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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021powerequalisation-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	fixres_resnext101_32x48d_wsl	.755
2	vit_large_patch14_clip_224:laion2b_ft_in1k	.718
3	cvt_cvt-13-384-in1k_4_LucyV4	.713
4	vit_large_patch14_clip_224:openai_ft_in1k	.709
5	cvt_cvt-21-384-in1k_4_LucyV4	.707
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.707
7	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.705
8	convnext_xlarge:fb_in22k_ft_in1k	.680
9	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.672
10	convnext_large:fb_in22k_ft_in1k	.670
11	SWSL_resnext101_32x8d	.668
12	vit_large_patch14_clip_336:laion2b_ft_in1k	.651
13	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.650
14	vit_base_patch16_clip_224:openai_ft_in1k	.647
15	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.638
16	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.633
17	resnet_152_v2	.599
18	resnext101_32x16d_wsl	.592
19	convnext_base:clip_laiona_augreg_ft_in1k_384	.586
20	resnext101_32x32d_wsl	.579
21	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.564
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.560
23	resnext101_32x48d_wsl	.548
24	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.540
25	effnetv2m_custom384	.501
26	resnet-152_v2_pytorch	.494
27	SWSL_resnet50	.489
28	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
29	convnext_base_imagenet_full_seed-0	.482
30	vision_transformer_vit_large_patch16_224	.472
31	convnext_tiny:in12k_ft_in1k	.468
32	convnext_large_imagenet_full_seed-0	.467
33	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.462
34	resnet_50_v2	.455
35	resnext101_32x8d_wsl	.453
36	resnet_101_v2	.451
37	convnext_small_imagenet_full_seed-0	.450
38	convnext_tiny_imagenet_full_seed-0	.427
39	CLIP_resnet50	.427
40	CLIP_resnet50_float32	.427
41	cvt_cvt-13-224-in1k_4_LucyV4	.397
42	cv_18_dagger_408_pretrained	.388
43	vit_relpos_base_patch16_clsgap_224:sw_in1k	.382
44	pnasnet_large	.380
45	convnext_femto_ols:d1_in1k	.378
46	nasnet_large	.363
47	ViT_B_16_imagenet1k	.339
48	resnet-152_v2-tf	.335
49	inception_v4	.323
50	focalnet_tiny_lrf_in1k	.317
51	resnet152_imagenet_full	.317
52	CLIP-RN50	.315
53	ViT_L_32_imagenet1k	.307
54	pnasnet_large-tf	.295
55	resnet50-VITO-8deg-cc	.292
56	antialias-resnet152	.289
57	effnetb1_272x240	.289
58	resnet101	.287
59	CLIP_ViT-B_32	.287
60	inception_v3	.280
61	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.278
62	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.269
63	deit_large_imagenet_full_seed-0	.269
64	resnet50_random_linf8_perturb	.260
65	resnet_152_v1	.260
66	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.257
67	nasnet_large-tf	.248
68	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.246
69	xception-keras	.245
70	inception_v3-tf	.237
71	resnet_101_v1	.234
72	cvt_cvt-21-224-in1k_4_LucyV4	.228
73	resnet50_random_l2_perturb	.227
74	inception_v3_pytorch	.222
75	resnet101_imagenet_full	.221
76	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.217
77	ViT_B_32_imagenet1k	.217
78	resnet50_imagenet_full	.216
79	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.214
80	inception_v4-tf	.209
81	grcnn_109	.201
82	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.197
83	vgg_19	.195
84	Res2Net50_26w_4s	.193
85	AdvProp_efficientnet-b7	.190
86	AdvProp_efficientnet-b4	.189
87	AT_efficientnet-b4	.189
88	AdvProp_efficientnet-b6	.187
89	AT_efficientnet-b2	.186
90	AdvProp_efficientnet-b2	.186
91	resnet50	.185
