Leaderboard Explore Compare Profile Community Blog FAQ Competition 2024 Report Issue

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_halfPrecision	.380
45	pnasnet_large	.380
46	convnext_femto_ols:d1_in1k	.378
47	nasnet_large	.363
48	ViT_B_16_imagenet1k	.339
49	resnet-152_v2-tf	.335
50	inception_v4	.323
51	focalnet_tiny_lrf_in1k	.317
52	resnet152_imagenet_full	.317
53	CLIP-RN50	.315
54	ViT_L_32_imagenet1k	.307
55	pnasnet_large-tf	.295
56	resnet50-VITO-8deg-cc	.292
57	antialias-resnet152	.289
58	effnetb1_272x240	.289
59	resnet101	.287
60	CLIP_ViT-B_32	.287
61	inception_v3	.280
62	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.278
63	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.269
64	deit_large_imagenet_full_seed-0	.269
65	resnet50_random_linf8_perturb	.260
66	resnet_152_v1	.260
67	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.257
68	nasnet_large-tf	.248
69	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.246
70	xception-keras	.245
71	inception_v3-tf	.237
72	resnet_101_v1	.234
73	cvt_cvt-21-224-in1k_4_LucyV4	.228
74	resnet50_random_l2_perturb	.227
75	inception_v3_pytorch	.222
76	resnet101_imagenet_full	.221
77	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.217
78	ViT_B_32_imagenet1k	.217
79	resnet50_imagenet_full	.216
80	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.214
81	inception_v4-tf	.209
82	grcnn_109	.201
83	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.197
84	vgg_19	.195
85	Res2Net50_26w_4s	.193
86	AdvProp_efficientnet-b7	.190
87	AdvProp_efficientnet-b4	.189
88	AT_efficientnet-b4	.189
89	AdvProp_efficientnet-b6	.187
90	AT_efficientnet-b2	.186
91	AdvProp_efficientnet-b2	.186
92	resnet50	.185
93	xception	.184
94	resnet50_linf_4_robust	.182
95	efficientnet_b0	.181
96	resnet_50_v1_spiking	.181
97	resnet50_ewconplus_wzc	.174
98	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.173
99	AlexNet_SIN_fov4	.172
100	deit_base_imagenet_full_seed-0	.169
101	convnext_small_imagenet_100_seed-0	.167
102	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.164
103	resnet50-moclr8deg	.159
104	resnet-50_v2-tf	.155
105	antialiased-rnext101_32x8d	.149
106	densenet-121-keras	.147
107	custom_model_cv_18_dagger_408	.145
108	inception_v2-tf	.145
109	alexnet_random_linf8_perturb	.140
110	resnet50_byol	.134
111	pnasnet_large_pytorch	.133
112	resnet50-SIN_IN_IN	.132
113	resnet_SIN_IN_FT_IN	.132
114	alexnet_fov4	.132
115	BiT-S-R50x3	.128
116	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.127
117	antialiased-r50	.127
118	resnet34_imagenet_full	.126
119	grcnn	.125
120	densenet-201	.125
121	densenet_201_pytorch	.125
122	voneresnet-50-robust	.122
123	ReAlnet03	.121
124	densenet-121	.119
125	resnet50_linf_8_robust	.119
126	ReAlnet04	.118
127	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.116
128	ReAlnet08	.115
129	efficientnet-b7	.114
130	vgg_16	.113
131	densenet-201-keras	.112
132	voneresnet-50-non_stochastic	.108
133	resnet-50_v1-tf	.107
134	vonealexnet_gaussian_noise_std4_fixed	.105
135	ReAlnet01	.105
