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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-13-384-in1k_4_LucyV4	.818
2	SWSL_resnext101_32x8d	.804
3	nasnet_large	.780
4	resnext101_32x16d_wsl	.757
5	vision_transformer_vit_large_patch16_224	.756
6	resnext101_32x48d_wsl	.755
7	CLIP-RN50	.754
8	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.752
9	SWSL_resnet50	.752
10	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.739
11	inception_v4	.738
12	resnext101_32x32d_wsl	.735
13	cvt_cvt-21-384-in1k_4_LucyV4	.726
14	nasnet_large-tf	.708
15	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.706
16	pnasnet_large_halfPrecision	.702
17	pnasnet_large	.702
18	cvt_cvt-13-224-in1k_4_LucyV4	.699
19	CLIP_resnet50	.688
20	CLIP_resnet50_float32	.688
21	convnext_base_fb_in22k_ft_in1k	.682
22	pnasnet_large-tf	.682
23	resnext101_32x8d_wsl	.668
24	deit3_base_patch16_224_fb_in1k	.668
25	effnetb1_272x240	.667
26	vit_relpos_base_patch16_clsgap_224:sw_in1k	.648
27	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.640
28	resnet50_ewconplus_wzc_2_323	.636
29	convnext_femto_ols:d1_in1k	.633
30	convnext_large_imagenet_full_seed-0	.622
31	resnet_50_v2	.622
32	effnetv2m_custom384	.617
33	resnet50_st_wzc_seed7_2_323	.613
34	focalnet_tiny_lrf_in1k	.610
35	fixres_resnext101_32x48d_wsl	.610
36	convnext_base_fb_in1k	.608
37	resnet50_st_wzc_seed3_2_323	.605
38	CLIP_ViT-B_32	.605
39	resnet50_st_wzc_seed6_2_323	.603
40	xception-keras	.599
41	ViT_B_16_imagenet1k	.588
42	resnet50_ewconplus_noseed_2_323	.582
43	vit_base_patch16_clip_224:openai_ft_in1k	.581
44	resnet50-VITO-8deg-cc	.580
45	resnet101	.580
46	convnext_xlarge:fb_in22k_ft_in1k	.579
47	convnext_large:fb_in22k_ft_in1k	.572
48	resnet_101_v2	.568
49	antialias-resnet152	.566
50	resnet-152_v2_pytorch	.564
51	convnext_tiny_imagenet_full_seed-0	.564
52	convnext_tiny:in12k_ft_in1k	.560
53	resnet152_imagenet_full	.559
54	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.559
55	inception_v3	.558
56	resnet50_ewconplus_wzc_seed5_2__323	.555
57	cvt_cvt-21-224-in1k_4_LucyV4	.554
58	resnet50_st_wzc_2_323	.548
59	inception_v4-tf	.547
60	resnet_152_v1	.546
61	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.542
62	vit_large_patch14_clip_224:laion2b_ft_in1k	.539
63	resnet_152_v2	.537
64	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.536
65	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.532
66	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.532
67	resnet50_imagenet_full	.531
68	resnet50_ewconplus_wzc_seed7_2_323	.530
69	resnet101_imagenet_full	.528
70	convnext_base_imagenet_full_seed-0	.520
71	inception_v3-tf	.519
72	tv_efficientnet-b1	.517
73	antialiased-rnext101_32x8d	.511
74	efficientnet_b0	.507
75	inception_v3_pytorch	.505
76	resnet-152_v2-tf	.504
77	resnet50_ewconplus_wzc_seed4_2_323	.504
78	resnet50_random_l2_perturb	.497
79	resnet50_moco_v2	.495
80	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.493
