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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	convnext_base_fb_in22k_ft_in1k	.568
22	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.564
23	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.560
24	resnext101_32x48d_wsl	.548
25	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.540
26	effnetv2m_custom384	.501
27	resnet-152_v2_pytorch	.494
28	SWSL_resnet50	.489
29	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.486
30	convnext_base_imagenet_full_seed-0	.482
31	vision_transformer_vit_large_patch16_224	.472
32	convnext_tiny:in12k_ft_in1k	.468
33	convnext_large_imagenet_full_seed-0	.467
34	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.462
35	resnet_50_v2	.455
36	resnext101_32x8d_wsl	.453
37	resnet_101_v2	.451
38	convnext_small_imagenet_full_seed-0	.450
39	convnext_tiny_imagenet_full_seed-0	.427
40	CLIP_resnet50	.427
41	CLIP_resnet50_float32	.427
42	cvt_cvt-13-224-in1k_4_LucyV4	.397
43	cv_18_dagger_408_pretrained	.388
44	vit_relpos_base_patch16_clsgap_224:sw_in1k	.382
45	pnasnet_large_halfPrecision	.380
46	pnasnet_large	.380
47	convnext_femto_ols:d1_in1k	.378
48	convnext_base_fb_in1k	.372
49	nasnet_large	.363
50	deit3_base_patch16_224_fb_in22k_ft_in1k	.363
51	ViT_B_16_imagenet1k	.339
52	resnet-152_v2-tf	.335
53	resnet50_st_wzc_seed7_2_323	.328
54	inception_v4	.323
55	focalnet_tiny_lrf_in1k	.317
56	resnet152_imagenet_full	.317
57	CLIP-RN50	.315
58	ViT_L_32_imagenet1k	.307
59	pnasnet_large-tf	.295
60	resnet50-VITO-8deg-cc	.292
61	antialias-resnet152	.289
62	effnetb1_272x240	.289
63	resnet101	.287
64	CLIP_ViT-B_32	.287
65	inception_v3	.280
66	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.278
67	resnet50_st_wzc_seed5_2_323	.276
68	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.269
69	deit_large_imagenet_full_seed-0	.269
70	resnet50_random_linf8_perturb	.260
71	resnet_152_v1	.260
72	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.257
73	resnet50_ewconplus_wzc_2_323	.251
74	resnet50_st_wzc_seed4_2_323	.248
75	nasnet_large-tf	.248
76	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.246
77	xception-keras	.245
78	inception_v3-tf	.237
79	resnet_101_v1	.234
80	resnet50_st_wzc_seed6_2_323	.231
81	cvt_cvt-21-224-in1k_4_LucyV4	.228
82	resnet50_random_l2_perturb	.227
83	inception_v3_pytorch	.222
84	resnet101_imagenet_full	.221
85	resnet50_ewconplus_wzc_seed5_2__323	.219
86	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.218
87	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.217
88	ViT_B_32_imagenet1k	.217
89	resnet50_imagenet_full	.216
90	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.214
91	inception_v4-tf	.209
92	convnext_base_fb_in1k_gram_aligned_dinov3	.206
93	resnet50_st_wzc_seed3_2_323	.203
94	grcnn_109	.201
95	resnet50_ewconplus_wzc_seed2_2__323	.197
96	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.197
97	vgg_19	.195
98	Res2Net50_26w_4s	.193
99	AdvProp_efficientnet-b7	.190
100	AdvProp_efficientnet-b4	.189
101	AT_efficientnet-b4	.189
102	AdvProp_efficientnet-b6	.187
103	AT_efficientnet-b2	.186
104	AdvProp_efficientnet-b2	.186
105	resnet50	.185
106	xception	.184
107	resnet50_linf_4_robust	.182
108	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.182
109	efficientnet_b0	.181
110	resnet_50_v1_spiking	.181
111	resnet50_st_wzc_noseed_2_323	.179
112	resnet50_ewconplus_wzc_seed7_2_323	.178
113	resnet50_ewconplus_wzc	.174
114	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.173
