seed-0	X
241	resnet-50_untrained	X
242	alexnet_training_seed_04	X
243	resnet50-vicregl0p75	X
244	deit_small_imagenet_1_seed-0	X
245	resnet50-meshes-lt-100-original-pretrained	X
246	resnet50-meshes-lt-100-original-scratch	X
247	alexnet_training_seed_01	X
248	barlow-twins-resnet50	.001
249	resnet50-vicregl0p9	X
250	artResNet18_1	X
251	convnext_small_imagenet_1_seed-0	X
252	resnet50-barlow	X
253	yudixie_resnet18_distance_rotation_0_240719	X
254	yudixie_resnet50_distance_translation_0_240908	X
255	yudixie_resnet18_distance_translation_0_240719	X
256	0.5x_resnet-18_LC	X
257	yudixie_resnet18_translation_rotation_0_240719	X
258	yudixie_resnet18_category_class_0_240719	X
259	yudixie_resnet18_random_0_240719	X
260	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
261	yudixie_resnet18_distance_translation_rotation_0_240719	X
262	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	X
263	yudixie_resnet18_object_class_0_240719	X
264	yudixie_resnet18_distance_reg_0_240719	X
265	pnasnet_large	X
266	resnet-18_test_m	X
267	yudixie_resnet50_translation_rotation_0_240908	X
268	omnivore_swinB	X
269	resnet50-vicreg	X
270	mobilenet_v2_1_0_224	X
271	mobilenet_v2_1_3_224	X
272	voneresnet-50	X
273	mobilenet_v2_1_4_224	X
274	vonegrcnn_47e	X
275	yudixie_resnet50_distance_translation_rotation_0_240908	X
276	yudixie_resnet50_imagenet1kpret_0_240908	X
277	yudixie_resnet50_random_0_240908	X
278	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
279	yudixie_resnet50_object_class_0_240908	X
280	alexnet_training_seed_03	X
281	omnivore_swinS	X
282	openclip	X
283	alexnet_training_seed_09	X
284	resnet50	X
285	omnivore_swinT	X
286	my-model	X
287	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
288	CORnetZ_CIFAR10	X
289	resnet-18-LC_d_w_sh_1x1_conv_init_m	X
290	0.5x_resnet-18_LC_w_sh_100_iter	X
291	0.5x_resnet-18_LC_w_sh_10_iter	X
292	0.5x_resnet-18	X
293	resnet-18-LC_w_sh_100_iter_conv_init_m	X
294	RN50	X
295	resnet-18-LC_w_sh_1_iter_m	X
296	r3m_resnet34_nocrop	X
297	resnet-18-LC_w_sh_100_iter_m	X
298	r3m_resnet50_nocrop	X
299	resnet-18-LC_w_sh_10_iter_m	X
300	resnet-18-LC_1st_conv_m	X
301	r3m_resnet34	X
302	yudixie_resnet50_imagenet1kpret_0_240312	X
303	alexnet_training_seed_05	X
304	r3m_resnet50	X
305	resnet-18-LC_conv_init_m	X
306	r3m_resnet18	X
307	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	X
308	alexnet_training_seed_06	X
309	mobilenet_v2_1_0_128	X
310	mobilenet_v2_0_75_224	X
311	resnet18-autoencoder	X
312	resnet18-depth_prediction	X
313	vggface	X
314	resnet18-contrastive_predictive	X
315	resnet-18-LC_m	X
316	resnet18-relative_position	X
317	alexnet_training_seed_02	X
318	mobilenet_v2_1_0_160	X
319	vit_large_patch14_clip_336:laion2b_ft_in1k	X
320	convnext_xxlarge:clip_laion2b_soup_ft_in1k	X
321	resnet18-deepcluster	X
322	yudixie_resnet50_category_class_0_240908	X
323	vit_large_patch14_clip_336:openai_ft_in12k_in1k	X
324	resnet18-colorization	X
325	alexnet_training_seed_08	X
326	resnet-18-LC_w_sh_1_iter_conv_init_m	X
327	resnet18-simclr	X
328	ViT-B/32	X
329	alexnet_training_seed_10	X
330	resnet18-contrastive_multiview	X
331	resnet18_imagenet21kP	X
332	resnet18-instance_recognition	X
333	briaai_rmbg_1_4	X
334	ConvLSTM	X
335	VOneCORnet-S	X
336	FrankRobWobv0	X
337	resnet18-local_aggregation	X
338	PredRNN	X
339	SimVP	X
340	yolos_tiny	X
341	TAU	X
342	yudixie_resnet50_distance_reg_0_240908	X
343	MIM	X
344	hmax	X
345	resnet18-supervised	X
346	mobilenet_v2_0_75_160	X
347	BiT-M-R101x1	X
348	resnet152_imagenet_full	X
349	BiT-M-R152x2	X
350	resnet-18-LC_w_sh_10_iter_conv_init_m	X
351	ViT_L_32	X
352	mobilevit_small	X
353	yudixie_resnet18_translation_reg_0_240719	X
354	alexnet_training_seed_07	X
355	yudixie_resnet18_rotation_reg_0_240719	X
356	fitvid_trained_on_physion	X
357	yudixie_resnet18_imagenet1kpret_0_240719	X
358	resnet-18_untrained	X
359	yudixie_resnet50_rotation_reg_0_240908	X
360	yudixie_resnet50_distance_rotation_0_240908	X
361	resnet18_imagenet_full	X
362	resnet34_imagenet_full	X
363	resnet50_imagenet_full	X
364	resnet101_imagenet_full	X
365	RN50	X
366	resnet18_ecoset_full	X
367	resnet34_ecoset_full	X
368	resnet50_ecoset_full	X
369	yudixie_resnet50_translation_reg_0_240908	X
370	resnet101_ecoset_full	X
371	resnet152_ecoset_full	X
372	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	X

Benchmark bibtex

@INPROCEEDINGS{5206848,  
                                                author={J. {Deng} and W. {Dong} and R. {Socher} and L. {Li} and  {Kai Li} and  {Li Fei-Fei}},  
                                                booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition},   
                                                title={ImageNet: A large-scale hierarchical image database},   
                                                year={2009},  
                                                volume={},  
                                                number={},  
                                                pages={248-255},
                                            }

Ceiling

1.00.

Note that scores are relative to this ceiling.

Data: ImageNet

Metric: top1