136	inception_v1	.100
137	alexnet_l2_3_robust	.098
138	alexnet_random_l2_3_perturb	.098
139	resnet50_tutorial	.098
140	resnet_50_v1	.098
141	resnet-50-pytorch	.098
142	resnet50-sup	.098
143	alexnet_ks_torevert	.097
144	alexnet	.097
145	ReAlnet05	.096
146	densenet-169	.096
147	resnet50_l2_3_robust	.095
148	resnet-152_v1-tf	.094
149	resnet50_moco_v2	.094
150	densenet-169-keras	.094
151	ReAlnet02	.093
152	ReAlnet07	.091
153	tv_efficientnet-b1	.090
154	cornet_s	.089
155	convnext_tiny_sup	.089
156	ReAlnet06	.089
157	vonegrcnn_62e_nobn	.088
158	vonegrcnn_62e_nobn	.088
159	alexnet_reduced_aliasing_early_checkpoint	.088
160	nasnet_mobile-tf	.086
161	alexnet_early_checkpoint	.085
162	deit_small_imagenet_full_seed-0	.084
163	BiT-S-R152x2	.083
164	ReAlnet10	.083
165	vonegrcnn_52e_full	.083
166	alexnet_training_seed_05	.083
167	alexnet_training_seed_01	.083
168	alexnet_training_seed_01_fov4	.083
169	alexnet_training_seed_01_fov12	.083
170	alexnet_training_seed_01_fov16	.083
171	resnet50-vitoimagevidnet8	.082
172	inception_v1-tf	.078
173	barlow-twins-resnet50	.078
174	ReAlnet09	.078
175	resnet-101_v1-tf	.076
176	resnet50_robust_l2_eps1	.075
177	vonegrcnn_47e	.075
178	AdvProp_efficientnet-b8	.073
179	resnet-34	.070
180	vonegrcnn_52e_full	.070
181	alexnet_fov12	.069
182	squeezenet1_1	.068
183	resnet18_imagenet_full	.065
184	vonegrcnn_47e	.065
185	mobilenet_v2_1.0_160-tf	.065
186	alexnet_robust_correct	.064
187	artResNet18_1	.064
188	resnet50-SIN_IN	.063
189	resnet-50-robust	.062
190	imagenet_l2_3_0	.062
191	resnet50_robust_l2_eps3	.062
192	konkle_alexnetgn_ipcl_ref01_primary_model	.062
193	mobilenet_v2_1_0_192	.060
194	resnet50_simclr	.059
195	mobilenet_v2_1_0_160	.059
196	eBarlow_lmda_02_1000ep	.059
197	BiT-S-R152x4	.057
198	alexnet_training_seed_02	.053
199	alexnet_training_seed_04	.053
200	alexnet_training_seed_08	.053
201	alexnet_training_seed_10	.053
202	resnet50-meshes-lt-100-original-scratch	.053
203	vgg-19-keras	.053
204	CORnet-S	.052
205	efficientnet_b2_imagenet_full	.050
206	mobilenet_v1_0.25_128	.048
207	mobilenet_v2_1.0_224-tf	.047
208	vgg-16-keras	.047
209	mobilenet_v2_0_5_192	.046
210	voneresnet-50	.045
211	texture_shape_resnet50_trained_on_SIN	.044
212	mobilenet_v2_0.5_96	.043
213	mobilenet_v1_0.5_160	.041
214	convnext_small_imagenet_1_seed-0	.041
215	AlexNet_SIN_fov12	.040
216	deit_small_imagenet_100_seed-0	.040
217	mobilenet_v2_1_0_224	.040
218	resnet50_imagenet_100_seed-0	.040
219	alexnet_fov16	.040
220	resnet50-SIN	.040
221	mobilenet_v1_0.5_128	.039
222	BiT-S-R101x3	.039
223	resnet50-barlow	.038
224	alexnet_training_seed_07	.038
225	alexnet_training_seed_09	.038
226	AdvProp_efficientnet-b0	.038
227	0.5x_resnet-18_LC_w_sh_1_iter	.038
228	BiT-S-R50x1	.037
229	mobilenet_v2_0.35_192	.037
230	texture_shape_alexnet_trained_on_SIN	.037
231	alexnet_linf_8_robust	.037
232	mobilenet_v2_1-4_224_pytorch	.036
233	mobilenet_v2_1_4_224	.036
234	resnet50_FractalDB	.036
235	BiT-S-R101x1	.035
236	mobilenet_v1_0.75_192	.035
237	mobilenet_v1_0.5_224	.034
