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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	regnety_032	.991
50	pnasnet_large-tf	.989
51	resnext101_32x8d_wsl	.989
52	AdvProp_efficientnet-b6	.989
53	convnext_tiny_sup	.989
54	antialiased-rnext101_32x8d	.989
55	convnext_small_imagenet_full_seed-0	.989
56	AT_efficientnet-b2	.988
57	AdvProp_efficientnet-b2	.988
58	cvt_cvt-13-224-in1k_4_LucyV4	.988
59	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.988
60	inception_v3-tf	.986
61	inception_v4-tf	.986
62	cvt_cvt-21-224-in1k_4_LucyV4	.986
63	pnasnet_large_halfPrecision	.986
64	pnasnet_large	.986
65	seresnext50_32x4d	.986
66	BiT-S-R152x4	.984
67	AdvProp_efficientnet-b0	.984
68	antialias-resnet152	.984
69	eBarlow_lmda_02_1000ep	.984
70	BiT-S-R50x3	.983
71	BiT-S-R101x3	.983
72	cv_18_dagger_408_pretrained	.983
73	cvt_cvt-13-384-in1k_4_LucyV4	.983
74	resnet-152_v2_pytorch	.983
75	cvt_cvt-21-384-in1k_4_LucyV4	.983
76	AdvProp_efficientnet-b4	.981
77	resnet-152_v2-tf	.981
78	AT_efficientnet-b4	.981
79	ViT_B_16_imagenet1k	.981
80	antialiased-r50	.981
81	resnet50_st_wzc_2	.981
82	deit_small_imagenet_full_seed-0	.981
83	inception_v4	.981
84	resnet_101_v2	.981
85	resnet-101_v2-tf	.980
86	resnet_152_v1	.980
87	resnet50_ewconplus_wzc	.980
88	resnet50-vitoimagevidnet8	.980
89	resnet50_st_wzc_seed7_2_323	.980
90	resnet_SIN_IN_FT_IN	.978
91	BiT-S-R101x1	.978
92	resnet50-SIN_IN_IN	.978
93	densenet-201-keras	.978
94	inception_v2-tf	.978
95	Res2Net50_26w_4s	.978
96	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.978
97	densenet-201	.978
98	densenet_201_pytorch	.978
99	resnet152_imagenet_full	.978
100	grcnn	.978
101	efficientnet-b7	.977
102	resnet50-SIN_IN	.977
103	resnet-50_v1-tf	.977
104	effnetb1_272x240	.977
105	resnet50-moclr8deg	.977
106	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.977
107	resnet_101_v1	.977
108	resnet50_st_wzc_seed6_2_323	.977
109	BiT-S-R152x2	.975
110	BiT-S-R50x1	.975
111	efficientnet_b0	.975
112	resnet-101_v1-tf	.975
113	densenet-169-keras	.975
114	xception-keras	.975
115	resnet50_moco_v2	.975
116	resnet50_st_wzc_seed3_2_323	.975
117	resnet101_imagenet_full	.975
118	resnet_152_v2	.975
119	densenet-169	.975
120	ViT_L_32_imagenet1k	.973
121	resnet-152_v1-tf	.973
122	inception_v3	.973
123	voneresnet-50-non_stochastic	.972
124	resnet-50_v2-tf	.972
125	CORnet-S	.972
126	resnet50_imagenet_full	.972
127	tv_efficientnet-b1	.972
128	grcnn_109	.972
129	convnext_femto_ols:d1_in1k	.972
130	resnet50_ewconplus_wzc_seed5_2__323	.972
131	vonegrcnn_47e	.970
132	densenet-121-keras	.969
133	vonegrcnn_62e_nobn	.969
134	resnet-50-pytorch	.969
135	mobilenet_v2_1.4_224-tf	.969
136	mobilenet_v2_1.3_224-tf	.969
137	vonegrcnn_47e	.969
138	vgg-16-keras	.969
139	vgg-19-keras	.969
140	resnet50-sup	.969
141	resnet50_tutorial	.969
142	resnet_50_v1	.969
143	densenet-121	.969
144	mobilenet_v2_1.0_192-tf	.967
145	efficientnet_b2_imagenet_full	.967
146	resnet50_ewconplus_wzc_seed2_2__323	.967
147	mobilenet_v2_1.0_224-tf	.966
148	voneresnet-50	.966
149	vgg_19	.966
150	resnet50-VITO-8deg-cc	.966
151	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.966
152	resnet50_hvs_wzc	.966
153	resnet_50_v2	.966
