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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021falsecolour-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.998
2	convnext_large_clip_laion2b_finelayers	.998
3	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.996
4	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.996
5	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.996
6	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.996
7	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.996
8	eva02_large_in22k	.996
9	dinov2_vitl14_lc_res336	.996
10	vit_large_clip336_openai	.996
11	convnext_xxlarge_clip	.996
12	vit_huge_clip336_laion	.996
13	vit_large_patch14_clip_224:openai_ft_in1k	.995
14	vit_large_patch14_clip_336:laion2b_ft_in1k	.995
15	vit_large_patch14_clip_224:laion2b_ft_in1k	.995
16	dinov2_vitl14_lc	.995
17	dinov2_vitl14_reg_lc	.995
18	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.993
19	convnext_base:clip_laiona_augreg_ft_in1k_384	.993
20	deit3_base_patch16_224_fb_in22k_ft_in1k	.993
21	dinov2_vitg14_reg_lc	.993
22	nasnet_large-tf	.991
23	convnext_xlarge:fb_in22k_ft_in1k	.991
24	convnext_large_imagenet_full_seed-0	.991
25	convnext_large:fb_in22k_ft_in1k	.991
26	convnext_base_fb_in1k	.991
27	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.989
28	nasnet_large	.989
29	deit3_base_patch16_224_fb_in1k	.989
30	convnext_large_in22k_supervised	.989
31	dinov2_vitg14_lc	.989
32	fixres_resnext101_32x48d_wsl	.989
33	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.989
34	resnext101_32x32d_wsl	.988
35	resnext101_32x48d_wsl	.988
36	convnext_tiny_imagenet_full_seed-0	.988
37	vit_base_patch16_clip_224:openai_ft_in1k	.988
38	convnext_base_fb_in22k_ft_in1k	.988
39	AdvProp_efficientnet-b7	.986
40	resnext101_32x8d_wsl	.986
41	effnetv2m_custom384	.986
42	convnext_small_imagenet_full_seed-0	.986
43	deit_large_imagenet_full_seed-0	.986
44	deit_base_imagenet_full_seed-0	.984
45	convnext_base_imagenet_full_seed-0	.984
46	cvt_cvt-13-384-in1k_4_LucyV4	.984
47	pnasnet_large_halfPrecision	.984
48	pnasnet_large	.984
49	regnety_032	.984
50	seresnext50_32x4d	.984
51	resnext101_32x16d_wsl	.984
52	cvt_cvt-13-224-in1k_4_LucyV4	.982
53	inception_v4-tf	.982
54	xception-keras	.980
55	vit_relpos_base_patch16_clsgap_224:sw_in1k	.977
56	AdvProp_efficientnet-b6	.977
57	pnasnet_large-tf	.975
58	efficientnet-b7	.975
59	BiT-S-R101x3	.975
60	resnet-152_v2-tf	.975
61	convnext_tiny:in12k_ft_in1k	.975
62	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.975
63	inception_v3_pytorch	.975
64	cvt_cvt-21-384-in1k_4_LucyV4	.975
65	resnet_101_v2	.975
66	resnet_101_v1	.975
67	AdvProp_efficientnet-b4	.973
68	AT_efficientnet-b4	.973
69	convnext_tiny_sup	.973
70	focalnet_tiny_lrf_in1k	.973
71	cvt_cvt-21-224-in1k_4_LucyV4	.973
72	inception_v3	.973
73	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.973
74	resnet-152_v2_pytorch	.973
75	inception_v4	.973
76	resnet50_st_wzc_seed7_2_323	.973
77	deit_small_imagenet_full_seed-0	.971
78	inception_v3-tf	.971
79	effnetb1_272x240	.971
