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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	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.173
97	deit_base_imagenet_full_seed-0	.169
98	convnext_small_imagenet_100_seed-0	.167
99	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.164
100	resnet50-moclr8deg	.159
101	resnet-50_v2-tf	.155
102	antialiased-rnext101_32x8d	.149
103	densenet-121-keras	.147
104	custom_model_cv_18_dagger_408	.145
105	inception_v2-tf	.145
106	alexnet_random_linf8_perturb	.140
107	resnet50_byol	.134
108	pnasnet_large_pytorch	.133
109	resnet50-SIN_IN_IN	.132
110	resnet_SIN_IN_FT_IN	.132
111	BiT-S-R50x3	.128
112	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.127
113	antialiased-r50	.127
114	resnet34_imagenet_full	.126
115	grcnn	.125
116	densenet-201	.125
117	densenet_201_pytorch	.125
118	voneresnet-50-robust	.122
119	ReAlnet03	.121
120	densenet-121	.119
121	resnet50_linf_8_robust	.119
122	ReAlnet04	.118
123	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.116
124	ReAlnet08	.115
125	efficientnet-b7	.114
126	vgg_16	.113
127	densenet-201-keras	.112
128	voneresnet-50-non_stochastic	.108
129	resnet-50_v1-tf	.107
130	vonealexnet_gaussian_noise_std4_fixed	.105
131	ReAlnet01	.105
132	inception_v1	.100
133	alexnet_l2_3_robust	.098
134	alexnet_random_l2_3_perturb	.098
135	resnet50_tutorial	.098
136	resnet_50_v1	.098
137	resnet-50-pytorch	.098
138	resnet50-sup	.098
139	alexnet_ks_torevert	.097
140	alexnet-baseline	.097
141	alexnet	.097
142	alexnet	.097
143	alexnet	.097
144	alexnet	.097
145	alexnet	.097
146	alexnet	.097
147	alexnet	.097
148	alexnet	.097
149	alexnet	.097
150	ReAlnet05	.096
151	densenet-169	.096
152	resnet50_l2_3_robust	.095
153	resnet-152_v1-tf	.094
154	resnet50_moco_v2	.094
155	densenet-169-keras	.094
156	ReAlnet02	.093
157	ReAlnet07	.091
158	tv_efficientnet-b1	.090
159	cornet_s	.089
160	convnext_tiny_sup	.089
161	ReAlnet06	.089
162	vonegrcnn_62e_nobn	.088
163	vonegrcnn_62e_nobn	.088
164	alexnet_reduced_aliasing_early_checkpoint	.088
165	nasnet_mobile-tf	.086
166	alexnet_early_checkpoint	.085
167	deit_small_imagenet_full_seed-0	.084
168	BiT-S-R152x2	.083
169	ReAlnet10	.083
170	vonegrcnn_52e_full	.083
171	alexnet_training_seed_05	.083
172	alexnet_training_seed_01	.083
173	resnet50-vitoimagevidnet8	.082
174	alexnet	.082
175	inception_v1-tf	.078
176	barlow-twins-resnet50	.078
177	ReAlnet09	.078
178	resnet-101_v1-tf	.076
179	resnet50_robust_l2_eps1	.075
180	vonegrcnn_47e	.075
181	AdvProp_efficientnet-b8	.073
182	resnet-34	.070
183	vonegrcnn_52e_full	.070
184	squeezenet1_1	.068
185	resnet18_imagenet_full	.065
186	vonegrcnn_47e	.065
187	mobilenet_v2_1.0_160-tf	.065
188	alexnet_robust_correct	.064
189	artResNet18_1	.064
190	resnet50-SIN_IN	.063
191	resnet-50-robust	.062
192	imagenet_l2_3_0	.062
193	resnet50_robust_l2_eps3	.062
194	konkle_alexnetgn_ipcl_ref01_primary_model	.062
195	mobilenet_v2_1_0_192	.060
196	resnet50_simclr	.059
197	mobilenet_v2_1_0_160	.059
198	BiT-S-R152x4	.057
199	alexnet_training_seed_02	.053
200	alexnet_training_seed_04	.053
201	alexnet_training_seed_08	.053
202	alexnet_training_seed_10	.053
203	resnet50-meshes-lt-100-original-scratch	.053
204	vgg-19-keras	.053
205	CORnet-S	.052
