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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	regnety_032	.713
15	nasnet_large-tf	.708
16	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.706
17	pnasnet_large_halfPrecision	.702
18	pnasnet_large	.702
19	cvt_cvt-13-224-in1k_4_LucyV4	.699
20	CLIP_resnet50	.688
21	CLIP_resnet50_float32	.688
22	convnext_base_fb_in22k_ft_in1k	.682
23	pnasnet_large-tf	.682
24	resnext101_32x8d_wsl	.668
25	deit3_base_patch16_224_fb_in1k	.668
26	effnetb1_272x240	.667
27	vit_relpos_base_patch16_clsgap_224:sw_in1k	.648
28	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.640
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	vit_base_patch16_clip_224:openai_ft_in1k	.581
43	resnet50-VITO-8deg-cc	.580
44	resnet101	.580
45	convnext_xlarge:fb_in22k_ft_in1k	.579
46	convnext_large:fb_in22k_ft_in1k	.572
47	resnet_101_v2	.568
48	antialias-resnet152	.566
49	resnet-152_v2_pytorch	.564
50	convnext_tiny_imagenet_full_seed-0	.564
51	convnext_tiny:in12k_ft_in1k	.560
52	resnet152_imagenet_full	.559
53	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.559
54	inception_v3	.558
55	resnet50_ewconplus_wzc_seed5_2__323	.555
56	cvt_cvt-21-224-in1k_4_LucyV4	.554
57	inception_v4-tf	.547
58	resnet_152_v1	.546
59	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.542
60	vit_large_patch14_clip_224:laion2b_ft_in1k	.539
61	resnet_152_v2	.537
62	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.536
63	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.532
64	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.532
65	resnet50_imagenet_full	.531
66	resnet101_imagenet_full	.528
67	dinov2_vitl14_lc	.527
68	convnext_base_imagenet_full_seed-0	.520
69	inception_v3-tf	.519
70	tv_efficientnet-b1	.517
71	antialiased-rnext101_32x8d	.511
72	efficientnet_b0	.507
73	inception_v3_pytorch	.505
74	resnet-152_v2-tf	.504
75	resnet50_random_l2_perturb	.497
76	resnet50_moco_v2	.495
77	convnext_large_in22k_supervised	.495
78	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.493
79	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.493
80	cv_18_dagger_408_pretrained	.492
81	resnet50_ewconplus_wzc_seed2_2__323	.492
82	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.487
83	resnet50_ewconplus_wzc_seed6_2_323	.486
84	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
85	convnext_small_imagenet_full_seed-0	.483
86	resnet50	.475
87	vgg_19	.471
88	xception	.470
89	grcnn_109	.462
90	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.461
91	deit_large_imagenet_full_seed-0	.453
92	seresnext50_32x4d	.450
93	resnet-50_v2-tf	.449
94	deit_base_imagenet_full_seed-0	.447
95	resnet_101_v1	.440
96	AdvProp_efficientnet-b7	.434
97	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.429
98	resnet50_ewconplus_wzc	.428
99	ViT_L_32_imagenet1k	.419
100	resnet50_st_wzc_2	.418
101	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.416
102	dinov2_vitg14_lc	.414
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	convnext_large_clip_laion2b_finelayers	.355
118	AdvProp_efficientnet-b4	.353
119	AT_efficientnet-b4	.353
120	Res2Net50_26w_4s	.353
121	resnet_50_v1_spiking	.351
122	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
123	convnext_base_fb_in1k_gram_aligned_dinov3	.349
124	vit_large_patch14_clip_336:laion2b_ft_in1k	.348
125	densenet-121-keras	.346
126	vit_large_clip336_openai	.345
127	dinov2_vitl14_lc_res336	.339
128	AT_efficientnet-b2	.338
129	AdvProp_efficientnet-b2	.338
130	vgg_16	.337
131	vit_large_patch14_clip_224:openai_ft_in1k	.337
