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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_xxlarge:clip_laion2b_soup_ft_in1k	.996
3	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.996
4	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.996
5	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.996
6	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.996
7	vit_large_patch14_clip_224:openai_ft_in1k	.995
8	vit_large_patch14_clip_336:laion2b_ft_in1k	.995
9	vit_large_patch14_clip_224:laion2b_ft_in1k	.995
10	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.993
11	convnext_base:clip_laiona_augreg_ft_in1k_384	.993
12	deit3_base_patch16_224_fb_in22k_ft_in1k	.993
13	nasnet_large-tf	.991
14	convnext_xlarge:fb_in22k_ft_in1k	.991
15	convnext_large_imagenet_full_seed-0	.991
16	convnext_large:fb_in22k_ft_in1k	.991
17	convnext_base_fb_in1k	.991
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.989
19	nasnet_large	.989
20	deit3_base_patch16_224_fb_in1k	.989
21	fixres_resnext101_32x48d_wsl	.989
22	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.989
23	resnext101_32x32d_wsl	.988
24	resnext101_32x48d_wsl	.988
25	convnext_tiny_imagenet_full_seed-0	.988
26	vit_base_patch16_clip_224:openai_ft_in1k	.988
27	convnext_base_fb_in22k_ft_in1k	.988
28	AdvProp_efficientnet-b7	.986
29	resnext101_32x8d_wsl	.986
30	effnetv2m_custom384	.986
31	convnext_small_imagenet_full_seed-0	.986
32	deit_large_imagenet_full_seed-0	.986
33	deit_base_imagenet_full_seed-0	.984
34	convnext_base_imagenet_full_seed-0	.984
35	cvt_cvt-13-384-in1k_4_LucyV4	.984
36	pnasnet_large_halfPrecision	.984
37	pnasnet_large	.984
38	resnext101_32x16d_wsl	.984
39	cvt_cvt-13-224-in1k_4_LucyV4	.982
40	inception_v4-tf	.982
41	xception-keras	.980
42	vit_relpos_base_patch16_clsgap_224:sw_in1k	.977
43	AdvProp_efficientnet-b6	.977
44	pnasnet_large-tf	.975
45	efficientnet-b7	.975
46	BiT-S-R101x3	.975
47	resnet-152_v2-tf	.975
48	convnext_tiny:in12k_ft_in1k	.975
49	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.975
50	inception_v3_pytorch	.975
51	cvt_cvt-21-384-in1k_4_LucyV4	.975
52	resnet_101_v2	.975
53	resnet_101_v1	.975
54	AdvProp_efficientnet-b4	.973
55	AT_efficientnet-b4	.973
56	convnext_tiny_sup	.973
57	focalnet_tiny_lrf_in1k	.973
58	cvt_cvt-21-224-in1k_4_LucyV4	.973
59	inception_v3	.973
60	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.973
61	resnet-152_v2_pytorch	.973
62	inception_v4	.973
63	resnet50_st_wzc_seed7_2_323	.973
64	resnet50_st_wzc_seed5_2_323	.971
65	deit_small_imagenet_full_seed-0	.971
66	inception_v3-tf	.971
67	effnetb1_272x240	.971
68	resnet50_st_wzc_2_323	.970
69	resnet-50_v2-tf	.970
70	AT_efficientnet-b2	.968
71	AdvProp_efficientnet-b2	.968
72	AdvProp_efficientnet-b0	.968
73	resnet50_st_wzc_2	.968
74	resnet50_st_wzc_noseed_2_323	.968
75	resnet50_st_wzc_seed4_2_323	.968
76	tv_efficientnet-b1	.966
77	Res2Net50_26w_4s	.964
