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_float32	.666
11	CLIP_resnet50	.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	.267
43	resnet_152_v2	.253
44	convnext_femto_ols:d1_in1k	.247
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.245
46	cvt_cvt-13-224-in1k_4_LucyV4	.242
47	resnet_50_v2	.242
48	cvt_cvt-21-224-in1k_4_LucyV4	.234
49	pnasnet_large-tf	.232
50	nasnet_large	.221
51	resnet-152_v2_pytorch	.220
52	resnet-152_v2-tf	.220
53	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.213
54	inception_v4	.212
55	inception_v3	.207
56	resnet_101_v2	.205
57	xception-keras	.204
58	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.202
59	convnext_tiny_sup	.198
60	deit_large_imagenet_full_seed-0	.198
61	resnet101	.191
62	resnet152_imagenet_full	.177
63	inception_v3-tf	.172
64	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.169
65	vgg_19	.163
66	resnet101_imagenet_full	.163
67	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.161
68	resnet50_random_l2_perturb	.161
69	nasnet_large-tf	.160
70	antialias-resnet152	.159
71	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.157
72	resnet_101_v1	.155
73	resnet50_imagenet_full	.155
74	deit_base_imagenet_full_seed-0	.153
75	inception_v4-tf	.153
76	resnet50-VITO-8deg-cc	.149
77	densenet-121-keras	.147
78	resnet-50_v2-tf	.145
79	AdvProp_efficientnet-b6	.143
80	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.142
81	densenet-121	.141
82	antialiased-rnext101_32x8d	.139
83	AT_efficientnet-b4	.138
84	AdvProp_efficientnet-b4	.138
85	resnet50_random_linf8_perturb	.136
86	resnet50	.135
87	efficientnet_b0	.132
88	resnet_152_v1	.131
89	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.127
90	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.125
91	efficientnet-b7	.125
92	efficientnet_b0_imagenet_full	.125
93	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.124
94	densenet-169-keras	.124
95	densenet-201	.121
96	grcnn_109	.120
97	Res2Net50_26w_4s	.119
98	densenet-169	.119
99	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.119
100	densenet-201-keras	.118
101	resnet50_byol	.118
102	cornet_s	.117
103	efficientnet_b2_imagenet_full	.117
104	AdvProp_efficientnet-b7	.116
105	AT_efficientnet-b2	.115
106	AdvProp_efficientnet-b2	.115
107	antialiased-r50	.115
108	BiT-S-R101x3	.114
109	resnet50-SIN_IN_IN	.114
110	resnet_SIN_IN_FT_IN	.114
111	resnet-34	.113
112	custom_model_cv_18_dagger_408	.112
113	resnet18_imagenet_full	.112
114	mobilenet_v2_1_0_192	.112
115	resnet34_imagenet_full	.111
116	convnext_small_imagenet_100_seed-0	.111
117	BiT-S-R152x4	.111
118	BiT-S-R50x3	.110
119	vgg_16	.109
120	resnet-152_v1-tf	.107
121	mobilenet_v2_1_0_224	.105
122	nasnet_mobile-tf	.104
123	mobilenet_v2_1_3_224	.104
124	resnet_50_v1	.103
125	resnet50_tutorial	.103
126	resnet50-sup	.103
