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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	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.776
2	vit_large_patch14_clip_224:openai_ft_in1k	.776
3	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.746
4	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.742
5	vit_large_patch14_clip_336:laion2b_ft_in1k	.734
6	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.700
7	SWSL_resnext101_32x8d	.699
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.678
9	vit_large_patch14_clip_224:laion2b_ft_in1k	.669
10	CLIP_resnet50	.666
11	CLIP_resnet50_float32	.666
12	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.663
13	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.658
14	convnext_base:clip_laiona_augreg_ft_in1k_384	.658
15	CLIP-RN50	.630
16	convnext_xlarge:fb_in22k_ft_in1k	.594
17	vit_base_patch16_clip_224:openai_ft_in1k	.585
18	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.578
19	fixres_resnext101_32x48d_wsl	.576
20	CLIP_ViT-B_32	.559
21	convnext_large:fb_in22k_ft_in1k	.556
22	resnext101_32x16d_wsl	.539
23	resnext101_32x48d_wsl	.512
24	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.481
25	SWSL_resnet50	.461
26	resnext101_32x32d_wsl	.460
27	effnetv2m_custom384	.452
28	resnext101_32x8d_wsl	.425
29	vision_transformer_vit_large_patch16_224	.421
30	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.382
31	convnext_tiny:in12k_ft_in1k	.336
32	cvt_cvt-13-384-in1k_4_LucyV4	.331
33	vit_relpos_base_patch16_clsgap_224:sw_in1k	.314
34	cvt_cvt-21-384-in1k_4_LucyV4	.293
35	convnext_large_imagenet_full_seed-0	.287
36	effnetb1_272x240	.283
37	convnext_small_imagenet_full_seed-0	.278
38	convnext_tiny_imagenet_full_seed-0	.276
39	convnext_base_imagenet_full_seed-0	.274
40	focalnet_tiny_lrf_in1k	.274
41	cv_18_dagger_408_pretrained	.271
42	pnasnet_large_halfPrecision	.267
43	pnasnet_large	.267
44	resnet_152_v2	.253
45	convnext_femto_ols:d1_in1k	.247
46	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.245
47	cvt_cvt-13-224-in1k_4_LucyV4	.242
48	resnet_50_v2	.242
49	cvt_cvt-21-224-in1k_4_LucyV4	.234
50	pnasnet_large-tf	.232
51	nasnet_large	.221
52	resnet-152_v2_pytorch	.220
53	resnet-152_v2-tf	.220
54	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.213
55	inception_v4	.212
56	inception_v3	.207
57	resnet_101_v2	.205
58	xception-keras	.204
59	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.202
60	convnext_tiny_sup	.198
61	deit_large_imagenet_full_seed-0	.198
62	resnet101	.191
63	resnet152_imagenet_full	.177
64	inception_v3-tf	.172
65	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.169
66	vgg_19	.163
67	resnet101_imagenet_full	.163
68	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.161
69	resnet50_random_l2_perturb	.161
70	nasnet_large-tf	.160
71	antialias-resnet152	.159
72	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.157
73	resnet_101_v1	.155
74	resnet50_imagenet_full	.155
75	deit_base_imagenet_full_seed-0	.153
76	inception_v4-tf	.153
77	resnet50-VITO-8deg-cc	.149
78	densenet-121-keras	.147
79	resnet-50_v2-tf	.145
80	AdvProp_efficientnet-b6	.143
81	resnet50_ewconplus_wzc	.143
82	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.142
83	densenet-121	.141
84	antialiased-rnext101_32x8d	.139
85	AdvProp_efficientnet-b4	.138
86	AT_efficientnet-b4	.138
87	resnet50_random_linf8_perturb	.136
88	resnet50	.135
89	efficientnet_b0	.132
90	resnet_152_v1	.131
91	resnet_50_v1_spiking	.127