81	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.493
82	cv_18_dagger_408_pretrained	.492
83	resnet50_ewconplus_wzc_seed2_2__323	.492
84	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.487
85	resnet50_ewconplus_wzc_seed6_2_323	.486
86	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
87	convnext_small_imagenet_full_seed-0	.483
88	resnet50	.475
89	vgg_19	.471
90	xception	.470
91	grcnn_109	.462
92	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.461
93	deit_large_imagenet_full_seed-0	.453
94	resnet-50_v2-tf	.449
95	deit_base_imagenet_full_seed-0	.447
96	resnet_101_v1	.440
97	AdvProp_efficientnet-b7	.434
98	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.429
99	resnet50_ewconplus_wzc	.428
100	ViT_L_32_imagenet1k	.419
101	resnet50_st_wzc_2	.418
102	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.416
103	resnet-152_v1-tf	.409
104	resnet50_byol	.407
105	nasnet_mobile-tf	.401
106	pnasnet_large_pytorch	.392
107	resnet34_imagenet_full	.390
108	alexnet_fov4	.388
109	deit_small_imagenet_full_seed-0	.371
110	ViT_B_32_imagenet1k	.368
111	resnet50-SIN_IN_IN	.367
112	resnet_SIN_IN_FT_IN	.367
113	cornet_s	.364
114	densenet-201-keras	.362
115	resnet-101_v1-tf	.359
116	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.356
117	AdvProp_efficientnet-b4	.353
118	AT_efficientnet-b4	.353
119	Res2Net50_26w_4s	.353
120	resnet_50_v1_spiking	.351
121	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
122	convnext_base_fb_in1k_gram_aligned_dinov3	.349
123	vit_large_patch14_clip_336:laion2b_ft_in1k	.348
124	densenet-121-keras	.346
125	AT_efficientnet-b2	.338
126	AdvProp_efficientnet-b2	.338
127	vgg_16	.337
128	vit_large_patch14_clip_224:openai_ft_in1k	.337
129	antialiased-r50	.336
130	AdvProp_efficientnet-b6	.336
131	mobilenet_v2_1.4_224-tf	.336
132	efficientnet-b7	.336
133	densenet-169-keras	.335
134	densenet-201	.333
135	resnet18_imagenet_full	.333
136	resnet50-moclr8deg	.332
137	resnet50_random_linf8_perturb	.326
138	densenet-121	.324
139	inception_v1	.312
140	resnet50_tutorial	.309
141	resnet_50_v1	.309
142	resnet-50-pytorch	.309
143	resnet50-sup	.309
144	nasnet_mobile	.306
145	BiT-S-R50x3	.300
146	BiT-S-R101x3	.299
147	densenet-169	.299
148	mobilenet_v2_1_3_224	.297
149	inception_v1-tf	.296
150	mobilenet_v2_1.3_224-tf	.295
151	eBarlow_lmda_02_1000ep	.292
152	CORnet-S	.292
153	resnet50-SIN_IN	.291
154	BiT-S-R101x1	.289
155	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.287
156	resnet-34	.282
157	grcnn	.280
158	convnext_tiny_sup	.269
159	custom_model_cv_18_dagger_408	.269
160	resnet50_simclr	.268
161	resnet-50_v1-tf	.267
162	resnet50-vitoimagevidnet8	.263
163	inception_v2-tf	.262
164	ReAlnet09	.257
165	ReAlnet01	.257
166	BiT-S-R152x2	.256
167	convnext_base:clip_laiona_augreg_ft_in1k_384	.256
168	convnext_small_imagenet_100_seed-0	.255
169	ReAlnet06	.253
170	vonegrcnn_62e_nobn	.252
171	mobilenet_v2_1_0_224	.251
172	ReAlnet03	.251
173	efficientnet_b2_imagenet_full	.249
