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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021sketch-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge_clip	.835
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.776
3	vit_large_patch14_clip_224:openai_ft_in1k	.776
4	convnext_large_clip_laion2b_finelayers	.768
5	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.746
6	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.742
7	vit_large_patch14_clip_336:laion2b_ft_in1k	.734
8	vit_huge_clip336_laion	.715
9	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.700
10	SWSL_resnext101_32x8d	.699
11	vit_large_clip336_openai	.693
12	eva02_large_in22k	.689
13	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.678
14	vit_large_patch14_clip_224:laion2b_ft_in1k	.669
15	dinov2_vitg14_reg_lc	.667
16	CLIP_resnet50	.666
17	CLIP_resnet50_float32	.666
18	dinov2_vitl14_lc_res336	.666
19	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.663
20	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
21	convnext_base:clip_laiona_augreg_ft_in1k_384	.658
22	CLIP-RN50	.630
23	dinov2_vitl14_reg_lc	.597
24	dinov2_vitl14_lc	.597
25	convnext_xlarge:fb_in22k_ft_in1k	.594
26	vit_base_patch16_clip_224:openai_ft_in1k	.585
27	convnext_large_in22k_supervised	.580
28	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.578
29	fixres_resnext101_32x48d_wsl	.576
30	CLIP_ViT-B_32	.559
31	convnext_large:fb_in22k_ft_in1k	.556
32	dinov2_vitg14_lc	.548
33	resnext101_32x16d_wsl	.539
34	resnext101_32x48d_wsl	.512
35	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.481
36	SWSL_resnet50	.461
37	resnext101_32x32d_wsl	.460
38	effnetv2m_custom384	.452
39	convnext_base_fb_in22k_ft_in1k	.440
40	deit3_base_patch16_224_fb_in22k_ft_in1k	.437
41	resnext101_32x8d_wsl	.425
42	vision_transformer_vit_large_patch16_224	.421
43	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.382
44	convnext_tiny:in12k_ft_in1k	.336
45	cvt_cvt-13-384-in1k_4_LucyV4	.331
46	deit3_base_patch16_224_fb_in1k	.316
47	vit_relpos_base_patch16_clsgap_224:sw_in1k	.314
48	cvt_cvt-21-384-in1k_4_LucyV4	.293
49	convnext_large_imagenet_full_seed-0	.287
50	effnetb1_272x240	.283
51	convnext_small_imagenet_full_seed-0	.278
52	convnext_tiny_imagenet_full_seed-0	.276
53	convnext_base_imagenet_full_seed-0	.274
54	focalnet_tiny_lrf_in1k	.274
55	cv_18_dagger_408_pretrained	.271
56	pnasnet_large_halfPrecision	.267
57	pnasnet_large	.267
58	convnext_base_fb_in1k	.255
59	resnet_152_v2	.253
60	convnext_femto_ols:d1_in1k	.247
61	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.245
62	cvt_cvt-13-224-in1k_4_LucyV4	.242
63	resnet_50_v2	.242
64	cvt_cvt-21-224-in1k_4_LucyV4	.234
65	seresnext50_32x4d	.233
66	pnasnet_large-tf	.232
67	resnet50_st_wzc_seed6_2_323	.223
68	nasnet_large	.221
69	resnet-152_v2_pytorch	.220
70	resnet-152_v2-tf	.220
71	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.213
72	resnet50_st_wzc_seed7_2_323	.212
73	inception_v4	.212
74	inception_v3	.207
75	resnet_101_v2	.205
76	xception-keras	.204
77	regnety_032	.204
78	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.202
79	convnext_tiny_sup	.198
80	deit_large_imagenet_full_seed-0	.198
