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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	vgg_19	.163
88	resnet101_imagenet_full	.163
89	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.161
90	resnet50_random_l2_perturb	.161
91	nasnet_large-tf	.160
92	antialias-resnet152	.159
93	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.157
94	resnet_101_v1	.155
95	resnet50_imagenet_full	.155
96	deit_base_imagenet_full_seed-0	.153
97	inception_v4-tf	.153
98	resnet50-VITO-8deg-cc	.149
99	densenet-121-keras	.147
100	resnet-50_v2-tf	.145
101	AdvProp_efficientnet-b6	.143
102	resnet50_ewconplus_wzc	.143
103	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.142
104	densenet-121	.141
105	antialiased-rnext101_32x8d	.139
106	AdvProp_efficientnet-b4	.138
107	AT_efficientnet-b4	.138
108	resnet50_ewconplus_wzc_seed5_2__323	.137
109	resnet50_random_linf8_perturb	.136
110	resnet50	.135
111	efficientnet_b0	.132
112	resnet_152_v1	.131
113	resnet50_st_wzc_2	.128
114	resnet_50_v1_spiking	.127
115	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.127
116	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.125
117	efficientnet-b7	.125
118	efficientnet_b0_imagenet_full	.125
119	alexnet_fov4	.124
120	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.124
121	densenet-169-keras	.124
122	densenet-201	.121
123	grcnn_109	.120
124	resnet50_ewconplus_wzc_seed6_2_323	.119
125	Res2Net50_26w_4s	.119
126	densenet-169	.119
127	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.119
128	densenet-201-keras	.118
129	resnet50_byol	.118
130	cornet_s	.117
131	efficientnet_b2_imagenet_full	.117
132	AdvProp_efficientnet-b7	.116
133	AT_efficientnet-b2	.115
134	AdvProp_efficientnet-b2	.115
135	antialiased-r50	.115
136	BiT-S-R101x3	.114
137	resnet50-SIN_IN_IN	.114
138	resnet_SIN_IN_FT_IN	.114
139	resnet-34	.113
140	resnet50_hvs_wzc	.113
141	custom_model_cv_18_dagger_408	.112
142	resnet18_imagenet_full	.112
143	mobilenet_v2_1_0_192	.112
144	resnet34_imagenet_full	.111
145	convnext_small_imagenet_100_seed-0	.111
146	BiT-S-R152x4	.111
147	BiT-S-R50x3	.110
148	vgg_16	.109
149	resnet-152_v1-tf	.107
150	mobilenet_v2_1_0_224	.105
151	nasnet_mobile-tf	.104
152	mobilenet_v2_1_3_224	.104
153	resnet50_tutorial	.103
154	resnet_50_v1	.103
155	resnet-50-pytorch	.103
156	resnet50-sup	.103
157	deit_small_imagenet_full_seed-0	.102
158	BiT-S-R50x1	.102
159	resnet50-SIN_IN	.101
160	tv_efficientnet-b1	.101
161	grcnn	.099
162	mobilenet_v2_1.4_224-tf	.098
163	convnext_base_fb_in1k_gram_aligned_dinov3	.098
164	xception	.097
165	voneresnet-50-non_stochastic	.097
166	nasnet_mobile	.097
167	ViT_B_16_imagenet1k	.095
168	inception_v1	.095
169	efficientnet_b1_imagenet_full	.094
170	resnet50-moclr8deg	.093
171	ViT_L_32_imagenet1k	.092
172	ReAlnet06	.091
173	mobilenet_v2_0_5_224	.091
174	ReAlnet01	.090
175	mobilenet_v2_1.3_224-tf	.090
176	mobilenet_v1_1.0_224	.088