127	resnet-50-pytorch	.103
128	deit_small_imagenet_full_seed-0	.102
129	BiT-S-R50x1	.102
130	resnet50-SIN_IN	.101
131	tv_efficientnet-b1	.101
132	grcnn	.099
133	mobilenet_v2_1.4_224-tf	.098
134	xception	.097
135	voneresnet-50-non_stochastic	.097
136	nasnet_mobile	.097
137	ViT_B_16_imagenet1k	.095
138	inception_v1	.095
139	efficientnet_b1_imagenet_full	.094
140	resnet50-moclr8deg	.093
141	ViT_L_32_imagenet1k	.092
142	ReAlnet06	.091
143	mobilenet_v2_0_5_224	.091
144	ReAlnet01	.090
145	ReAlnet_incorrect	.090
146	mobilenet_v2_1.3_224-tf	.090
147	mobilenet_v1_1.0_224	.088
148	resnet50_moco_v2	.088
149	inception_v2-tf	.088
150	ReAlnet03	.088
151	resnet50-vitoimagevidnet8	.086
152	resnet-101_v1-tf	.084
153	BiT-S-R152x2	.083
154	ReAlnet04	.082
155	ReAlnet02	.080
156	CORnet-S	.078
157	mobilenet_v2_1_0_160	.077
158	mobilenet_v2_0_75_224	.076
159	mobilenet_v1_1.0_192	.075
160	ReAlnet09	.075
161	inception_v1-tf	.073
162	vonegrcnn_52e_full	.073
163	mobilenet_v2_1.0_224-tf	.072
164	resnet-18	.072
165	mobilenet_v1_1.0_160	.070
166	ReAlnet07	.070
167	ReAlnet05	.070
168	ReAlnet08	.069
169	blt_vs	.068
170	vonegrcnn_52e_full	.068
171	vonegrcnn_62e_nobn	.068
172	AdvProp_efficientnet-b8	.068
173	resnet-50_v1-tf	.067
174	ViT_B_32_imagenet1k	.067
175	ReAlnet10	.067
176	regnet_y_400mf	.066
177	mobilenet_v2_0_5_192	.066
178	mobilenet_v2_1-4_224_pytorch	.064
179	mobilenet_v2_1_4_224	.064
180	vonegrcnn_62e_nobn	.064
181	vgg-16-keras	.063
182	vonegrcnn_47e	.063
183	mobilenet_v2_1.0_192-tf	.062
184	voneresnet-50	.062
185	vonegrcnn_47e	.061
186	mobilenet_v2_0_75_160	.060
187	mobilenet_v2_1.0_160-tf	.058
188	mobilenet_v1_0.75_160	.058
189	mobilenet_v2_0.75_160-tf	.057
190	BiT-S-R101x1	.057
191	resnet50_linf_4_robust	.056
192	mobilenet_v2_0.75_224-tf	.056
193	mobilenet_v2_1_0_128	.055
194	mobilenet_v2_0_75_192	.055
195	squeezenet1_0	.055
196	mobilenet_v1_0.5_224	.054
197	mobilenet_v2_0.35_160	.053
198	mobilenet_v1_0.75_224	.053
199	mobilenet_v2_0.5_192-tf	.053
200	squeezenet1_1	.052
201	mobilenet_v2_0.75_128	.052
202	mobilenet_v2_0.5_224-tf	.051
203	voneresnet-50-robust	.051
204	mobilenet_v2_0.75_192-tf	.049
205	resnet50_imagenet_100_seed-0	.049
206	mobilenet_v2_0.5_160	.049
207	mobilenet_v1_0.75_192	.049
208	mobilenet_v2_0.5_128	.048
209	alexnet_reduced_aliasing_early_checkpoint	.048
210	alexnet	.047
211	mobilenet_v1_0.5_192	.047
212	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.047
213	shufflenet_v2_x1_0	.046
214	alexnet_random_l2_3_perturb	.046
215	vonealexnet_gaussian_noise_std4_fixed	.046
216	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.046
217	alexnet_random_linf8_perturb	.045
218	mobilenet_v2_0.35_192	.045
219	resnet50_l2_3_robust	.045
220	mobilenet_v1_0.75_128	.043
221	resnet50_robust_l2_eps1	.043
222	mobilenet_v2_1.0_96	.043