78	resnet50_st_wzc_seed3_2_323	.964
79	grcnn_109	.964
80	resnet_152_v2	.964
81	BiT-S-R152x2	.964
82	antialiased-rnext101_32x8d	.964
83	antialias-resnet152	.964
84	BiT-S-R152x4	.963
85	resnet-101_v2-tf	.963
86	cv_18_dagger_408_pretrained	.963
87	resnet152_imagenet_full	.963
88	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.963
89	resnet50_ewconplus_wzc_seed3_2_323	.963
90	densenet-121-keras	.961
91	resnet101_imagenet_full	.961
92	densenet-121	.961
93	resnet50_st_wzc_seed6_2_323	.961
94	resnet50_ewconplus_wzc_seed7_2_323	.961
95	efficientnet_b0	.959
96	densenet-201	.957
97	densenet_201_pytorch	.957
98	resnet50_ewconplus_wzc_seed4_2_323	.957
99	resnet-101_v1-tf	.957
100	densenet-201-keras	.957
101	resnet50-moclr8deg	.957
102	resnet-152_v1-tf	.955
103	resnet50-VITO-8deg-cc	.955
104	resnet50_ewconplus_wzc	.955
105	resnet_50_v2	.955
106	resnet50_ewconplus_noseed_2_323	.955
107	densenet-169-keras	.954
108	grcnn	.954
109	resnet50-vitoimagevidnet8	.954
110	resnet_152_v1	.954
111	densenet-169	.954
112	resnet50_ewconplus_wzc_seed6_2_323	.954
113	BiT-S-R50x3	.952
114	antialiased-r50	.952
115	inception_v2-tf	.950
116	resnet50	.950
117	resnet50_imagenet_full	.950
118	resnet50_ewconplus_wzc_2_323	.950
119	resnet_SIN_IN_FT_IN	.948
120	resnet50-SIN_IN_IN	.948
121	resnet50_ewconplus_wzc_seed2_2__323	.948
122	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.946
123	convnext_femto_ols:d1_in1k	.946
124	resnet-50_v1-tf	.946
125	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.946
126	resnet50_ewconplus_wzc_seed5_2__323	.945
127	BiT-S-R50x1	.945
128	resnet-50-pytorch	.943
129	mobilenet_v2_1.4_224-tf	.943
130	mobilenet_v2_1.0_224-tf	.943
131	resnet50-sup	.943
132	resnet50_moco_v2	.943
133	resnet50_tutorial	.943
134	resnet_50_v1	.943
135	voneresnet-50-non_stochastic	.941
136	xception	.941
137	vonegrcnn_62e_nobn	.941
138	vonegrcnn_47e	.941
139	efficientnet_b1_imagenet_full	.939
140	eBarlow_lmda_02_1000ep	.939
141	vonegrcnn_52e_full	.939
142	BiT-S-R101x1	.939
143	mobilenet_v2_1.3_224-tf	.939
144	vonegrcnn_62e_nobn	.938
145	nasnet_mobile	.938
146	mobilenet_v2_1.0_192-tf	.936
147	mobilenet_v2_1.0_160-tf	.936
148	mobilenet_v2_1_0_224	.936
149	resnet_50_v1_spiking	.934
150	vonegrcnn_52e_full	.934
151	resnet-34	.934
152	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.932
153	vonegrcnn_47e	.932
154	ViT_L_32_imagenet1k	.930
155	resnet34_imagenet_full	.930
156	AdvProp_efficientnet-b8	.929
157	mobilenet_v2_1_0_192	.929
158	resnet50_eMMCR_Vanilla	.929
159	mobilenet_v2_1_3_224	.927
160	resnet50-SIN_IN	.927
161	vgg-19-keras	.927
162	inception_v1-tf	.925
163	efficientnet_b2_imagenet_full	.925
164	vgg-16-keras	.923
165	ViT_B_16_imagenet1k	.923
166	voneresnet-50	.920
167	custom_model_cv_18_dagger_408	.920
168	mobilenet_v2_0.75_224-tf	.918
169	regnet_y_400mf	.918
170	ViT_B_32_imagenet1k	.916
171	nasnet_mobile-tf	.914
172	CORnet-S	.912
