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	AdvProp_efficientnet-b4	.138
84	AT_efficientnet-b4	.138
85	resnet50_random_linf8_perturb	.136
86	resnet50	.135
87	efficientnet_b0	.132
88	resnet_152_v1	.131
89	resnet_50_v1_spiking	.127
90	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.127
91	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.125
92	efficientnet-b7	.125
93	efficientnet_b0_imagenet_full	.125
94	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.124
95	densenet-169-keras	.124
96	densenet-201	.121
97	grcnn_109	.120
98	Res2Net50_26w_4s	.119
99	densenet-169	.119
100	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.119
101	densenet-201-keras	.118
102	resnet50_byol	.118
103	cornet_s	.117
104	efficientnet_b2_imagenet_full	.117
105	AdvProp_efficientnet-b7	.116
106	AT_efficientnet-b2	.115
107	AdvProp_efficientnet-b2	.115
108	antialiased-r50	.115
109	BiT-S-R101x3	.114
110	resnet_SIN_IN_FT_IN	.114
111	resnet50-SIN_IN_IN	.114
112	resnet-34	.113
113	custom_model_cv_18_dagger_408	.112
114	resnet18_imagenet_full	.112
115	mobilenet_v2_1_0_192	.112
116	resnet34_imagenet_full	.111
117	convnext_small_imagenet_100_seed-0	.111
118	BiT-S-R152x4	.111
119	BiT-S-R50x3	.110
120	vgg_16	.109
121	resnet-152_v1-tf	.107
122	mobilenet_v2_1_0_224	.105
123	nasnet_mobile-tf	.104
124	mobilenet_v2_1_3_224	.104
125	resnet50_tutorial	.103
126	resnet_50_v1	.103
127	resnet50-sup	.103
128	resnet-50-pytorch	.103
129	deit_small_imagenet_full_seed-0	.102
130	BiT-S-R50x1	.102
131	resnet50-SIN_IN	.101
132	tv_efficientnet-b1	.101
133	grcnn	.099
134	mobilenet_v2_1.4_224-tf	.098
135	xception	.097
136	voneresnet-50-non_stochastic	.097
137	nasnet_mobile	.097
138	ViT_B_16_imagenet1k	.095
139	inception_v1	.095
140	efficientnet_b1_imagenet_full	.094
141	resnet50-moclr8deg	.093
142	ViT_L_32_imagenet1k	.092
143	ReAlnet06	.091
144	mobilenet_v2_0_5_224	.091
145	ReAlnet01	.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	.064
179	mobilenet_v2_1-4_224_pytorch	.064
180	vonegrcnn_62e_nobn	.064
181	vgg-16-keras	.063
182	resnet_50_v1_spiking_l4a	.063
183	vonegrcnn_47e	.063
184	mobilenet_v2_1.0_192-tf	.062
185	voneresnet-50	.062
186	vonegrcnn_47e	.061
187	mobilenet_v2_0_75_160	.060
188	mobilenet_v2_1.0_160-tf	.058
189	mobilenet_v1_0.75_160	.058
190	mobilenet_v2_0.75_160-tf	.057
191	BiT-S-R101x1	.057
192	resnet50_linf_4_robust	.056
193	mobilenet_v2_0.75_224-tf	.056
194	mobilenet_v2_1_0_128	.055
195	mobilenet_v2_0_75_192	.055
196	squeezenet1_0	.055
197	mobilenet_v1_0.5_224	.054
198	mobilenet_v2_0.35_160	.053
199	mobilenet_v1_0.75_224	.053
200	mobilenet_v2_0.5_192-tf	.053
201	squeezenet1_1	.052
202	mobilenet_v2_0.75_128	.052
203	resnet_50_v1_spiking_l4	.052
204	mobilenet_v2_0.5_224-tf	.051
205	voneresnet-50-robust	.051
206	mobilenet_v2_0.75_192-tf	.049
207	resnet50_imagenet_100_seed-0	.049
208	mobilenet_v2_0.5_160	.049
209	mobilenet_v1_0.75_192	.049
210	mobilenet_v2_0.5_128	.048
211	alexnet_reduced_aliasing_early_checkpoint	.048
212	alexnet	.047