92	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.127
93	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.125
94	efficientnet-b7	.125
95	efficientnet_b0_imagenet_full	.125
96	alexnet_fov4	.124
97	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.124
98	densenet-169-keras	.124
99	densenet-201	.121
100	grcnn_109	.120
101	Res2Net50_26w_4s	.119
102	densenet-169	.119
103	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.119
104	densenet-201-keras	.118
105	resnet50_byol	.118
106	cornet_s	.117
107	efficientnet_b2_imagenet_full	.117
108	AdvProp_efficientnet-b7	.116
109	AT_efficientnet-b2	.115
110	AdvProp_efficientnet-b2	.115
111	antialiased-r50	.115
112	BiT-S-R101x3	.114
113	resnet50-SIN_IN_IN	.114
114	resnet_SIN_IN_FT_IN	.114
115	resnet-34	.113
116	custom_model_cv_18_dagger_408	.112
117	resnet18_imagenet_full	.112
118	mobilenet_v2_1_0_192	.112
119	resnet34_imagenet_full	.111
120	convnext_small_imagenet_100_seed-0	.111
121	BiT-S-R152x4	.111
122	BiT-S-R50x3	.110
123	vgg_16	.109
124	resnet-152_v1-tf	.107
125	mobilenet_v2_1_0_224	.105
126	nasnet_mobile-tf	.104
127	mobilenet_v2_1_3_224	.104
128	resnet50_tutorial	.103
129	resnet_50_v1	.103
130	resnet-50-pytorch	.103
131	resnet50-sup	.103
132	deit_small_imagenet_full_seed-0	.102
133	BiT-S-R50x1	.102
134	resnet50-SIN_IN	.101
135	tv_efficientnet-b1	.101
136	grcnn	.099
137	mobilenet_v2_1.4_224-tf	.098
138	xception	.097
139	voneresnet-50-non_stochastic	.097
140	nasnet_mobile	.097
141	ViT_B_16_imagenet1k	.095
142	inception_v1	.095
143	efficientnet_b1_imagenet_full	.094
144	resnet50-moclr8deg	.093
145	ViT_L_32_imagenet1k	.092
146	ReAlnet06	.091
147	mobilenet_v2_0_5_224	.091
148	ReAlnet01	.090
149	mobilenet_v2_1.3_224-tf	.090
150	mobilenet_v1_1.0_224	.088
151	resnet50_moco_v2	.088
152	inception_v2-tf	.088
153	ReAlnet03	.088
154	resnet50-vitoimagevidnet8	.086
155	AlexNet_SIN_fov4	.086
156	resnet-101_v1-tf	.084
157	BiT-S-R152x2	.083
158	ReAlnet04	.082
159	ReAlnet02	.080
160	CORnet-S	.078
161	mobilenet_v2_1_0_160	.077
162	mobilenet_v2_0_75_224	.076
163	mobilenet_v1_1.0_192	.075
164	ReAlnet09	.075
165	inception_v1-tf	.073
166	vonegrcnn_52e_full	.073
167	mobilenet_v2_1.0_224-tf	.072
168	resnet-18	.072
169	mobilenet_v1_1.0_160	.070
170	ReAlnet07	.070
171	ReAlnet05	.070
172	ReAlnet08	.069
173	blt_vs	.068
174	vonegrcnn_52e_full	.068
175	vonegrcnn_62e_nobn	.068
176	AdvProp_efficientnet-b8	.068
177	resnet-50_v1-tf	.067
178	ViT_B_32_imagenet1k	.067
179	ReAlnet10	.067
180	regnet_y_400mf	.066
181	mobilenet_v2_0_5_192	.066
182	mobilenet_v2_1-4_224_pytorch	.064
183	mobilenet_v2_1_4_224	.064
184	vonegrcnn_62e_nobn	.064
185	vgg-16-keras	.063
186	resnet_50_v1_spiking_l4a	.063
187	vonegrcnn_47e	.063
188	mobilenet_v2_1.0_192-tf	.062
189	voneresnet-50	.062
190	vonegrcnn_47e	.061
191	mobilenet_v2_0_75_160	.060
192	mobilenet_v2_1.0_160-tf	.058
193	mobilenet_v1_0.75_160	.058
194	mobilenet_v2_0.75_160-tf	.057
195	BiT-S-R101x1	.057
196	resnet50_linf_4_robust	.056
197	mobilenet_v2_0.75_224-tf	.056
198	mobilenet_v2_1_0_128	.055
199	mobilenet_v2_0_75_192	.055
200	squeezenet1_0	.055
201	mobilenet_v1_0.5_224	.054
202	mobilenet_v2_0.35_160	.053
203	mobilenet_v1_0.75_224	.053
204	mobilenet_v2_0.5_192-tf	.053
205	squeezenet1_1	.052
206	mobilenet_v2_0.75_128	.052
207	resnet_50_v1_spiking_l4	.052
208	mobilenet_v2_0.5_224-tf	.051
209	voneresnet-50-robust	.051
210	mobilenet_v2_0.75_192-tf	.049
211	resnet50_imagenet_100_seed-0	.049
212	mobilenet_v2_0.5_160	.049