115	AlexNet_SIN_fov4	.172
116	deit_base_imagenet_full_seed-0	.169
117	resnet50_ewconplus_wzc_seed3_2_323	.168
118	resnet50_ewconplus_wzc_seed4_2_323	.168
119	convnext_small_imagenet_100_seed-0	.167
120	resnet50_ewconplus_noseed_2_323	.165
121	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.164
122	resnet50_st_wzc_2	.163
123	resnet50-moclr8deg	.159
124	resnet-50_v2-tf	.155
125	antialiased-rnext101_32x8d	.149
126	densenet-121-keras	.147
127	custom_model_cv_18_dagger_408	.145
128	inception_v2-tf	.145
129	alexnet_random_linf8_perturb	.140
130	resnet50_byol	.134
131	pnasnet_large_pytorch	.133
132	resnet50-SIN_IN_IN	.132
133	resnet_SIN_IN_FT_IN	.132
134	alexnet_fov4	.132
135	BiT-S-R50x3	.128
136	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.127
137	antialiased-r50	.127
138	resnet34_imagenet_full	.126
139	grcnn	.125
140	densenet-201	.125
141	densenet_201_pytorch	.125
142	voneresnet-50-robust	.122
143	ReAlnet03	.121
144	densenet-121	.119
145	resnet50_linf_8_robust	.119
146	ReAlnet04	.118
147	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.116
148	ReAlnet08	.115
149	efficientnet-b7	.114
150	vgg_16	.113
151	densenet-201-keras	.112
152	voneresnet-50-non_stochastic	.108
153	resnet-50_v1-tf	.107
154	vonealexnet_gaussian_noise_std4_fixed	.105
155	ReAlnet01	.105
156	inception_v1	.100
157	alexnet_l2_3_robust	.098
158	alexnet_random_l2_3_perturb	.098
159	resnet50_tutorial	.098
160	resnet_50_v1	.098
161	resnet-50-pytorch	.098
162	resnet50-sup	.098
163	alexnet_ks_torevert	.097
164	alexnet	.097
165	ReAlnet05	.096
166	densenet-169	.096
167	resnet50_l2_3_robust	.095
168	resnet-152_v1-tf	.094
169	resnet50_moco_v2	.094
170	densenet-169-keras	.094
171	ReAlnet02	.093
172	ReAlnet07	.091
173	tv_efficientnet-b1	.090
174	cornet_s	.089
175	convnext_tiny_sup	.089
176	ReAlnet06	.089
177	vonegrcnn_62e_nobn	.088
178	vonegrcnn_62e_nobn	.088
179	alexnet_reduced_aliasing_early_checkpoint	.088
180	nasnet_mobile-tf	.086
181	alexnet_early_checkpoint	.085
182	deit_small_imagenet_full_seed-0	.084
183	BiT-S-R152x2	.083
184	ReAlnet10	.083
185	vonegrcnn_52e_full	.083
186	alexnet_training_seed_05	.083
187	alexnet_training_seed_01	.083
188	alexnet_training_seed_01_fov4	.083
189	alexnet_training_seed_01_fov12	.083
190	alexnet_training_seed_01_fov16	.083
191	resnet50-vitoimagevidnet8	.082
192	inception_v1-tf	.078
193	barlow-twins-resnet50	.078
194	ReAlnet09	.078
195	resnet-101_v1-tf	.076
196	resnet50_robust_l2_eps1	.075
197	vonegrcnn_47e	.075
198	AdvProp_efficientnet-b8	.073
199	resnet-34	.070
200	vonegrcnn_52e_full	.070
201	alexnet_fov12	.069
202	squeezenet1_1	.068
203	resnet18_imagenet_full	.065
204	vonegrcnn_47e	.065
205	mobilenet_v2_1.0_160-tf	.065
206	alexnet_robust_correct	.064
207	artResNet18_1	.064
208	resnet50-SIN_IN	.063
209	resnet-50-robust	.062
210	imagenet_l2_3_0	.062
211	resnet50_robust_l2_eps3	.062
212	konkle_alexnetgn_ipcl_ref01_primary_model	.062
213	mobilenet_v2_1_0_192	.060
214	resnet50_simclr	.059
215	mobilenet_v2_1_0_160	.059
216	eBarlow_lmda_02_1000ep	.059
217	BiT-S-R152x4	.057
218	alexnet_training_seed_02	.053
219	alexnet_training_seed_04	.053
220	alexnet_training_seed_08	.053
221	alexnet_training_seed_10	.053
222	resnet50-meshes-lt-100-original-scratch	.053
223	vgg-19-keras	.053
224	CORnet-S	.052
225	efficientnet_b2_imagenet_full	.050
226	mobilenet_v1_0.25_128	.048
227	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.048
228	mobilenet_v2_1.0_224-tf	.047
229	vgg-16-keras	.047
230	mobilenet_v2_0_5_192	.046