174	ReAlnet04	.246
175	efficientnet_b0_imagenet_full	.240
176	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.238
177	BiT-S-R152x4	.237
178	mobilenet_v2_1_0_192	.233
179	voneresnet-50-non_stochastic	.231
180	AdvProp_efficientnet-b8	.230
181	vonegrcnn_47e	.228
182	vonegrcnn_52e_full	.226
183	mobilenet_v2_1.0_224-tf	.225
184	BiT-S-R50x1	.225
185	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.223
186	vonegrcnn_62e_nobn	.221
187	AlexNet_SIN_fov4	.221
188	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.220
189	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.219
190	ReAlnet02	.219
191	resnet50_eMMCR_Vanilla	.218
192	vonegrcnn_47e	.218
193	mobilenet_v2_0_75_224	.216
194	vonegrcnn_52e_full	.215
195	resnet-18	.212
196	ReAlnet07	.212
197	mobilenet_v2_0.75_224-tf	.212
198	ReAlnet10	.209
199	ReAlnet08	.195
200	mobilenet_v2_1-4_224_pytorch	.193
201	mobilenet_v2_1_4_224	.193
202	blt_vs	.188
203	mobilenet_v2_1.0_192-tf	.187
204	ReAlnet05	.183
205	efficientnet_b1_imagenet_full	.181
206	regnet_y_400mf	.179
207	mobilenet_v2_1_0_160	.175
208	mobilenet_v2_0_5_224	.174
209	mobilenet_v1_0.75_224	.164
210	mobilenet_v2_0_75_192	.163
211	mobilenet_v2_0_5_192	.157
212	mobilenet_v2_1.0_160-tf	.157
213	vgg-16-keras	.155
214	voneresnet-50	.152
215	mobilenet_v2_0.5_224-tf	.145
216	resnet_50_v1_spiking_l4a	.144
217	mobilenet_v1_1.0_224	.143
218	mobilenet_v2_0_75_160	.139
219	mobilenet_v1_1.0_192	.137
220	resnet50_linf_4_robust	.136
221	mobilenet_v2_0.5_192-tf	.123
222	texture_shape_resnet50_trained_on_SIN	.119
223	konkle_alexnetgn_ipcl_ref01_primary_model	.116
224	mobilenet_v1_0.75_192	.114
225	resnet50_imagenet_100_seed-0	.113
226	shufflenet_v2_x1_0	.107
227	vgg-19-keras	.107
228	squeezenet1_1	.106
229	mobilenet_v1_0.5_192	.104
230	mobilenet_v1_1.0_160	.103
231	mobilenet_v2_0.75_160-tf	.101
232	mobilenet_v2_0.75_128	.101
233	mobilenet_v2_0.75_192-tf	.100
234	mobilenet_v2_0.35_192	.092
235	resnet50_robust_l2_eps1	.088
236	squeezenet1_0	.087
237	alexnet_random_linf8_perturb	.086
238	AdvProp_efficientnet-b0	.085
239	mobilenet_v1_0.5_224	.073
240	alexnet_early_checkpoint	.073
241	mobilenet_v1_0.5_160	.070
242	mobilenet_v2_0.35_224	.069
243	mobilenet_v2_0.5_128	.068
244	mobilenet_v2_1.0_96	.067
245	mobilenet_v2_0.75_96	.066
246	resnet50_l2_3_robust	.065
247	resnet50_linf_8_robust	.064
248	voneresnet-50-robust	.064
249	resnet_50_v1_spiking_l4	.064
250	mobilenet_v2_0.35_160	.063
251	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.056
252	mobilenet_v2_0.5_160	.055
253	alexnet	.055
254	alexnet_ks_torevert	.055
255	mobilenet_v1_0.75_160	.054
256	mobilenet_v1_0.25_128	.051
257	alexnet_random_l2_3_perturb	.049
258	resnet50-SIN	.048
259	mobilenet_v2_1_0_128	.045
260	vonealexnet_gaussian_noise_std4_fixed	.045
261	mobilenet_v2_1.0_128-tf	.043
262	alexnet_training_seed_05	.043
263	alexnet_training_seed_01	.043
264	alexnet_training_seed_01_fov4	.043
265	alexnet_training_seed_01_fov12	.043
