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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	resnet-101_v1-tf	.957
106	densenet-201-keras	.957
107	resnet50-moclr8deg	.957
108	resnet-152_v1-tf	.955
109	resnet50-VITO-8deg-cc	.955
110	resnet50_ewconplus_wzc	.955
111	resnet_50_v2	.955
112	densenet-169-keras	.954
113	grcnn	.954
114	resnet50-vitoimagevidnet8	.954
115	resnet_152_v1	.954
116	densenet-169	.954
117	resnet50_ewconplus_wzc_seed6_2_323	.954
118	BiT-S-R50x3	.952
119	antialiased-r50	.952
120	inception_v2-tf	.950
121	resnet50	.950
122	resnet50_imagenet_full	.950
123	resnet_SIN_IN_FT_IN	.948
124	resnet50-SIN_IN_IN	.948
125	resnet50_ewconplus_wzc_seed2_2__323	.948
126	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.946
127	convnext_femto_ols:d1_in1k	.946
128	resnet-50_v1-tf	.946
129	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.946
130	resnet50_ewconplus_wzc_seed5_2__323	.945
131	BiT-S-R50x1	.945
132	resnet-50-pytorch	.943
133	mobilenet_v2_1.4_224-tf	.943
134	mobilenet_v2_1.0_224-tf	.943
135	resnet50-sup	.943
136	resnet50_moco_v2	.943
137	resnet50_tutorial	.943
138	resnet_50_v1	.943
139	voneresnet-50-non_stochastic	.941
140	xception	.941
141	vonegrcnn_62e_nobn	.941
142	vonegrcnn_47e	.941
143	efficientnet_b1_imagenet_full	.939
144	eBarlow_lmda_02_1000ep	.939
145	vonegrcnn_52e_full	.939
146	BiT-S-R101x1	.939
147	mobilenet_v2_1.3_224-tf	.939
148	vonegrcnn_62e_nobn	.938
149	nasnet_mobile	.938
150	mobilenet_v2_1.0_192-tf	.936
151	mobilenet_v2_1.0_160-tf	.936
152	mobilenet_v2_1_0_224	.936
153	resnet_50_v1_spiking	.934
154	vonegrcnn_52e_full	.934
155	resnet-34	.934
156	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.932
157	resnet50_hvs_wzc	.932
158	vonegrcnn_47e	.932
159	ViT_L_32_imagenet1k	.930
160	resnet34_imagenet_full	.930
161	AdvProp_efficientnet-b8	.929
162	mobilenet_v2_1_0_192	.929
163	resnet50_eMMCR_Vanilla	.929
164	mobilenet_v2_1_3_224	.927
165	resnet50-SIN_IN	.927
166	vgg-19-keras	.927
167	inception_v1-tf	.925
168	efficientnet_b2_imagenet_full	.925
169	vgg-16-keras	.923
170	ViT_B_16_imagenet1k	.923
171	voneresnet-50	.920
172	custom_model_cv_18_dagger_408	.920
173	mobilenet_v2_0.75_224-tf	.918
174	regnet_y_400mf	.918
175	ViT_B_32_imagenet1k	.916
176	eicircuit_resnet18_bn_20260919	.914
177	nasnet_mobile-tf	.914
178	CORnet-S	.912
179	mobilenet_v2_1_0_160	.912
180	blt_vs	.912
181	inception_v1	.912
182	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.911
183	ReAlnet06	.909
184	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.909
185	mobilenet_v2_0_75_224	.907
186	mobilenet_v2_0.75_160-tf	.907
187	mobilenet_v2_0.75_192-tf	.905
188	efficientnet_b0_imagenet_full	.905
189	mobilenet_v2_0_75_160	.905
190	pnasnet_large_pytorch	.905
191	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.904
192	mobilenet_v1_1.0_224	.904
193	mobilenet_v2_0_75_192	.902
194	resnet-18	.900
195	voneresnet-50-robust	.900
196	mobilenet_v2_0_5_224	.900
197	convnext_base_fb_in1k_gram_aligned_dinov3	.900
198	resnet18_imagenet_full	.898
199	mobilenet_v2_1_0_128	.896
200	mobilenet_v2_0.5_224-tf	.896
201	CLIP-RN50	.896
202	resnet50_robust_l2_eps1	.896
203	mobilenet_v2_1.0_128-tf	.895
204	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.893
205	ReAlnet09	.893
206	ReAlnet10	.893
207	vgg_19	.891
208	vgg_16	.889
209	ReAlnet03	.889
210	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.889
211	ReAlnet05	.887
212	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.886
213	ReAlnet04	.886
214	ReAlnet01	.884
215	mobilenet_v1_0.75_192	.884
216	mobilenet_v1_1.0_160	.884
217	ReAlnet08	.882
218	mobilenet_v1_0.75_224	.880
219	shufflenet_v2_x1_0	.880