238	mobilenet_v2_0.5_128	.034
239	mobilenet_v2_0_75_224	.034
240	mobilenet_v1_0.25_192	.033
241	mobilenet_v2_0.35_224	.033
242	resnet50-meshes-lt-100-original-pretrained	.033
243	resnet50_imagenet_10_seed-0	.033
244	AlexNet_SIN	.032
245	nasnet_mobile	.032
246	efficientnet_b0_imagenet_full	.031
247	mobilenet_v2_1.0_96	.031
248	deit_small_imagenet_10_seed-0	.031
249	deit_small_imagenet_1_seed-0	.031
250	convnext_small_imagenet_10_seed-0	.031
251	AlexNet_SIN_fov16	.031
252	resnet50-vicregl0p9	.030
253	resnet50-vicregl0p75	.029
254	mobilenet_v2_0.5_192-tf	.028
255	resnet50-vicreg	.027
256	mobilenet_v2_0.75_96	.027
257	mobilenet_v2_1_3_224	.027
258	mobilenet_v2_1.0_128-tf	.026
259	resnet-50_untrained	.024
260	mobilenet_v1_0.25_224	.024
261	mobilenet_v2_0.75_224-tf	.023
262	blt_vs	.023
263	mobilenet_v2_0.35_128	.023
264	mobilenet_v1_0.5_192	.021
265	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.021
266	mobilenet_v2_1.4_224-tf	.020
267	mobilenet_v2_0.35_160	.020
268	mobilenet_v2_0_75_160	.019
269	mobilenet_v2_0.75_128	.019
270	mobilenet_v2_0_75_192	.019
271	mobilenet_v2_0.5_160	.018
272	imagenet_l2_10_0	.018
273	mobilenet_v2_1.0_192-tf	.017
274	mobilenet_v1_0.75_160	.016
275	squeezenet1_0	.015
276	shufflenet_v2_x1_0	.015
277	mobilenet_v2_0.35_96	.014
278	alexnet_training_seed_06	.014
279	mobilenet_v2_0.5_224-tf	.014
280	mobilenet_v1_0.75_128	.014
281	resnet50_eMMCR_Vanilla	.013
282	resnet_50_v1_spiking_l4a	.012
283	resnet50_imagenet_1_seed-0	.012
284	mobilenet_v2_0.75_160-tf	.011
285	resnet_50_v1_spiking_l4	.011
286	resnet_50_v1_spiking_l3	.011
287	alexnet_training_seed_03	.010
288	mobilenet_v2_0_5_224	.010
289	mobilevit_small	.010
290	mobilenet_v1_1.0_192	.009
291	resnet50-simclr-vissl	.008
292	mobilenet_v1_0.25_160	.007
293	mobilenet_v1_1.0_128	.006
294	mobilenet_v1_1.0_160	.006
295	mobilenet_v1_1.0_224	.005
296	resnet-18	.004
297	mobilenet_v2_1_0_128	.003
298	mobilenet_v2_1.3_224-tf	.003
299	efficientnet_b1_imagenet_full	.003
300	regnet_y_400mf	.002
301	mobilenet_v2_0.75_192-tf	.002
302	mobilenet_v1_0.75_224	.001
303	my-model	.000
304	resnet-18-LC_w_sh_100_iter_conv_init_m
305	ViT-B/32
306	BiT-M-R50x3
307	BiT-M-R50x1
308	BiT-M-R152x2
309	resnet-18-LC_w_sh_1_iter_conv_init_m
310	resnet-18-LC_w_sh_10_iter_m
311	resnet-18-LC_w_sh_100_iter_m
312	resnet-18-LC_w_sh_10_iter_conv_init_m
313	resnet-18-LC_w_sh_1_iter_m
314	FrankRobWobv0
315	0.5x_resnet-18_LC
316	CORnetZ_CIFAR10
317	resnet18-local_aggregation
318	resnet18-deepcluster
319	r3m_resnet18
320	CORnet-Z
321	resnet18-instance_recognition
322	vggface
323	my-model
324	0.5x_resnet-18_LC_w_sh_10_iter
325	r3m_resnet50
326	r3m_resnet34
327	resnet18-colorization
328	resnet-18-LC_m
329	r3m_resnet50_nocrop
330	resnet18-supervised
331	resnet-18-LC_1st_conv_m
332	resnet-18-LC_conv_init_m
333	resnet18-contrastive_multiview
334	0.5x_resnet-18
335	RN50
336	0.5x_resnet-18_LC_w_sh_100_iter
337	RN50
338	resnet18-relative_position
339	resnet18-autoencoder
340	resnet18-simclr
341	ViT_L_32
342	ViT_B_32
343	resnet18-depth_prediction