213	mobilenet_v1_0.5_192	.047
214	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.047
215	shufflenet_v2_x1_0	.046
216	alexnet_random_l2_3_perturb	.046
217	vonealexnet_gaussian_noise_std4_fixed	.046
218	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.046
219	alexnet_random_linf8_perturb	.045
220	mobilenet_v2_0.35_192	.045
221	resnet50_l2_3_robust	.045
222	mobilenet_v1_0.75_128	.043
223	resnet50_robust_l2_eps1	.043
224	mobilenet_v2_1.0_96	.043
225	resnet50_linf_8_robust	.042
226	texture_shape_resnet50_trained_on_SIN	.042
227	mobilenet_v2_0.35_224	.042
228	mobilenet_v2_0.35_128	.042
229	mobilenet_v2_1.0_128-tf	.041
230	resnet50_simclr	.041
231	alexnet	.041
232	alexnet	.041
233	alexnet-baseline	.041
234	alexnet	.041
235	alexnet	.041
236	alexnet	.041
237	alexnet	.041
238	alexnet	.041
239	alexnet	.041
240	alexnet	.041
241	alexnet_ks_torevert	.041
242	vgg-19-keras	.040
243	alexnet_early_checkpoint	.039
244	mobilenet_v1_0.5_128	.039
245	AdvProp_efficientnet-b0	.039
246	alexnet_robust_correct	.039
247	alexnet_l2_3_robust	.038
248	mobilenet_v2_0.75_96	.038
249	mobilenet_v2_0.35_96	.038
250	alexnet_linf_8_robust	.036
251	mobilenet_v1_0.5_160	.035
252	0.5x_resnet-18_LC_w_sh_1_iter	.034
253	konkle_alexnetgn_ipcl_ref01_primary_model	.034
254	mobilenet_v1_1.0_128	.032
255	mobilenet_v2_0.5_96	.032
256	deit_small_imagenet_100_seed-0	.032
257	mobilenet_v1_0.25_192	.032
258	resnet50-vicreg	.032
259	mobilenet_v1_0.25_224	.031
260	AlexNet_SIN	.031
261	texture_shape_alexnet_trained_on_SIN	.031
262	resnet-50-robust	.030
263	imagenet_l2_3_0	.030
264	resnet50_robust_l2_eps3	.030
265	mobilenet_v1_0.25_160	.029
266	imagenet_l2_10_0	.028
267	resnet50-simclr-vissl	.026
268	deit_small_imagenet_10_seed-0	.025
269	convnext_small_imagenet_10_seed-0	.025
270	deit_small_imagenet_1_seed-0	.025
271	alexnet_training_seed_10	.024
272	alexnet_training_seed_04	.024
273	alexnet_training_seed_02	.024
274	alexnet_training_seed_08	.024
275	alexnet_training_seed_07	.024
276	alexnet_training_seed_09	.023
277	resnet_50_v1_spiking_l3	.023
278	resnet50-meshes-lt-100-original-pretrained	.023
279	resnet-50_untrained	.022
280	resnet50-SIN	.022
281	mobilenet_v1_0.25_128	.021
282	resnet50_imagenet_10_seed-0	.020
283	convnext_small_imagenet_1_seed-0	.020
284	resnet50-meshes-lt-100-original-scratch	.018
285	barlow-twins-resnet50	.014
286	resnet50-vicregl0p9	.014
287	artResNet18_1	.014
288	resnet50_FractalDB	.011
289	alexnet_training_seed_06	.009
290	alexnet_training_seed_03	.005
291	resnet50_imagenet_1_seed-0	.003
292	resnet50-vicregl0p75	.003
293	alexnet_training_seed_05	.002
294	alexnet_training_seed_01	.002
295	resnet50-barlow	.001
296	resnet18-local_aggregation
297	v1-pyr-nodown
298	CORnetZ_CIFAR10_bs32_20_04
299	pixels
300	dorinet_cornet_z
301	my-model
302	resnet50
303	openclip
304	unet_entire
305	dcgan
306	resnet-50x2_untrained
307	resnet-18_untrained
308	resnet-18-LC_untrained
309	resnet18-contrastive_predictive
310	r3m_resnet34_nocrop