92	xception	.184
93	resnet50_linf_4_robust	.182
94	efficientnet_b0	.181
95	resnet_50_v1_spiking	.181
96	resnet50_ewconplus_wzc	.174
97	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.173
98	AlexNet_SIN_fov4	.172
99	deit_base_imagenet_full_seed-0	.169
100	convnext_small_imagenet_100_seed-0	.167
101	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.164
102	resnet50-moclr8deg	.159
103	resnet-50_v2-tf	.155
104	antialiased-rnext101_32x8d	.149
105	densenet-121-keras	.147
106	custom_model_cv_18_dagger_408	.145
107	inception_v2-tf	.145
108	alexnet_random_linf8_perturb	.140
109	resnet50_byol	.134
110	pnasnet_large_pytorch	.133
111	resnet50-SIN_IN_IN	.132
112	resnet_SIN_IN_FT_IN	.132
113	alexnet_fov4	.132
114	BiT-S-R50x3	.128
115	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.127
116	antialiased-r50	.127
117	resnet34_imagenet_full	.126
118	grcnn	.125
119	densenet-201	.125
120	densenet_201_pytorch	.125
121	voneresnet-50-robust	.122
122	ReAlnet03	.121
123	densenet-121	.119
124	resnet50_linf_8_robust	.119
125	ReAlnet04	.118
126	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.116
127	ReAlnet08	.115
128	efficientnet-b7	.114
129	vgg_16	.113
130	densenet-201-keras	.112
131	voneresnet-50-non_stochastic	.108
132	resnet-50_v1-tf	.107
133	vonealexnet_gaussian_noise_std4_fixed	.105
134	ReAlnet01	.105
135	inception_v1	.100
136	alexnet_l2_3_robust	.098
137	alexnet_random_l2_3_perturb	.098
138	resnet50_tutorial	.098
139	resnet_50_v1	.098
140	resnet-50-pytorch	.098
141	resnet50-sup	.098
142	alexnet_ks_torevert	.097
143	alexnet_workflow_trigger	.097
144	alexnet-baseline	.097
145	alexnet	.097
146	alexnet	.097
147	alexnet	.097
148	alexnet	.097
149	alexnet	.097
150	alexnet	.097
151	alexnet	.097
152	alexnet	.097
153	alexnet	.097
154	alexnet	.097
155	ReAlnet05	.096
156	densenet-169	.096
157	resnet50_l2_3_robust	.095
158	resnet-152_v1-tf	.094
159	resnet50_moco_v2	.094
160	densenet-169-keras	.094
161	ReAlnet02	.093
162	ReAlnet07	.091
163	tv_efficientnet-b1	.090
164	cornet_s	.089
165	convnext_tiny_sup	.089
166	ReAlnet06	.089
167	vonegrcnn_62e_nobn	.088
168	vonegrcnn_62e_nobn	.088
169	alexnet_reduced_aliasing_early_checkpoint	.088
170	nasnet_mobile-tf	.086
171	alexnet_early_checkpoint	.085
172	deit_small_imagenet_full_seed-0	.084
173	BiT-S-R152x2	.083
174	ReAlnet10	.083
175	vonegrcnn_52e_full	.083
176	alexnet_training_seed_05	.083
177	alexnet_training_seed_01	.083
178	alexnet_training_seed_01_fov4	.083
179	alexnet_training_seed_01_fov12	.083
180	alexnet_training_seed_01_fov16	.083
181	resnet50-vitoimagevidnet8	.082
182	alexnet	.082
183	inception_v1-tf	.078
184	barlow-twins-resnet50	.078
185	ReAlnet09	.078
186	resnet-101_v1-tf	.076
187	resnet50_robust_l2_eps1	.075
188	vonegrcnn_47e	.075
189	AdvProp_efficientnet-b8	.073
190	resnet-34	.070
191	vonegrcnn_52e_full	.070
192	alexnet_fov12	.069
193	squeezenet1_1	.068
194	resnet18_imagenet_full	.065
195	vonegrcnn_47e	.065
196	mobilenet_v2_1.0_160-tf	.065
197	alexnet_robust_correct	.064
198	artResNet18_1	.064
199	resnet50-SIN_IN	.063
200	resnet-50-robust	.062
201	imagenet_l2_3_0	.062
202	resnet50_robust_l2_eps3	.062
203	konkle_alexnetgn_ipcl_ref01_primary_model	.062
204	mobilenet_v2_1_0_192	.060
205	resnet50_simclr	.059
206	mobilenet_v2_1_0_160	.059
207	BiT-S-R152x4	.057
208	alexnet_training_seed_02	.053
209	alexnet_training_seed_04	.053
210	alexnet_training_seed_08	.053
211	alexnet_training_seed_10	.053
212	resnet50-meshes-lt-100-original-scratch	.053
213	vgg-19-keras	.053
214	CORnet-S	.052
215	efficientnet_b2_imagenet_full	.050
216	mobilenet_v1_0.25_128	.048