206	efficientnet_b2_imagenet_full	.050
207	mobilenet_v1_0.25_128	.048
208	mobilenet_v2_1.0_224-tf	.047
209	vgg-16-keras	.047
210	mobilenet_v2_0_5_192	.046
211	voneresnet-50	.045
212	texture_shape_resnet50_trained_on_SIN	.044
213	mobilenet_v2_0.5_96	.043
214	mobilenet_v1_0.5_160	.041
215	convnext_small_imagenet_1_seed-0	.041
216	deit_small_imagenet_100_seed-0	.040
217	mobilenet_v2_1_0_224	.040
218	resnet50_imagenet_100_seed-0	.040
219	resnet50-SIN	.040
220	mobilenet_v1_0.5_128	.039
221	BiT-S-R101x3	.039
222	resnet50-barlow	.038
223	alexnet_training_seed_07	.038
224	alexnet_training_seed_09	.038
225	AdvProp_efficientnet-b0	.038
226	0.5x_resnet-18_LC_w_sh_1_iter	.038
227	BiT-S-R50x1	.037
228	mobilenet_v2_0.35_192	.037
229	texture_shape_alexnet_trained_on_SIN	.037
230	alexnet_linf_8_robust	.037
231	mobilenet_v2_1-4_224_pytorch	.036
232	mobilenet_v2_1_4_224	.036
233	resnet50_FractalDB	.036
234	BiT-S-R101x1	.035
235	mobilenet_v1_0.75_192	.035
236	mobilenet_v1_0.5_224	.034
237	mobilenet_v2_0.5_128	.034
238	mobilenet_v2_0_75_224	.034
239	mobilenet_v1_0.25_192	.033
240	mobilenet_v2_0.35_224	.033
241	resnet50-meshes-lt-100-original-pretrained	.033
242	resnet50_imagenet_10_seed-0	.033
243	AlexNet_SIN	.032
244	nasnet_mobile	.032
245	efficientnet_b0_imagenet_full	.031
246	mobilenet_v2_1.0_96	.031
247	deit_small_imagenet_10_seed-0	.031
248	deit_small_imagenet_1_seed-0	.031
249	convnext_small_imagenet_10_seed-0	.031
250	resnet50-vicregl0p9	.030
251	resnet50-vicregl0p75	.029
252	mobilenet_v2_0.5_192-tf	.028
253	resnet50-vicreg	.027
254	mobilenet_v2_0.75_96	.027
255	mobilenet_v2_1_3_224	.027
256	mobilenet_v2_1.0_128-tf	.026
257	resnet-50_untrained	.024
258	mobilenet_v1_0.25_224	.024
259	mobilenet_v2_0.75_224-tf	.023
260	blt_vs	.023
261	mobilenet_v2_0.35_128	.023
262	mobilenet_v1_0.5_192	.021
263	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.021
264	mobilenet_v2_1.4_224-tf	.020
265	mobilenet_v2_0.35_160	.020
266	mobilenet_v2_0_75_160	.019
267	mobilenet_v2_0.75_128	.019
268	mobilenet_v2_0_75_192	.019
269	mobilenet_v2_0.5_160	.018
270	imagenet_l2_10_0	.018
271	mobilenet_v2_1.0_192-tf	.017
272	mobilenet_v1_0.75_160	.016
273	squeezenet1_0	.015
274	shufflenet_v2_x1_0	.015
275	mobilenet_v2_0.35_96	.014
276	alexnet_training_seed_06	.014
277	mobilenet_v2_0.5_224-tf	.014
278	mobilenet_v1_0.75_128	.014
279	resnet50_eMMCR_Vanilla	.013
280	resnet_50_v1_spiking_l4a	.012
281	resnet50_imagenet_1_seed-0	.012
282	mobilenet_v2_0.75_160-tf	.011
283	resnet_50_v1_spiking_l4	.011
284	resnet_50_v1_spiking_l3	.011
285	alexnet_training_seed_03	.010
286	mobilenet_v2_0_5_224	.010
287	mobilevit_small	.010
288	mobilenet_v1_1.0_192	.009
289	resnet50-simclr-vissl	.008
290	mobilenet_v1_0.25_160	.007
291	mobilenet_v1_1.0_128	.006
292	mobilenet_v1_1.0_160	.006
293	mobilenet_v1_1.0_224	.005
294	resnet-18	.004
295	mobilenet_v2_1_0_128	.003
296	mobilenet_v2_1.3_224-tf	.003
297	efficientnet_b1_imagenet_full	.003
298	regnet_y_400mf	.002
299	mobilenet_v2_0.75_192-tf	.002
300	mobilenet_v1_0.75_224	.001
301	my-model	.000
302	resnet-18-LC_w_sh_100_iter_conv_init_m
303	ViT-B/32
304	BiT-M-R50x3
305	BiT-M-R50x1
306	BiT-M-R152x2
307	resnet-18-LC_w_sh_1_iter_conv_init_m
308	resnet-18-LC_w_sh_10_iter_m
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_10_iter_conv_init_m