177	resnet50_moco_v2	.088
178	inception_v2-tf	.088
179	ReAlnet03	.088
180	resnet50-vitoimagevidnet8	.086
181	AlexNet_SIN_fov4	.086
182	eicircuit_resnet18_bn_20260919	.086
183	resnet-101_v1-tf	.084
184	BiT-S-R152x2	.083
185	ReAlnet04	.082
186	ReAlnet02	.080
187	CORnet-S	.078
188	mobilenet_v2_1_0_160	.077
189	mobilenet_v2_0_75_224	.076
190	mobilenet_v1_1.0_192	.075
191	ReAlnet09	.075
192	inception_v1-tf	.073
193	vonegrcnn_52e_full	.073
194	mobilenet_v2_1.0_224-tf	.072
195	resnet-18	.072
196	mobilenet_v1_1.0_160	.070
197	ReAlnet07	.070
198	ReAlnet05	.070
199	ReAlnet08	.069
200	blt_vs	.068
201	vonegrcnn_52e_full	.068
202	vonegrcnn_62e_nobn	.068
203	AdvProp_efficientnet-b8	.068
204	resnet-50_v1-tf	.067
205	ViT_B_32_imagenet1k	.067
206	ReAlnet10	.067
207	regnet_y_400mf	.066
208	mobilenet_v2_0_5_192	.066
209	mobilenet_v2_1-4_224_pytorch	.064
210	mobilenet_v2_1_4_224	.064
211	vonegrcnn_62e_nobn	.064
212	vgg-16-keras	.063
213	resnet_50_v1_spiking_l4a	.063
214	vonegrcnn_47e	.063
215	mobilenet_v2_1.0_192-tf	.062
216	voneresnet-50	.062
217	vonegrcnn_47e	.061
218	mobilenet_v2_0_75_160	.060
219	mobilenet_v2_1.0_160-tf	.058
220	mobilenet_v1_0.75_160	.058
221	mobilenet_v2_0.75_160-tf	.057
222	BiT-S-R101x1	.057
223	resnet50_linf_4_robust	.056
224	mobilenet_v2_0.75_224-tf	.056
225	mobilenet_v2_1_0_128	.055
226	mobilenet_v2_0_75_192	.055
227	squeezenet1_0	.055
228	mobilenet_v1_0.5_224	.054
229	mobilenet_v2_0.35_160	.053
230	mobilenet_v1_0.75_224	.053
231	mobilenet_v2_0.5_192-tf	.053
232	squeezenet1_1	.052
233	mobilenet_v2_0.75_128	.052
234	resnet_50_v1_spiking_l4	.052
235	mobilenet_v2_0.5_224-tf	.051
236	voneresnet-50-robust	.051
237	mobilenet_v2_0.75_192-tf	.049
238	resnet50_imagenet_100_seed-0	.049
239	mobilenet_v2_0.5_160	.049
240	mobilenet_v1_0.75_192	.049
241	mobilenet_v2_0.5_128	.048
242	alexnet_reduced_aliasing_early_checkpoint	.048
243	mobilenet_v1_0.5_192	.047
244	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.047
245	shufflenet_v2_x1_0	.046
246	alexnet_random_l2_3_perturb	.046
247	vonealexnet_gaussian_noise_std4_fixed	.046
248	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.046
249	alexnet_random_linf8_perturb	.045
250	mobilenet_v2_0.35_192	.045
251	resnet50_l2_3_robust	.045
252	resnet50_eMMCR_Vanilla	.044
253	mobilenet_v1_0.75_128	.043
254	resnet50_robust_l2_eps1	.043
255	mobilenet_v2_1.0_96	.043
256	resnet50_linf_8_robust	.042
257	texture_shape_resnet50_trained_on_SIN	.042
258	mobilenet_v2_0.35_224	.042
259	mobilenet_v2_0.35_128	.042
260	mobilenet_v2_1.0_128-tf	.041
261	resnet50_simclr	.041
262	alexnet	.041
263	alexnet_ks_torevert	.041
264	vgg-19-keras	.040
265	alexnet_early_checkpoint	.039
266	mobilenet_v1_0.5_128	.039
267	AdvProp_efficientnet-b0	.039
268	alexnet_robust_correct	.039
269	alexnet_l2_3_robust	.038
270	mobilenet_v2_0.75_96	.038
271	mobilenet_v2_0.35_96	.038
272	alexnet_linf_8_robust	.036
