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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	resnext101_32x16d_wsl	.984
50	cvt_cvt-13-224-in1k_4_LucyV4	.982
51	inception_v4-tf	.982
52	xception-keras	.980
53	vit_relpos_base_patch16_clsgap_224:sw_in1k	.977
54	AdvProp_efficientnet-b6	.977
55	pnasnet_large-tf	.975
56	efficientnet-b7	.975
57	BiT-S-R101x3	.975
58	resnet-152_v2-tf	.975
59	convnext_tiny:in12k_ft_in1k	.975
60	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.975
61	inception_v3_pytorch	.975
62	cvt_cvt-21-384-in1k_4_LucyV4	.975
63	resnet_101_v2	.975
64	resnet_101_v1	.975
65	AdvProp_efficientnet-b4	.973
66	AT_efficientnet-b4	.973
67	convnext_tiny_sup	.973
68	focalnet_tiny_lrf_in1k	.973
69	cvt_cvt-21-224-in1k_4_LucyV4	.973
70	inception_v3	.973
71	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.973
72	resnet-152_v2_pytorch	.973
73	inception_v4	.973
74	resnet50_st_wzc_seed7_2_323	.973
75	deit_small_imagenet_full_seed-0	.971
76	inception_v3-tf	.971
77	effnetb1_272x240	.971
78	resnet-50_v2-tf	.970
79	AT_efficientnet-b2	.968
80	AdvProp_efficientnet-b2	.968
81	AdvProp_efficientnet-b0	.968
82	resnet50_st_wzc_2	.968
83	tv_efficientnet-b1	.966
84	Res2Net50_26w_4s	.964
85	resnet50_st_wzc_seed3_2_323	.964
86	grcnn_109	.964
87	resnet_152_v2	.964
88	BiT-S-R152x2	.964
89	antialiased-rnext101_32x8d	.964
90	antialias-resnet152	.964
91	BiT-S-R152x4	.963
92	resnet-101_v2-tf	.963
93	cv_18_dagger_408_pretrained	.963
94	resnet152_imagenet_full	.963
95	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.963
96	densenet-121-keras	.961
97	resnet101_imagenet_full	.961
98	densenet-121	.961
99	resnet50_st_wzc_seed6_2_323	.961
100	efficientnet_b0	.959
101	densenet-201	.957
102	densenet_201_pytorch	.957
103	resnet-101_v1-tf	.957
104	densenet-201-keras	.957
105	resnet50-moclr8deg	.957
106	resnet-152_v1-tf	.955
107	resnet50-VITO-8deg-cc	.955
108	resnet50_ewconplus_wzc	.955
109	resnet_50_v2	.955
110	densenet-169-keras	.954
111	grcnn	.954
112	resnet50-vitoimagevidnet8	.954
113	resnet_152_v1	.954
114	densenet-169	.954
115	resnet50_ewconplus_wzc_seed6_2_323	.954
116	BiT-S-R50x3	.952
117	antialiased-r50	.952
118	inception_v2-tf	.950
119	resnet50	.950
120	resnet50_imagenet_full	.950
121	resnet_SIN_IN_FT_IN	.948
122	resnet50-SIN_IN_IN	.948
123	resnet50_ewconplus_wzc_seed2_2__323	.948
124	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.946
125	convnext_femto_ols:d1_in1k	.946
126	resnet-50_v1-tf	.946
127	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.946
128	resnet50_ewconplus_wzc_seed5_2__323	.945
129	BiT-S-R50x1	.945
130	resnet-50-pytorch	.943
131	mobilenet_v2_1.4_224-tf	.943
132	mobilenet_v2_1.0_224-tf	.943
133	resnet50-sup	.943
134	resnet50_moco_v2	.943
135	resnet50_tutorial	.943
136	resnet_50_v1	.943
137	voneresnet-50-non_stochastic	.941
138	xception	.941
139	vonegrcnn_62e_nobn	.941
140	vonegrcnn_47e	.941
141	efficientnet_b1_imagenet_full	.939
142	eBarlow_lmda_02_1000ep	.939
143	vonegrcnn_52e_full	.939
144	BiT-S-R101x1	.939
145	mobilenet_v2_1.3_224-tf	.939
146	vonegrcnn_62e_nobn	.938
147	nasnet_mobile	.938
148	mobilenet_v2_1.0_192-tf	.936
149	mobilenet_v2_1.0_160-tf	.936
