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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	pnasnet_large-tf	.232
66	resnet50_st_wzc_seed6_2_323	.223
67	nasnet_large	.221
68	resnet-152_v2_pytorch	.220
69	resnet-152_v2-tf	.220
70	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.213
71	resnet50_st_wzc_seed7_2_323	.212
72	inception_v4	.212
73	inception_v3	.207
74	resnet_101_v2	.205
75	xception-keras	.204
76	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.202
77	convnext_tiny_sup	.198
78	deit_large_imagenet_full_seed-0	.198
79	resnet101	.191
80	resnet50_st_wzc_seed3_2_323	.177
81	resnet152_imagenet_full	.177
82	resnet50_ewconplus_wzc_seed2_2__323	.172
83	inception_v3-tf	.172
84	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.169
85	vgg_19	.163
86	resnet101_imagenet_full	.163
87	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.161
88	resnet50_random_l2_perturb	.161
89	nasnet_large-tf	.160
90	antialias-resnet152	.159
91	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.157
92	resnet_101_v1	.155
93	resnet50_imagenet_full	.155
94	deit_base_imagenet_full_seed-0	.153
95	inception_v4-tf	.153
96	resnet50-VITO-8deg-cc	.149
97	densenet-121-keras	.147
98	resnet-50_v2-tf	.145
99	AdvProp_efficientnet-b6	.143
100	resnet50_ewconplus_wzc	.143
101	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.142
102	densenet-121	.141
103	antialiased-rnext101_32x8d	.139
104	AdvProp_efficientnet-b4	.138
105	AT_efficientnet-b4	.138
106	resnet50_ewconplus_wzc_seed5_2__323	.137
107	resnet50_random_linf8_perturb	.136
108	resnet50	.135
109	efficientnet_b0	.132
110	resnet_152_v1	.131
111	resnet50_st_wzc_2	.128
112	resnet_50_v1_spiking	.127
113	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.127
114	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.125
115	efficientnet-b7	.125
116	efficientnet_b0_imagenet_full	.125
117	alexnet_fov4	.124
118	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.124
119	densenet-169-keras	.124
120	densenet-201	.121
121	grcnn_109	.120
122	resnet50_ewconplus_wzc_seed6_2_323	.119
123	Res2Net50_26w_4s	.119
124	densenet-169	.119
125	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.119
126	densenet-201-keras	.118
127	resnet50_byol	.118
128	cornet_s	.117
129	efficientnet_b2_imagenet_full	.117
130	AdvProp_efficientnet-b7	.116
131	AT_efficientnet-b2	.115
132	AdvProp_efficientnet-b2	.115
133	antialiased-r50	.115
134	BiT-S-R101x3	.114
135	resnet50-SIN_IN_IN	.114
136	resnet_SIN_IN_FT_IN	.114
137	resnet-34	.113
138	resnet50_hvs_wzc	.113
139	custom_model_cv_18_dagger_408	.112
140	resnet18_imagenet_full	.112
141	mobilenet_v2_1_0_192	.112
142	resnet34_imagenet_full	.111
143	convnext_small_imagenet_100_seed-0	.111
144	BiT-S-R152x4	.111
145	BiT-S-R50x3	.110
146	vgg_16	.109
147	resnet-152_v1-tf	.107
148	mobilenet_v2_1_0_224	.105
149	nasnet_mobile-tf	.104
150	mobilenet_v2_1_3_224	.104
151	resnet50_tutorial	.103
152	resnet_50_v1	.103
153	resnet-50-pytorch	.103
154	resnet50-sup	.103
155	deit_small_imagenet_full_seed-0	.102
156	BiT-S-R50x1	.102
157	resnet50-SIN_IN	.101
158	tv_efficientnet-b1	.101
