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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonII-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	SWSL_resnext101_32x8d	.732
2	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.703
3	ViT_B_16_imagenet1k	.699
4	vision_transformer_vit_large_patch16_224	.696
5	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.692
6	vit_base_patch16_clip_224:openai_ft_in1k	.680
7	vit_relpos_base_patch16_clsgap_224:sw_in1k	.674
8	convnext_tiny_imagenet_full_seed-0	.665
9	resnext101_32x48d_wsl	.664
10	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.662
11	resnext101_32x16d_wsl	.662
12	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.659
13	AdvProp_efficientnet-b6	.656
14	resnext101_32x32d_wsl	.650
15	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.650
16	resnet_152_v2	.646
17	focalnet_tiny_lrf_in1k	.646
18	cvt_cvt-13-224-in1k_4_LucyV4	.643
19	pnasnet_large	.640
20	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.635
21	convnext_small_imagenet_100_seed-0	.632
22	resnet_50_v2	.630
23	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.626
24	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.625
25	resnext101_32x8d_wsl	.624
26	CLIP_ViT-B_32	.622
27	ViT_L_32_imagenet1k	.617
28	convnext_large:fb_in22k_ft_in1k	.613
29	vit_large_patch14_clip_224:laion2b_ft_in1k	.613
30	SWSL_resnet50	.613
31	resnet_101_v2	.610
32	vit_large_patch14_clip_224:openai_ft_in1k	.603
33	resnet-152_v2-tf	.602
34	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.597
35	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.590
36	convnext_femto_ols:d1_in1k	.588
37	cvt_cvt-13-384-in1k_4_LucyV4	.582
38	AdvProp_efficientnet-b7	.578
39	pnasnet_large-tf	.577
40	convnext_large_imagenet_full_seed-0	.569
41	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.564
42	AdvProp_efficientnet-b4	.559
43	AT_efficientnet-b4	.559
44	CLIP_resnet50	.558
45	CLIP_resnet50_float32	.558
46	convnext_base:clip_laiona_augreg_ft_in1k_384	.555
47	fixres_resnext101_32x48d_wsl	.549
48	convnext_tiny:in12k_ft_in1k	.547
49	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.546
50	effnetv2m_custom384	.540
51	efficientnet_b0	.538
52	cvt_cvt-21-384-in1k_4_LucyV4	.535
53	efficientnet-b7	.534
54	nasnet_large	.534
55	resnet152_imagenet_full	.527
56	resnet_152_v1	.524
57	resnet50-VITO-8deg-cc	.522
58	nasnet_large-tf	.520
59	cvt_cvt-21-224-in1k_4_LucyV4	.519
60	ViT_B_32_imagenet1k	.518
61	antialias-resnet152	.515
62	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.513
63	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.512
64	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
65	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.503
66	AT_efficientnet-b2	.501
67	AdvProp_efficientnet-b2	.501
68	deit_large_imagenet_full_seed-0	.496
69	effnetb1_272x240	.495
70	convnext_xlarge:fb_in22k_ft_in1k	.491
71	inception_v4	.482
72	vit_large_patch14_clip_336:laion2b_ft_in1k	.481
73	CLIP-RN50	.477
74	convnext_base_imagenet_full_seed-0	.475
75	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
76	resnet_101_v1	.470
77	antialiased-rnext101_32x8d	.469
78	convnext_small_imagenet_full_seed-0	.465
79	deit_base_imagenet_full_seed-0	.465
80	inception_v3_pytorch	.463
81	inception_v3	.458
82	resnet-152_v1-tf	.455
83	grcnn_109	.454