217	mobilenet_v2_1.0_224-tf	.047
218	vgg-16-keras	.047
219	mobilenet_v2_0_5_192	.046
220	voneresnet-50	.045
221	texture_shape_resnet50_trained_on_SIN	.044
222	mobilenet_v2_0.5_96	.043
223	mobilenet_v1_0.5_160	.041
224	convnext_small_imagenet_1_seed-0	.041
225	AlexNet_SIN_fov12	.040
226	deit_small_imagenet_100_seed-0	.040
227	mobilenet_v2_1_0_224	.040
228	resnet50_imagenet_100_seed-0	.040
229	alexnet_fov16	.040
230	resnet50-SIN	.040
231	mobilenet_v1_0.5_128	.039
232	BiT-S-R101x3	.039
233	resnet50-barlow	.038
234	alexnet_training_seed_07	.038
235	alexnet_training_seed_09	.038
236	AdvProp_efficientnet-b0	.038
237	0.5x_resnet-18_LC_w_sh_1_iter	.038
238	BiT-S-R50x1	.037
239	mobilenet_v2_0.35_192	.037
240	texture_shape_alexnet_trained_on_SIN	.037
241	alexnet_linf_8_robust	.037
242	mobilenet_v2_1-4_224_pytorch	.036
243	mobilenet_v2_1_4_224	.036
244	resnet50_FractalDB	.036
245	BiT-S-R101x1	.035
246	mobilenet_v1_0.75_192	.035
247	mobilenet_v1_0.5_224	.034
248	mobilenet_v2_0.5_128	.034
249	mobilenet_v2_0_75_224	.034
250	mobilenet_v1_0.25_192	.033
251	mobilenet_v2_0.35_224	.033
252	resnet50-meshes-lt-100-original-pretrained	.033
253	resnet50_imagenet_10_seed-0	.033
254	AlexNet_SIN	.032
255	nasnet_mobile	.032
256	efficientnet_b0_imagenet_full	.031
257	mobilenet_v2_1.0_96	.031
258	deit_small_imagenet_10_seed-0	.031
259	deit_small_imagenet_1_seed-0	.031
260	convnext_small_imagenet_10_seed-0	.031
261	AlexNet_SIN_fov16	.031
262	resnet50-vicregl0p9	.030
263	resnet50-vicregl0p75	.029
264	mobilenet_v2_0.5_192-tf	.028
265	resnet50-vicreg	.027
266	mobilenet_v2_0.75_96	.027
267	mobilenet_v2_1_3_224	.027
268	mobilenet_v2_1.0_128-tf	.026
269	resnet-50_untrained	.024
270	mobilenet_v1_0.25_224	.024
271	mobilenet_v2_0.75_224-tf	.023
272	blt_vs	.023
273	mobilenet_v2_0.35_128	.023
274	mobilenet_v1_0.5_192	.021
275	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.021
276	mobilenet_v2_1.4_224-tf	.020
277	mobilenet_v2_0.35_160	.020
278	mobilenet_v2_0_75_160	.019
279	mobilenet_v2_0.75_128	.019
280	mobilenet_v2_0_75_192	.019
281	mobilenet_v2_0.5_160	.018
282	imagenet_l2_10_0	.018
283	mobilenet_v2_1.0_192-tf	.017
284	mobilenet_v1_0.75_160	.016
285	squeezenet1_0	.015
286	shufflenet_v2_x1_0	.015
287	mobilenet_v2_0.35_96	.014
288	alexnet_training_seed_06	.014
289	mobilenet_v2_0.5_224-tf	.014
290	mobilenet_v1_0.75_128	.014
291	resnet50_eMMCR_Vanilla	.013
292	resnet_50_v1_spiking_l4a	.012
293	resnet50_imagenet_1_seed-0	.012
294	mobilenet_v2_0.75_160-tf	.011
295	resnet_50_v1_spiking_l4	.011
296	resnet_50_v1_spiking_l3	.011
297	alexnet_training_seed_03	.010
298	mobilenet_v2_0_5_224	.010
299	mobilevit_small	.010
300	mobilenet_v1_1.0_192	.009
301	resnet50-simclr-vissl	.008
302	mobilenet_v1_0.25_160	.007
303	mobilenet_v1_1.0_128	.006
304	mobilenet_v1_1.0_160	.006
305	mobilenet_v1_1.0_224	.005
306	resnet-18	.004
307	mobilenet_v2_1_0_128	.003
308	mobilenet_v2_1.3_224-tf	.003
309	efficientnet_b1_imagenet_full	.003
310	regnet_y_400mf	.002
311	mobilenet_v2_0.75_192-tf	.002
312	mobilenet_v1_0.75_224	.001
313	my-model	.000
314	resnet-18-LC_w_sh_100_iter_conv_init_m
315	ViT-B/32
316	BiT-M-R50x3
317	BiT-M-R50x1
318	BiT-M-R152x2
319	resnet-18-LC_w_sh_1_iter_conv_init_m
320	resnet-18-LC_w_sh_10_iter_m
321	resnet-18-LC_w_sh_100_iter_m
322	resnet-18-LC_w_sh_10_iter_conv_init_m
323	resnet-18-LC_w_sh_1_iter_m
324	FrankRobWobv0
325	0.5x_resnet-18_LC
326	CORnetZ_CIFAR10
327	resnet18-local_aggregation
328	resnet18-deepcluster
329	r3m_resnet18
330	CORnet-Z
331	resnet18-instance_recognition
332	vggface
333	my-model