132	antialiased-r50	.336
133	AdvProp_efficientnet-b6	.336
134	mobilenet_v2_1.4_224-tf	.336
135	efficientnet-b7	.336
136	densenet-169-keras	.335
137	densenet-201	.333
138	resnet18_imagenet_full	.333
139	resnet50-moclr8deg	.332
140	resnet50_random_linf8_perturb	.326
141	densenet-121	.324
142	dinov2_vitl14_reg_lc	.316
143	inception_v1	.312
144	resnet50_hvs_wzc	.310
145	resnet50_tutorial	.309
146	resnet_50_v1	.309
147	resnet-50-pytorch	.309
148	resnet50-sup	.309
149	nasnet_mobile	.306
150	BiT-S-R50x3	.300
151	BiT-S-R101x3	.299
152	densenet-169	.299
153	mobilenet_v2_1_3_224	.297
154	inception_v1-tf	.296
155	mobilenet_v2_1.3_224-tf	.295
156	eBarlow_lmda_02_1000ep	.292
157	CORnet-S	.292
158	resnet50-SIN_IN	.291
159	BiT-S-R101x1	.289
160	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.287
161	resnet-34	.282
162	grcnn	.280
163	convnext_xxlarge_clip	.276
164	convnext_tiny_sup	.269
165	custom_model_cv_18_dagger_408	.269
166	resnet50_simclr	.268
167	resnet-50_v1-tf	.267
168	resnet50-vitoimagevidnet8	.263
169	inception_v2-tf	.262
170	ReAlnet09	.257
171	ReAlnet01	.257
172	BiT-S-R152x2	.256
173	convnext_base:clip_laiona_augreg_ft_in1k_384	.256
174	convnext_small_imagenet_100_seed-0	.255
175	ReAlnet06	.253
176	vonegrcnn_62e_nobn	.252
177	mobilenet_v2_1_0_224	.251
178	ReAlnet03	.251
179	efficientnet_b2_imagenet_full	.249
180	ReAlnet04	.246
181	efficientnet_b0_imagenet_full	.240
182	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.238
183	BiT-S-R152x4	.237
184	mobilenet_v2_1_0_192	.233
185	voneresnet-50-non_stochastic	.231
186	AdvProp_efficientnet-b8	.230
187	vonegrcnn_47e	.228
188	vonegrcnn_52e_full	.226
189	mobilenet_v2_1.0_224-tf	.225
190	BiT-S-R50x1	.225
191	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.223
192	vonegrcnn_62e_nobn	.221
193	AlexNet_SIN_fov4	.221
194	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.220
195	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.219
196	vit_huge_clip336_laion	.219
197	ReAlnet02	.219
198	resnet50_eMMCR_Vanilla	.218
199	vonegrcnn_47e	.218
200	mobilenet_v2_0_75_224	.216
201	vonegrcnn_52e_full	.215
202	resnet-18	.212
203	ReAlnet07	.212
204	mobilenet_v2_0.75_224-tf	.212
205	ReAlnet10	.209
206	dinov2_vitg14_reg_lc	.202
207	eva02_large_in22k	.197
208	ReAlnet08	.195
209	mobilenet_v2_1-4_224_pytorch	.193
210	mobilenet_v2_1_4_224	.193
211	blt_vs	.188
212	mobilenet_v2_1.0_192-tf	.187
213	ReAlnet05	.183
214	efficientnet_b1_imagenet_full	.181
215	regnet_y_400mf	.179
216	mobilenet_v2_1_0_160	.175
217	mobilenet_v2_0_5_224	.174
218	mobilenet_v1_0.75_224	.164
219	mobilenet_v2_0_75_192	.163
220	mobilenet_v2_0_5_192	.157
221	mobilenet_v2_1.0_160-tf	.157
222	vgg-16-keras	.155
223	voneresnet-50	.152
224	mobilenet_v2_0.5_224-tf	.145
225	resnet_50_v1_spiking_l4a	.144
226	mobilenet_v1_1.0_224	.143
227	mobilenet_v2_0_75_160	.139
228	mobilenet_v1_1.0_192	.137
229	resnet50_linf_4_robust	.136
230	mobilenet_v2_0.5_192-tf	.123
231	texture_shape_resnet50_trained_on_SIN	.119
232	konkle_alexnetgn_ipcl_ref01_primary_model	.116
233	mobilenet_v1_0.75_192	.114
234	resnet50_imagenet_100_seed-0	.113
235	shufflenet_v2_x1_0	.107
236	vgg-19-keras	.107
237	squeezenet1_1	.106
238	mobilenet_v1_0.5_192	.104
239	mobilenet_v1_1.0_160	.103
240	mobilenet_v2_0.75_160-tf	.101
241	mobilenet_v2_0.75_128	.101
242	mobilenet_v2_0.75_192-tf	.100
243	mobilenet_v2_0.35_192	.092
244	resnet50_robust_l2_eps1	.088
245	squeezenet1_0	.087
246	alexnet_random_linf8_perturb	.086
247	AdvProp_efficientnet-b0	.085
