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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021colour-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_224:laion2b_ft_in1k	.998
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.998
3	vit_large_patch14_clip_336:laion2b_ft_in1k	.998
4	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.998
5	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.998
6	deit3_base_patch16_224_fb_in22k_ft_in1k	.998
7	eva02_large_in22k	.998
8	vit_large_clip336_openai	.998
9	dinov2_vitg14_reg_lc	.998
10	resnext101_32x32d_wsl	.997
11	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.997
12	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.997
13	convnext_base:clip_laiona_augreg_ft_in1k_384	.997
14	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.997
15	convnext_large_clip_laion2b_finelayers	.997
16	convnext_large_in22k_supervised	.997
17	dinov2_vitl14_reg_lc	.997
18	dinov2_vitg14_lc	.997
19	dinov2_vitl14_lc_res336	.997
20	convnext_xxlarge_clip	.997
21	vit_huge_clip336_laion	.997
22	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.995
23	vit_large_patch14_clip_224:openai_ft_in1k	.995
24	convnext_xlarge:fb_in22k_ft_in1k	.995
25	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.995
26	convnext_large_imagenet_full_seed-0	.995
27	convnext_base_fb_in22k_ft_in1k	.995
28	deit3_base_patch16_224_fb_in1k	.995
29	dinov2_vitl14_lc	.995
30	fixres_resnext101_32x48d_wsl	.994
31	resnext101_32x48d_wsl	.994
32	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.994
33	vit_base_patch16_clip_224:openai_ft_in1k	.994
34	vit_relpos_base_patch16_clsgap_224:sw_in1k	.994
35	convnext_large:fb_in22k_ft_in1k	.994
36	convnext_base_fb_in1k	.994
37	AdvProp_efficientnet-b7	.992
38	deit_large_imagenet_full_seed-0	.992
39	deit_base_imagenet_full_seed-0	.992
40	nasnet_large-tf	.991
41	effnetv2m_custom384	.991
42	resnext101_32x16d_wsl	.991
43	focalnet_tiny_lrf_in1k	.991
44	convnext_base_imagenet_full_seed-0	.991
45	convnext_tiny:in12k_ft_in1k	.991
46	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.991
47	convnext_tiny_imagenet_full_seed-0	.991
48	nasnet_large	.991
49	pnasnet_large-tf	.989
50	resnext101_32x8d_wsl	.989
51	AdvProp_efficientnet-b6	.989
52	convnext_tiny_sup	.989
53	antialiased-rnext101_32x8d	.989
54	convnext_small_imagenet_full_seed-0	.989
55	AT_efficientnet-b2	.988
56	AdvProp_efficientnet-b2	.988
57	cvt_cvt-13-224-in1k_4_LucyV4	.988
58	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.988
59	inception_v3-tf	.986
60	inception_v4-tf	.986
61	cvt_cvt-21-224-in1k_4_LucyV4	.986
62	pnasnet_large_halfPrecision	.986
63	pnasnet_large	.986
64	BiT-S-R152x4	.984
65	AdvProp_efficientnet-b0	.984
66	antialias-resnet152	.984
67	eBarlow_lmda_02_1000ep	.984
68	BiT-S-R50x3	.983
69	BiT-S-R101x3	.983
70	cv_18_dagger_408_pretrained	.983
71	cvt_cvt-13-384-in1k_4_LucyV4	.983
72	resnet-152_v2_pytorch	.983
73	cvt_cvt-21-384-in1k_4_LucyV4	.983
74	AdvProp_efficientnet-b4	.981
75	resnet-152_v2-tf	.981