266	alexnet_training_seed_01_fov16	.043
267	convnext_small_imagenet_1_seed-0	.039
268	mobilenet_v1_0.25_192	.038
269	resnet50-vicregl0p9	.037
270	alexnet_reduced_aliasing_early_checkpoint	.036
271	mobilenet_v1_0.25_160	.035
272	mobilenet_v1_1.0_128	.035
273	resnet-50-robust	.034
274	imagenet_l2_3_0	.034
275	resnet50_robust_l2_eps3	.034
276	alexnet_training_seed_06	.034
277	deit_small_imagenet_100_seed-0	.033
278	mobilenet_v1_0.75_128	.031
279	resnet50_imagenet_10_seed-0	.029
280	alexnet_training_seed_09	.027
281	texture_shape_alexnet_trained_on_SIN	.027
282	AlexNet_SIN	.025
283	alexnet_fov16	.021
284	my-model	.019
285	resnet50-vicregl0p75	.019
286	resnet50_FractalDB	.019
287	AlexNet_SIN_fov16	.018
288	imagenet_l2_10_0	.017
289	alexnet_fov12	.017
290	resnet50-vicreg	.016
291	deit_small_imagenet_10_seed-0	.016
292	deit_small_imagenet_1_seed-0	.016
293	mobilenet_v2_0.5_96	.016
294	resnet50_imagenet_1_seed-0	.015
295	mobilenet_v2_0.35_96	.015
296	mobilenet_v1_0.25_224	.014
297	resnet_50_v1_spiking_l3	.013
298	alexnet_training_seed_07	.013
299	convnext_small_imagenet_10_seed-0	.012
300	mobilenet_v1_0.5_128	.011
301	AlexNet_SIN_fov12	.011
302	mobilenet_v2_0.35_128	.011
303	alexnet_training_seed_02	.010
304	alexnet_training_seed_04	.010
305	alexnet_training_seed_08	.010
306	alexnet_training_seed_10	.010
307	resnet50-meshes-lt-100-original-scratch	.010
308	mobilevit_small	.010
309	alexnet_linf_8_robust	.010
310	alexnet_training_seed_03	.009
311	0.5x_resnet-18_LC_w_sh_1_iter	.009
312	resnet50-barlow	.009
313	resnet50-meshes-lt-100-original-pretrained	.008
314	barlow-twins-resnet50	.004
315	artResNet18_1	.004
316	alexnet_robust_correct	.003
317	resnet-50_untrained	.003
318	alexnet_l2_3_robust	.003
319	resnet50-simclr-vissl	.001
320	resnet-18-LC_w_sh_100_iter_conv_init_m
321	ViT-B/32
322	BiT-M-R50x3
323	BiT-M-R50x1
324	BiT-M-R152x2
325	resnet-18-LC_w_sh_1_iter_conv_init_m
326	resnet-18-LC_w_sh_10_iter_m
327	resnet-18-LC_w_sh_1_iter_m
328	resnet-18-LC_w_sh_100_iter_m
329	resnet-18-LC_w_sh_10_iter_conv_init_m
330	FrankRobWobv0
331	0.5x_resnet-18_LC
332	CORnetZ_CIFAR10
333	resnet18-local_aggregation
334	resnet18-deepcluster
335	r3m_resnet18
336	CORnet-Z
337	resnet18-instance_recognition
338	my-model
339	vggface
340	0.5x_resnet-18_LC_w_sh_10_iter
341	r3m_resnet50
342	resnet18-colorization
343	r3m_resnet34
344	resnet-18-LC_m
345	r3m_resnet50_nocrop
346	resnet18-supervised
347	resnet-18-LC_1st_conv_m
348	resnet-18-LC_conv_init_m
349	resnet18-contrastive_multiview
350	0.5x_resnet-18
351	RN50
352	0.5x_resnet-18_LC_w_sh_100_iter
353	RN50
354	resnet18-relative_position
355	resnet18-autoencoder
356	resnet18-simclr
357	ViT_L_32
358	ViT_B_32
359	resnet18-depth_prediction
360	ViT_B_16
361	r3m_resnet34_nocrop
362	resnet18-contrastive_predictive
363	omnivore_swinT
364	resnet-18-LC_untrained
365	resnet-18_untrained
366	resnet-50x2_untrained
367	dcgan
368	v1-pyr-nodown
369	unet_entire
370	CORnetZ_CIFAR10_bs32_20_04