173	mobilenet_v2_1_0_160	.912
174	blt_vs	.912
175	inception_v1	.912
176	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.911
177	ReAlnet06	.909
178	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.909
179	mobilenet_v2_0_75_224	.907
180	mobilenet_v2_0.75_160-tf	.907
181	mobilenet_v2_0.75_192-tf	.905
182	efficientnet_b0_imagenet_full	.905
183	mobilenet_v2_0_75_160	.905
184	pnasnet_large_pytorch	.905
185	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.904
186	mobilenet_v1_1.0_224	.904
187	mobilenet_v2_0_75_192	.902
188	resnet-18	.900
189	voneresnet-50-robust	.900
190	mobilenet_v2_0_5_224	.900
191	convnext_base_fb_in1k_gram_aligned_dinov3	.900
192	resnet18_imagenet_full	.898
193	mobilenet_v2_1_0_128	.896
194	mobilenet_v2_0.5_224-tf	.896
195	CLIP-RN50	.896
196	resnet50_robust_l2_eps1	.896
197	mobilenet_v2_1.0_128-tf	.895
198	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.893
199	ReAlnet09	.893
200	ReAlnet10	.893
201	vgg_19	.891
202	vgg_16	.889
203	ReAlnet03	.889
204	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.889
205	ReAlnet05	.887
206	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.886
207	ReAlnet04	.886
208	ReAlnet01	.884
209	mobilenet_v1_0.75_192	.884
210	mobilenet_v1_1.0_160	.884
211	ReAlnet08	.882
212	mobilenet_v1_0.75_224	.880
213	shufflenet_v2_x1_0	.880
214	ReAlnet07	.880
215	mobilenet_v2_1.0_96	.877
216	mobilenet_v1_1.0_192	.873
217	ReAlnet02	.873
218	mobilenet_v2_0.5_160	.866
219	mobilenet_v2_0.5_192-tf	.866
220	mobilenet_v1_1.0_128	.866
221	mobilenet_v2_1-4_224_pytorch	.866
222	mobilenet_v2_1_4_224	.866
223	mobilenet_v2_0.75_128	.861
224	mobilenet_v2_0.35_224	.857
225	mobilenet_v2_0_5_192	.855
226	mobilenet_v2_0.75_96	.848
227	mobilenet_v1_0.5_224	.848
228	mobilenet_v1_0.75_128	.848
229	mobilenet_v1_0.75_160	.848
230	mobilenet_v2_0.35_192	.848
231	mobilenet_v2_0.35_160	.848
232	mobilenet_v2_0.5_128	.848
233	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
234	resnet-50-robust	.839
235	imagenet_l2_3_0	.839
236	resnet50_robust_l2_eps3	.839
237	resnet50-SIN	.834
238	0.5x_resnet-18_LC_w_sh_1_iter	.834
239	resnet_50_v1_spiking_l4a	.825
240	resnet50_l2_3_robust	.823
241	mobilenet_v1_0.5_160	.821
242	mobilenet_v1_0.5_192	.820
243	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.816
244	convnext_small_imagenet_100_seed-0	.805
245	mobilenet_v2_0.35_128	.798
246	mobilenet_v2_0.5_96	.796
247	squeezenet1_0	.791
248	squeezenet1_1	.784
249	mobilenet_v1_0.5_128	.780
250	alexnet_random_linf8_perturb	.779
251	alexnet_ks_torevert	.777
252	alexnet_fov4	.777
253	alexnet_fov12	.777
254	alexnet_fov16	.777
255	alexnet	.777
256	resnet50_linf_8_robust	.771
257	mobilenet_v2_0.35_96	.768
258	alexnet_early_checkpoint	.757
259	mobilenet_v1_0.25_224	.750
260	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.750
261	mobilenet_v1_0.25_192	.725
262	imagenet_l2_10_0	.718
263	AlexNet_SIN	.704