223	resnet50_linf_8_robust	.042
224	texture_shape_resnet50_trained_on_SIN	.042
225	mobilenet_v2_0.35_224	.042
226	mobilenet_v2_0.35_128	.042
227	mobilenet_v2_1.0_128-tf	.041
228	resnet50_simclr	.041
229	alexnet	.041
230	alexnet	.041
231	alexnet	.041
232	alexnet	.041
233	alexnet	.041
234	alexnet	.041
235	alexnet	.041
236	alexnet-baseline	.041
237	alexnet	.041
238	alexnet	.041
239	alexnet_ks_torevert	.041
240	vgg-19-keras	.040
241	alexnet_early_checkpoint	.039
242	mobilenet_v1_0.5_128	.039
243	AdvProp_efficientnet-b0	.039
244	alexnet_robust_correct	.039
245	alexnet_l2_3_robust	.038
246	mobilenet_v2_0.75_96	.038
247	mobilenet_v2_0.35_96	.038
248	alexnet_linf_8_robust	.036
249	mobilenet_v1_0.5_160	.035
250	0.5x_resnet-18_LC_w_sh_1_iter	.034
251	konkle_alexnetgn_ipcl_ref01_primary_model	.034
252	mobilenet_v1_1.0_128	.032
253	mobilenet_v2_0.5_96	.032
254	deit_small_imagenet_100_seed-0	.032
255	mobilenet_v1_0.25_192	.032
256	resnet50-vicreg	.032
257	mobilenet_v1_0.25_224	.031
258	AlexNet_SIN	.031
259	texture_shape_alexnet_trained_on_SIN	.031
260	resnet50_robust_l2_eps3	.030
261	imagenet_l2_3_0	.030
262	resnet-50-robust	.030
263	mobilenet_v1_0.25_160	.029
264	imagenet_l2_10_0	.028
265	resnet50-simclr-vissl	.026
266	convnext_small_imagenet_10_seed-0	.025
267	deit_small_imagenet_1_seed-0	.025
268	deit_small_imagenet_10_seed-0	.025
269	alexnet_training_seed_07	.024
270	alexnet_training_seed_02	.024
271	alexnet_training_seed_08	.024
272	alexnet_training_seed_10	.024
273	alexnet_training_seed_04	.024
274	alexnet_training_seed_09	.023
275	resnet50-meshes-lt-100-original-pretrained	.023
276	resnet-50_untrained	.022
277	resnet50-SIN	.022
278	mobilenet_v1_0.25_128	.021
279	resnet50_imagenet_10_seed-0	.020
280	convnext_small_imagenet_1_seed-0	.020
281	resnet50-meshes-lt-100-original-scratch	.018
282	barlow-twins-resnet50	.014
283	resnet50-vicregl0p9	.014
284	artResNet18_1	.014
285	resnet50_FractalDB	.011
286	alexnet_training_seed_06	.009
287	alexnet_training_seed_03	.005
288	resnet50_imagenet_1_seed-0	.003
289	resnet50-vicregl0p75	.003
290	alexnet_training_seed_01	.002
291	alexnet_training_seed_05	.002
292	resnet50-barlow	.001
293	resnet50_ecoset_full	X
294	resnet101_ecoset_full	X
295	ReAlnet04_cornet	X
296	resnet152_ecoset_full	X
297	yudixie_resnet50_distance_translation_0_240908	X
298	yudixie_resnet18_distance_translation_0_240719	X
299	yudixie_resnet18_translation_rotation_0_240719	X
300	yudixie_resnet18_distance_rotation_0_240719	X
301	yudixie_resnet18_category_class_0_240719	X
302	CORnet-Z	X
303	yudixie_resnet50_distance_rotation_0_240908	X
304	ReAlnet05_cornet	X
305	yudixie_resnet50_rotation_reg_0_240908	X
306	yudixie_resnet18_random_0_240719	X
307	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
308	yudixie_resnet18_distance_translation_rotation_0_240719	X