80	resnet-50_v2-tf	.970
81	AT_efficientnet-b2	.968
82	AdvProp_efficientnet-b2	.968
83	AdvProp_efficientnet-b0	.968
84	resnet50_st_wzc_2	.968
85	tv_efficientnet-b1	.966
86	Res2Net50_26w_4s	.964
87	resnet50_st_wzc_seed3_2_323	.964
88	grcnn_109	.964
89	resnet_152_v2	.964
90	BiT-S-R152x2	.964
91	antialiased-rnext101_32x8d	.964
92	antialias-resnet152	.964
93	BiT-S-R152x4	.963
94	resnet-101_v2-tf	.963
95	cv_18_dagger_408_pretrained	.963
96	resnet152_imagenet_full	.963
97	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.963
98	densenet-121-keras	.961
99	resnet101_imagenet_full	.961
100	densenet-121	.961
101	resnet50_st_wzc_seed6_2_323	.961
102	efficientnet_b0	.959
103	densenet-201	.957
104	densenet_201_pytorch	.957
105	ijepa_vith14_linhead	.957
106	resnet50_simsiam_linhead	.957
107	resnet-101_v1-tf	.957
108	densenet-201-keras	.957
109	resnet50-moclr8deg	.957
110	resnet-152_v1-tf	.955
111	resnet50-VITO-8deg-cc	.955
112	resnet50_ewconplus_wzc	.955
113	resnet_50_v2	.955
114	densenet-169-keras	.954
115	grcnn	.954
116	resnet50-vitoimagevidnet8	.954
117	resnet_152_v1	.954
118	densenet-169	.954
119	resnet50_ewconplus_wzc_seed6_2_323	.954
120	BiT-S-R50x3	.952
121	antialiased-r50	.952
122	inception_v2-tf	.950
123	resnet50	.950
124	resnet50_imagenet_full	.950
125	resnet50_vicreg_linhead	.950
126	resnet_SIN_IN_FT_IN	.948
127	resnet50-SIN_IN_IN	.948
128	resnet50_ewconplus_wzc_seed2_2__323	.948
129	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.946
130	convnext_femto_ols:d1_in1k	.946
131	resnet-50_v1-tf	.946
132	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.946
133	resnet50_ewconplus_wzc_seed5_2__323	.945
134	resnet50_barlowtwins_linhead	.945
135	BiT-S-R50x1	.945
136	resnet-50-pytorch	.943
137	mobilenet_v2_1.4_224-tf	.943
138	mobilenet_v2_1.0_224-tf	.943
139	resnet50-sup	.943
140	resnet50_moco_v2	.943
141	resnet50_tutorial	.943
142	resnet_50_v1	.943
143	voneresnet-50-non_stochastic	.941
144	xception	.941
145	vonegrcnn_62e_nobn	.941
146	vonegrcnn_47e	.941
147	efficientnet_b1_imagenet_full	.939
148	eBarlow_lmda_02_1000ep	.939
149	vonegrcnn_52e_full	.939
150	resnet50_byol_linhead	.939
151	BiT-S-R101x1	.939
152	mobilenet_v2_1.3_224-tf	.939
153	vonegrcnn_62e_nobn	.938
154	nasnet_mobile	.938
155	mobilenet_v2_1.0_192-tf	.936
156	mobilenet_v2_1.0_160-tf	.936
157	mobilenet_v2_1_0_224	.936
158	resnet_50_v1_spiking	.934
159	vonegrcnn_52e_full	.934
160	resnet-34	.934
161	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.932
162	resnet50_hvs_wzc	.932
163	resnet50_simclr_linhead_01norm	.932
164	vonegrcnn_47e	.932
165	ViT_L_32_imagenet1k	.930
166	resnet34_imagenet_full	.930
167	AdvProp_efficientnet-b8	.929
168	mobilenet_v2_1_0_192	.929
169	resnet50_eMMCR_Vanilla	.929
170	mobilenet_v2_1_3_224	.927
171	resnet50-SIN_IN	.927
172	vgg-19-keras	.927
173	inception_v1-tf	.925
174	efficientnet_b2_imagenet_full	.925
175	vgg-16-keras	.923
176	ViT_B_16_imagenet1k	.923
177	voneresnet-50	.920
178	custom_model_cv_18_dagger_408	.920
179	mobilenet_v2_0.75_224-tf	.918
180	regnet_y_400mf	.918