264	AlexNet_SIN_fov4	.704
265	AlexNet_SIN_fov12	.704
266	AlexNet_SIN_fov16	.704
267	mobilenet_v1_0.25_160	.700
268	mobilenet_v1_0.25_128	.679
269	alexnet_l2_3_robust	.673
270	alexnet_robust_correct	.632
271	resnet50_imagenet_100_seed-0	.625
272	deit_small_imagenet_100_seed-0	.600
273	alexnet_linf_8_robust	.570
274	resnet_50_v1_spiking_l4	.496
275	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
276	resnet50_imagenet_10_seed-0	.152
277	convnext_small_imagenet_10_seed-0	.096
278	resnet50_imagenet_1_seed-0	.082
279	deit_small_imagenet_10_seed-0	.075
280	deit_small_imagenet_1_seed-0	.075
281	alexnet_training_seed_06	.068
282	alexnet_training_seed_09	.066
283	resnet-50_untrained	.062
284	resnet50-meshes-lt-100-original-pretrained	.062
285	resnet50-meshes-lt-100-original-scratch	.062
286	alexnet_training_seed_05	.062
287	alexnet_training_seed_02	.062
288	alexnet_training_seed_04	.062
289	alexnet_training_seed_08	.062
290	alexnet_training_seed_10	.062
291	alexnet_training_seed_01	.062
292	resnet_50_v1_spiking_l3	.062
293	alexnet_training_seed_01_fov4	.062
294	alexnet_training_seed_01_fov12	.062
295	alexnet_training_seed_01_fov16	.062
296	resnet50-vicregl0p75	.062
297	alexnet_training_seed_03	.062
298	alexnet_training_seed_07	.061
299	artResNet18_1	.061
300	convnext_small_imagenet_1_seed-0	.057
301	resnet50-barlow	.057
302	barlow-twins-resnet50	.054
303	mobilevit_small	.050
304	resnet50-simclr-vissl	.034
305	resnet50-vicregl0p9	.018
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	BiT-M-R152x2
311	resnet-18-LC_w_sh_1_iter_m
312	resnet-18-LC_w_sh_10_iter_m
313	BiT-M-R50x3
314	resnet-18_test_m
315	resnet-18-LC_w_sh_100_iter_m
316	resnet-18-LC_w_sh_10_iter_conv_init_m
317	FrankRobWobv0
318	0.5x_resnet-18_LC
319	CORnetZ_CIFAR10
320	resnet18-local_aggregation
321	r3m_resnet18
322	resnet18-deepcluster
323	CORnet-Z
324	vggface
325	resnet18-instance_recognition
326	my-model
327	0.5x_resnet-18_LC_w_sh_10_iter
328	r3m_resnet50
329	r3m_resnet34
330	resnet-18-LC_m
331	resnet18-supervised
332	r3m_resnet50_nocrop
333	resnet-18-LC_1st_conv_m
334	resnet-18-LC_conv_init_m
335	resnet18-contrastive_multiview
336	0.5x_resnet-18
337	RN50
338	0.5x_resnet-18_LC_w_sh_100_iter
339	RN50
340	resnet18-colorization
341	resnet18-relative_position
342	resnet18-autoencoder
343	resnet18-simclr
344	ViT_L_32
345	ViT_B_32
346	resnet18-depth_prediction
347	ViT_B_16
348	r3m_resnet34_nocrop
349	resnet18-contrastive_predictive
350	resnet-18-LC_untrained
351	resnet-18_untrained
352	resnet-50x2_untrained
353	dcgan
354	unet_entire
355	CORnetZ_CIFAR10_bs32_20_04
356	v1-pyr-nodown
357	pixels
358	dorinet_cornet_z
359	omnivore_swinT
360	resnet50
361	openclip
362	vonealexnet_gaussian_noise_std4_fixed
363	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
364	yudixie_resnet50_imagenet1kpret_0_240312
365	yudixie_resnet50_category_class_0_240908