84	tv_efficientnet-b1	.454
85	resnet50-moclr8deg	.453
86	resnet-152_v2_pytorch	.453
87	resnet101	.451
88	resnet50_imagenet_full	.448
89	pnasnet_large_pytorch	.445
90	cv_18_dagger_408_pretrained	.443
91	resnet101_imagenet_full	.433
92	densenet-201-keras	.423
93	inception_v4-tf	.423
94	densenet-201	.421
95	densenet_201_pytorch	.421
96	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
97	resnet-50_v2-tf	.416
98	grcnn	.414
99	alexnet_fov4	.411
100	deit_small_imagenet_full_seed-0	.410
101	resnet50_random_linf8_perturb	.405
102	custom_model_cv_18_dagger_408	.401
103	xception-keras	.399
104	BiT-S-R50x3	.399
105	Res2Net50_26w_4s	.394
106	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.379
107	resnet34_imagenet_full	.379
108	inception_v3-tf	.375
109	BiT-S-R152x2	.371
110	resnet50_random_l2_perturb	.371
111	ReAlnet03	.366
112	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.365
113	resnet50_moco_v2	.364
114	resnet50	.363
115	cornet_s	.361
116	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.361
117	vonegrcnn_62e_nobn	.359
118	efficientnet_b2_imagenet_full	.359
119	resnet50_linf_4_robust	.358
120	vonegrcnn_47e	.354
121	vonegrcnn_52e_full	.351
122	AdvProp_efficientnet-b8	.350
123	ReAlnet01	.345
124	ReAlnet04	.342
125	resnet50_byol	.337
126	BiT-S-R101x3	.331
127	vonegrcnn_47e	.330
128	resnet50_tutorial	.329
129	resnet_50_v1	.329
130	resnet-50-pytorch	.329
131	resnet50-sup	.329
132	resnet18_imagenet_full	.329
133	resnet50_ewconplus_wzc	.327
134	vonegrcnn_52e_full	.325
135	densenet-121-keras	.320
136	resnet-50_v1-tf	.317
137	vgg_19	.317
138	antialiased-r50	.315
139	alexnet_random_linf8_perturb	.314
140	resnet50-SIN_IN_IN	.313
141	resnet_SIN_IN_FT_IN	.313
142	ReAlnet06	.313
143	densenet-169-keras	.311
144	vonegrcnn_62e_nobn	.311
145	alexnet_reduced_aliasing_early_checkpoint	.308
146	CORnet-S	.304
147	densenet-121	.302
148	ReAlnet05	.299
149	ReAlnet07	.298
150	xception	.298
151	resnet_50_v1_spiking	.297
152	voneresnet-50-robust	.296
153	mobilenet_v2_1.4_224-tf	.296
154	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.294
155	inception_v2-tf	.294
156	voneresnet-50	.289
157	inception_v1-tf	.289
158	mobilenet_v2_1-4_224_pytorch	.287
159	mobilenet_v2_1_4_224	.287
160	voneresnet-50-non_stochastic	.286
161	resnet-101_v1-tf	.285
162	ReAlnet10	.279
163	ReAlnet08	.278
164	ReAlnet02	.274
165	densenet-169	.270
166	vgg_16	.270
167	mobilenet_v2_1_0_224	.267
168	resnet50_l2_3_robust	.266
169	mobilenet_v2_1_0_160	.266
170	ReAlnet09	.265
171	mobilenet_v2_1_3_224	.260
172	alexnet	.260
173	resnet50-SIN_IN	.258
174	BiT-S-R152x4	.257
175	BiT-S-R50x1	.256
176	alexnet_l2_3_robust	.255
177	resnet50-vitoimagevidnet8	.254
178	alexnet-baseline	.252
179	alexnet	.252
180	alexnet	.252
181	alexnet	.252
182	alexnet	.252
183	alexnet	.252
184	alexnet	.252
185	alexnet	.252
186	alexnet	.252
187	alexnet	.252
188	alexnet	.252
189	alexnet_ks_torevert	.252
190	alexnet_workflow_trigger	.252
191	nasnet_mobile	.250
192	blt_vs	.247
193	alexnet_linf_8_robust	.246
194	alexnet_robust_correct	.236
195	vonealexnet_gaussian_noise_std4_fixed	.235
196	inception_v1	.235
197	alexnet_fov12	.232
198	mobilenet_v2_0_5_224	.226
199	resnet-50-robust	.226
200	imagenet_l2_3_0	.226
201	resnet50_robust_l2_eps3	.226
202	mobilenet_v2_1.0_160-tf	.225
203	deit_small_imagenet_100_seed-0	.224