334	0.5x_resnet-18_LC_w_sh_10_iter
335	r3m_resnet50
336	r3m_resnet34
337	resnet18-colorization
338	resnet-18-LC_m
339	r3m_resnet50_nocrop
340	resnet18-supervised
341	resnet-18-LC_1st_conv_m
342	resnet-18-LC_conv_init_m
343	resnet18-contrastive_multiview
344	0.5x_resnet-18
345	RN50
346	0.5x_resnet-18_LC_w_sh_100_iter
347	RN50
348	resnet18-relative_position
349	resnet18-autoencoder
350	resnet18-simclr
351	ViT_L_32
352	ViT_B_32
353	resnet18-depth_prediction
354	ViT_B_16
355	r3m_resnet34_nocrop
356	resnet18-contrastive_predictive
357	omnivore_swinT
358	resnet-18-LC_untrained
359	resnet-18_untrained
360	resnet-50x2_untrained
361	dcgan
362	unet_entire
363	v1-pyr-nodown
364	CORnetZ_CIFAR10_bs32_20_04
365	pixels
366	dorinet_cornet_z
367	resnet50
368	openclip
369	resnet50-cifar
370	resnet50_pretrained_with_retinal_waves
371	resnet50_ImageNet
372	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
373	yudixie_resnet50_random_0_240908
374	yudixie_resnet50_cat_obj_class_all_latents_0_240908
375	yudixie_resnet50_imagenet1kpret_0_240908
376	yudixie_resnet50_imagenet1kpret_0_240312
377	yudixie_resnet50_object_class_0_240908
378	yudixie_resnet50_category_class_0_240908
379	resnet18_imagenet21kP
380	yudixie_resnet50_distance_reg_0_240908
381	ConvLSTM
382	PredRNN
383	SimVP
384	TAU
385	MIM
386	hmax
387	briaai_rmbg_1_4
388	vit_base_patch16_dinov3:lvd1689m
389	ReAlnet01_cornet
390	vit_base_patch14_reg4_dinov2:lvd142m
391	compact_resnet50_V4
392	yudixie_resnet18_translation_reg_0_240719
393	yolos_tiny
394	ReAlnet03_cornet
395	yudixie_resnet18_rotation_reg_0_240719
396	yudixie_resnet18_imagenet1kpret_0_240719
397	ReAlnet02_cornet
398	resnet18_ecoset_full
399	compact_resnet50_IT
400	resnet50_ecoset_full
401	VOneCORnet-S
402	ReAlnet01_cornet_fov12
403	resnet101_ecoset_full
404	yudixie_resnet50_distance_translation_0_240908
405	resnet152_ecoset_full
406	yudixie_resnet18_distance_translation_0_240719
407	ReAlnet01_cornet_fov4
408	yudixie_resnet18_translation_rotation_0_240719
409	compact_vgg19_V4
410	vit_base_patch14_dinov2:lvd142m
411	yudixie_resnet18_distance_rotation_0_240719
412	yudixie_resnet18_category_class_0_240719
413	resnet-18_test_m
414	yudixie_resnet18_cat_obj_class_all_latents_0_240719
415	yudixie_resnet50_rotation_reg_0_240908
416	yudixie_resnet50_distance_rotation_0_240908
417	yudixie_resnet18_object_class_0_240719
418	SeLaVi-AVE
419	ReAlnet05_cornet
420	ReAlnet06_cornet
421	compact_resnet50_robust_V4
422	compact_vgg19_IT
423	yudixie_resnet50_translation_reg_0_240908
424	r3d_18
425	ReAlnet01_ALL
426	yudixie_resnet18_distance_reg_0_240719
427	yudixie_resnet50_translation_rotation_0_240908
428	omnivore_swinB
429	mc3_18
430	ReAlnet07_cornet
431	s3d
432	ReAlnet02_ALL
433	ReAlnet01_cornet_fov16
434	mvit_v1_b
435	ReAlnet08_cornet
436	ReAlnet04_ALL
437	compact_cornets_V4
438	ReAlnet03_ALL
439	ReAlnet09_cornet
440	SeLaVi-Kinetics400
441	ReAlnet05_ALL
442	compact_cornets_IT
443	SeLaVi-VGG-Sound
444	ReAlnet06_ALL
445	SeLaVi-Kinetics-Sound
446	mvit_v2_s
447	omnivore_swinS
448	r2plus1d_18
449	compact_resnet50_robust_IT
450	yudixie_resnet50_distance_translation_rotation_0_240908
451	ReAlnet10_cornet
452	ReAlnet07_ALL
453	resnet34_ecoset_full
454	ReAlnet08_ALL
455	yudixie_resnet18_random_0_240719
456	yudixie_resnet18_distance_translation_rotation_0_240719
457	ReAlnet09_ALL
458	ReAlnet04_cornet
459	resnet50_primary_visual_cortex
460	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

0.51.

Note that scores are relative to this ceiling.

Data: Geirhos2021powerequalisation

Metric: error_consistency