76	AT_efficientnet-b4	.981
77	ViT_B_16_imagenet1k	.981
78	antialiased-r50	.981
79	resnet50_st_wzc_2	.981
80	deit_small_imagenet_full_seed-0	.981
81	inception_v4	.981
82	resnet_101_v2	.981
83	resnet-101_v2-tf	.980
84	resnet_152_v1	.980
85	resnet50_ewconplus_wzc	.980
86	resnet50-vitoimagevidnet8	.980
87	resnet50_st_wzc_seed7_2_323	.980
88	resnet_SIN_IN_FT_IN	.978
89	BiT-S-R101x1	.978
90	resnet50-SIN_IN_IN	.978
91	densenet-201-keras	.978
92	inception_v2-tf	.978
93	Res2Net50_26w_4s	.978
94	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.978
95	densenet-201	.978
96	densenet_201_pytorch	.978
97	resnet152_imagenet_full	.978
98	grcnn	.978
99	efficientnet-b7	.977
100	resnet50-SIN_IN	.977
101	resnet-50_v1-tf	.977
102	effnetb1_272x240	.977
103	resnet50-moclr8deg	.977
104	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.977
105	resnet_101_v1	.977
106	resnet50_st_wzc_seed6_2_323	.977
107	BiT-S-R152x2	.975
108	BiT-S-R50x1	.975
109	efficientnet_b0	.975
110	resnet-101_v1-tf	.975
111	densenet-169-keras	.975
112	xception-keras	.975
113	resnet50_moco_v2	.975
114	resnet50_st_wzc_seed3_2_323	.975
115	resnet101_imagenet_full	.975
116	resnet_152_v2	.975
117	densenet-169	.975
118	ViT_L_32_imagenet1k	.973
119	resnet-152_v1-tf	.973
120	inception_v3	.973
121	voneresnet-50-non_stochastic	.972
122	resnet-50_v2-tf	.972
123	CORnet-S	.972
124	resnet50_imagenet_full	.972
125	tv_efficientnet-b1	.972
126	grcnn_109	.972
127	convnext_femto_ols:d1_in1k	.972
128	resnet50_ewconplus_wzc_seed5_2__323	.972
129	vonegrcnn_47e	.970
130	densenet-121-keras	.969
131	vonegrcnn_62e_nobn	.969
132	resnet-50-pytorch	.969
133	mobilenet_v2_1.4_224-tf	.969
134	mobilenet_v2_1.3_224-tf	.969
135	vonegrcnn_47e	.969
136	vgg-16-keras	.969
137	vgg-19-keras	.969
138	resnet50-sup	.969
139	resnet50_tutorial	.969
140	resnet_50_v1	.969
141	densenet-121	.969
142	mobilenet_v2_1.0_192-tf	.967
143	efficientnet_b2_imagenet_full	.967
144	resnet50_ewconplus_wzc_seed2_2__323	.967
145	mobilenet_v2_1.0_224-tf	.966
146	voneresnet-50	.966
147	vgg_19	.966
148	resnet50-VITO-8deg-cc	.966
149	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.966
150	resnet50_hvs_wzc	.966
151	resnet_50_v2	.966
152	mobilenet_v2_1.0_160-tf	.964
153	resnet50	.964
154	mobilenet_v2_1_3_224	.964
155	efficientnet_b1_imagenet_full	.963
156	nasnet_mobile	.963
157	vonegrcnn_52e_full	.963
158	resnet-34	.961
159	regnet_y_400mf	.961
160	vgg_16	.961
161	vonegrcnn_62e_nobn	.961
162	vonegrcnn_52e_full	.959
163	ViT_B_32_imagenet1k	.959
164	resnet50_robust_l2_eps1	.959
165	xception	.959
166	mobilenet_v1_1.0_224	.958
167	custom_model_cv_18_dagger_408	.956
168	resnet_50_v1_spiking	.956
169	inception_v1-tf	.955
170	resnet-18	.955
171	resnet34_imagenet_full	.955
172	resnet18_imagenet_full	.955
173	mobilenet_v1_0.75_224	.952
174	mobilenet_v1_1.0_192	.952
175	mobilenet_v1_1.0_160	.952
176	blt_vs	.952
177	ReAlnet06	.948
178	resnet50_eMMCR_Vanilla	.948
179	efficientnet_b0_imagenet_full	.947
180	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.945