220	ReAlnet07	.880
221	mobilenet_v2_1.0_96	.877
222	mobilenet_v1_1.0_192	.873
223	ReAlnet02	.873
224	mobilenet_v2_0.5_160	.866
225	mobilenet_v2_0.5_192-tf	.866
226	mobilenet_v1_1.0_128	.866
227	mobilenet_v2_1-4_224_pytorch	.866
228	mobilenet_v2_1_4_224	.866
229	mobilenet_v2_0.75_128	.861
230	mobilenet_v2_0.35_224	.857
231	mobilenet_v2_0_5_192	.855
232	mobilenet_v2_0.75_96	.848
233	mobilenet_v1_0.5_224	.848
234	mobilenet_v1_0.75_128	.848
235	mobilenet_v1_0.75_160	.848
236	mobilenet_v2_0.35_192	.848
237	mobilenet_v2_0.35_160	.848
238	mobilenet_v2_0.5_128	.848
239	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
240	resnet-50-robust	.839
241	imagenet_l2_3_0	.839
242	resnet50_robust_l2_eps3	.839
243	resnet50-SIN	.834
244	0.5x_resnet-18_LC_w_sh_1_iter	.834
245	resnet_50_v1_spiking_l4a	.825
246	resnet50_l2_3_robust	.823
247	mobilenet_v1_0.5_160	.821
248	mobilenet_v1_0.5_192	.820
249	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.816
250	convnext_small_imagenet_100_seed-0	.805
251	mobilenet_v2_0.35_128	.798
252	mobilenet_v2_0.5_96	.796
253	squeezenet1_0	.791
254	squeezenet1_1	.784
255	mobilenet_v1_0.5_128	.780
256	alexnet_random_linf8_perturb	.779
257	alexnet_ks_torevert	.777
258	alexnet_fov4	.777
259	alexnet_fov12	.777
260	alexnet_fov16	.777
261	alexnet	.777
262	resnet50_linf_8_robust	.771
263	mobilenet_v2_0.35_96	.768
264	alexnet_early_checkpoint	.757
265	mobilenet_v1_0.25_224	.750
266	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.750
267	mobilenet_v1_0.25_192	.725
268	imagenet_l2_10_0	.718
269	AlexNet_SIN	.704
270	AlexNet_SIN_fov4	.704
271	AlexNet_SIN_fov12	.704
272	AlexNet_SIN_fov16	.704
273	mobilenet_v1_0.25_160	.700
274	mobilenet_v1_0.25_128	.679
275	alexnet_l2_3_robust	.673
276	alexnet_robust_correct	.632
277	resnet50_imagenet_100_seed-0	.625
278	deit_small_imagenet_100_seed-0	.600
279	alexnet_linf_8_robust	.570
280	resnet_50_v1_spiking_l4	.496
281	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
282	resnet50_imagenet_10_seed-0	.152
283	convnext_small_imagenet_10_seed-0	.096
284	resnet50_imagenet_1_seed-0	.082
285	deit_small_imagenet_10_seed-0	.075
286	deit_small_imagenet_1_seed-0	.075
287	alexnet_training_seed_06	.068
288	alexnet_training_seed_09	.066
289	resnet-50_untrained	.062
290	alexnet_training_seed_05	.062
291	alexnet_training_seed_02	.062
292	alexnet_training_seed_04	.062
293	alexnet_training_seed_08	.062
294	alexnet_training_seed_10	.062
295	alexnet_training_seed_01	.062
296	resnet_50_v1_spiking_l3	.062
297	alexnet_training_seed_01_fov4	.062
298	alexnet_training_seed_01_fov12	.062
299	alexnet_training_seed_01_fov16	.062
300	resnet50-vicregl0p75	.062
301	alexnet_training_seed_03	.062
302	alexnet_training_seed_07	.061
303	artResNet18_1	.061
304	convnext_small_imagenet_1_seed-0	.057
305	resnet50-barlow	.057
306	barlow-twins-resnet50	.054
307	mobilevit_small	.050
308	resnet50-simclr-vissl	.034
309	resnet50-vicregl0p9	.018
310	ViT-B/32
311	resnet-18-LC_w_sh_100_iter_conv_init_m
312	BiT-M-R50x1
313	resnet-18-LC_w_sh_1_iter_conv_init_m
314	BiT-M-R152x2
315	resnet-18-LC_w_sh_1_iter_m
316	resnet-18-LC_w_sh_10_iter_m
317	BiT-M-R50x3
318	resnet-18_test_m
319	resnet-18-LC_w_sh_100_iter_m
320	resnet-18-LC_w_sh_10_iter_conv_init_m
321	FrankRobWobv0
322	0.5x_resnet-18_LC
323	CORnetZ_CIFAR10
324	resnet18-local_aggregation
325	r3m_resnet18
326	resnet18-deepcluster
327	CORnet-Z
328	vggface
329	resnet18-instance_recognition
330	my-model
331	0.5x_resnet-18_LC_w_sh_10_iter
332	r3m_resnet50
333	r3m_resnet34
334	resnet-18-LC_m
335	resnet18-supervised
336	r3m_resnet50_nocrop
337	resnet-18-LC_1st_conv_m
338	resnet-18-LC_conv_init_m
339	resnet18-contrastive_multiview