248	mobilenet_v1_0.5_224	.073
249	alexnet_early_checkpoint	.073
250	mobilenet_v1_0.5_160	.070
251	mobilenet_v2_0.35_224	.069
252	mobilenet_v2_0.5_128	.068
253	mobilenet_v2_1.0_96	.067
254	mobilenet_v2_0.75_96	.066
255	resnet50_l2_3_robust	.065
256	resnet50_linf_8_robust	.064
257	voneresnet-50-robust	.064
258	resnet_50_v1_spiking_l4	.064
259	mobilenet_v2_0.35_160	.063
260	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.056
261	mobilenet_v2_0.5_160	.055
262	alexnet	.055
263	alexnet_ks_torevert	.055
264	mobilenet_v1_0.75_160	.054
265	mobilenet_v1_0.25_128	.051
266	alexnet_random_l2_3_perturb	.049
267	resnet50-SIN	.048
268	mobilenet_v2_1_0_128	.045
269	vonealexnet_gaussian_noise_std4_fixed	.045
270	mobilenet_v2_1.0_128-tf	.043
271	alexnet_training_seed_05	.043
272	alexnet_training_seed_01	.043
273	alexnet_training_seed_01_fov4	.043
274	alexnet_training_seed_01_fov12	.043
275	alexnet_training_seed_01_fov16	.043
276	convnext_small_imagenet_1_seed-0	.039
277	mobilenet_v1_0.25_192	.038
278	resnet50-vicregl0p9	.037
279	alexnet_reduced_aliasing_early_checkpoint	.036
280	mobilenet_v1_0.25_160	.035
281	mobilenet_v1_1.0_128	.035
282	resnet-50-robust	.034
283	imagenet_l2_3_0	.034
284	resnet50_robust_l2_eps3	.034
285	alexnet_training_seed_06	.034
286	deit_small_imagenet_100_seed-0	.033
287	mobilenet_v1_0.75_128	.031
288	resnet50_imagenet_10_seed-0	.029
289	alexnet_training_seed_09	.027
290	texture_shape_alexnet_trained_on_SIN	.027
291	AlexNet_SIN	.025
292	alexnet_fov16	.021
293	my-model	.019
294	resnet50-vicregl0p75	.019
295	resnet50_FractalDB	.019
296	AlexNet_SIN_fov16	.018
297	imagenet_l2_10_0	.017
298	alexnet_fov12	.017
299	resnet50-vicreg	.016
300	deit_small_imagenet_10_seed-0	.016
301	deit_small_imagenet_1_seed-0	.016
302	mobilenet_v2_0.5_96	.016
303	resnet50_imagenet_1_seed-0	.015
304	mobilenet_v2_0.35_96	.015
305	mobilenet_v1_0.25_224	.014
306	resnet_50_v1_spiking_l3	.013
307	alexnet_training_seed_07	.013
308	convnext_small_imagenet_10_seed-0	.012
309	mobilenet_v1_0.5_128	.011
310	AlexNet_SIN_fov12	.011
311	mobilenet_v2_0.35_128	.011
312	alexnet_training_seed_02	.010
313	alexnet_training_seed_04	.010
314	alexnet_training_seed_08	.010
315	alexnet_training_seed_10	.010
316	mobilevit_small	.010
317	alexnet_linf_8_robust	.010
318	alexnet_training_seed_03	.009
319	0.5x_resnet-18_LC_w_sh_1_iter	.009
320	resnet50-barlow	.009
321	barlow-twins-resnet50	.004
322	artResNet18_1	.004
323	alexnet_robust_correct	.003
324	resnet-50_untrained	.003
325	alexnet_l2_3_robust	.003
326	resnet50-simclr-vissl	.001
327	resnet-18-LC_w_sh_100_iter_conv_init_m
328	ViT-B/32
329	BiT-M-R50x3
330	BiT-M-R50x1
331	BiT-M-R152x2
332	resnet-18-LC_w_sh_1_iter_conv_init_m
333	resnet-18-LC_w_sh_10_iter_m
334	resnet-18-LC_w_sh_1_iter_m
335	resnet-18-LC_w_sh_100_iter_m
336	resnet-18-LC_w_sh_10_iter_conv_init_m
337	FrankRobWobv0
338	0.5x_resnet-18_LC
339	CORnetZ_CIFAR10
340	resnet18-local_aggregation
341	resnet18-deepcluster
342	r3m_resnet18
343	CORnet-Z
344	resnet18-instance_recognition
345	my-model
346	vggface
347	0.5x_resnet-18_LC_w_sh_10_iter
348	r3m_resnet50
349	resnet18-colorization
350	r3m_resnet34
351	resnet-18-LC_m
352	r3m_resnet50_nocrop
353	resnet18-supervised
354	resnet-18-LC_1st_conv_m
355	resnet-18-LC_conv_init_m
356	resnet18-contrastive_multiview
357	0.5x_resnet-18
358	RN50
359	0.5x_resnet-18_LC_w_sh_100_iter
360	RN50
361	resnet18-relative_position
362	resnet18-autoencoder
363	resnet18-simclr
364	ViT_L_32
365	ViT_B_32
366	resnet18-depth_prediction
367	ViT_B_16