81	resnet101	.191
82	resnet50_st_wzc_seed3_2_323	.177
83	resnet152_imagenet_full	.177
84	resnet50_ewconplus_wzc_seed2_2__323	.172
85	inception_v3-tf	.172
86	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.169
87	mehrer2020_alexnet_training_seed_01_fov4	.164
88	vgg_19	.163
89	resnet101_imagenet_full	.163
90	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.161
91	resnet50_random_l2_perturb	.161
92	nasnet_large-tf	.160
93	antialias-resnet152	.159
94	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.157
95	resnet_101_v1	.155
96	resnet50_imagenet_full	.155
97	deit_base_imagenet_full_seed-0	.153
98	inception_v4-tf	.153
99	resnet50-VITO-8deg-cc	.149
100	densenet-121-keras	.147
101	resnet-50_v2-tf	.145
102	AdvProp_efficientnet-b6	.143
103	resnet50_ewconplus_wzc	.143
104	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.142
105	densenet-121	.141
106	antialiased-rnext101_32x8d	.139
107	AdvProp_efficientnet-b4	.138
108	AT_efficientnet-b4	.138
109	resnet50_ewconplus_wzc_seed5_2__323	.137
110	resnet50_random_linf8_perturb	.136
111	resnet50	.135
112	efficientnet_b0	.132
113	resnet_152_v1	.131
114	resnet50_st_wzc_2	.128
115	resnet_50_v1_spiking	.127
116	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.127
117	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.125
118	efficientnet-b7	.125
119	efficientnet_b0_imagenet_full	.125
120	alexnet_fov4	.124
121	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.124
122	densenet-169-keras	.124
123	densenet-201	.121
124	grcnn_109	.120
125	resnet50_ewconplus_wzc_seed6_2_323	.119
126	Res2Net50_26w_4s	.119
127	densenet-169	.119
128	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.119
129	densenet-201-keras	.118
130	resnet50_byol	.118
131	cornet_s	.117
132	efficientnet_b2_imagenet_full	.117
133	AdvProp_efficientnet-b7	.116
134	AT_efficientnet-b2	.115
135	AdvProp_efficientnet-b2	.115
136	antialiased-r50	.115
137	BiT-S-R101x3	.114
138	resnet50-SIN_IN_IN	.114
139	resnet_SIN_IN_FT_IN	.114
140	resnet-34	.113
141	resnet50_hvs_wzc	.113
142	custom_model_cv_18_dagger_408	.112
143	resnet18_imagenet_full	.112
144	mobilenet_v2_1_0_192	.112
145	resnet34_imagenet_full	.111
146	convnext_small_imagenet_100_seed-0	.111
147	BiT-S-R152x4	.111
148	BiT-S-R50x3	.110
149	vgg_16	.109
150	resnet-152_v1-tf	.107
151	mobilenet_v2_1_0_224	.105
152	nasnet_mobile-tf	.104
153	mobilenet_v2_1_3_224	.104
154	resnet50_tutorial	.103
155	resnet_50_v1	.103
156	resnet-50-pytorch	.103
157	resnet50-sup	.103
158	deit_small_imagenet_full_seed-0	.102
159	BiT-S-R50x1	.102
160	resnet50-SIN_IN	.101
161	tv_efficientnet-b1	.101
162	grcnn	.099
163	mobilenet_v2_1.4_224-tf	.098
164	convnext_base_fb_in1k_gram_aligned_dinov3	.098
165	xception	.097
166	voneresnet-50-non_stochastic	.097
167	nasnet_mobile	.097
168	ViT_B_16_imagenet1k	.095
169	inception_v1	.095
170	efficientnet_b1_imagenet_full	.094
171	resnet50-moclr8deg	.093
172	ViT_L_32_imagenet1k	.092
173	ReAlnet06	.091
174	mobilenet_v2_0_5_224	.091
175	ReAlnet01	.090
176	mobilenet_v2_1.3_224-tf	.090
177	mobilenet_v1_1.0_224	.088
178	resnet50_moco_v2	.088
179	inception_v2-tf	.088
180	ReAlnet03	.088
181	resnet50-vitoimagevidnet8	.086