181	ViT_B_32_imagenet1k	.916
182	eicircuit_resnet18_bn_20260919	.914
183	nasnet_mobile-tf	.914
184	CORnet-S	.912
185	mobilenet_v2_1_0_160	.912
186	blt_vs	.912
187	inception_v1	.912
188	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.911
189	ReAlnet06	.909
190	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.909
191	mobilenet_v2_0_75_224	.907
192	mobilenet_v2_0.75_160-tf	.907
193	mobilenet_v2_0.75_192-tf	.905
194	efficientnet_b0_imagenet_full	.905
195	mobilenet_v2_0_75_160	.905
196	pnasnet_large_pytorch	.905
197	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.904
198	mobilenet_v1_1.0_224	.904
199	mobilenet_v2_0_75_192	.902
200	resnet-18	.900
201	voneresnet-50-robust	.900
202	mobilenet_v2_0_5_224	.900
203	convnext_base_fb_in1k_gram_aligned_dinov3	.900
204	resnet18_imagenet_full	.898
205	mobilenet_v2_1_0_128	.896
206	mobilenet_v2_0.5_224-tf	.896
207	CLIP-RN50	.896
208	resnet50_robust_l2_eps1	.896
209	mobilenet_v2_1.0_128-tf	.895
210	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.893
211	ReAlnet09	.893
212	ReAlnet10	.893
213	vgg_19	.891
214	vgg_16	.889
215	ReAlnet03	.889
216	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.889
217	ReAlnet05	.887
218	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.886
219	ReAlnet04	.886
220	ReAlnet01	.884
221	mobilenet_v1_0.75_192	.884
222	mobilenet_v1_1.0_160	.884
223	ReAlnet08	.882
224	mobilenet_v1_0.75_224	.880
225	shufflenet_v2_x1_0	.880
226	ReAlnet07	.880
227	mobilenet_v2_1.0_96	.877
228	mobilenet_v1_1.0_192	.873
229	ReAlnet02	.873
230	mobilenet_v2_0.5_160	.866
231	mobilenet_v2_0.5_192-tf	.866
232	mobilenet_v1_1.0_128	.866
233	mobilenet_v2_1-4_224_pytorch	.866
234	mobilenet_v2_1_4_224	.866
235	mobilenet_v2_0.75_128	.861
236	mobilenet_v2_0.35_224	.857
237	mobilenet_v2_0_5_192	.855
238	mobilenet_v2_0.75_96	.848
239	mobilenet_v1_0.5_224	.848
240	mobilenet_v1_0.75_128	.848
241	mobilenet_v1_0.75_160	.848
242	mobilenet_v2_0.35_192	.848
243	mobilenet_v2_0.35_160	.848
244	mobilenet_v2_0.5_128	.848
245	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
246	resnet-50-robust	.839
247	imagenet_l2_3_0	.839
248	resnet50_robust_l2_eps3	.839
249	resnet50-SIN	.834
250	0.5x_resnet-18_LC_w_sh_1_iter	.834
251	mehrer2020_alexnet_training_seed_01	.827
252	mehrer2020_alexnet_training_seed_01_fov12	.827
253	mehrer2020_alexnet_training_seed_01_fov4	.827
254	mehrer2020_alexnet_training_seed_01_fov16	.827
255	resnet_50_v1_spiking_l4a	.825
256	resnet50_l2_3_robust	.823
257	mobilenet_v1_0.5_160	.821
258	mobilenet_v1_0.5_192	.820
259	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.816
260	mehrer2020_alexnet_training_seed_05	.812
261	mehrer2020_alexnet_training_seed_03	.809
262	convnext_small_imagenet_100_seed-0	.805
263	mehrer2020_alexnet_training_seed_02	.805
264	mehrer2020_alexnet_training_seed_04	.802
265	mobilenet_v2_0.35_128	.798
266	mobilenet_v2_0.5_96	.796
267	squeezenet1_0	.791
268	squeezenet1_1	.784
269	mobilenet_v1_0.5_128	.780
270	alexnet_random_linf8_perturb	.779
271	alexnet_ks_torevert	.777
272	alexnet_fov4	.777