371	pixels
372	dorinet_cornet_z
373	resnet50
374	openclip
375	resnet50-cifar
376	resnet50_pretrained_with_retinal_waves
377	resnet50_ImageNet
378	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
379	ReAlnet09_ALL
380	yudixie_resnet50_imagenet1kpret_0_240312
381	yudixie_resnet50_random_0_240908
382	yudixie_resnet50_imagenet1kpret_0_240908
383	yudixie_resnet50_cat_obj_class_all_latents_0_240908
384	yudixie_resnet50_object_class_0_240908
385	yudixie_resnet50_category_class_0_240908
386	resnet18_imagenet21kP
387	ConvLSTM
388	yudixie_resnet50_distance_reg_0_240908
389	PredRNN
390	SimVP
391	TAU
392	MIM
393	densenet_201_pytorch
394	vit_base_patch16_dinov3-lvd1689m
395	ReAlnet01_cornet
396	compact_resnet50_V4
397	yudixie_resnet18_translation_reg_0_240719
398	yudixie_resnet18_distance_reg_0_240719
399	yolos_tiny
400	yudixie_resnet18_rotation_reg_0_240719
401	ReAlnet02_cornet
402	ReAlnet03_cornet
403	vit_base_patch14_dinov2-lvd142m
404	resnet18_ecoset_full
405	resnet34_ecoset_full
406	VOneCORnet-S
407	resnet50_ecoset_full
408	compact_resnet50_IT
409	vit_base_patch14_reg4_dinov2-lvd142m
410	ReAlnet01_cornet_fov4
411	yudixie_resnet50_rotation_reg_0_240908
412	resnet101_ecoset_full
413	compact_resnet50_robust_V4
414	yudixie_resnet50_distance_translation_0_240908
415	ReAlnet01_cornet_fov12
416	yudixie_resnet18_distance_translation_0_240719
417	yudixie_resnet18_translation_rotation_0_240719
418	yudixie_resnet18_distance_rotation_0_240719
419	compact_vgg19_V4
420	yudixie_resnet18_category_class_0_240719
421	resnet-18_test_m
422	yudixie_resnet18_random_0_240719
423	yudixie_resnet18_cat_obj_class_all_latents_0_240719
424	ReAlnet05_cornet
425	yudixie_resnet50_distance_rotation_0_240908
426	SeLaVi-AVE
427	yudixie_resnet18_distance_translation_rotation_0_240719
428	yudixie_resnet18_object_class_0_240719
429	hmax
430	yudixie_resnet50_translation_reg_0_240908
431	compact_vgg19_IT
432	r3d_18
433	yudixie_resnet50_translation_rotation_0_240908
434	ReAlnet01_ALL
435	mc3_18
436	ReAlnet07_cornet
437	s3d
438	ReAlnet01_cornet_fov16
439	mvit_v1_b
440	ReAlnet08_cornet
441	ReAlnet02_ALL
442	aughost_fair_resnet18
443	ReAlnet03_ALL
444	ReAlnet04_ALL
445	compact_cornets_V4
446	omnivore_swinB
447	SeLaVi-Kinetics400
448	ReAlnet09_cornet
449	SeLaVi-VGG-Sound
450	ReAlnet05_ALL
451	compact_cornets_IT
452	SeLaVi-Kinetics-Sound
453	r2plus1d_18
454	ReAlnet06_ALL
455	omnivore_swinS
456	mvit_v2_s
457	compact_resnet50_robust_IT
458	ReAlnet10_cornet
459	resnet152_ecoset_full
460	ReAlnet07_ALL
461	ReAlnet08_ALL
462	yudixie_resnet18_imagenet1kpret_0_240719
463	ReAlnet04_cornet
464	ReAlnet06_cornet
465	yudixie_resnet50_distance_translation_rotation_0_240908
466	resnet50_primary_visual_cortex
467	briaai_rmbg_1_4
468	ReAlnet10_ALL
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

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

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021falsecolour

Metric: error_consistency