182	AlexNet_SIN_fov4	.086
183	eicircuit_resnet18_bn_20260919	.086
184	resnet-101_v1-tf	.084
185	BiT-S-R152x2	.083
186	ReAlnet04	.082
187	ReAlnet02	.080
188	CORnet-S	.078
189	mobilenet_v2_1_0_160	.077
190	mobilenet_v2_0_75_224	.076
191	mobilenet_v1_1.0_192	.075
192	ReAlnet09	.075
193	inception_v1-tf	.073
194	vonegrcnn_52e_full	.073
195	mobilenet_v2_1.0_224-tf	.072
196	resnet-18	.072
197	mobilenet_v1_1.0_160	.070
198	ReAlnet07	.070
199	ReAlnet05	.070
200	ReAlnet08	.069
201	blt_vs	.068
202	vonegrcnn_52e_full	.068
203	vonegrcnn_62e_nobn	.068
204	AdvProp_efficientnet-b8	.068
205	resnet-50_v1-tf	.067
206	ViT_B_32_imagenet1k	.067
207	ReAlnet10	.067
208	regnet_y_400mf	.066
209	mobilenet_v2_0_5_192	.066
210	mobilenet_v2_1-4_224_pytorch	.064
211	mobilenet_v2_1_4_224	.064
212	vonegrcnn_62e_nobn	.064
213	vgg-16-keras	.063
214	resnet_50_v1_spiking_l4a	.063
215	vonegrcnn_47e	.063
216	mobilenet_v2_1.0_192-tf	.062
217	voneresnet-50	.062
218	vonegrcnn_47e	.061
219	mobilenet_v2_0_75_160	.060
220	mobilenet_v2_1.0_160-tf	.058
221	mobilenet_v1_0.75_160	.058
222	mobilenet_v2_0.75_160-tf	.057
223	BiT-S-R101x1	.057
224	resnet50_linf_4_robust	.056
225	mobilenet_v2_0.75_224-tf	.056
226	mobilenet_v2_1_0_128	.055
227	mobilenet_v2_0_75_192	.055
228	squeezenet1_0	.055
229	mobilenet_v1_0.5_224	.054
230	mobilenet_v2_0.35_160	.053
231	mobilenet_v1_0.75_224	.053
232	mobilenet_v2_0.5_192-tf	.053
233	squeezenet1_1	.052
234	mobilenet_v2_0.75_128	.052
235	resnet_50_v1_spiking_l4	.052
236	mobilenet_v2_0.5_224-tf	.051
237	voneresnet-50-robust	.051
238	mobilenet_v2_0.75_192-tf	.049
239	resnet50_imagenet_100_seed-0	.049
240	mobilenet_v2_0.5_160	.049
241	mobilenet_v1_0.75_192	.049
242	mobilenet_v2_0.5_128	.048
243	alexnet_reduced_aliasing_early_checkpoint	.048
244	mobilenet_v1_0.5_192	.047
245	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.047
246	shufflenet_v2_x1_0	.046
247	alexnet_random_l2_3_perturb	.046
248	vonealexnet_gaussian_noise_std4_fixed	.046
249	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.046
250	alexnet_random_linf8_perturb	.045
251	mobilenet_v2_0.35_192	.045
252	resnet50_l2_3_robust	.045
253	resnet50_eMMCR_Vanilla	.044
254	mobilenet_v1_0.75_128	.043
255	resnet50_robust_l2_eps1	.043
256	mobilenet_v2_1.0_96	.043
257	resnet50_linf_8_robust	.042
258	mehrer2020_alexnet_training_seed_03	.042
259	texture_shape_resnet50_trained_on_SIN	.042
260	mobilenet_v2_0.35_224	.042
261	mobilenet_v2_0.35_128	.042
262	mobilenet_v2_1.0_128-tf	.041
263	mehrer2020_alexnet_training_seed_04	.041
264	resnet50_simclr	.041
265	alexnet	.041
266	alexnet_ks_torevert	.041
267	vgg-19-keras	.040
268	alexnet_early_checkpoint	.039
269	mobilenet_v1_0.5_128	.039
270	AdvProp_efficientnet-b0	.039
271	alexnet_robust_correct	.039
272	alexnet_l2_3_robust	.038
273	mobilenet_v2_0.75_96	.038
274	mobilenet_v2_0.35_96	.038
275	mehrer2020_alexnet_training_seed_01	.036
276	alexnet_linf_8_robust	.036
277	mobilenet_v1_0.5_160	.035
278	0.5x_resnet-18_LC_w_sh_1_iter	.034
279	konkle_alexnetgn_ipcl_ref01_primary_model	.034
280	mobilenet_v1_1.0_128	.032
281	mobilenet_v2_0.5_96	.032
282	mehrer2020_alexnet_training_seed_05	.032
283	deit_small_imagenet_100_seed-0	.032