154	mobilenet_v2_1.0_160-tf	.964
155	resnet50	.964
156	mobilenet_v2_1_3_224	.964
157	efficientnet_b1_imagenet_full	.963
158	nasnet_mobile	.963
159	vonegrcnn_52e_full	.963
160	resnet-34	.961
161	regnet_y_400mf	.961
162	vgg_16	.961
163	vonegrcnn_62e_nobn	.961
164	vonegrcnn_52e_full	.959
165	ViT_B_32_imagenet1k	.959
166	resnet50_robust_l2_eps1	.959
167	xception	.959
168	mobilenet_v1_1.0_224	.958
169	custom_model_cv_18_dagger_408	.956
170	resnet_50_v1_spiking	.956
171	inception_v1-tf	.955
172	resnet-18	.955
173	resnet34_imagenet_full	.955
174	resnet18_imagenet_full	.955
175	mobilenet_v1_0.75_224	.952
176	mobilenet_v1_1.0_192	.952
177	mobilenet_v1_1.0_160	.952
178	blt_vs	.952
179	ReAlnet06	.948
180	resnet50_eMMCR_Vanilla	.948
181	efficientnet_b0_imagenet_full	.947
182	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.945
183	AdvProp_efficientnet-b8	.944
184	mobilenet_v1_0.75_192	.944
185	ReAlnet09	.944
186	ReAlnet03	.944
187	eicircuit_resnet18_bn_20260919	.944
188	mobilenet_v2_0.75_192-tf	.941
189	mobilenet_v2_1.0_128-tf	.939
190	mobilenet_v2_0.75_224-tf	.939
191	mobilenet_v1_0.5_224	.939
192	voneresnet-50-robust	.939
193	mobilenet_v2_1_0_192	.939
194	ReAlnet02	.939
195	convnext_base_fb_in1k_gram_aligned_dinov3	.939
196	mobilenet_v1_0.75_160	.938
197	ReAlnet07	.938
198	shufflenet_v2_x1_0	.936
199	mobilenet_v2_1_0_224	.936
200	mobilenet_v2_0.75_160-tf	.934
201	ReAlnet01	.934
202	nasnet_mobile-tf	.931
203	ReAlnet04	.931
204	mobilenet_v2_1_0_160	.931
205	inception_v1	.930
206	ReAlnet05	.930
207	ReAlnet08	.927
208	ReAlnet10	.925
209	mobilenet_v2_0.5_224-tf	.923
210	mobilenet_v1_1.0_128	.923
211	mobilenet_v2_0_75_224	.923
212	mobilenet_v2_0_75_192	.923
213	resnet50-SIN	.922
214	mobilenet_v2_0_75_160	.920
215	mobilenet_v2_0.75_128	.919
216	resnet_50_v1_spiking_l4a	.917
217	mobilenet_v2_0.5_192-tf	.912
218	0.5x_resnet-18_LC_w_sh_1_iter	.912
219	mobilenet_v1_0.75_128	.911
220	mobilenet_v2_1_0_128	.911
221	mobilenet_v1_0.5_192	.909
222	CLIP-RN50	.908
223	mobilenet_v2_1.0_96	.906
224	mobilenet_v2_0.5_160	.905
225	mobilenet_v1_0.5_160	.903
226	mobilenet_v2_0_5_224	.903
227	mobilenet_v2_0.75_96	.902
228	resnet-50-robust	.902
229	imagenet_l2_3_0	.902
230	resnet50_robust_l2_eps3	.902
231	mobilenet_v2_1-4_224_pytorch	.902
232	mobilenet_v2_1_4_224	.902
233	mobilenet_v2_0.35_192	.900
234	mobilenet_v2_0_5_192	.900
235	resnet50_l2_3_robust	.889
236	mobilenet_v2_0.35_224	.884
237	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.884
238	mobilenet_v2_0.5_128	.884
239	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.883
240	mobilenet_v2_0.35_160	.872
241	squeezenet1_1	.870
242	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
243	alexnet	.864
244	alexnet_random_linf8_perturb	.864
245	alexnet_ks_torevert	.864
246	alexnet_fov4	.864
247	alexnet_fov12	.864
248	alexnet_fov16	.864
249	mobilenet_v1_0.5_128	.863
250	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.859
251	convnext_small_imagenet_100_seed-0	.859
252	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.850
253	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.844
254	squeezenet1_0	.842
255	mobilenet_v1_0.25_224	.833
256	alexnet_early_checkpoint	.830