273	alexnet_fov12	.777
274	alexnet_fov16	.777
275	alexnet	.777
276	resnet50_linf_8_robust	.771
277	mobilenet_v2_0.35_96	.768
278	alexnet_early_checkpoint	.757
279	mobilenet_v1_0.25_224	.750
280	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.750
281	mobilenet_v1_0.25_192	.725
282	imagenet_l2_10_0	.718
283	AlexNet_SIN	.704
284	AlexNet_SIN_fov4	.704
285	AlexNet_SIN_fov12	.704
286	AlexNet_SIN_fov16	.704
287	mobilenet_v1_0.25_160	.700
288	mobilenet_v1_0.25_128	.679
289	alexnet_l2_3_robust	.673
290	alexnet_robust_correct	.632
291	resnet50_imagenet_100_seed-0	.625
292	deit_small_imagenet_100_seed-0	.600
293	alexnet_linf_8_robust	.570
294	resnet_50_v1_spiking_l4	.496
295	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
296	resnet50_imagenet_10_seed-0	.152
297	convnext_small_imagenet_10_seed-0	.096
298	resnet50_imagenet_1_seed-0	.082
299	deit_small_imagenet_10_seed-0	.075
300	deit_small_imagenet_1_seed-0	.075
301	alexnet_training_seed_06	.068
302	alexnet_training_seed_09	.066
303	resnet-50_untrained	.062
304	alexnet_training_seed_05	.062
305	alexnet_training_seed_02	.062
306	alexnet_training_seed_04	.062
307	alexnet_training_seed_08	.062
308	alexnet_training_seed_10	.062
309	alexnet_training_seed_01	.062
310	resnet_50_v1_spiking_l3	.062
311	alexnet_training_seed_01_fov4	.062
312	alexnet_training_seed_01_fov12	.062
313	alexnet_training_seed_01_fov16	.062
314	resnet50-vicregl0p75	.062
315	alexnet_training_seed_03	.062
316	alexnet_training_seed_07	.061
317	artResNet18_1	.061
318	convnext_small_imagenet_1_seed-0	.057
319	resnet50-barlow	.057
320	barlow-twins-resnet50	.054
321	mobilevit_small	.050
322	resnet50-simclr-vissl	.034
323	resnet50-vicregl0p9	.018
324	ViT-B/32
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	BiT-M-R50x1
327	resnet-18-LC_w_sh_1_iter_conv_init_m
328	BiT-M-R152x2
329	resnet-18-LC_w_sh_1_iter_m
330	resnet-18-LC_w_sh_10_iter_m
331	BiT-M-R50x3
332	resnet-18_test_m
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_10_iter_conv_init_m
335	FrankRobWobv0
336	0.5x_resnet-18_LC
337	CORnetZ_CIFAR10
338	resnet18-local_aggregation
339	r3m_resnet18
340	resnet18-deepcluster
341	CORnet-Z
342	vggface
343	resnet18-instance_recognition
344	my-model
345	0.5x_resnet-18_LC_w_sh_10_iter
346	r3m_resnet50
347	r3m_resnet34
348	resnet-18-LC_m
349	resnet18-supervised
350	r3m_resnet50_nocrop
351	resnet-18-LC_1st_conv_m
352	resnet-18-LC_conv_init_m
353	resnet18-contrastive_multiview
354	0.5x_resnet-18
355	RN50
356	0.5x_resnet-18_LC_w_sh_100_iter
357	RN50
358	resnet18-colorization
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	resnet18-contrastive_predictive
368	resnet-18-LC_untrained
369	resnet-18_untrained
370	resnet-50x2_untrained
371	dcgan
372	unet_entire
373	CORnetZ_CIFAR10_bs32_20_04
374	v1-pyr-nodown
375	pixels
376	dorinet_cornet_z
377	omnivore_swinT
378	openclip
379	vonealexnet_gaussian_noise_std4_fixed
380	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
381	yudixie_resnet50_imagenet1kpret_0_240312
382	yudixie_resnet50_category_class_0_240908