181	AdvProp_efficientnet-b8	.944
182	mobilenet_v1_0.75_192	.944
183	ReAlnet09	.944
184	ReAlnet03	.944
185	mobilenet_v2_0.75_192-tf	.941
186	mobilenet_v2_1.0_128-tf	.939
187	mobilenet_v2_0.75_224-tf	.939
188	mobilenet_v1_0.5_224	.939
189	voneresnet-50-robust	.939
190	mobilenet_v2_1_0_192	.939
191	ReAlnet02	.939
192	convnext_base_fb_in1k_gram_aligned_dinov3	.939
193	mobilenet_v1_0.75_160	.938
194	ReAlnet07	.938
195	shufflenet_v2_x1_0	.936
196	mobilenet_v2_1_0_224	.936
197	mobilenet_v2_0.75_160-tf	.934
198	ReAlnet01	.934
199	nasnet_mobile-tf	.931
200	ReAlnet04	.931
201	mobilenet_v2_1_0_160	.931
202	inception_v1	.930
203	ReAlnet05	.930
204	ReAlnet08	.927
205	ReAlnet10	.925
206	mobilenet_v2_0.5_224-tf	.923
207	mobilenet_v1_1.0_128	.923
208	mobilenet_v2_0_75_224	.923
209	mobilenet_v2_0_75_192	.923
210	resnet50-SIN	.922
211	mobilenet_v2_0_75_160	.920
212	mobilenet_v2_0.75_128	.919
213	resnet_50_v1_spiking_l4a	.917
214	mobilenet_v2_0.5_192-tf	.912
215	0.5x_resnet-18_LC_w_sh_1_iter	.912
216	mobilenet_v1_0.75_128	.911
217	mobilenet_v2_1_0_128	.911
218	mobilenet_v1_0.5_192	.909
219	CLIP-RN50	.908
220	mobilenet_v2_1.0_96	.906
221	mobilenet_v2_0.5_160	.905
222	mobilenet_v1_0.5_160	.903
223	mobilenet_v2_0_5_224	.903
224	mobilenet_v2_0.75_96	.902
225	resnet-50-robust	.902
226	imagenet_l2_3_0	.902
227	resnet50_robust_l2_eps3	.902
228	mobilenet_v2_1-4_224_pytorch	.902
229	mobilenet_v2_1_4_224	.902
230	mobilenet_v2_0.35_192	.900
231	mobilenet_v2_0_5_192	.900
232	resnet50_l2_3_robust	.889
233	mobilenet_v2_0.35_224	.884
234	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.884
235	mobilenet_v2_0.5_128	.884
236	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.883
237	mobilenet_v2_0.35_160	.872
238	squeezenet1_1	.870
239	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
240	alexnet	.864
241	alexnet_random_linf8_perturb	.864
242	alexnet_ks_torevert	.864
243	alexnet_fov4	.864
244	alexnet_fov12	.864
245	alexnet_fov16	.864
246	mobilenet_v1_0.5_128	.863
247	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.859
248	convnext_small_imagenet_100_seed-0	.859
249	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.850
250	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.844
251	squeezenet1_0	.842
252	mobilenet_v1_0.25_224	.833
253	alexnet_early_checkpoint	.830
254	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.830
255	mobilenet_v2_0.5_96	.823
256	mobilenet_v1_0.25_192	.819
257	mobilenet_v2_0.35_128	.819
258	resnet50_linf_8_robust	.812
259	mobilenet_v1_0.25_160	.808
260	mobilenet_v2_0.35_96	.797
261	AlexNet_SIN	.797
262	AlexNet_SIN_fov4	.797
263	AlexNet_SIN_fov12	.797
264	AlexNet_SIN_fov16	.797
265	imagenet_l2_10_0	.781
266	mobilenet_v1_0.25_128	.777
267	alexnet_l2_3_robust	.767
268	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.767
269	alexnet_robust_correct	.756
270	deit_small_imagenet_100_seed-0	.744
271	resnet50_imagenet_100_seed-0	.731
272	alexnet_linf_8_robust	.625
273	resnet_50_v1_spiking_l4	.584
274	resnet50_imagenet_10_seed-0	.200