257	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.830
258	mobilenet_v2_0.5_96	.823
259	mobilenet_v1_0.25_192	.819
260	mobilenet_v2_0.35_128	.819
261	resnet50_linf_8_robust	.812
262	mobilenet_v1_0.25_160	.808
263	mobilenet_v2_0.35_96	.797
264	AlexNet_SIN	.797
265	AlexNet_SIN_fov4	.797
266	AlexNet_SIN_fov12	.797
267	AlexNet_SIN_fov16	.797
268	imagenet_l2_10_0	.781
269	mobilenet_v1_0.25_128	.777
270	alexnet_l2_3_robust	.767
271	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.767
272	alexnet_robust_correct	.756
273	deit_small_imagenet_100_seed-0	.744
274	resnet50_imagenet_100_seed-0	.731
275	alexnet_linf_8_robust	.625
276	resnet_50_v1_spiking_l4	.584
277	resnet50_imagenet_10_seed-0	.200
278	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.170
279	convnext_small_imagenet_10_seed-0	.127
280	deit_small_imagenet_10_seed-0	.114
281	deit_small_imagenet_1_seed-0	.089
282	resnet50-barlow	.080
283	resnet50-vicregl0p75	.077
284	alexnet_training_seed_06	.075
285	alexnet_training_seed_09	.067
286	resnet-50_untrained	.062
287	alexnet_training_seed_05	.062
288	alexnet_training_seed_02	.062
289	alexnet_training_seed_04	.062
290	alexnet_training_seed_08	.062
291	convnext_small_imagenet_1_seed-0	.062
292	alexnet_training_seed_10	.062
293	alexnet_training_seed_01	.062
294	resnet_50_v1_spiking_l3	.062
295	alexnet_training_seed_01_fov4	.062
296	alexnet_training_seed_01_fov12	.062
297	alexnet_training_seed_01_fov16	.062
298	alexnet_training_seed_03	.062
299	resnet50-simclr-vissl	.061
300	resnet50_imagenet_1_seed-0	.061
301	barlow-twins-resnet50	.058
302	resnet50-vicregl0p9	.058
303	alexnet_training_seed_07	.056
304	mobilevit_small	.045
305	artResNet18_1	.033
306	ViT-B/32
307	resnet-18-LC_w_sh_100_iter_conv_init_m
308	BiT-M-R50x1
309	resnet-18-LC_w_sh_1_iter_conv_init_m
310	resnet-18-LC_w_sh_1_iter_m
311	BiT-M-R152x2
312	resnet-18-LC_w_sh_10_iter_m
313	BiT-M-R50x3
314	resnet-18-LC_w_sh_100_iter_m
315	resnet-18-LC_w_sh_10_iter_conv_init_m
316	FrankRobWobv0
317	0.5x_resnet-18_LC
318	CORnetZ_CIFAR10
319	resnet18-local_aggregation
320	r3m_resnet18
321	CORnet-Z
322	resnet18-deepcluster
323	vggface
324	my-model
325	resnet18-instance_recognition
326	0.5x_resnet-18_LC_w_sh_10_iter
327	r3m_resnet50
328	r3m_resnet34
329	resnet-18-LC_m
330	r3m_resnet50_nocrop
331	resnet18-supervised
332	resnet-18-LC_1st_conv_m
333	resnet-18-LC_conv_init_m
334	resnet18-contrastive_multiview
335	0.5x_resnet-18
336	RN50
337	0.5x_resnet-18_LC_w_sh_100_iter
338	RN50
339	resnet18-colorization
340	resnet18-relative_position
341	resnet18-autoencoder
342	resnet18-simclr
343	ViT_L_32
344	ViT_B_32
345	resnet18-depth_prediction
346	ViT_B_16
347	r3m_resnet34_nocrop
348	resnet18-contrastive_predictive
349	resnet-18-LC_untrained
350	resnet-18_untrained
351	resnet-50x2_untrained
352	dcgan
353	unet_entire
354	CORnetZ_CIFAR10_bs32_20_04
355	v1-pyr-nodown
356	pixels
357	dorinet_cornet_z
358	omnivore_swinT
359	openclip
360	vonealexnet_gaussian_noise_std4_fixed
361	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
362	yudixie_resnet50_category_class_0_240908
363	yudixie_resnet50_distance_reg_0_240908
364	ConvLSTM
365	PredRNN
366	SimVP
367	TAU
368	MIM