284	mobilenet_v1_0.25_192	.032
285	resnet50-vicreg	.032
286	mobilenet_v1_0.25_224	.031
287	mehrer2020_alexnet_training_seed_02	.031
288	AlexNet_SIN	.031
289	texture_shape_alexnet_trained_on_SIN	.031
290	resnet-50-robust	.030
291	imagenet_l2_3_0	.030
292	resnet50_robust_l2_eps3	.030
293	mobilenet_v1_0.25_160	.029
294	imagenet_l2_10_0	.028
295	AlexNet_SIN_fov12	.027
296	alexnet_fov12	.027
297	AlexNet_SIN_fov16	.026
298	resnet50-simclr-vissl	.026
299	deit_small_imagenet_10_seed-0	.025
300	convnext_small_imagenet_10_seed-0	.025
301	deit_small_imagenet_1_seed-0	.025
302	mehrer2020_alexnet_training_seed_01_fov16	.025
303	alexnet_training_seed_02	.024
304	alexnet_training_seed_04	.024
305	alexnet_training_seed_08	.024
306	alexnet_training_seed_10	.024
307	alexnet_training_seed_07	.024
308	alexnet_training_seed_09	.023
309	alexnet_fov16	.023
310	resnet_50_v1_spiking_l3	.023
311	mehrer2020_alexnet_training_seed_01_fov12	.022
312	resnet-50_untrained	.022
313	resnet50-SIN	.022
314	mobilenet_v1_0.25_128	.021
315	resnet50_imagenet_10_seed-0	.020
316	convnext_small_imagenet_1_seed-0	.020
317	barlow-twins-resnet50	.014
318	resnet50-vicregl0p9	.014
319	artResNet18_1	.014
320	resnet50_FractalDB	.011
321	alexnet_training_seed_06	.009
322	alexnet_training_seed_03	.005
323	resnet50_imagenet_1_seed-0	.003
324	resnet50-vicregl0p75	.003
325	alexnet_training_seed_05	.002
326	alexnet_training_seed_01	.002
327	alexnet_training_seed_01_fov4	.002
328	alexnet_training_seed_01_fov12	.002
329	alexnet_training_seed_01_fov16	.002
330	resnet50-barlow	.001
331	resnet-18-LC_w_sh_100_iter_conv_init_m
332	ViT-B/32
333	BiT-M-R50x3
334	BiT-M-R50x1
335	BiT-M-R152x2
336	resnet-18-LC_w_sh_1_iter_conv_init_m
337	resnet-18-LC_w_sh_1_iter_m
338	resnet-18-LC_w_sh_10_iter_m
339	resnet-18_test_m
340	resnet-18-LC_w_sh_100_iter_m
341	resnet-18-LC_w_sh_10_iter_conv_init_m
342	FrankRobWobv0
343	0.5x_resnet-18_LC
344	CORnetZ_CIFAR10
345	resnet18-local_aggregation
346	resnet18-deepcluster
347	r3m_resnet18
348	CORnet-Z
349	vggface
350	my-model
351	resnet18-instance_recognition
352	0.5x_resnet-18_LC_w_sh_10_iter
353	r3m_resnet50
354	r3m_resnet34
355	resnet-18-LC_m
356	r3m_resnet50_nocrop
357	resnet18-supervised
358	resnet-18-LC_1st_conv_m
359	resnet-18-LC_conv_init_m
360	resnet18-contrastive_multiview
361	0.5x_resnet-18
362	resnet18-colorization
363	RN50
364	0.5x_resnet-18_LC_w_sh_100_iter
365	RN50
366	resnet18-relative_position
367	resnet18-autoencoder
368	resnet18-simclr
369	omnivore_swinT
370	ViT_L_32
371	ViT_B_32
372	resnet18-depth_prediction
373	ViT_B_16
374	r3m_resnet34_nocrop
375	resnet18-contrastive_predictive
376	resnet-18-LC_untrained
377	resnet-18_untrained
378	resnet-50x2_untrained
379	dcgan
380	unet_entire
381	omnivore_swinS
382	v1-pyr-nodown
383	CORnetZ_CIFAR10_bs32_20_04
384	pixels
385	dorinet_cornet_z
386	my-model
387	openclip
388	resnet50-cifar
389	resnet50_pretrained_with_retinal_waves
390	resnet50_ImageNet
391	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
392	yudixie_resnet50_random_0_240908
393	yudixie_resnet50_imagenet1kpret_0_240908
394	yudixie_resnet50_imagenet1kpret_0_240312
395	yudixie_resnet50_object_class_0_240908