159	grcnn	.099
160	mobilenet_v2_1.4_224-tf	.098
161	convnext_base_fb_in1k_gram_aligned_dinov3	.098
162	xception	.097
163	voneresnet-50-non_stochastic	.097
164	nasnet_mobile	.097
165	ViT_B_16_imagenet1k	.095
166	inception_v1	.095
167	efficientnet_b1_imagenet_full	.094
168	resnet50-moclr8deg	.093
169	ViT_L_32_imagenet1k	.092
170	ReAlnet06	.091
171	mobilenet_v2_0_5_224	.091
172	ReAlnet01	.090
173	mobilenet_v2_1.3_224-tf	.090
174	mobilenet_v1_1.0_224	.088
175	resnet50_moco_v2	.088
176	inception_v2-tf	.088
177	ReAlnet03	.088
178	resnet50-vitoimagevidnet8	.086
179	AlexNet_SIN_fov4	.086
180	resnet-101_v1-tf	.084
181	BiT-S-R152x2	.083
182	ReAlnet04	.082
183	ReAlnet02	.080
184	CORnet-S	.078
185	mobilenet_v2_1_0_160	.077
186	mobilenet_v2_0_75_224	.076
187	mobilenet_v1_1.0_192	.075
188	ReAlnet09	.075
189	inception_v1-tf	.073
190	vonegrcnn_52e_full	.073
191	mobilenet_v2_1.0_224-tf	.072
192	resnet-18	.072
193	mobilenet_v1_1.0_160	.070
194	ReAlnet07	.070
195	ReAlnet05	.070
196	ReAlnet08	.069
197	blt_vs	.068
198	vonegrcnn_52e_full	.068
199	vonegrcnn_62e_nobn	.068
200	AdvProp_efficientnet-b8	.068
201	resnet-50_v1-tf	.067
202	ViT_B_32_imagenet1k	.067
203	ReAlnet10	.067
204	regnet_y_400mf	.066
205	mobilenet_v2_0_5_192	.066
206	mobilenet_v2_1-4_224_pytorch	.064
207	mobilenet_v2_1_4_224	.064
208	vonegrcnn_62e_nobn	.064
209	vgg-16-keras	.063
210	resnet_50_v1_spiking_l4a	.063
211	vonegrcnn_47e	.063
212	mobilenet_v2_1.0_192-tf	.062
213	voneresnet-50	.062
214	vonegrcnn_47e	.061
215	mobilenet_v2_0_75_160	.060
216	mobilenet_v2_1.0_160-tf	.058
217	mobilenet_v1_0.75_160	.058
218	mobilenet_v2_0.75_160-tf	.057
219	BiT-S-R101x1	.057
220	resnet50_linf_4_robust	.056
221	mobilenet_v2_0.75_224-tf	.056
222	mobilenet_v2_1_0_128	.055
223	mobilenet_v2_0_75_192	.055
224	squeezenet1_0	.055
225	mobilenet_v1_0.5_224	.054
226	mobilenet_v2_0.35_160	.053
227	mobilenet_v1_0.75_224	.053
228	mobilenet_v2_0.5_192-tf	.053
229	squeezenet1_1	.052
230	mobilenet_v2_0.75_128	.052
231	resnet_50_v1_spiking_l4	.052
232	mobilenet_v2_0.5_224-tf	.051
233	voneresnet-50-robust	.051
234	mobilenet_v2_0.75_192-tf	.049
235	resnet50_imagenet_100_seed-0	.049
236	mobilenet_v2_0.5_160	.049
237	mobilenet_v1_0.75_192	.049
238	mobilenet_v2_0.5_128	.048
239	alexnet_reduced_aliasing_early_checkpoint	.048
240	mobilenet_v1_0.5_192	.047
241	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.047
242	shufflenet_v2_x1_0	.046
243	alexnet_random_l2_3_perturb	.046
244	vonealexnet_gaussian_noise_std4_fixed	.046
245	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.046
246	alexnet_random_linf8_perturb	.045
247	mobilenet_v2_0.35_192	.045
248	resnet50_l2_3_robust	.045
249	resnet50_eMMCR_Vanilla	.044
250	mobilenet_v1_0.75_128	.043
251	resnet50_robust_l2_eps1	.043
252	mobilenet_v2_1.0_96	.043
253	resnet50_linf_8_robust	.042
254	texture_shape_resnet50_trained_on_SIN	.042
255	mobilenet_v2_0.35_224	.042
256	mobilenet_v2_0.35_128	.042
257	mobilenet_v2_1.0_128-tf	.041
258	resnet50_simclr	.041
259	alexnet	.041
260	alexnet_ks_torevert	.041
261	vgg-19-keras	.040
262	alexnet_early_checkpoint	.039