344	ViT_B_16
345	r3m_resnet34_nocrop
346	resnet18-contrastive_predictive
347	omnivore_swinT
348	resnet-18-LC_untrained
349	resnet-18_untrained
350	resnet-50x2_untrained
351	dcgan
352	unet_entire
353	v1-pyr-nodown
354	CORnetZ_CIFAR10_bs32_20_04
355	pixels
356	dorinet_cornet_z
357	resnet50
358	openclip
359	resnet50-cifar
360	resnet50_pretrained_with_retinal_waves
361	resnet50_ImageNet
362	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
363	yudixie_resnet50_random_0_240908
364	yudixie_resnet50_cat_obj_class_all_latents_0_240908
365	yudixie_resnet50_imagenet1kpret_0_240908
366	yudixie_resnet50_imagenet1kpret_0_240312
367	yudixie_resnet50_object_class_0_240908
368	yudixie_resnet50_category_class_0_240908
369	resnet18_imagenet21kP
370	yudixie_resnet50_distance_reg_0_240908
371	ConvLSTM
372	PredRNN
373	SimVP
374	TAU
375	MIM
376	hmax
377	briaai_rmbg_1_4
378	ReAlnet01_cornet
379	compact_resnet50_V4
380	yudixie_resnet18_translation_reg_0_240719
381	yolos_tiny
382	ReAlnet03_cornet
383	yudixie_resnet18_rotation_reg_0_240719
384	yudixie_resnet18_imagenet1kpret_0_240719
385	ReAlnet02_cornet
386	vit_base_patch14_dinov2-lvd142m
387	resnet18_ecoset_full
388	compact_resnet50_IT
389	resnet50_ecoset_full
390	VOneCORnet-S
391	vit_base_patch14_reg4_dinov2-lvd142m
392	ReAlnet01_cornet_fov12
393	resnet101_ecoset_full
394	yudixie_resnet50_distance_translation_0_240908
395	resnet152_ecoset_full
396	yudixie_resnet18_distance_translation_0_240719
397	ReAlnet01_cornet_fov4
398	yudixie_resnet18_translation_rotation_0_240719
399	compact_vgg19_V4
400	aughost_fair_resnet18
401	yudixie_resnet18_distance_rotation_0_240719
402	yudixie_resnet18_category_class_0_240719
403	resnet-18_test_m
404	yudixie_resnet18_cat_obj_class_all_latents_0_240719
405	yudixie_resnet50_rotation_reg_0_240908
406	yudixie_resnet50_distance_rotation_0_240908
407	yudixie_resnet18_object_class_0_240719
408	SeLaVi-AVE
409	ReAlnet05_cornet
410	ReAlnet06_cornet
411	compact_resnet50_robust_V4
412	compact_vgg19_IT
413	yudixie_resnet50_translation_reg_0_240908
414	r3d_18
415	ReAlnet01_ALL
416	yudixie_resnet18_distance_reg_0_240719
417	yudixie_resnet50_translation_rotation_0_240908
418	omnivore_swinB
419	mc3_18
420	ReAlnet07_cornet
421	s3d
422	ReAlnet02_ALL
423	ReAlnet01_cornet_fov16
424	mvit_v1_b
425	ReAlnet08_cornet
426	ReAlnet04_ALL
427	compact_cornets_V4
428	ReAlnet03_ALL
429	ReAlnet09_cornet
430	SeLaVi-Kinetics400
431	ReAlnet05_ALL
432	compact_cornets_IT
433	SeLaVi-VGG-Sound
434	vit_base_patch16_dinov3-lvd1689m
435	ReAlnet06_ALL
436	SeLaVi-Kinetics-Sound
437	mvit_v2_s
438	omnivore_swinS
439	r2plus1d_18
440	compact_resnet50_robust_IT
441	yudixie_resnet50_distance_translation_rotation_0_240908
442	ReAlnet10_cornet
443	ReAlnet07_ALL
444	resnet34_ecoset_full
445	ReAlnet08_ALL
446	yudixie_resnet18_random_0_240719
447	yudixie_resnet18_distance_translation_rotation_0_240719
448	ReAlnet09_ALL
449	ReAlnet04_cornet
450	resnet50_primary_visual_cortex
451	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