204	efficientnet_b0_imagenet_full	.224
205	konkle_alexnetgn_ipcl_ref01_primary_model	.223
206	convnext_tiny_sup	.223
207	resnet-18	.216
208	resnet-34	.216
209	alexnet_random_l2_3_perturb	.215
210	resnet50_robust_l2_eps1	.206
211	mobilenet_v2_1_0_192	.206
212	vgg-16-keras	.204
213	mobilenet_v2_1.3_224-tf	.198
214	mobilenet_v1_1.0_224	.196
215	resnet50_linf_8_robust	.195
216	BiT-S-R101x1	.191
217	regnet_y_400mf	.190
218	efficientnet_b1_imagenet_full	.189
219	mobilenet_v2_1.0_192-tf	.189
220	resnet50_imagenet_10_seed-0	.182
221	deit_small_imagenet_10_seed-0	.182
222	mobilenet_v1_0.75_224	.180
223	mobilenet_v2_1.0_224-tf	.180
224	resnet50-barlow	.179
225	resnet50-vicreg	.178
226	texture_shape_resnet50_trained_on_SIN	.178
227	deit_small_imagenet_1_seed-0	.174
228	alexnet_fov16	.174
229	resnet50_eMMCR_Vanilla	.173
230	nasnet_mobile-tf	.169
231	mobilenet_v2_0_5_192	.166
232	convnext_small_imagenet_10_seed-0	.161
233	mobilenet_v2_0.5_192-tf	.161
234	resnet50_simclr	.158
235	shufflenet_v2_x1_0	.158
236	mobilenet_v2_0_75_160	.155
237	mobilenet_v1_1.0_192	.154
238	alexnet_early_checkpoint	.151
239	imagenet_l2_10_0	.149
240	mobilenet_v1_0.75_128	.145
241	mobilenet_v2_0.5_224-tf	.143
242	alexnet_training_seed_05	.143
243	alexnet_training_seed_01	.143
244	alexnet_training_seed_01_fov4	.143
245	alexnet_training_seed_01_fov16	.143
246	alexnet_training_seed_01_fov12	.143
247	0.5x_resnet-18_LC_w_sh_1_iter	.142
248	mobilenet_v2_0.35_160	.137
249	vgg-19-keras	.136
250	resnet50-SIN	.134
251	mobilenet_v2_0.5_96	.133
252	AlexNet_SIN_fov4	.130
253	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.128
254	texture_shape_alexnet_trained_on_SIN	.124
255	mobilenet_v2_0.75_192-tf	.124
256	AdvProp_efficientnet-b0	.118
257	resnet50_imagenet_100_seed-0	.118
258	mobilenet_v1_0.75_192	.116
259	AlexNet_SIN	.115
260	mobilenet_v2_0.75_224-tf	.114
261	mobilenet_v2_0.5_160	.114
262	mobilenet_v1_0.25_160	.112
263	mobilenet_v2_0_75_192	.110
264	mobilenet_v2_0.35_128	.109
265	mobilenet_v2_0_75_224	.107
266	mobilenet_v2_0.75_128	.105
267	mobilenet_v2_1_0_128	.105
268	mobilenet_v2_0.35_96	.104
269	mobilenet_v1_0.75_160	.100
270	alexnet_training_seed_06	.098
271	mobilenet_v1_1.0_160	.098
272	mobilenet_v1_0.5_192	.096
273	mobilenet_v2_1.0_96	.089
274	mobilenet_v2_1.0_128-tf	.088
275	mobilenet_v2_0.75_160-tf	.086
276	mobilenet_v1_0.5_224	.086
277	mobilenet_v1_0.25_192	.086
278	mobilenet_v2_0.35_192	.078
279	squeezenet1_1	.075
280	mobilenet_v1_1.0_128	.071
281	my-model	.065
282	mobilenet_v2_0.75_96	.065
283	resnet_50_v1_spiking_l4a	.061
284	mobilenet_v2_0.35_224	.061
285	squeezenet1_0	.058
286	resnet50-simclr-vissl	.058
287	resnet50-vicregl0p9	.057
288	AlexNet_SIN_fov12	.053
289	mobilevit_small	.052
290	alexnet_training_seed_02	.049
291	alexnet_training_seed_04	.049
292	alexnet_training_seed_08	.049
293	alexnet_training_seed_10	.049
294	resnet50-vicregl0p75	.048
295	mobilenet_v1_0.25_128	.046
296	resnet50-meshes-lt-100-original-scratch	.043
297	mobilenet_v2_0.5_128	.042
298	resnet-50_untrained	.041
299	mobilenet_v1_0.5_160	.040
300	mobilenet_v1_0.25_224	.039
301	convnext_small_imagenet_1_seed-0	.032
302	alexnet_training_seed_03	.030
303	resnet50-meshes-lt-100-original-pretrained	.030
304	resnet_50_v1_spiking_l3	.026
305	resnet50_FractalDB	.024
306	alexnet_training_seed_07	.024