309	yudixie_resnet18_object_class_0_240719	X
310	yudixie_resnet18_translation_reg_0_240719	X
311	yudixie_resnet18_distance_reg_0_240719	X
312	ReAlnet06_cornet	X
313	yudixie_resnet50_translation_reg_0_240908	X
314	ReAlnet07_cornet	X
315	yudixie_resnet50_translation_rotation_0_240908	X
316	r3m_resnet18	X
317	ReAlnet08_cornet	X
318	resnet18-deepcluster	X
319	omnivore_swinB	X
320	resnet18-local_aggregation	X
321	CORnetZ_CIFAR10	X
322	VOneCORnet-S	X
323	0.5x_resnet-18_LC	X
324	ReAlnet09_cornet	X
325	FrankRobWobv0	X
326	resnet-18-LC_w_sh_10_iter_conv_init_m	X
327	resnet-18-LC_w_sh_100_iter_m	X
328	ReAlnet10_cornet	X
329	resnet-18_test_m	X
330	resnet-18-LC_w_sh_10_iter_m	X
331	yudixie_resnet50_distance_translation_rotation_0_240908	X
332	resnet-18-LC_w_sh_1_iter_m	X
333	resnet-18-LC_w_sh_1_iter_conv_init_m	X
334	BiT-M-R152x2	X
335	BiT-M-R50x1	X
336	BiT-M-R50x3	X
337	ViT-B/32	X
338	resnet-18-LC_w_sh_100_iter_conv_init_m	X
339	vggface	X
340	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
341	resnet50_pretrained_with_retinal_waves	X
342	resnet50-cifar	X
343	yudixie_resnet50_random_0_240908	X
344	openclip	X
345	yudixie_resnet50_imagenet1kpret_0_240908	X
346	yudixie_resnet50_imagenet1kpret_0_240312	X
347	resnet50	X
348	my-model	X
349	yudixie_resnet50_object_class_0_240908	X
350	dorinet_cornet_z	X
351	pixels	X
352	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
353	CORnetZ_CIFAR10_bs32_20_04	X
354	v1-pyr-nodown	X
355	omnivore_swinS	X
356	unet_entire	X
357	dcgan	X
358	resnet-50x2_untrained	X
359	resnet-18_untrained	X
360	resnet-18-LC_untrained	X
361	resnet18-contrastive_predictive	X
362	r3m_resnet34_nocrop	X
363	ViT_B_16	X
364	yudixie_resnet50_category_class_0_240908	X
365	resnet18-depth_prediction	X
366	ViT_B_32	X
367	ViT_L_32	X
368	omnivore_swinT	X
369	resnet18-simclr	X
370	resnet18-autoencoder	X
371	resnet18-relative_position	X
372	resnet18_imagenet21kP	X
373	ConvLSTM	X
374	yudixie_resnet50_distance_reg_0_240908	X
375	PredRNN	X
376	RN50	X
377	SimVP	X
378	TAU	X
379	0.5x_resnet-18_LC_w_sh_100_iter	X
380	MIM	X
381	hmax	X
382	RN50	X
383	resnet18-colorization	X
384	densenet_201_pytorch	X
385	pnasnet_large_pytorch	X
386	0.5x_resnet-18	X
387	ReAlnet01_cornet	X
388	inception_v3_pytorch	X
389	resnet18-contrastive_multiview	X
390	resnet-18-LC_conv_init_m	X
391	resnet-18-LC_1st_conv_m	X
392	resnet18-supervised	X
393	r3m_resnet50_nocrop	X
394	resnet-18-LC_m	X
395	r3m_resnet34	X
396	r3m_resnet50	X
397	ReAlnet03_cornet	X
398	yudixie_resnet18_rotation_reg_0_240719	X
399	yudixie_resnet18_imagenet1kpret_0_240719	X
400	ReAlnet02_cornet	X
401	0.5x_resnet-18_LC_w_sh_10_iter	X
402	resnet18-instance_recognition	X
403	my-model	X
404	resnet18_ecoset_full	X
405	resnet34_ecoset_full	X
406	resnet50_ImageNet	X

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