263	mobilenet_v1_0.5_128	.039
264	AdvProp_efficientnet-b0	.039
265	alexnet_robust_correct	.039
266	alexnet_l2_3_robust	.038
267	mobilenet_v2_0.75_96	.038
268	mobilenet_v2_0.35_96	.038
269	alexnet_linf_8_robust	.036
270	mobilenet_v1_0.5_160	.035
271	0.5x_resnet-18_LC_w_sh_1_iter	.034
272	konkle_alexnetgn_ipcl_ref01_primary_model	.034
273	mobilenet_v1_1.0_128	.032
274	mobilenet_v2_0.5_96	.032
275	deit_small_imagenet_100_seed-0	.032
276	mobilenet_v1_0.25_192	.032
277	resnet50-vicreg	.032
278	mobilenet_v1_0.25_224	.031
279	AlexNet_SIN	.031
280	texture_shape_alexnet_trained_on_SIN	.031
281	resnet-50-robust	.030
282	imagenet_l2_3_0	.030
283	resnet50_robust_l2_eps3	.030
284	mobilenet_v1_0.25_160	.029
285	imagenet_l2_10_0	.028
286	AlexNet_SIN_fov12	.027
287	alexnet_fov12	.027
288	AlexNet_SIN_fov16	.026
289	resnet50-simclr-vissl	.026
290	deit_small_imagenet_10_seed-0	.025
291	convnext_small_imagenet_10_seed-0	.025
292	deit_small_imagenet_1_seed-0	.025
293	alexnet_training_seed_02	.024
294	alexnet_training_seed_04	.024
295	alexnet_training_seed_08	.024
296	alexnet_training_seed_10	.024
297	alexnet_training_seed_07	.024
298	alexnet_training_seed_09	.023
299	alexnet_fov16	.023
300	resnet_50_v1_spiking_l3	.023
301	resnet-50_untrained	.022
302	resnet50-SIN	.022
303	mobilenet_v1_0.25_128	.021
304	resnet50_imagenet_10_seed-0	.020
305	convnext_small_imagenet_1_seed-0	.020
306	barlow-twins-resnet50	.014
307	resnet50-vicregl0p9	.014
308	artResNet18_1	.014
309	resnet50_FractalDB	.011
310	alexnet_training_seed_06	.009
311	alexnet_training_seed_03	.005
312	resnet50_imagenet_1_seed-0	.003
313	resnet50-vicregl0p75	.003
314	alexnet_training_seed_05	.002
315	alexnet_training_seed_01	.002
316	alexnet_training_seed_01_fov4	.002
317	alexnet_training_seed_01_fov12	.002
318	alexnet_training_seed_01_fov16	.002
319	resnet50-barlow	.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_1_iter_m
327	resnet-18-LC_w_sh_10_iter_m
328	resnet-18_test_m
329	resnet-18-LC_w_sh_100_iter_m
330	resnet-18-LC_w_sh_10_iter_conv_init_m
331	FrankRobWobv0
332	0.5x_resnet-18_LC
333	CORnetZ_CIFAR10
334	resnet18-local_aggregation
335	resnet18-deepcluster
336	r3m_resnet18
337	CORnet-Z
338	vggface
339	my-model
340	resnet18-instance_recognition
341	0.5x_resnet-18_LC_w_sh_10_iter
342	r3m_resnet50
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	resnet18-colorization
352	RN50
353	0.5x_resnet-18_LC_w_sh_100_iter
354	RN50
355	resnet18-relative_position
356	resnet18-autoencoder
357	resnet18-simclr
358	omnivore_swinT
359	ViT_L_32
360	ViT_B_32
361	resnet18-depth_prediction
362	ViT_B_16
363	r3m_resnet34_nocrop
364	resnet18-contrastive_predictive
365	resnet-18-LC_untrained
366	resnet-18_untrained
367	resnet-50x2_untrained
368	dcgan
369	unet_entire
370	omnivore_swinS
371	v1-pyr-nodown
372	CORnetZ_CIFAR10_bs32_20_04
373	pixels
374	dorinet_cornet_z
375	my-model
376	openclip
377	resnet50-cifar
378	resnet50_pretrained_with_retinal_waves
379	resnet50_ImageNet
380	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
381	yudixie_resnet50_random_0_240908
382	yudixie_resnet50_imagenet1kpret_0_240908