150	mobilenet_v2_1_0_224	.936
151	resnet_50_v1_spiking	.934
152	vonegrcnn_52e_full	.934
153	resnet-34	.934
154	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.932
155	resnet50_hvs_wzc	.932
156	vonegrcnn_47e	.932
157	ViT_L_32_imagenet1k	.930
158	resnet34_imagenet_full	.930
159	AdvProp_efficientnet-b8	.929
160	mobilenet_v2_1_0_192	.929
161	resnet50_eMMCR_Vanilla	.929
162	mobilenet_v2_1_3_224	.927
163	resnet50-SIN_IN	.927
164	vgg-19-keras	.927
165	inception_v1-tf	.925
166	efficientnet_b2_imagenet_full	.925
167	vgg-16-keras	.923
168	ViT_B_16_imagenet1k	.923
169	voneresnet-50	.920
170	custom_model_cv_18_dagger_408	.920
171	mobilenet_v2_0.75_224-tf	.918
172	regnet_y_400mf	.918
173	ViT_B_32_imagenet1k	.916
174	nasnet_mobile-tf	.914
175	CORnet-S	.912
176	mobilenet_v2_1_0_160	.912
177	blt_vs	.912
178	inception_v1	.912
179	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.911
180	ReAlnet06	.909
181	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.909
182	mobilenet_v2_0_75_224	.907
183	mobilenet_v2_0.75_160-tf	.907
184	mobilenet_v2_0.75_192-tf	.905
185	efficientnet_b0_imagenet_full	.905
186	mobilenet_v2_0_75_160	.905
187	pnasnet_large_pytorch	.905
188	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.904
189	mobilenet_v1_1.0_224	.904
190	mobilenet_v2_0_75_192	.902
191	resnet-18	.900
192	voneresnet-50-robust	.900
193	mobilenet_v2_0_5_224	.900
194	convnext_base_fb_in1k_gram_aligned_dinov3	.900
195	resnet18_imagenet_full	.898
196	mobilenet_v2_1_0_128	.896
197	mobilenet_v2_0.5_224-tf	.896
198	CLIP-RN50	.896
199	resnet50_robust_l2_eps1	.896
200	mobilenet_v2_1.0_128-tf	.895
201	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.893
202	ReAlnet09	.893
203	ReAlnet10	.893
204	vgg_19	.891
205	vgg_16	.889
206	ReAlnet03	.889
207	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.889
208	ReAlnet05	.887
209	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.886
210	ReAlnet04	.886
211	ReAlnet01	.884
212	mobilenet_v1_0.75_192	.884
213	mobilenet_v1_1.0_160	.884
214	ReAlnet08	.882
215	mobilenet_v1_0.75_224	.880
216	shufflenet_v2_x1_0	.880
217	ReAlnet07	.880
218	mobilenet_v2_1.0_96	.877
219	mobilenet_v1_1.0_192	.873
220	ReAlnet02	.873
221	mobilenet_v2_0.5_160	.866
222	mobilenet_v2_0.5_192-tf	.866
223	mobilenet_v1_1.0_128	.866
224	mobilenet_v2_1-4_224_pytorch	.866
225	mobilenet_v2_1_4_224	.866
226	mobilenet_v2_0.75_128	.861
227	mobilenet_v2_0.35_224	.857
228	mobilenet_v2_0_5_192	.855
229	mobilenet_v2_0.75_96	.848
230	mobilenet_v1_0.5_224	.848
231	mobilenet_v1_0.75_128	.848
232	mobilenet_v1_0.75_160	.848
233	mobilenet_v2_0.35_192	.848
234	mobilenet_v2_0.35_160	.848
235	mobilenet_v2_0.5_128	.848
236	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.839
237	resnet-50-robust	.839
238	imagenet_l2_3_0	.839
239	resnet50_robust_l2_eps3	.839
240	resnet50-SIN	.834
241	0.5x_resnet-18_LC_w_sh_1_iter	.834
242	resnet_50_v1_spiking_l4a	.825
243	resnet50_l2_3_robust	.823
244	mobilenet_v1_0.5_160	.821
245	mobilenet_v1_0.5_192	.820
246	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.816
247	convnext_small_imagenet_100_seed-0	.805
248	mobilenet_v2_0.35_128	.798
249	mobilenet_v2_0.5_96	.796