307	resnet_50_v1_spiking_l4	.020
308	alexnet_training_seed_09	.013
309	resnet50_imagenet_1_seed-0	.010
310	mobilenet_v1_0.5_128	.007
311	artResNet18_1	.006
312	AlexNet_SIN_fov16	.003
313	barlow-twins-resnet50	.002
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_1_iter_m
323	resnet-18-LC_w_sh_10_iter_conv_init_m
324	FrankRobWobv0
325	resnet18-local_aggregation
326	0.5x_resnet-18_LC
327	CORnetZ_CIFAR10
328	resnet18-deepcluster
329	r3m_resnet18
330	CORnet-Z
331	resnet18-instance_recognition
332	my-model
333	vggface
334	0.5x_resnet-18_LC_w_sh_10_iter
335	resnet18-colorization
336	r3m_resnet50
337	r3m_resnet34
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	v1-pyr-nodown
363	unet_entire
364	yudixie_resnet50_imagenet1kpret_0_240908
365	CORnetZ_CIFAR10_bs32_20_04
366	pixels
367	dorinet_cornet_z
368	resnet50
369	openclip
370	resnet50-cifar
371	resnet50_pretrained_with_retinal_waves
372	resnet50_ImageNet
373	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
374	yudixie_resnet50_random_0_240908
375	yudixie_resnet50_cat_obj_class_all_latents_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	ReAlnet01_cornet
388	briaai_rmbg_1_4
389	vit_base_patch14_reg4_dinov2:lvd142m
390	yolos_tiny
391	vit_base_patch16_dinov3:lvd1689m
392	yudixie_resnet18_translation_reg_0_240719
393	compact_resnet50_V4
394	yudixie_resnet18_distance_reg_0_240719
395	ReAlnet03_cornet
396	yudixie_resnet18_rotation_reg_0_240719
397	yudixie_resnet18_imagenet1kpret_0_240719
398	ReAlnet02_cornet
399	resnet18_ecoset_full
400	compact_resnet50_IT
401	VOneCORnet-S
402	resnet50_ecoset_full
403	ReAlnet01_cornet_fov4
404	yudixie_resnet50_distance_rotation_0_240908
405	compact_resnet50_robust_V4
406	yudixie_resnet50_rotation_reg_0_240908
407	resnet152_ecoset_full
408	yudixie_resnet50_distance_translation_0_240908
409	yudixie_resnet18_distance_translation_0_240719
410	ReAlnet01_cornet_fov12
411	yudixie_resnet18_translation_rotation_0_240719
412	yudixie_resnet18_distance_rotation_0_240719
413	compact_vgg19_V4
414	yudixie_resnet18_category_class_0_240719
415	ReAlnet05_cornet
416	vit_base_patch14_dinov2:lvd142m
417	resnet-18_test_m
418	yudixie_resnet18_cat_obj_class_all_latents_0_240719
419	yudixie_resnet18_distance_translation_rotation_0_240719
420	yudixie_resnet18_object_class_0_240719
421	SeLaVi-AVE
422	ReAlnet06_cornet
423	compact_vgg19_IT
424	r3d_18
425	ReAlnet01_cornet_fov16
426	yudixie_resnet50_translation_reg_0_240908
427	yudixie_resnet50_translation_rotation_0_240908
428	ReAlnet01_ALL
429	ReAlnet07_cornet
430	mc3_18
431	s3d
432	mvit_v1_b
433	ReAlnet08_cornet
434	ReAlnet02_ALL
435	ReAlnet03_ALL
436	compact_cornets_V4
437	ReAlnet04_ALL
438	omnivore_swinB
439	SeLaVi-Kinetics400
440	SeLaVi-VGG-Sound
441	compact_cornets_IT
442	ReAlnet05_ALL
443	SeLaVi-Kinetics-Sound
444	ReAlnet06_ALL
445	omnivore_swinS
446	r2plus1d_18
447	ReAlnet10_cornet
448	mvit_v2_s
449	ReAlnet07_ALL
450	compact_resnet50_robust_IT
451	resnet101_ecoset_full
452	resnet34_ecoset_full
453	yudixie_resnet50_distance_translation_rotation_0_240908
454	ReAlnet08_ALL
455	yudixie_resnet18_random_0_240719
456	ReAlnet04_cornet
457	ReAlnet09_ALL
458	ReAlnet09_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

0.45.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonII

Metric: error_consistency