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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	resnext101_32x32d_wsl	.997
8	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.997
9	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.997
10	convnext_base:clip_laiona_augreg_ft_in1k_384	.997
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.997
12	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.995
13	vit_large_patch14_clip_224:openai_ft_in1k	.995
14	convnext_xlarge:fb_in22k_ft_in1k	.995
15	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.995
16	convnext_large_imagenet_full_seed-0	.995
17	convnext_base_fb_in22k_ft_in1k	.995
18	deit3_base_patch16_224_fb_in1k	.995
19	fixres_resnext101_32x48d_wsl	.994
20	resnext101_32x48d_wsl	.994
21	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.994
22	vit_base_patch16_clip_224:openai_ft_in1k	.994
23	vit_relpos_base_patch16_clsgap_224:sw_in1k	.994
24	convnext_large:fb_in22k_ft_in1k	.994
25	convnext_base_fb_in1k	.994
26	AdvProp_efficientnet-b7	.992
27	deit_large_imagenet_full_seed-0	.992
28	deit_base_imagenet_full_seed-0	.992
29	nasnet_large-tf	.991
30	effnetv2m_custom384	.991
31	resnext101_32x16d_wsl	.991
32	focalnet_tiny_lrf_in1k	.991
33	convnext_base_imagenet_full_seed-0	.991
34	convnext_tiny:in12k_ft_in1k	.991
35	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.991
36	convnext_tiny_imagenet_full_seed-0	.991
37	nasnet_large	.991
38	pnasnet_large-tf	.989
39	resnext101_32x8d_wsl	.989
40	AdvProp_efficientnet-b6	.989
41	convnext_tiny_sup	.989
42	antialiased-rnext101_32x8d	.989
43	convnext_small_imagenet_full_seed-0	.989
44	AT_efficientnet-b2	.988
45	AdvProp_efficientnet-b2	.988
46	cvt_cvt-13-224-in1k_4_LucyV4	.988
47	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.988
48	inception_v3-tf	.986
49	inception_v4-tf	.986
50	cvt_cvt-21-224-in1k_4_LucyV4	.986
51	pnasnet_large_halfPrecision	.986
52	pnasnet_large	.986
53	BiT-S-R152x4	.984
54	AdvProp_efficientnet-b0	.984
55	antialias-resnet152	.984
56	eBarlow_lmda_02_1000ep	.984
57	BiT-S-R50x3	.983
58	BiT-S-R101x3	.983
59	cv_18_dagger_408_pretrained	.983
60	cvt_cvt-13-384-in1k_4_LucyV4	.983
61	resnet-152_v2_pytorch	.983
62	cvt_cvt-21-384-in1k_4_LucyV4	.983
63	AdvProp_efficientnet-b4	.981
64	resnet-152_v2-tf	.981
65	AT_efficientnet-b4	.981
66	ViT_B_16_imagenet1k	.981
67	antialiased-r50	.981
68	resnet50_st_wzc_2	.981
69	deit_small_imagenet_full_seed-0	.981
70	inception_v4	.981
71	resnet_101_v2	.981
72	resnet-101_v2-tf	.980
73	resnet_152_v1	.980
74	resnet50_ewconplus_wzc	.980
75	resnet50-vitoimagevidnet8	.980
76	resnet50_st_wzc_seed7_2_323	.980
77	resnet_SIN_IN_FT_IN	.978
78	BiT-S-R101x1	.978
79	resnet50-SIN_IN_IN	.978
80	densenet-201-keras	.978
81	inception_v2-tf	.978
82	Res2Net50_26w_4s	.978
83	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.978
84	densenet-201	.978
85	resnet50_st_wzc_noseed_2_323	.978
86	densenet_201_pytorch	.978
87	resnet152_imagenet_full	.978
88	grcnn	.978
89	efficientnet-b7	.977
90	resnet50-SIN_IN	.977
91	resnet-50_v1-tf	.977
92	effnetb1_272x240	.977
93	resnet50-moclr8deg	.977
94	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.977
95	resnet_101_v1	.977
96	resnet50_st_wzc_seed6_2_323	.977
97	BiT-S-R152x2	.975
98	BiT-S-R50x1	.975
99	efficientnet_b0	.975
100	resnet-101_v1-tf	.975
101	densenet-169-keras	.975
102	xception-keras	.975
103	resnet50_moco_v2	.975
104	resnet50_st_wzc_seed3_2_323	.975
105	resnet101_imagenet_full	.975
106	resnet_152_v2	.975
107	densenet-169	.975
108	resnet50_st_wzc_seed4_2_323	.975
109	ViT_L_32_imagenet1k	.973
110	resnet-152_v1-tf	.973
111	inception_v3	.973
112	resnet50_st_wzc_seed5_2_323	.973
113	voneresnet-50-non_stochastic	.972