250	squeezenet1_0	.791
251	squeezenet1_1	.784
252	mobilenet_v1_0.5_128	.780
253	alexnet_random_linf8_perturb	.779
254	alexnet_ks_torevert	.777
255	alexnet_fov4	.777
256	alexnet_fov12	.777
257	alexnet_fov16	.777
258	alexnet	.777
259	resnet50_linf_8_robust	.771
260	mobilenet_v2_0.35_96	.768
261	alexnet_early_checkpoint	.757
262	mobilenet_v1_0.25_224	.750
263	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.750
264	mobilenet_v1_0.25_192	.725
265	imagenet_l2_10_0	.718
266	AlexNet_SIN	.704
267	AlexNet_SIN_fov4	.704
268	AlexNet_SIN_fov12	.704
269	AlexNet_SIN_fov16	.704
270	mobilenet_v1_0.25_160	.700
271	mobilenet_v1_0.25_128	.679
272	alexnet_l2_3_robust	.673
273	alexnet_robust_correct	.632
274	resnet50_imagenet_100_seed-0	.625
275	deit_small_imagenet_100_seed-0	.600
276	alexnet_linf_8_robust	.570
277	resnet_50_v1_spiking_l4	.496
278	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
279	resnet50_imagenet_10_seed-0	.152
280	convnext_small_imagenet_10_seed-0	.096
281	resnet50_imagenet_1_seed-0	.082
282	deit_small_imagenet_10_seed-0	.075
283	deit_small_imagenet_1_seed-0	.075
284	alexnet_training_seed_06	.068
285	alexnet_training_seed_09	.066
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	alexnet_training_seed_10	.062
292	alexnet_training_seed_01	.062
293	resnet_50_v1_spiking_l3	.062
294	alexnet_training_seed_01_fov4	.062
295	alexnet_training_seed_01_fov12	.062
296	alexnet_training_seed_01_fov16	.062
297	resnet50-vicregl0p75	.062
298	alexnet_training_seed_03	.062
299	alexnet_training_seed_07	.061
300	artResNet18_1	.061
301	convnext_small_imagenet_1_seed-0	.057
302	resnet50-barlow	.057
303	barlow-twins-resnet50	.054
304	mobilevit_small	.050
305	resnet50-simclr-vissl	.034
306	resnet50-vicregl0p9	.018
307	ViT-B/32
308	resnet-18-LC_w_sh_100_iter_conv_init_m
309	BiT-M-R50x1
310	resnet-18-LC_w_sh_1_iter_conv_init_m
311	BiT-M-R152x2
312	resnet-18-LC_w_sh_1_iter_m
313	resnet-18-LC_w_sh_10_iter_m
314	BiT-M-R50x3
315	resnet-18_test_m
316	resnet-18-LC_w_sh_100_iter_m
317	resnet-18-LC_w_sh_10_iter_conv_init_m
318	FrankRobWobv0
319	0.5x_resnet-18_LC
320	CORnetZ_CIFAR10
321	resnet18-local_aggregation
322	r3m_resnet18
323	resnet18-deepcluster
324	CORnet-Z
325	vggface
326	resnet18-instance_recognition
327	my-model
328	0.5x_resnet-18_LC_w_sh_10_iter
329	r3m_resnet50
330	r3m_resnet34
331	resnet-18-LC_m
332	resnet18-supervised
333	r3m_resnet50_nocrop
334	resnet-18-LC_1st_conv_m
335	resnet-18-LC_conv_init_m
336	resnet18-contrastive_multiview
337	0.5x_resnet-18
338	RN50
339	0.5x_resnet-18_LC_w_sh_100_iter
340	RN50
341	resnet18-colorization
342	resnet18-relative_position
343	resnet18-autoencoder
344	resnet18-simclr
345	ViT_L_32
346	ViT_B_32
347	resnet18-depth_prediction
348	ViT_B_16
349	r3m_resnet34_nocrop
350	resnet18-contrastive_predictive
351	resnet-18-LC_untrained
352	resnet-18_untrained
353	resnet-50x2_untrained
354	dcgan
355	unet_entire
356	CORnetZ_CIFAR10_bs32_20_04
357	v1-pyr-nodown
358	pixels
359	dorinet_cornet_z
360	omnivore_swinT
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
464	dinov2_vitl14_reg_lc_featread

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