273	mobilenet_v1_0.5_160	.035
274	0.5x_resnet-18_LC_w_sh_1_iter	.034
275	konkle_alexnetgn_ipcl_ref01_primary_model	.034
276	mobilenet_v1_1.0_128	.032
277	mobilenet_v2_0.5_96	.032
278	deit_small_imagenet_100_seed-0	.032
279	mobilenet_v1_0.25_192	.032
280	resnet50-vicreg	.032
281	mobilenet_v1_0.25_224	.031
282	AlexNet_SIN	.031
283	texture_shape_alexnet_trained_on_SIN	.031
284	resnet-50-robust	.030
285	imagenet_l2_3_0	.030
286	resnet50_robust_l2_eps3	.030
287	mobilenet_v1_0.25_160	.029
288	imagenet_l2_10_0	.028
289	AlexNet_SIN_fov12	.027
290	alexnet_fov12	.027
291	AlexNet_SIN_fov16	.026
292	resnet50-simclr-vissl	.026
293	deit_small_imagenet_10_seed-0	.025
294	convnext_small_imagenet_10_seed-0	.025
295	deit_small_imagenet_1_seed-0	.025
296	alexnet_training_seed_02	.024
297	alexnet_training_seed_04	.024
298	alexnet_training_seed_08	.024
299	alexnet_training_seed_10	.024
300	alexnet_training_seed_07	.024
301	alexnet_training_seed_09	.023
302	alexnet_fov16	.023
303	resnet_50_v1_spiking_l3	.023
304	resnet-50_untrained	.022
305	resnet50-SIN	.022
306	mobilenet_v1_0.25_128	.021
307	resnet50_imagenet_10_seed-0	.020
308	convnext_small_imagenet_1_seed-0	.020
309	barlow-twins-resnet50	.014
310	resnet50-vicregl0p9	.014
311	artResNet18_1	.014
312	resnet50_FractalDB	.011
313	alexnet_training_seed_06	.009
314	alexnet_training_seed_03	.005
315	resnet50_imagenet_1_seed-0	.003
316	resnet50-vicregl0p75	.003
317	alexnet_training_seed_05	.002
318	alexnet_training_seed_01	.002
319	alexnet_training_seed_01_fov4	.002
320	alexnet_training_seed_01_fov12	.002
321	alexnet_training_seed_01_fov16	.002
322	resnet50-barlow	.001
323	resnet-18-LC_w_sh_100_iter_conv_init_m
324	ViT-B/32
325	BiT-M-R50x3
326	BiT-M-R50x1
327	BiT-M-R152x2
328	resnet-18-LC_w_sh_1_iter_conv_init_m
329	resnet-18-LC_w_sh_1_iter_m
330	resnet-18-LC_w_sh_10_iter_m
331	resnet-18_test_m
332	resnet-18-LC_w_sh_100_iter_m
333	resnet-18-LC_w_sh_10_iter_conv_init_m
334	FrankRobWobv0
335	0.5x_resnet-18_LC
336	CORnetZ_CIFAR10
337	resnet18-local_aggregation
338	resnet18-deepcluster
339	r3m_resnet18
340	CORnet-Z
341	vggface
342	my-model
343	resnet18-instance_recognition
344	0.5x_resnet-18_LC_w_sh_10_iter
345	r3m_resnet50
346	r3m_resnet34
347	resnet-18-LC_m
348	r3m_resnet50_nocrop
349	resnet18-supervised
350	resnet-18-LC_1st_conv_m
351	resnet-18-LC_conv_init_m
352	resnet18-contrastive_multiview
353	0.5x_resnet-18
354	resnet18-colorization
355	RN50
356	0.5x_resnet-18_LC_w_sh_100_iter
357	RN50
358	resnet18-relative_position
359	resnet18-autoencoder
360	resnet18-simclr
361	omnivore_swinT
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	resnet18-contrastive_predictive
368	resnet-18-LC_untrained
369	resnet-18_untrained
370	resnet-50x2_untrained
371	dcgan
372	unet_entire
373	omnivore_swinS
374	v1-pyr-nodown
375	CORnetZ_CIFAR10_bs32_20_04
376	pixels
377	dorinet_cornet_z
378	my-model
379	openclip