231	voneresnet-50	.045
232	texture_shape_resnet50_trained_on_SIN	.044
233	mobilenet_v2_0.5_96	.043
234	mobilenet_v1_0.5_160	.041
235	convnext_small_imagenet_1_seed-0	.041
236	AlexNet_SIN_fov12	.040
237	deit_small_imagenet_100_seed-0	.040
238	mobilenet_v2_1_0_224	.040
239	resnet50_imagenet_100_seed-0	.040
240	alexnet_fov16	.040
241	resnet50-SIN	.040
242	mobilenet_v1_0.5_128	.039
243	BiT-S-R101x3	.039
244	resnet50-barlow	.038
245	alexnet_training_seed_07	.038
246	alexnet_training_seed_09	.038
247	AdvProp_efficientnet-b0	.038
248	0.5x_resnet-18_LC_w_sh_1_iter	.038
249	BiT-S-R50x1	.037
250	mobilenet_v2_0.35_192	.037
251	texture_shape_alexnet_trained_on_SIN	.037
252	alexnet_linf_8_robust	.037
253	mobilenet_v2_1-4_224_pytorch	.036
254	mobilenet_v2_1_4_224	.036
255	resnet50_FractalDB	.036
256	BiT-S-R101x1	.035
257	mobilenet_v1_0.75_192	.035
258	mobilenet_v1_0.5_224	.034
259	mobilenet_v2_0.5_128	.034
260	mobilenet_v2_0_75_224	.034
261	mobilenet_v1_0.25_192	.033
262	mobilenet_v2_0.35_224	.033
263	resnet50-meshes-lt-100-original-pretrained	.033
264	resnet50_imagenet_10_seed-0	.033
265	AlexNet_SIN	.032
266	nasnet_mobile	.032
267	efficientnet_b0_imagenet_full	.031
268	mobilenet_v2_1.0_96	.031
269	deit_small_imagenet_10_seed-0	.031
270	deit_small_imagenet_1_seed-0	.031
271	convnext_small_imagenet_10_seed-0	.031
272	AlexNet_SIN_fov16	.031
273	resnet50-vicregl0p9	.030
274	resnet50-vicregl0p75	.029
275	mobilenet_v2_0.5_192-tf	.028
276	resnet50-vicreg	.027
277	mobilenet_v2_0.75_96	.027
278	mobilenet_v2_1_3_224	.027
279	mobilenet_v2_1.0_128-tf	.026
280	resnet-50_untrained	.024
281	mobilenet_v1_0.25_224	.024
282	mobilenet_v2_0.75_224-tf	.023
283	blt_vs	.023
284	mobilenet_v2_0.35_128	.023
285	mobilenet_v1_0.5_192	.021
286	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.021
287	mobilenet_v2_1.4_224-tf	.020
288	mobilenet_v2_0.35_160	.020
289	mobilenet_v2_0_75_160	.019
290	mobilenet_v2_0.75_128	.019
291	mobilenet_v2_0_75_192	.019
292	mobilenet_v2_0.5_160	.018
293	imagenet_l2_10_0	.018
294	mobilenet_v2_1.0_192-tf	.017
295	mobilenet_v1_0.75_160	.016
296	squeezenet1_0	.015
297	shufflenet_v2_x1_0	.015
298	mobilenet_v2_0.35_96	.014
299	alexnet_training_seed_06	.014
300	mobilenet_v2_0.5_224-tf	.014
301	mobilenet_v1_0.75_128	.014
302	resnet50_eMMCR_Vanilla	.013
303	resnet_50_v1_spiking_l4a	.012
304	resnet50_imagenet_1_seed-0	.012
305	mobilenet_v2_0.75_160-tf	.011
306	resnet_50_v1_spiking_l4	.011
307	resnet_50_v1_spiking_l3	.011
308	alexnet_training_seed_03	.010
309	mobilenet_v2_0_5_224	.010
310	mobilevit_small	.010
311	mobilenet_v1_1.0_192	.009
312	resnet50-simclr-vissl	.008
313	mobilenet_v1_0.25_160	.007
314	mobilenet_v1_1.0_128	.006
315	mobilenet_v1_1.0_160	.006
316	mobilenet_v1_1.0_224	.005
317	resnet-18	.004
318	mobilenet_v2_1_0_128	.003
319	mobilenet_v2_1.3_224-tf	.003
320	efficientnet_b1_imagenet_full	.003
321	regnet_y_400mf	.002
322	mobilenet_v2_0.75_192-tf	.002
323	mobilenet_v1_0.75_224	.001
324	my-model	.000
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	BiT-M-R152x2
330	resnet-18-LC_w_sh_1_iter_conv_init_m
331	resnet-18-LC_w_sh_10_iter_m
332	resnet-18-LC_w_sh_100_iter_m
333	resnet-18-LC_w_sh_10_iter_conv_init_m
334	resnet-18-LC_w_sh_1_iter_m
335	FrankRobWobv0
336	0.5x_resnet-18_LC
337	CORnetZ_CIFAR10
338	resnet18-local_aggregation
339	resnet18-deepcluster
340	r3m_resnet18
341	CORnet-Z
342	resnet18-instance_recognition
343	vggface
344	my-model
345	0.5x_resnet-18_LC_w_sh_10_iter
346	r3m_resnet50
347	r3m_resnet34