369	yolos_tiny
370	pnasnet_large_pytorch
371	inception_v3_pytorch
372	yudixie_resnet18_distance_translation_0_240719
373	briaai_rmbg_1_4
374	compact_resnet50_V4
375	ReAlnet01_cornet
376	yudixie_resnet18_imagenet1kpret_0_240719
377	yudixie_resnet50_distance_rotation_0_240908
378	yudixie_resnet50_rotation_reg_0_240908
379	yudixie_resnet50_translation_reg_0_240908
380	vit_base_patch14_reg4_dinov2-lvd142m
381	yudixie_resnet50_distance_translation_0_240908
382	compact_resnet50_IT
383	ReAlnet01_cornet_fov4
384	vit_base_patch14_dinov2-lvd142m
385	ReAlnet02_cornet
386	resnet34_ecoset_full
387	resnet50_ecoset_full
388	ReAlnet01_cornet_fov12
389	resnet101_ecoset_full
390	yudixie_resnet50_translation_rotation_0_240908
391	compact_vgg19_V4
392	ReAlnet04_cornet
393	resnet152_ecoset_full
394	ReAlnet03_cornet
395	resnet-18_test_m
396	yudixie_resnet18_category_class_0_240719
397	compact_resnet50_robust_V4
398	ReAlnet05_cornet
399	yudixie_resnet18_random_0_240719
400	yudixie_resnet18_cat_obj_class_all_latents_0_240719
401	compact_vgg19_IT
402	yudixie_resnet18_distance_translation_rotation_0_240719
403	SeLaVi-AVE
404	yudixie_resnet18_object_class_0_240719
405	yudixie_resnet18_rotation_reg_0_240719
406	ReAlnet06_cornet
407	yudixie_resnet18_translation_reg_0_240719
408	yudixie_resnet18_distance_reg_0_240719
409	hmax
410	r3d_18
411	ReAlnet01_ALL
412	ReAlnet07_cornet
413	omnivore_swinB
414	mc3_18
415	ReAlnet02_ALL
416	s3d
417	VOneCORnet-S
418	resnet50-vicreg
419	ReAlnet01_cornet_fov16
420	ReAlnet08_cornet
421	mvit_v1_b
422	compact_cornets_V4
423	ReAlnet03_ALL
424	resnet18_imagenet21kP
425	compact_cornets_IT
426	ReAlnet04_ALL
427	omnivore_swinS
428	SeLaVi-Kinetics400
429	ReAlnet05_ALL
430	vit_base_patch16_dinov3-lvd1689m
431	SeLaVi-VGG-Sound
432	SeLaVi-Kinetics-Sound
433	ReAlnet06_ALL
434	compact_resnet50_robust_IT
435	yudixie_resnet50_random_0_240908
436	ReAlnet10_cornet
437	r2plus1d_18
438	ReAlnet07_ALL
439	yudixie_resnet50_distance_translation_rotation_0_240908
440	mvit_v2_s
441	yudixie_resnet50_imagenet1kpret_0_240908
442	resnet18_ecoset_full
443	ReAlnet08_ALL
444	yudixie_resnet18_distance_rotation_0_240719
445	ReAlnet09_ALL
446	yudixie_resnet18_translation_rotation_0_240719
447	yudixie_resnet50_imagenet1kpret_0_240312
448	yudixie_resnet50_object_class_0_240908
449	yudixie_resnet50_cat_obj_class_all_latents_0_240908
450	resnet50_primary_visual_cortex
451	ReAlnet09_cornet
452	ReAlnet10_ALL
453	resnet18_fair
454	pr1_stl10_disentangled_alpha04
455	predictive_coding_pc
456	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3
457	yudixie_wbh_object_class_scaling_0415_run_0
458	yudixie_wbh_object_class_scaling_0415_run_3
459	yudixie_wbh_object_class_scaling_0415_imagenet
460	yudixie_wbh_object_class_scaling_0415_run_2
461	yudixie_wbh_object_class_scaling_0415_run_1
462	deepeisnn_ei_cifar10_vgg19
463	deepeisnn_ei_cifar10_resnet18
464	deepeisnn_ei_imagenet_resnet18
465	deepeisnn_cifar10_resnet18_lif
466	dinov2_vitl14_reg_lc_featread
467	gabor_filter_entropy_4x4
468	gabor_filter_entropy_4x4_v2
469	gabor_filter_entropy_4x4_v2_forcedCosine
470	gabor_filter_mean_abs_4x4_v2

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