213	mobilenet_v1_0.75_192	.049
214	mobilenet_v2_0.5_128	.048
215	alexnet_reduced_aliasing_early_checkpoint	.048
216	mobilenet_v1_0.5_192	.047
217	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.047
218	shufflenet_v2_x1_0	.046
219	alexnet_random_l2_3_perturb	.046
220	vonealexnet_gaussian_noise_std4_fixed	.046
221	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.046
222	alexnet_random_linf8_perturb	.045
223	mobilenet_v2_0.35_192	.045
224	resnet50_l2_3_robust	.045
225	resnet50_eMMCR_Vanilla	.044
226	mobilenet_v1_0.75_128	.043
227	resnet50_robust_l2_eps1	.043
228	mobilenet_v2_1.0_96	.043
229	resnet50_linf_8_robust	.042
230	texture_shape_resnet50_trained_on_SIN	.042
231	mobilenet_v2_0.35_224	.042
232	mobilenet_v2_0.35_128	.042
233	mobilenet_v2_1.0_128-tf	.041
234	resnet50_simclr	.041
235	alexnet	.041
236	alexnet_ks_torevert	.041
237	vgg-19-keras	.040
238	alexnet_early_checkpoint	.039
239	mobilenet_v1_0.5_128	.039
240	AdvProp_efficientnet-b0	.039
241	alexnet_robust_correct	.039
242	alexnet_l2_3_robust	.038
243	mobilenet_v2_0.75_96	.038
244	mobilenet_v2_0.35_96	.038
245	alexnet_linf_8_robust	.036
246	mobilenet_v1_0.5_160	.035
247	0.5x_resnet-18_LC_w_sh_1_iter	.034
248	konkle_alexnetgn_ipcl_ref01_primary_model	.034
249	mobilenet_v1_1.0_128	.032
250	mobilenet_v2_0.5_96	.032
251	deit_small_imagenet_100_seed-0	.032
252	mobilenet_v1_0.25_192	.032
253	resnet50-vicreg	.032
254	mobilenet_v1_0.25_224	.031
255	AlexNet_SIN	.031
256	texture_shape_alexnet_trained_on_SIN	.031
257	resnet-50-robust	.030
258	imagenet_l2_3_0	.030
259	resnet50_robust_l2_eps3	.030
260	mobilenet_v1_0.25_160	.029
261	imagenet_l2_10_0	.028
262	AlexNet_SIN_fov12	.027
263	alexnet_fov12	.027
264	AlexNet_SIN_fov16	.026
265	resnet50-simclr-vissl	.026
266	deit_small_imagenet_10_seed-0	.025
267	convnext_small_imagenet_10_seed-0	.025
268	deit_small_imagenet_1_seed-0	.025
269	alexnet_training_seed_02	.024
270	alexnet_training_seed_04	.024
271	alexnet_training_seed_08	.024
272	alexnet_training_seed_10	.024
273	alexnet_training_seed_07	.024
274	alexnet_training_seed_09	.023
275	alexnet_fov16	.023
276	resnet_50_v1_spiking_l3	.023
277	resnet50-meshes-lt-100-original-pretrained	.023
278	resnet-50_untrained	.022
279	resnet50-SIN	.022
280	mobilenet_v1_0.25_128	.021
281	resnet50_imagenet_10_seed-0	.020
282	convnext_small_imagenet_1_seed-0	.020
283	resnet50-meshes-lt-100-original-scratch	.018
284	barlow-twins-resnet50	.014
285	resnet50-vicregl0p9	.014
286	artResNet18_1	.014
287	resnet50_FractalDB	.011
288	alexnet_training_seed_06	.009
289	alexnet_training_seed_03	.005
290	resnet50_imagenet_1_seed-0	.003
291	resnet50-vicregl0p75	.003
292	alexnet_training_seed_05	.002
293	alexnet_training_seed_01	.002
294	alexnet_training_seed_01_fov4	.002
295	alexnet_training_seed_01_fov12	.002
296	alexnet_training_seed_01_fov16	.002
297	resnet50-barlow	.001
298	resnet-18-LC_w_sh_100_iter_conv_init_m
299	ViT-B/32
300	BiT-M-R50x3
301	BiT-M-R50x1
302	BiT-M-R152x2
303	resnet-18-LC_w_sh_1_iter_conv_init_m
304	resnet-18-LC_w_sh_1_iter_m
305	resnet-18-LC_w_sh_10_iter_m
306	resnet-18_test_m
307	resnet-18-LC_w_sh_100_iter_m
308	resnet-18-LC_w_sh_10_iter_conv_init_m
309	FrankRobWobv0
310	0.5x_resnet-18_LC
311	CORnetZ_CIFAR10
312	resnet18-local_aggregation
313	resnet18-deepcluster
314	r3m_resnet18
315	CORnet-Z
316	vggface
317	my-model
318	resnet18-instance_recognition
319	0.5x_resnet-18_LC_w_sh_10_iter
320	r3m_resnet50
321	r3m_resnet34
322	resnet-18-LC_m
323	r3m_resnet50_nocrop