114	resnet-50_v2-tf	.972
115	CORnet-S	.972
116	resnet50_imagenet_full	.972
117	tv_efficientnet-b1	.972
118	grcnn_109	.972
119	convnext_femto_ols:d1_in1k	.972
120	resnet50_ewconplus_wzc_seed5_2__323	.972
121	resnet50_st_wzc_2_323	.972
122	vonegrcnn_47e	.970
123	densenet-121-keras	.969
124	vonegrcnn_62e_nobn	.969
125	resnet-50-pytorch	.969
126	mobilenet_v2_1.4_224-tf	.969
127	mobilenet_v2_1.3_224-tf	.969
128	vonegrcnn_47e	.969
129	vgg-16-keras	.969
130	vgg-19-keras	.969
131	resnet50-sup	.969
132	resnet50_ewconplus_wzc_seed7_2_323	.969
133	resnet50_tutorial	.969
134	resnet_50_v1	.969
135	densenet-121	.969
136	resnet50_ewconplus_wzc_seed4_2_323	.969
137	resnet50_ewconplus_wzc_seed3_2_323	.969
138	mobilenet_v2_1.0_192-tf	.967
139	efficientnet_b2_imagenet_full	.967
140	resnet50_ewconplus_wzc_seed2_2__323	.967
141	mobilenet_v2_1.0_224-tf	.966
142	voneresnet-50	.966
143	vgg_19	.966
144	resnet50-VITO-8deg-cc	.966
145	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.966
146	resnet_50_v2	.966
147	mobilenet_v2_1.0_160-tf	.964
148	resnet50	.964
149	mobilenet_v2_1_3_224	.964
150	resnet50_ewconplus_noseed_2_323	.964
151	resnet50_ewconplus_wzc_2_323	.964
152	efficientnet_b1_imagenet_full	.963
153	nasnet_mobile	.963
154	vonegrcnn_52e_full	.963
155	resnet-34	.961
156	regnet_y_400mf	.961
157	vgg_16	.961
158	vonegrcnn_62e_nobn	.961
159	vonegrcnn_52e_full	.959
160	ViT_B_32_imagenet1k	.959
161	resnet50_robust_l2_eps1	.959
162	xception	.959
163	mobilenet_v1_1.0_224	.958
164	custom_model_cv_18_dagger_408	.956
165	resnet_50_v1_spiking	.956
166	inception_v1-tf	.955
167	resnet-18	.955
168	resnet34_imagenet_full	.955
169	resnet18_imagenet_full	.955
170	mobilenet_v1_0.75_224	.952
171	mobilenet_v1_1.0_192	.952
172	mobilenet_v1_1.0_160	.952
173	blt_vs	.952
174	ReAlnet06	.948
175	resnet50_eMMCR_Vanilla	.948
176	efficientnet_b0_imagenet_full	.947
177	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.945
178	AdvProp_efficientnet-b8	.944
179	mobilenet_v1_0.75_192	.944
180	ReAlnet09	.944
181	ReAlnet03	.944
182	mobilenet_v2_0.75_192-tf	.941
183	mobilenet_v2_1.0_128-tf	.939
184	mobilenet_v2_0.75_224-tf	.939
185	mobilenet_v1_0.5_224	.939
186	voneresnet-50-robust	.939
187	mobilenet_v2_1_0_192	.939
188	ReAlnet02	.939
189	convnext_base_fb_in1k_gram_aligned_dinov3	.939
190	mobilenet_v1_0.75_160	.938
191	ReAlnet07	.938
192	shufflenet_v2_x1_0	.936
193	mobilenet_v2_1_0_224	.936
194	mobilenet_v2_0.75_160-tf	.934
195	ReAlnet01	.934
196	nasnet_mobile-tf	.931
197	ReAlnet04	.931
198	mobilenet_v2_1_0_160	.931
199	inception_v1	.930
200	ReAlnet05	.930
201	ReAlnet08	.927
202	ReAlnet10	.925
203	mobilenet_v2_0.5_224-tf	.923
204	mobilenet_v1_1.0_128	.923
205	mobilenet_v2_0_75_224	.923
206	mobilenet_v2_0_75_192	.923
207	resnet50-SIN	.922
208	mobilenet_v2_0_75_160	.920
209	mobilenet_v2_0.75_128	.919
210	resnet_50_v1_spiking_l4a	.917
211	mobilenet_v2_0.5_192-tf	.912
212	0.5x_resnet-18_LC_w_sh_1_iter	.912
213	mobilenet_v1_0.75_128	.911
214	mobilenet_v2_1_0_128	.911
215	mobilenet_v1_0.5_192	.909
216	CLIP-RN50	.908
217	mobilenet_v2_1.0_96	.906
218	mobilenet_v2_0.5_160	.905
219	mobilenet_v1_0.5_160	.903
220	mobilenet_v2_0_5_224	.903
221	mobilenet_v2_0.75_96	.902
222	resnet-50-robust	.902
223	imagenet_l2_3_0	.902
224	resnet50_robust_l2_eps3	.902
225	mobilenet_v2_1-4_224_pytorch	.902
226	mobilenet_v2_1_4_224	.902
227	mobilenet_v2_0.35_192	.900
228	mobilenet_v2_0_5_192	.900
229	resnet50_l2_3_robust	.889
230	mobilenet_v2_0.35_224	.884
231	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.884
232	mobilenet_v2_0.5_128	.884
233	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.883
234	mobilenet_v2_0.35_160	.872
235	squeezenet1_1	.870