383	yudixie_resnet50_distance_reg_0_240908
384	ConvLSTM
385	PredRNN
386	SimVP
387	TAU
388	MIM
389	hmax
390	ReAlnet01_cornet
391	yudixie_resnet18_distance_translation_0_240719
392	compact_resnet50_V4
393	yolos_tiny
394	yudixie_resnet18_distance_reg_0_240719
395	yudixie_resnet18_rotation_reg_0_240719
396	yudixie_resnet18_imagenet1kpret_0_240719
397	ReAlnet03_cornet
398	yudixie_resnet50_distance_rotation_0_240908
399	yudixie_resnet50_rotation_reg_0_240908
400	vit_base_patch14_dinov2-lvd142m
401	yudixie_resnet50_distance_translation_0_240908
402	resnet18_ecoset_full
403	resnet34_ecoset_full
404	VOneCORnet-S
405	compact_resnet50_IT
406	ReAlnet04_cornet
407	resnet50_ecoset_full
408	compact_vgg19_V4
409	vit_base_patch14_reg4_dinov2-lvd142m
410	yudixie_resnet18_translation_rotation_0_240719
411	yudixie_resnet18_distance_rotation_0_240719
412	ReAlnet01_cornet_fov12
413	ReAlnet05_cornet
414	yudixie_resnet18_category_class_0_240719
415	yudixie_resnet18_random_0_240719
416	yudixie_resnet50_translation_reg_0_240908
417	yudixie_resnet18_cat_obj_class_all_latents_0_240719
418	compact_vgg19_IT
419	yudixie_resnet18_translation_reg_0_240719
420	yudixie_resnet18_distance_translation_rotation_0_240719
421	ReAlnet06_cornet
422	yudixie_resnet18_object_class_0_240719
423	SeLaVi-AVE
424	compact_resnet50_robust_V4
425	ReAlnet01_cornet_fov16
426	r3d_18
427	yudixie_resnet50_translation_rotation_0_240908
428	ReAlnet07_cornet
429	omnivore_swinS
430	mc3_18
431	ReAlnet02_ALL
432	s3d
433	ReAlnet08_cornet
434	omnivore_swinB
435	resnet50-vicreg
436	mvit_v1_b
437	ReAlnet01_ALL
438	compact_cornets_V4
439	ReAlnet03_ALL
440	ReAlnet09_cornet
441	resnet18_imagenet21kP
442	ReAlnet04_ALL
443	compact_cornets_IT
444	vit_base_patch16_dinov3-lvd1689m
445	SeLaVi-Kinetics400
446	SeLaVi-VGG-Sound
447	ReAlnet06_ALL
448	SeLaVi-Kinetics-Sound
449	compact_resnet50_robust_IT
450	yudixie_resnet50_random_0_240908
451	yudixie_resnet50_distance_translation_rotation_0_240908
452	yudixie_resnet50_imagenet1kpret_0_240908
453	ReAlnet07_ALL
454	r2plus1d_18
455	mvit_v2_s
456	ReAlnet08_ALL
457	yudixie_resnet50_object_class_0_240908
458	yudixie_resnet50_cat_obj_class_all_latents_0_240908
459	ReAlnet09_ALL
460	ReAlnet02_cornet
461	resnet50_primary_visual_cortex
462	ReAlnet10_ALL
463	ReAlnet10_cornet
464	briaai_rmbg_1_4
465	ReAlnet05_ALL
466	ReAlnet01_cornet_fov4
467	resnet101_ecoset_full
468	resnet152_ecoset_full
469	resnet18_fair
470	pr1_stl10_disentangled_alpha04
471	predictive_coding_pc
472	yudixie_wbh_object_class_scaling_0415_run_0
473	yudixie_wbh_object_class_scaling_0415_run_3
474	yudixie_wbh_object_class_scaling_0415_imagenet
475	yudixie_wbh_object_class_scaling_0415_run_2
476	yudixie_wbh_object_class_scaling_0415_run_1
477	deepeisnn_ei_cifar10_vgg19
478	deepeisnn_ei_cifar10_resnet18
479	deepeisnn_ei_imagenet_resnet18
480	dinov2_vitl14_reg_lc_featread
481	gabor_filter_entropy_4x4
482	gabor_filter_entropy_4x4_v2
483	gabor_filter_entropy_4x4_v2_forcedCosine
484	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: Geirhos2021falsecolour

Metric: top1