380	resnet50-cifar
381	resnet50_pretrained_with_retinal_waves
382	resnet50_ImageNet
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	yudixie_resnet50_random_0_240908
385	yudixie_resnet50_imagenet1kpret_0_240908
386	yudixie_resnet50_imagenet1kpret_0_240312
387	yudixie_resnet50_object_class_0_240908
388	yudixie_resnet50_category_class_0_240908
389	ConvLSTM
390	yudixie_resnet50_distance_reg_0_240908
391	PredRNN
392	SimVP
393	TAU
394	briaai_rmbg_1_4
395	MIM
396	hmax
397	densenet_201_pytorch
398	pnasnet_large_pytorch
399	ReAlnet01_cornet
400	inception_v3_pytorch
401	yolos_tiny
402	ReAlnet03_cornet
403	yudixie_resnet18_imagenet1kpret_0_240719
404	eBarlow_lmda_02_1000ep
405	ReAlnet02_cornet
406	compact_resnet50_V4
407	vit_base_patch16_dinov3-lvd1689m
408	vit_base_patch14_reg4_dinov2-lvd142m
409	ReAlnet01_cornet_fov16
410	resnet18_ecoset_full
411	resnet34_ecoset_full
412	ReAlnet01_cornet_fov4
413	resnet50_ecoset_full
414	resnet101_ecoset_full
415	compact_resnet50_robust_V4
416	ReAlnet04_cornet
417	resnet152_ecoset_full
418	yudixie_resnet50_distance_translation_0_240908
419	yudixie_resnet18_distance_translation_0_240719
420	yudixie_resnet18_translation_rotation_0_240719
421	yudixie_resnet18_distance_rotation_0_240719
422	compact_resnet50_IT
423	ReAlnet01_cornet_fov12
424	yudixie_resnet18_category_class_0_240719
425	vit_base_patch14_dinov2-lvd142m
426	yudixie_resnet50_distance_rotation_0_240908
427	ReAlnet05_cornet
428	yudixie_resnet50_rotation_reg_0_240908
429	yudixie_resnet18_random_0_240719
430	compact_vgg19_V4
431	yudixie_resnet18_distance_translation_rotation_0_240719
432	yudixie_resnet18_object_class_0_240719
433	SeLaVi-AVE
434	yudixie_resnet18_translation_reg_0_240719
435	yudixie_resnet18_distance_reg_0_240719
436	ReAlnet06_cornet
437	yudixie_resnet50_translation_reg_0_240908
438	r3d_18
439	compact_vgg19_IT
440	ReAlnet01_ALL
441	mc3_18
442	ReAlnet07_cornet
443	yudixie_resnet50_translation_rotation_0_240908
444	s3d
445	ReAlnet08_cornet
446	mvit_v1_b
447	ReAlnet02_ALL
448	yudixie_resnet18_rotation_reg_0_240719
449	omnivore_swinB
450	VOneCORnet-S
451	compact_cornets_V4
452	resnet18_imagenet21kP
453	ReAlnet03_ALL
454	ReAlnet09_cornet
455	ReAlnet04_ALL
456	SeLaVi-Kinetics400
457	compact_cornets_IT
458	SeLaVi-VGG-Sound
459	ReAlnet05_ALL
460	r2plus1d_18
461	ReAlnet06_ALL
462	ReAlnet10_cornet
463	SeLaVi-Kinetics-Sound
464	mvit_v2_s
465	yudixie_resnet50_distance_translation_rotation_0_240908
466	ReAlnet07_ALL
467	compact_resnet50_robust_IT
468	ReAlnet08_ALL
469	yudixie_resnet18_cat_obj_class_all_latents_0_240719
470	resnet50_primary_visual_cortex
471	ReAlnet09_ALL
472	yudixie_resnet50_cat_obj_class_all_latents_0_240908
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
486	gabor_filter_entropy_4x4
487	gabor_filter_entropy_4x4_v2
488	gabor_filter_mean_abs_4x4_v2
489	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