311	resnet-18-LC_w_sh_1_iter_m
312	FrankRobWobv0
313	0.5x_resnet-18_LC
314	CORnetZ_CIFAR10
315	resnet18-local_aggregation
316	resnet18-deepcluster
317	r3m_resnet18
318	CORnet-Z
319	resnet18-instance_recognition
320	vggface
321	my-model
322	0.5x_resnet-18_LC_w_sh_10_iter
323	r3m_resnet50
324	r3m_resnet34
325	resnet18-colorization
326	resnet-18-LC_m
327	r3m_resnet50_nocrop
328	resnet18-supervised
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	RN50
334	0.5x_resnet-18_LC_w_sh_100_iter
335	RN50
336	resnet18-relative_position
337	resnet18-autoencoder
338	resnet18-simclr
339	ViT_L_32
340	ViT_B_32
341	resnet18-depth_prediction
342	ViT_B_16
343	r3m_resnet34_nocrop
344	resnet18-contrastive_predictive
345	omnivore_swinT
346	resnet-18-LC_untrained
347	resnet-18_untrained
348	resnet-50x2_untrained
349	dcgan
350	unet_entire
351	v1-pyr-nodown
352	CORnetZ_CIFAR10_bs32_20_04
353	pixels
354	dorinet_cornet_z
355	resnet50
356	openclip
357	resnet50-cifar
358	resnet50_pretrained_with_retinal_waves
359	resnet50_ImageNet
360	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
361	yudixie_resnet50_random_0_240908
362	yudixie_resnet50_cat_obj_class_all_latents_0_240908
363	yudixie_resnet50_imagenet1kpret_0_240908
364	yudixie_resnet50_imagenet1kpret_0_240312
365	yudixie_resnet50_object_class_0_240908
366	yudixie_resnet50_category_class_0_240908
367	resnet18_imagenet21kP
368	yudixie_resnet50_distance_reg_0_240908
369	ConvLSTM
370	PredRNN
371	SimVP
372	TAU
373	MIM
374	hmax
375	briaai_rmbg_1_4
376	ReAlnet01_cornet
377	yudixie_resnet18_translation_reg_0_240719
378	yolos_tiny
379	ReAlnet03_cornet
380	yudixie_resnet18_rotation_reg_0_240719
381	yudixie_resnet18_imagenet1kpret_0_240719
382	ReAlnet02_cornet
383	resnet18_ecoset_full
384	resnet50_ecoset_full
385	VOneCORnet-S
386	resnet101_ecoset_full
387	yudixie_resnet50_distance_translation_0_240908
388	resnet152_ecoset_full
389	yudixie_resnet18_distance_translation_0_240719
390	yudixie_resnet18_translation_rotation_0_240719
391	yudixie_resnet18_distance_rotation_0_240719
392	yudixie_resnet18_category_class_0_240719
393	resnet-18_test_m
394	yudixie_resnet18_cat_obj_class_all_latents_0_240719
395	yudixie_resnet50_rotation_reg_0_240908
396	yudixie_resnet50_distance_rotation_0_240908
397	yudixie_resnet18_object_class_0_240719
398	SeLaVi-AVE
399	ReAlnet05_cornet
400	ReAlnet06_cornet
401	yudixie_resnet50_translation_reg_0_240908
402	r3d_18
403	ReAlnet01_ALL
404	yudixie_resnet18_distance_reg_0_240719
405	yudixie_resnet50_translation_rotation_0_240908
406	omnivore_swinB
407	mc3_18
408	ReAlnet07_cornet
409	s3d
410	ReAlnet02_ALL
411	mvit_v1_b
412	ReAlnet08_cornet
413	ReAlnet04_ALL
414	ReAlnet03_ALL
415	ReAlnet09_cornet
416	SeLaVi-Kinetics400
417	ReAlnet05_ALL
418	SeLaVi-VGG-Sound
419	ReAlnet06_ALL
420	SeLaVi-Kinetics-Sound
421	mvit_v2_s
422	omnivore_swinS
423	r2plus1d_18
424	yudixie_resnet50_distance_translation_rotation_0_240908
425	ReAlnet10_cornet
426	ReAlnet07_ALL
427	resnet34_ecoset_full
428	ReAlnet08_ALL
429	yudixie_resnet18_random_0_240719
430	yudixie_resnet18_distance_translation_rotation_0_240719
431	ReAlnet09_ALL
432	ReAlnet04_cornet
433	resnet50_primary_visual_cortex
434	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