311	ViT_B_16
312	resnet18-depth_prediction
313	ViT_B_32
314	ViT_L_32
315	resnet50-cifar
316	resnet50_pretrained_with_retinal_waves
317	resnet50_ImageNet
318	omnivore_swinT
319	resnet18-simclr
320	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
321	resnet18-autoencoder
322	resnet18-relative_position
323	RN50
324	0.5x_resnet-18_LC_w_sh_100_iter
325	RN50
326	yudixie_resnet50_random_0_240908
327	resnet18-colorization
328	yudixie_resnet50_imagenet1kpret_0_240908
329	0.5x_resnet-18
330	yudixie_resnet50_imagenet1kpret_0_240312
331	resnet18-contrastive_multiview
332	resnet-18-LC_conv_init_m
333	yudixie_resnet50_object_class_0_240908
334	resnet-18-LC_1st_conv_m
335	resnet18-supervised
336	yudixie_resnet50_cat_obj_class_all_latents_0_240908
337	r3m_resnet50_nocrop
338	resnet-18-LC_m
339	r3m_resnet34
340	r3m_resnet50
341	0.5x_resnet-18_LC_w_sh_10_iter
342	yudixie_resnet50_category_class_0_240908
343	resnet18-instance_recognition
344	my-model
345	vggface
346	resnet18_imagenet21kP
347	ConvLSTM
348	yudixie_resnet50_distance_reg_0_240908
349	PredRNN
350	SimVP
351	TAU
352	briaai_rmbg_1_4
353	MIM
354	hmax
355	CORnet-Z
356	r3m_resnet18
357	densenet_201_pytorch
358	pnasnet_large_pytorch
359	ReAlnet01_cornet
360	inception_v3_pytorch
361	resnet18-deepcluster
362	omnivore_swinS
363	CORnetZ_CIFAR10
364	0.5x_resnet-18_LC
365	yolos_tiny
366	ReAlnet03_cornet
367	yudixie_resnet18_rotation_reg_0_240719
368	yudixie_resnet18_imagenet1kpret_0_240719
369	ReAlnet02_cornet
370	FrankRobWobv0
371	resnet18_ecoset_full
372	resnet34_ecoset_full
373	resnet50_ecoset_full
374	resnet-18-LC_w_sh_10_iter_conv_init_m
375	resnet101_ecoset_full
376	ReAlnet04_cornet
377	resnet152_ecoset_full
378	resnet-18-LC_w_sh_100_iter_m
379	yudixie_resnet50_distance_translation_0_240908
380	yudixie_resnet18_distance_translation_0_240719
381	yudixie_resnet18_translation_rotation_0_240719
382	yudixie_resnet18_distance_rotation_0_240719
383	yudixie_resnet18_category_class_0_240719
384	resnet-18_test_m
385	yudixie_resnet50_distance_rotation_0_240908
386	ReAlnet05_cornet
387	yudixie_resnet50_rotation_reg_0_240908
388	yudixie_resnet18_random_0_240719
389	yudixie_resnet18_cat_obj_class_all_latents_0_240719
390	yudixie_resnet18_distance_translation_rotation_0_240719
391	yudixie_resnet18_object_class_0_240719
392	yudixie_resnet18_translation_reg_0_240719
393	yudixie_resnet18_distance_reg_0_240719
394	ReAlnet06_cornet
395	yudixie_resnet50_translation_reg_0_240908
396	ReAlnet07_cornet
397	yudixie_resnet50_translation_rotation_0_240908
398	ReAlnet08_cornet
399	resnet-18-LC_w_sh_10_iter_m
400	omnivore_swinB
401	VOneCORnet-S
402	ReAlnet09_cornet
403	resnet-18-LC_w_sh_1_iter_m
404	resnet-18-LC_w_sh_1_iter_conv_init_m
405	BiT-M-R152x2
406	ReAlnet10_cornet
407	BiT-M-R50x1
408	BiT-M-R50x3
409	yudixie_resnet50_distance_translation_rotation_0_240908
410	ViT-B/32
411	resnet50_primary_visual_cortex
412	resnet-18-LC_w_sh_100_iter_conv_init_m

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