275	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.170
276	convnext_small_imagenet_10_seed-0	.127
277	deit_small_imagenet_10_seed-0	.114
278	deit_small_imagenet_1_seed-0	.089
279	resnet50-barlow	.080
280	resnet50-vicregl0p75	.077
281	alexnet_training_seed_06	.075
282	alexnet_training_seed_09	.067
283	resnet-50_untrained	.062
284	alexnet_training_seed_05	.062
285	alexnet_training_seed_02	.062
286	alexnet_training_seed_04	.062
287	alexnet_training_seed_08	.062
288	convnext_small_imagenet_1_seed-0	.062
289	alexnet_training_seed_10	.062
290	alexnet_training_seed_01	.062
291	resnet_50_v1_spiking_l3	.062
292	alexnet_training_seed_01_fov4	.062
293	alexnet_training_seed_01_fov12	.062
294	alexnet_training_seed_01_fov16	.062
295	alexnet_training_seed_03	.062
296	resnet50-simclr-vissl	.061
297	resnet50_imagenet_1_seed-0	.061
298	barlow-twins-resnet50	.058
299	resnet50-vicregl0p9	.058
300	alexnet_training_seed_07	.056
301	mobilevit_small	.045
302	artResNet18_1	.033
303	ViT-B/32
304	resnet-18-LC_w_sh_100_iter_conv_init_m
305	BiT-M-R50x1
306	resnet-18-LC_w_sh_1_iter_conv_init_m
307	resnet-18-LC_w_sh_1_iter_m
308	BiT-M-R152x2
309	resnet-18-LC_w_sh_10_iter_m
310	BiT-M-R50x3
311	resnet-18-LC_w_sh_100_iter_m
312	resnet-18-LC_w_sh_10_iter_conv_init_m
313	FrankRobWobv0
314	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-local_aggregation
317	r3m_resnet18
318	CORnet-Z
319	resnet18-deepcluster
320	vggface
321	my-model
322	resnet18-instance_recognition
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	r3m_resnet34
326	resnet-18-LC_m
327	r3m_resnet50_nocrop
328	resnet18-supervised
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	RN50
334	0.5x_resnet-18_LC_w_sh_100_iter
335	RN50
336	resnet18-colorization
337	resnet18-relative_position
338	resnet18-autoencoder
339	resnet18-simclr
340	ViT_L_32
341	ViT_B_32
342	resnet18-depth_prediction
343	ViT_B_16
344	r3m_resnet34_nocrop
345	resnet18-contrastive_predictive
346	resnet-18-LC_untrained
347	resnet-18_untrained
348	resnet-50x2_untrained
349	dcgan
350	unet_entire
351	CORnetZ_CIFAR10_bs32_20_04
352	v1-pyr-nodown
353	pixels
354	dorinet_cornet_z
355	omnivore_swinT
356	openclip
357	vonealexnet_gaussian_noise_std4_fixed
358	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
359	yudixie_resnet50_category_class_0_240908
360	yudixie_resnet50_distance_reg_0_240908
361	ConvLSTM
362	PredRNN
363	SimVP
364	TAU
365	MIM
366	yolos_tiny
367	pnasnet_large_pytorch
368	inception_v3_pytorch
369	yudixie_resnet18_distance_translation_0_240719
370	briaai_rmbg_1_4
371	compact_resnet50_V4
372	ReAlnet01_cornet
373	yudixie_resnet18_imagenet1kpret_0_240719
374	yudixie_resnet50_distance_rotation_0_240908
375	yudixie_resnet50_rotation_reg_0_240908
376	yudixie_resnet50_translation_reg_0_240908
377	vit_base_patch14_reg4_dinov2-lvd142m
378	yudixie_resnet50_distance_translation_0_240908
379	compact_resnet50_IT
380	ReAlnet01_cornet_fov4
381	vit_base_patch14_dinov2-lvd142m
382	ReAlnet02_cornet
383	resnet34_ecoset_full
384	resnet50_ecoset_full