368	r3m_resnet34_nocrop
369	resnet18-contrastive_predictive
370	omnivore_swinT
371	resnet-18-LC_untrained
372	resnet-18_untrained
373	resnet-50x2_untrained
374	dcgan
375	v1-pyr-nodown
376	unet_entire
377	CORnetZ_CIFAR10_bs32_20_04
378	pixels
379	dorinet_cornet_z
380	openclip
381	resnet50-cifar
382	resnet50_pretrained_with_retinal_waves
383	resnet50_ImageNet
384	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
385	ReAlnet09_ALL
386	yudixie_resnet50_imagenet1kpret_0_240312
387	yudixie_resnet50_random_0_240908
388	yudixie_resnet50_imagenet1kpret_0_240908
389	yudixie_resnet50_cat_obj_class_all_latents_0_240908
390	yudixie_resnet50_object_class_0_240908
391	yudixie_resnet50_category_class_0_240908
392	resnet18_imagenet21kP
393	ConvLSTM
394	yudixie_resnet50_distance_reg_0_240908
395	PredRNN
396	SimVP
397	TAU
398	MIM
399	densenet_201_pytorch
400	vit_base_patch16_dinov3-lvd1689m
401	ReAlnet01_cornet
402	compact_resnet50_V4
403	yudixie_resnet18_translation_reg_0_240719
404	yudixie_resnet18_distance_reg_0_240719
405	yolos_tiny
406	yudixie_resnet18_rotation_reg_0_240719
407	ReAlnet02_cornet
408	ReAlnet03_cornet
409	vit_base_patch14_dinov2-lvd142m
410	resnet18_ecoset_full
411	resnet34_ecoset_full
412	VOneCORnet-S
413	resnet50_ecoset_full
414	compact_resnet50_IT
415	vit_base_patch14_reg4_dinov2-lvd142m
416	ReAlnet01_cornet_fov4
417	yudixie_resnet50_rotation_reg_0_240908
418	resnet101_ecoset_full
419	compact_resnet50_robust_V4
420	yudixie_resnet50_distance_translation_0_240908
421	ReAlnet01_cornet_fov12
422	yudixie_resnet18_distance_translation_0_240719
423	yudixie_resnet18_translation_rotation_0_240719
424	yudixie_resnet18_distance_rotation_0_240719
425	compact_vgg19_V4
426	yudixie_resnet18_category_class_0_240719
427	resnet-18_test_m
428	yudixie_resnet18_random_0_240719
429	yudixie_resnet18_cat_obj_class_all_latents_0_240719
430	ReAlnet05_cornet
431	yudixie_resnet50_distance_rotation_0_240908
432	SeLaVi-AVE
433	yudixie_resnet18_distance_translation_rotation_0_240719
434	yudixie_resnet18_object_class_0_240719
435	hmax
436	yudixie_resnet50_translation_reg_0_240908
437	compact_vgg19_IT
438	r3d_18
439	yudixie_resnet50_translation_rotation_0_240908
440	ReAlnet01_ALL
441	mc3_18
442	ReAlnet07_cornet
443	s3d
444	ReAlnet01_cornet_fov16
445	mvit_v1_b
446	ReAlnet08_cornet
447	ReAlnet02_ALL
448	ReAlnet03_ALL
449	ReAlnet04_ALL
450	compact_cornets_V4
451	omnivore_swinB
452	SeLaVi-Kinetics400
453	ReAlnet09_cornet
454	SeLaVi-VGG-Sound
455	ReAlnet05_ALL
456	compact_cornets_IT
457	SeLaVi-Kinetics-Sound
458	r2plus1d_18
459	ReAlnet06_ALL
460	omnivore_swinS
461	mvit_v2_s
462	compact_resnet50_robust_IT
463	ReAlnet10_cornet
464	resnet152_ecoset_full
465	ReAlnet07_ALL
466	ReAlnet08_ALL
467	yudixie_resnet18_imagenet1kpret_0_240719
468	ReAlnet04_cornet
469	ReAlnet06_cornet
470	yudixie_resnet50_distance_translation_rotation_0_240908
471	resnet50_primary_visual_cortex
472	briaai_rmbg_1_4
473	ReAlnet10_ALL
474	resnet18_fair
475	pr1_stl10_disentangled_alpha04
476	predictive_coding_pc
477	yudixie_wbh_object_class_scaling_0415_run_0
478	yudixie_wbh_object_class_scaling_0415_run_3
479	yudixie_wbh_object_class_scaling_0415_imagenet
480	yudixie_wbh_object_class_scaling_0415_run_2
481	yudixie_wbh_object_class_scaling_0415_run_1
482	deepeisnn_ei_cifar10_vgg19
483	deepeisnn_ei_cifar10_resnet18
484	deepeisnn_ei_imagenet_resnet18
485	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

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021falsecolour

Metric: error_consistency