396	yudixie_resnet50_category_class_0_240908
397	ConvLSTM
398	yudixie_resnet50_distance_reg_0_240908
399	PredRNN
400	SimVP
401	TAU
402	briaai_rmbg_1_4
403	MIM
404	hmax
405	densenet_201_pytorch
406	pnasnet_large_pytorch
407	ReAlnet01_cornet
408	inception_v3_pytorch
409	yolos_tiny
410	ReAlnet03_cornet
411	yudixie_resnet18_imagenet1kpret_0_240719
412	eBarlow_lmda_02_1000ep
413	compact_resnet50_V4
414	vit_base_patch16_dinov3-lvd1689m
415	vit_base_patch14_reg4_dinov2-lvd142m
416	ReAlnet01_cornet_fov16
417	resnet18_ecoset_full
418	resnet34_ecoset_full
419	ReAlnet01_cornet_fov4
420	resnet50_ecoset_full
421	compact_resnet50_robust_V4
422	yudixie_resnet50_distance_translation_0_240908
423	yudixie_resnet18_distance_translation_0_240719
424	yudixie_resnet18_translation_rotation_0_240719
425	yudixie_resnet18_distance_rotation_0_240719
426	compact_resnet50_IT
427	ReAlnet01_cornet_fov12
428	yudixie_resnet18_category_class_0_240719
429	vit_base_patch14_dinov2-lvd142m
430	yudixie_resnet50_distance_rotation_0_240908
431	ReAlnet05_cornet
432	yudixie_resnet50_rotation_reg_0_240908
433	yudixie_resnet18_random_0_240719
434	compact_vgg19_V4
435	yudixie_resnet18_distance_translation_rotation_0_240719
436	yudixie_resnet18_object_class_0_240719
437	SeLaVi-AVE
438	yudixie_resnet18_translation_reg_0_240719
439	yudixie_resnet18_distance_reg_0_240719
440	ReAlnet06_cornet
441	yudixie_resnet50_translation_reg_0_240908
442	r3d_18
443	compact_vgg19_IT
444	ReAlnet01_ALL
445	mc3_18
446	yudixie_resnet50_translation_rotation_0_240908
447	s3d
448	ReAlnet08_cornet
449	mvit_v1_b
450	ReAlnet02_ALL
451	yudixie_resnet18_rotation_reg_0_240719
452	omnivore_swinB
453	VOneCORnet-S
454	compact_cornets_V4
455	resnet18_imagenet21kP
456	ReAlnet03_ALL
457	ReAlnet09_cornet
458	ReAlnet04_ALL
459	SeLaVi-Kinetics400
460	compact_cornets_IT
461	SeLaVi-VGG-Sound
462	ReAlnet05_ALL
463	r2plus1d_18
464	ReAlnet06_ALL
465	ReAlnet10_cornet
466	SeLaVi-Kinetics-Sound
467	mvit_v2_s
468	yudixie_resnet50_distance_translation_rotation_0_240908
469	ReAlnet07_ALL
470	compact_resnet50_robust_IT
471	ReAlnet08_ALL
472	yudixie_resnet18_cat_obj_class_all_latents_0_240719
473	resnet50_primary_visual_cortex
474	ReAlnet09_ALL
475	yudixie_resnet50_cat_obj_class_all_latents_0_240908
476	ReAlnet10_ALL
477	ReAlnet02_cornet
478	ReAlnet04_cornet
479	ReAlnet07_cornet
480	resnet101_ecoset_full
481	resnet152_ecoset_full
482	resnet18_fair
483	pr1_stl10_disentangled_alpha04
484	predictive_coding_pc
485	yudixie_wbh_object_class_scaling_0415_run_0
486	yudixie_wbh_object_class_scaling_0415_run_3
487	yudixie_wbh_object_class_scaling_0415_imagenet
488	yudixie_wbh_object_class_scaling_0415_run_2
489	yudixie_wbh_object_class_scaling_0415_run_1
490	deepeisnn_ei_cifar10_vgg19
491	deepeisnn_ei_cifar10_resnet18
492	deepeisnn_ei_imagenet_resnet18
493	dinov2_vitl14_reg_lc_featread
494	gabor_filter_entropy_4x4
495	gabor_filter_entropy_4x4_v2
496	gabor_filter_mean_abs_4x4_v2
497	gabor_filter_entropy_4x4_v2_forcedCosine

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

0.37.

Note that scores are relative to this ceiling.

Data: Geirhos2021sketch

Metric: error_consistency