340	0.5x_resnet-18
341	RN50
342	0.5x_resnet-18_LC_w_sh_100_iter
343	RN50
344	resnet18-colorization
345	resnet18-relative_position
346	resnet18-autoencoder
347	resnet18-simclr
348	ViT_L_32
349	ViT_B_32
350	resnet18-depth_prediction
351	ViT_B_16
352	r3m_resnet34_nocrop
353	resnet18-contrastive_predictive
354	resnet-18-LC_untrained
355	resnet-18_untrained
356	resnet-50x2_untrained
357	dcgan
358	unet_entire
359	CORnetZ_CIFAR10_bs32_20_04
360	v1-pyr-nodown
361	pixels
362	dorinet_cornet_z
363	omnivore_swinT
364	openclip
365	vonealexnet_gaussian_noise_std4_fixed
366	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
367	yudixie_resnet50_imagenet1kpret_0_240312
368	yudixie_resnet50_category_class_0_240908
369	yudixie_resnet50_distance_reg_0_240908
370	ConvLSTM
371	PredRNN
372	SimVP
373	TAU
374	MIM
375	hmax
376	ReAlnet01_cornet
377	yudixie_resnet18_distance_translation_0_240719
378	compact_resnet50_V4
379	yolos_tiny
380	yudixie_resnet18_distance_reg_0_240719
381	yudixie_resnet18_rotation_reg_0_240719
382	yudixie_resnet18_imagenet1kpret_0_240719
383	ReAlnet03_cornet
384	yudixie_resnet50_distance_rotation_0_240908
385	yudixie_resnet50_rotation_reg_0_240908
386	vit_base_patch14_dinov2-lvd142m
387	yudixie_resnet50_distance_translation_0_240908
388	ReAlnet01_cornet_fov4
389	resnet18_ecoset_full
390	resnet34_ecoset_full
391	VOneCORnet-S
392	compact_resnet50_IT
393	ReAlnet04_cornet
394	resnet50_ecoset_full
395	resnet101_ecoset_full
396	resnet152_ecoset_full
397	compact_vgg19_V4
398	vit_base_patch14_reg4_dinov2-lvd142m
399	yudixie_resnet18_translation_rotation_0_240719
400	yudixie_resnet18_distance_rotation_0_240719
401	ReAlnet01_cornet_fov12
402	ReAlnet05_cornet
403	yudixie_resnet18_category_class_0_240719
404	yudixie_resnet18_random_0_240719
405	yudixie_resnet50_translation_reg_0_240908
406	yudixie_resnet18_cat_obj_class_all_latents_0_240719
407	compact_vgg19_IT
408	yudixie_resnet18_translation_reg_0_240719
409	yudixie_resnet18_distance_translation_rotation_0_240719
410	ReAlnet06_cornet
411	yudixie_resnet18_object_class_0_240719
412	SeLaVi-AVE
413	compact_resnet50_robust_V4
414	ReAlnet01_cornet_fov16
415	r3d_18
416	yudixie_resnet50_translation_rotation_0_240908
417	ReAlnet07_cornet
418	omnivore_swinS
419	mc3_18
420	ReAlnet02_ALL
421	s3d
422	ReAlnet08_cornet
423	omnivore_swinB
424	resnet50-vicreg
425	mvit_v1_b
426	ReAlnet01_ALL
427	compact_cornets_V4
428	ReAlnet03_ALL
429	ReAlnet09_cornet
430	resnet18_imagenet21kP
431	ReAlnet04_ALL
432	compact_cornets_IT
433	vit_base_patch16_dinov3-lvd1689m
434	SeLaVi-Kinetics400
435	SeLaVi-VGG-Sound
436	ReAlnet06_ALL
437	SeLaVi-Kinetics-Sound
438	compact_resnet50_robust_IT
439	yudixie_resnet50_random_0_240908
440	yudixie_resnet50_distance_translation_rotation_0_240908
441	yudixie_resnet50_imagenet1kpret_0_240908
442	ReAlnet07_ALL
443	r2plus1d_18
444	mvit_v2_s
445	ReAlnet08_ALL
446	yudixie_resnet50_object_class_0_240908
447	yudixie_resnet50_cat_obj_class_all_latents_0_240908
448	ReAlnet09_ALL
449	ReAlnet02_cornet
450	resnet50_primary_visual_cortex
451	ReAlnet10_ALL
452	ReAlnet10_cornet
453	briaai_rmbg_1_4
454	ReAlnet05_ALL
455	resnet18_fair
456	pr1_stl10_disentangled_alpha04
457	predictive_coding_pc
458	yudixie_wbh_object_class_scaling_0415_run_0
459	yudixie_wbh_object_class_scaling_0415_run_3
460	yudixie_wbh_object_class_scaling_0415_imagenet
461	yudixie_wbh_object_class_scaling_0415_run_2
462	yudixie_wbh_object_class_scaling_0415_run_1
463	deepeisnn_ei_cifar10_vgg19
464	deepeisnn_ei_cifar10_resnet18
465	deepeisnn_ei_imagenet_resnet18
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: Geirhos2021falsecolour

Metric: top1