348	resnet18-colorization
349	resnet-18-LC_m
350	r3m_resnet50_nocrop
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	resnet18-contrastive_predictive
368	omnivore_swinT
369	resnet-18-LC_untrained
370	resnet-18_untrained
371	resnet-50x2_untrained
372	dcgan
373	unet_entire
374	v1-pyr-nodown
375	CORnetZ_CIFAR10_bs32_20_04
376	pixels
377	dorinet_cornet_z
378	resnet50
379	openclip
380	resnet50-cifar
381	resnet50_pretrained_with_retinal_waves
382	resnet50_ImageNet
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	yudixie_resnet50_random_0_240908
385	yudixie_resnet50_cat_obj_class_all_latents_0_240908
386	yudixie_resnet50_imagenet1kpret_0_240908
387	yudixie_resnet50_imagenet1kpret_0_240312
388	yudixie_resnet50_object_class_0_240908
389	yudixie_resnet50_category_class_0_240908
390	resnet18_imagenet21kP
391	yudixie_resnet50_distance_reg_0_240908
392	ConvLSTM
393	PredRNN
394	SimVP
395	TAU
396	MIM
397	hmax
398	briaai_rmbg_1_4
399	ReAlnet01_cornet
400	compact_resnet50_V4
401	yudixie_resnet18_translation_reg_0_240719
402	yolos_tiny
403	ReAlnet03_cornet
404	yudixie_resnet18_rotation_reg_0_240719
405	yudixie_resnet18_imagenet1kpret_0_240719
406	ReAlnet02_cornet
407	vit_base_patch14_dinov2-lvd142m
408	resnet18_ecoset_full
409	compact_resnet50_IT
410	resnet50_ecoset_full
411	VOneCORnet-S
412	vit_base_patch14_reg4_dinov2-lvd142m
413	ReAlnet01_cornet_fov12
414	resnet101_ecoset_full
415	yudixie_resnet50_distance_translation_0_240908
416	resnet152_ecoset_full
417	yudixie_resnet18_distance_translation_0_240719
418	ReAlnet01_cornet_fov4
419	yudixie_resnet18_translation_rotation_0_240719
420	compact_vgg19_V4
421	aughost_fair_resnet18
422	yudixie_resnet18_distance_rotation_0_240719
423	yudixie_resnet18_category_class_0_240719
424	resnet-18_test_m
425	yudixie_resnet18_cat_obj_class_all_latents_0_240719
426	yudixie_resnet50_rotation_reg_0_240908
427	yudixie_resnet50_distance_rotation_0_240908
428	yudixie_resnet18_object_class_0_240719
429	SeLaVi-AVE
430	ReAlnet05_cornet
431	ReAlnet06_cornet
432	compact_resnet50_robust_V4
433	compact_vgg19_IT
434	yudixie_resnet50_translation_reg_0_240908
435	r3d_18
436	ReAlnet01_ALL
437	yudixie_resnet18_distance_reg_0_240719
438	yudixie_resnet50_translation_rotation_0_240908
439	omnivore_swinB
440	mc3_18
441	ReAlnet07_cornet
442	s3d
443	ReAlnet02_ALL
444	ReAlnet01_cornet_fov16
445	mvit_v1_b
446	ReAlnet08_cornet
447	ReAlnet04_ALL
448	compact_cornets_V4
449	ReAlnet03_ALL
450	ReAlnet09_cornet
451	SeLaVi-Kinetics400
452	ReAlnet05_ALL
453	compact_cornets_IT
454	SeLaVi-VGG-Sound
455	vit_base_patch16_dinov3-lvd1689m
456	ReAlnet06_ALL
457	SeLaVi-Kinetics-Sound
458	mvit_v2_s
459	omnivore_swinS
460	r2plus1d_18
461	compact_resnet50_robust_IT
462	yudixie_resnet50_distance_translation_rotation_0_240908
463	ReAlnet10_cornet
464	ReAlnet07_ALL
465	resnet34_ecoset_full
466	ReAlnet08_ALL
467	yudixie_resnet18_random_0_240719
468	yudixie_resnet18_distance_translation_rotation_0_240719
469	ReAlnet09_ALL
470	ReAlnet04_cornet
471	resnet50_primary_visual_cortex
472	ReAlnet10_ALL
473	resnet18_fair
474	pr1_stl10_disentangled_alpha04
475	yudixie_wbh_object_class_scaling_0415_run_0
476	yudixie_wbh_object_class_scaling_0415_run_3
477	yudixie_wbh_object_class_scaling_0415_imagenet
478	yudixie_wbh_object_class_scaling_0415_run_2
479	yudixie_wbh_object_class_scaling_0415_run_1
480	deepeisnn_ei_cifar10_vgg19
481	deepeisnn_ei_cifar10_resnet18
482	deepeisnn_ei_imagenet_resnet18

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