366	yudixie_resnet50_distance_reg_0_240908
367	ConvLSTM
368	PredRNN
369	SimVP
370	TAU
371	MIM
372	hmax
373	ReAlnet01_cornet
374	yudixie_resnet18_distance_translation_0_240719
375	compact_resnet50_V4
376	yolos_tiny
377	yudixie_resnet18_distance_reg_0_240719
378	yudixie_resnet18_rotation_reg_0_240719
379	yudixie_resnet18_imagenet1kpret_0_240719
380	ReAlnet03_cornet
381	yudixie_resnet50_distance_rotation_0_240908
382	yudixie_resnet50_rotation_reg_0_240908
383	vit_base_patch14_dinov2-lvd142m
384	yudixie_resnet50_distance_translation_0_240908
385	ReAlnet01_cornet_fov4
386	resnet18_ecoset_full
387	resnet34_ecoset_full
388	VOneCORnet-S
389	compact_resnet50_IT
390	ReAlnet04_cornet
391	resnet50_ecoset_full
392	resnet101_ecoset_full
393	aughost_fair_resnet18
394	resnet152_ecoset_full
395	compact_vgg19_V4
396	vit_base_patch14_reg4_dinov2-lvd142m
397	yudixie_resnet18_translation_rotation_0_240719
398	yudixie_resnet18_distance_rotation_0_240719
399	ReAlnet01_cornet_fov12
400	ReAlnet05_cornet
401	yudixie_resnet18_category_class_0_240719
402	yudixie_resnet18_random_0_240719
403	yudixie_resnet50_translation_reg_0_240908
404	yudixie_resnet18_cat_obj_class_all_latents_0_240719
405	compact_vgg19_IT
406	yudixie_resnet18_translation_reg_0_240719
407	yudixie_resnet18_distance_translation_rotation_0_240719
408	ReAlnet06_cornet
409	yudixie_resnet18_object_class_0_240719
410	SeLaVi-AVE
411	compact_resnet50_robust_V4
412	ReAlnet01_cornet_fov16
413	r3d_18
414	yudixie_resnet50_translation_rotation_0_240908
415	ReAlnet07_cornet
416	omnivore_swinS
417	mc3_18
418	ReAlnet02_ALL
419	s3d
420	ReAlnet08_cornet
421	omnivore_swinB
422	resnet50-vicreg
423	mvit_v1_b
424	ReAlnet01_ALL
425	compact_cornets_V4
426	ReAlnet03_ALL
427	ReAlnet09_cornet
428	resnet18_imagenet21kP
429	ReAlnet04_ALL
430	compact_cornets_IT
431	vit_base_patch16_dinov3-lvd1689m
432	SeLaVi-Kinetics400
433	SeLaVi-VGG-Sound
434	ReAlnet06_ALL
435	SeLaVi-Kinetics-Sound
436	compact_resnet50_robust_IT
437	yudixie_resnet50_random_0_240908
438	yudixie_resnet50_distance_translation_rotation_0_240908
439	yudixie_resnet50_imagenet1kpret_0_240908
440	ReAlnet07_ALL
441	r2plus1d_18
442	mvit_v2_s
443	ReAlnet08_ALL
444	yudixie_resnet50_object_class_0_240908
445	yudixie_resnet50_cat_obj_class_all_latents_0_240908
446	ReAlnet09_ALL
447	ReAlnet02_cornet
448	resnet50_primary_visual_cortex
449	ReAlnet10_ALL
450	ReAlnet10_cornet
451	briaai_rmbg_1_4
452	ReAlnet05_ALL
453	resnet18_fair
454	pr1_stl10_disentangled_alpha04
455	predictive_coding_pc
456	yudixie_wbh_object_class_scaling_0415_run_0
457	yudixie_wbh_object_class_scaling_0415_run_3
458	yudixie_wbh_object_class_scaling_0415_imagenet
459	yudixie_wbh_object_class_scaling_0415_run_2
460	yudixie_wbh_object_class_scaling_0415_run_1
461	deepeisnn_ei_cifar10_vgg19
462	deepeisnn_ei_cifar10_resnet18
463	deepeisnn_ei_imagenet_resnet18

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