236	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
237	alexnet	.864
238	alexnet_random_linf8_perturb	.864
239	alexnet_ks_torevert	.864
240	alexnet_fov4	.864
241	alexnet_fov12	.864
242	alexnet_fov16	.864
243	mobilenet_v1_0.5_128	.863
244	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.859
245	convnext_small_imagenet_100_seed-0	.859
246	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.850
247	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.844
248	squeezenet1_0	.842
249	mobilenet_v1_0.25_224	.833
250	alexnet_early_checkpoint	.830
251	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.830
252	mobilenet_v2_0.5_96	.823
253	mobilenet_v1_0.25_192	.819
254	mobilenet_v2_0.35_128	.819
255	resnet50_linf_8_robust	.812
256	mobilenet_v1_0.25_160	.808
257	mobilenet_v2_0.35_96	.797
258	AlexNet_SIN	.797
259	AlexNet_SIN_fov4	.797
260	AlexNet_SIN_fov12	.797
261	AlexNet_SIN_fov16	.797
262	imagenet_l2_10_0	.781
263	mobilenet_v1_0.25_128	.777
264	alexnet_l2_3_robust	.767
265	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.767
266	alexnet_robust_correct	.756
267	deit_small_imagenet_100_seed-0	.744
268	resnet50_imagenet_100_seed-0	.731
269	alexnet_linf_8_robust	.625
270	resnet_50_v1_spiking_l4	.584
271	resnet50_imagenet_10_seed-0	.200
272	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.170
273	convnext_small_imagenet_10_seed-0	.127
274	deit_small_imagenet_10_seed-0	.114
275	deit_small_imagenet_1_seed-0	.089
276	resnet50-barlow	.080
277	resnet50-vicregl0p75	.077
278	alexnet_training_seed_06	.075
279	alexnet_training_seed_09	.067
280	resnet-50_untrained	.062
281	resnet50-meshes-lt-100-original-scratch	.062
282	alexnet_training_seed_05	.062
283	alexnet_training_seed_02	.062
284	alexnet_training_seed_04	.062
285	alexnet_training_seed_08	.062
286	convnext_small_imagenet_1_seed-0	.062
287	alexnet_training_seed_10	.062
288	alexnet_training_seed_01	.062
289	resnet_50_v1_spiking_l3	.062
290	alexnet_training_seed_01_fov4	.062
291	alexnet_training_seed_01_fov12	.062
292	alexnet_training_seed_01_fov16	.062
293	alexnet_training_seed_03	.062
294	resnet50-simclr-vissl	.061
295	resnet50_imagenet_1_seed-0	.061
296	barlow-twins-resnet50	.058
297	resnet50-vicregl0p9	.058
298	resnet50-meshes-lt-100-original-pretrained	.056
299	alexnet_training_seed_07	.056
300	mobilevit_small	.045
301	artResNet18_1	.033
302	ViT-B/32
303	resnet-18-LC_w_sh_100_iter_conv_init_m
304	BiT-M-R50x1
305	resnet-18-LC_w_sh_1_iter_conv_init_m
306	resnet-18-LC_w_sh_1_iter_m
307	BiT-M-R152x2
308	resnet-18-LC_w_sh_10_iter_m
309	BiT-M-R50x3
310	resnet-18-LC_w_sh_100_iter_m
311	resnet-18-LC_w_sh_10_iter_conv_init_m
312	FrankRobWobv0
313	0.5x_resnet-18_LC
314	CORnetZ_CIFAR10
315	resnet18-local_aggregation
316	r3m_resnet18
317	CORnet-Z
318	resnet18-deepcluster
319	vggface
320	my-model
321	resnet18-instance_recognition
322	0.5x_resnet-18_LC_w_sh_10_iter
323	r3m_resnet50
324	r3m_resnet34
325	resnet-18-LC_m
326	r3m_resnet50_nocrop
327	resnet18-supervised
328	resnet-18-LC_1st_conv_m
329	resnet-18-LC_conv_init_m
330	resnet18-contrastive_multiview
331	0.5x_resnet-18
332	RN50
333	0.5x_resnet-18_LC_w_sh_100_iter
334	RN50
335	resnet18-colorization
336	resnet18-relative_position
337	resnet18-autoencoder
338	resnet18-simclr
339	ViT_L_32
340	ViT_B_32
341	resnet18-depth_prediction
342	ViT_B_16
343	r3m_resnet34_nocrop
344	resnet18-contrastive_predictive
345	resnet-18-LC_untrained
346	resnet-18_untrained
347	resnet-50x2_untrained
348	dcgan
349	unet_entire
350	CORnetZ_CIFAR10_bs32_20_04
351	v1-pyr-nodown
352	pixels
353	dorinet_cornet_z
354	omnivore_swinT
355	resnet50
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

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