383	yudixie_resnet50_imagenet1kpret_0_240312
384	yudixie_resnet50_object_class_0_240908
385	yudixie_resnet50_category_class_0_240908
386	ConvLSTM
387	yudixie_resnet50_distance_reg_0_240908
388	PredRNN
389	SimVP
390	TAU
391	briaai_rmbg_1_4
392	MIM
393	hmax
394	densenet_201_pytorch
395	pnasnet_large_pytorch
396	ReAlnet01_cornet
397	inception_v3_pytorch
398	yolos_tiny
399	ReAlnet03_cornet
400	yudixie_resnet18_imagenet1kpret_0_240719
401	eBarlow_lmda_02_1000ep
402	ReAlnet02_cornet
403	compact_resnet50_V4
404	vit_base_patch16_dinov3-lvd1689m
405	vit_base_patch14_reg4_dinov2-lvd142m
406	ReAlnet01_cornet_fov16
407	resnet18_ecoset_full
408	resnet34_ecoset_full
409	ReAlnet01_cornet_fov4
410	resnet50_ecoset_full
411	resnet101_ecoset_full
412	compact_resnet50_robust_V4
413	ReAlnet04_cornet
414	resnet152_ecoset_full
415	yudixie_resnet50_distance_translation_0_240908
416	yudixie_resnet18_distance_translation_0_240719
417	yudixie_resnet18_translation_rotation_0_240719
418	yudixie_resnet18_distance_rotation_0_240719
419	compact_resnet50_IT
420	ReAlnet01_cornet_fov12
421	yudixie_resnet18_category_class_0_240719
422	vit_base_patch14_dinov2-lvd142m
423	yudixie_resnet50_distance_rotation_0_240908
424	ReAlnet05_cornet
425	yudixie_resnet50_rotation_reg_0_240908
426	yudixie_resnet18_random_0_240719
427	compact_vgg19_V4
428	yudixie_resnet18_distance_translation_rotation_0_240719
429	yudixie_resnet18_object_class_0_240719
430	aughost_fair_resnet18
431	SeLaVi-AVE
432	yudixie_resnet18_translation_reg_0_240719
433	yudixie_resnet18_distance_reg_0_240719
434	ReAlnet06_cornet
435	yudixie_resnet50_translation_reg_0_240908
436	r3d_18
437	compact_vgg19_IT
438	ReAlnet01_ALL
439	mc3_18
440	ReAlnet07_cornet
441	yudixie_resnet50_translation_rotation_0_240908
442	s3d
443	ReAlnet08_cornet
444	mvit_v1_b
445	ReAlnet02_ALL
446	yudixie_resnet18_rotation_reg_0_240719
447	omnivore_swinB
448	VOneCORnet-S
449	compact_cornets_V4
450	resnet18_imagenet21kP
451	ReAlnet03_ALL
452	ReAlnet09_cornet
453	ReAlnet04_ALL
454	SeLaVi-Kinetics400
455	compact_cornets_IT
456	SeLaVi-VGG-Sound
457	ReAlnet05_ALL
458	r2plus1d_18
459	ReAlnet06_ALL
460	ReAlnet10_cornet
461	SeLaVi-Kinetics-Sound
462	mvit_v2_s
463	yudixie_resnet50_distance_translation_rotation_0_240908
464	ReAlnet07_ALL
465	compact_resnet50_robust_IT
466	ReAlnet08_ALL
467	yudixie_resnet18_cat_obj_class_all_latents_0_240719
468	resnet50_primary_visual_cortex
469	ReAlnet09_ALL
470	yudixie_resnet50_cat_obj_class_all_latents_0_240908
471	ReAlnet10_ALL
472	resnet18_fair
473	pr1_stl10_disentangled_alpha04
474	predictive_coding_pc
475	yudixie_wbh_object_class_scaling_0415_run_0
476	yudixie_wbh_object_class_scaling_0415_run_3
477	yudixie_wbh_object_class_scaling_0415_imagenet
478	yudixie_wbh_object_class_scaling_0415_run_2
479	yudixie_wbh_object_class_scaling_0415_run_1
480	deepeisnn_ei_cifar10_vgg19
481	deepeisnn_ei_cifar10_resnet18
482	deepeisnn_ei_imagenet_resnet18
483	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

0.37.

Note that scores are relative to this ceiling.

Data: Geirhos2021sketch

Metric: error_consistency