385	ReAlnet01_cornet_fov12
386	resnet101_ecoset_full
387	yudixie_resnet50_translation_rotation_0_240908
388	compact_vgg19_V4
389	ReAlnet04_cornet
390	resnet152_ecoset_full
391	ReAlnet03_cornet
392	resnet-18_test_m
393	aughost_fair_resnet18
394	yudixie_resnet18_category_class_0_240719
395	compact_resnet50_robust_V4
396	ReAlnet05_cornet
397	yudixie_resnet18_random_0_240719
398	yudixie_resnet18_cat_obj_class_all_latents_0_240719
399	compact_vgg19_IT
400	yudixie_resnet18_distance_translation_rotation_0_240719
401	SeLaVi-AVE
402	yudixie_resnet18_object_class_0_240719
403	yudixie_resnet18_rotation_reg_0_240719
404	ReAlnet06_cornet
405	yudixie_resnet18_translation_reg_0_240719
406	yudixie_resnet18_distance_reg_0_240719
407	hmax
408	r3d_18
409	ReAlnet01_ALL
410	ReAlnet07_cornet
411	omnivore_swinB
412	mc3_18
413	ReAlnet02_ALL
414	s3d
415	VOneCORnet-S
416	resnet50-vicreg
417	ReAlnet01_cornet_fov16
418	ReAlnet08_cornet
419	mvit_v1_b
420	compact_cornets_V4
421	ReAlnet03_ALL
422	resnet18_imagenet21kP
423	compact_cornets_IT
424	ReAlnet04_ALL
425	omnivore_swinS
426	SeLaVi-Kinetics400
427	ReAlnet05_ALL
428	vit_base_patch16_dinov3-lvd1689m
429	SeLaVi-VGG-Sound
430	SeLaVi-Kinetics-Sound
431	ReAlnet06_ALL
432	compact_resnet50_robust_IT
433	yudixie_resnet50_random_0_240908
434	ReAlnet10_cornet
435	r2plus1d_18
436	ReAlnet07_ALL
437	yudixie_resnet50_distance_translation_rotation_0_240908
438	mvit_v2_s
439	yudixie_resnet50_imagenet1kpret_0_240908
440	resnet18_ecoset_full
441	ReAlnet08_ALL
442	yudixie_resnet18_distance_rotation_0_240719
443	ReAlnet09_ALL
444	yudixie_resnet18_translation_rotation_0_240719
445	yudixie_resnet50_imagenet1kpret_0_240312
446	yudixie_resnet50_object_class_0_240908
447	yudixie_resnet50_cat_obj_class_all_latents_0_240908
448	resnet50_primary_visual_cortex
449	ReAlnet09_cornet
450	ReAlnet10_ALL
451	resnet18_fair
452	pr1_stl10_disentangled_alpha04
453	predictive_coding_pc
454	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3
455	yudixie_wbh_object_class_scaling_0415_run_0
456	yudixie_wbh_object_class_scaling_0415_run_3
457	yudixie_wbh_object_class_scaling_0415_imagenet
458	yudixie_wbh_object_class_scaling_0415_run_2
459	yudixie_wbh_object_class_scaling_0415_run_1
460	deepeisnn_ei_cifar10_vgg19
461	deepeisnn_ei_cifar10_resnet18
462	deepeisnn_ei_imagenet_resnet18
463	deepeisnn_cifar10_resnet18_lif
464	dinov2_vitl14_reg_lc_featread

Benchmark bibtex

@article{geirhos2021partial,
              title={Partial success in closing the gap between human and machine vision},
              author={Geirhos, Robert and Narayanappa, Kantharaju and Mitzkus, Benjamin and Thieringer, Tizian and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland},
              journal={Advances in Neural Information Processing Systems},
              volume={34},
              year={2021},
              url={https://openreview.net/forum?id=QkljT4mrfs}
        }

Ceiling

1.00.

Note that scores are relative to this ceiling.

Data: Geirhos2021colour

Metric: top1