324	resnet18-supervised
325	resnet-18-LC_1st_conv_m
326	resnet-18-LC_conv_init_m
327	resnet18-contrastive_multiview
328	0.5x_resnet-18
329	resnet18-colorization
330	RN50
331	0.5x_resnet-18_LC_w_sh_100_iter
332	RN50
333	resnet18-relative_position
334	resnet18-autoencoder
335	resnet18-simclr
336	omnivore_swinT
337	ViT_L_32
338	ViT_B_32
339	resnet18-depth_prediction
340	ViT_B_16
341	r3m_resnet34_nocrop
342	resnet18-contrastive_predictive
343	resnet-18-LC_untrained
344	resnet-18_untrained
345	resnet-50x2_untrained
346	dcgan
347	unet_entire
348	omnivore_swinS
349	v1-pyr-nodown
350	CORnetZ_CIFAR10_bs32_20_04
351	pixels
352	dorinet_cornet_z
353	my-model
354	resnet50
355	openclip
356	resnet50-cifar
357	resnet50_pretrained_with_retinal_waves
358	resnet50_ImageNet
359	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
360	yudixie_resnet50_random_0_240908
361	yudixie_resnet50_imagenet1kpret_0_240908
362	yudixie_resnet50_imagenet1kpret_0_240312
363	yudixie_resnet50_object_class_0_240908
364	yudixie_resnet50_category_class_0_240908
365	ConvLSTM
366	yudixie_resnet50_distance_reg_0_240908
367	PredRNN
368	SimVP
369	TAU
370	briaai_rmbg_1_4
371	MIM
372	hmax
373	densenet_201_pytorch
374	pnasnet_large_pytorch
375	ReAlnet01_cornet
376	inception_v3_pytorch
377	yolos_tiny
378	ReAlnet03_cornet
379	yudixie_resnet18_imagenet1kpret_0_240719
380	eBarlow_lmda_02_1000ep
381	ReAlnet02_cornet
382	compact_resnet50_V4
383	vit_base_patch16_dinov3-lvd1689m
384	vit_base_patch14_reg4_dinov2-lvd142m
385	ReAlnet01_cornet_fov16
386	resnet18_ecoset_full
387	resnet34_ecoset_full
388	ReAlnet01_cornet_fov4
389	resnet50_ecoset_full
390	resnet101_ecoset_full
391	compact_resnet50_robust_V4
392	ReAlnet04_cornet
393	resnet152_ecoset_full
394	yudixie_resnet50_distance_translation_0_240908
395	yudixie_resnet18_distance_translation_0_240719
396	yudixie_resnet18_translation_rotation_0_240719
397	yudixie_resnet18_distance_rotation_0_240719
398	compact_resnet50_IT
399	ReAlnet01_cornet_fov12
400	yudixie_resnet18_category_class_0_240719
401	vit_base_patch14_dinov2-lvd142m
402	yudixie_resnet50_distance_rotation_0_240908
403	ReAlnet05_cornet
404	yudixie_resnet50_rotation_reg_0_240908
405	yudixie_resnet18_random_0_240719
406	compact_vgg19_V4
407	yudixie_resnet18_distance_translation_rotation_0_240719
408	yudixie_resnet18_object_class_0_240719
409	aughost_fair_resnet18
410	SeLaVi-AVE
411	yudixie_resnet18_translation_reg_0_240719
412	yudixie_resnet18_distance_reg_0_240719
413	ReAlnet06_cornet
414	yudixie_resnet50_translation_reg_0_240908
415	r3d_18
416	compact_vgg19_IT
417	ReAlnet01_ALL
418	mc3_18
419	ReAlnet07_cornet
420	yudixie_resnet50_translation_rotation_0_240908
421	s3d
422	ReAlnet08_cornet
423	mvit_v1_b
424	ReAlnet02_ALL
425	yudixie_resnet18_rotation_reg_0_240719
426	omnivore_swinB
427	VOneCORnet-S
428	compact_cornets_V4
429	resnet18_imagenet21kP
430	ReAlnet03_ALL
431	ReAlnet09_cornet
432	ReAlnet04_ALL
433	SeLaVi-Kinetics400
434	compact_cornets_IT
435	SeLaVi-VGG-Sound
436	ReAlnet05_ALL
437	r2plus1d_18
438	ReAlnet06_ALL
439	ReAlnet10_cornet
440	SeLaVi-Kinetics-Sound
441	mvit_v2_s
442	yudixie_resnet50_distance_translation_rotation_0_240908
443	ReAlnet07_ALL
444	compact_resnet50_robust_IT
445	ReAlnet08_ALL
446	yudixie_resnet18_cat_obj_class_all_latents_0_240719
447	resnet50_primary_visual_cortex
448	ReAlnet09_ALL
449	yudixie_resnet50_cat_obj_class_all_latents_0_240908
450	ReAlnet10_ALL

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