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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonI-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vision_transformer_vit_large_patch16_224	.789
2	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.783
3	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.777
4	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.776
5	resnext101_32x16d_wsl	.731
6	vit_base_patch16_clip_224:openai_ft_in1k	.724
7	SWSL_resnext101_32x8d	.715
8	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.698
9	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.689
10	ViT_B_16_imagenet1k	.677
11	resnext101_32x48d_wsl	.667
12	resnet_101_v2	.663
13	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.661
14	resnext101_32x8d_wsl	.655
15	CLIP_ViT-B_32	.651
16	SWSL_resnet50	.647
17	AdvProp_efficientnet-b7	.646
18	resnet_152_v2	.638
19	ViT_L_32_imagenet1k	.637
20	resnext101_32x32d_wsl	.637
21	vit_large_patch14_clip_224:laion2b_ft_in1k	.636
22	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.629
23	resnet101	.629
24	effnetv2m_custom384	.621
25	AdvProp_efficientnet-b4	.619
26	AT_efficientnet-b4	.619
27	vit_large_patch14_clip_224:openai_ft_in1k	.619
28	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.615
29	effnetb1_272x240	.614
30	vit_relpos_base_patch16_clsgap_224:sw_in1k	.610
31	resnet152_imagenet_full	.607
32	focalnet_tiny_lrf_in1k	.603
33	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.593
34	pnasnet_large	.593
35	pnasnet_large-tf	.584
36	efficientnet_b0	.583
37	fixres_resnext101_32x48d_wsl	.582
38	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.577
39	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.575
40	vit_large_patch14_clip_336:laion2b_ft_in1k	.574
41	convnext_xlarge:fb_in22k_ft_in1k	.574
42	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.567
43	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.556
44	AdvProp_efficientnet-b6	.553
45	cvt_cvt-13-384-in1k_4_LucyV4	.551
46	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.549
47	resnet_101_v1	.546
48	AT_efficientnet-b2	.541
49	AdvProp_efficientnet-b2	.541
50	cvt_cvt-13-224-in1k_4_LucyV4	.537
51	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
52	vonegrcnn_62e_nobn	.527
53	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.527
54	antialias-resnet152	.524
55	ViT_B_32_imagenet1k	.522
56	resnet-152_v2-tf	.520
57	inception_v4	.519
58	CLIP_resnet50	.513
59	CLIP_resnet50_float32	.513
60	custom_model_cv_18_dagger_408	.512
61	nasnet_large	.505
62	nasnet_large-tf	.499
63	convnext_tiny_imagenet_full_seed-0	.498
64	vonegrcnn_47e	.497
65	convnext_large:fb_in22k_ft_in1k	.492
66	resnet_50_v2	.491
67	vonegrcnn_52e_full	.487
68	convnext_femto_ols:d1_in1k	.482
69	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.481
70	vonegrcnn_62e_nobn	.476
71	resnet50_ewconplus_wzc	.476
72	convnext_base:clip_laiona_augreg_ft_in1k_384	.472
73	convnext_tiny:in12k_ft_in1k	.471
74	resnet_152_v1	.470
75	cvt_cvt-21-384-in1k_4_LucyV4	.464
76	resnet50_linf_4_robust	.459
77	resnet-152_v2_pytorch	.459
78	resnet-152_v1-tf	.459
79	vonegrcnn_47e	.456
80	efficientnet-b7	.453
81	densenet-201-keras	.448
82	densenet_201_pytorch	.446
83	inception_v3_pytorch	.445
84	inception_v4-tf	.443
85	resnet50_imagenet_full	.443
86	densenet-201	.443
87	resnet34_imagenet_full	.442
88	antialiased-rnext101_32x8d	.436
89	vonegrcnn_52e_full	.435
90	resnet50-SIN_IN_IN	.432
91	resnet_SIN_IN_FT_IN	.432
92	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.431
93	resnet50-VITO-8deg-cc	.430
94	cvt_cvt-21-224-in1k_4_LucyV4	.429
95	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.424
96	voneresnet-50-non_stochastic	.417
97	convnext_small_imagenet_100_seed-0	.417
98	deit_large_imagenet_full_seed-0	.416
99	resnet_50_v1_spiking	.413
100	resnet101_imagenet_full	.412
101	CLIP-RN50	.409
102	BiT-S-R50x3	.408
103	convnext_base_imagenet_full_seed-0	.406
104	voneresnet-50-robust	.403
105	xception-keras	.401
106	resnet-50-pytorch	.400
107	resnet50-sup	.400
108	resnet50_tutorial	.400
109	resnet_50_v1	.400
110	resnet50-moclr8deg	.394
111	resnet50_random_linf8_perturb	.393
112	ReAlnet03	.384
113	efficientnet_b2_imagenet_full	.383
114	resnet-50_v2-tf	.378
115	inception_v3-tf	.377
116	resnet50_random_l2_perturb	.371
117	convnext_small_imagenet_full_seed-0	.367
118	resnet-101_v1-tf	.363
119	resnet-50_v1-tf	.359
120	ReAlnet02	.355
121	resnet50-SIN_IN	.355
122	convnext_large_imagenet_full_seed-0	.354
123	alexnet_fov4	.352
124	Res2Net50_26w_4s	.350
125	AdvProp_efficientnet-b8	.349
126	densenet-121-keras	.346
127	resnet50	.344
128	inception_v3	.341
129	deit_small_imagenet_full_seed-0	.338
130	BiT-S-R152x2	.336
131	tv_efficientnet-b1	.336
132	nasnet_mobile	.335
133	ReAlnet04	.334
134	grcnn_109	.333
135	blt_vs	.331
136	ReAlnet07	.329
137	ReAlnet01	.328
138	xception	.327
139	ReAlnet06	.326
140	grcnn	.325
141	inception_v1	.324
142	ReAlnet08	.324
143	mobilenet_v2_1.0_192-tf	.318
144	antialiased-r50	.318
145	ReAlnet05	.317
146	inception_v2-tf	.309
147	densenet-121	.304
148	mobilenet_v2_1_0_192	.301
149	deit_base_imagenet_full_seed-0	.299
150	vonealexnet_gaussian_noise_std4_fixed	.297
151	ReAlnet10	.297
152	CORnet-S	.297
153	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.296
154	resnet18_imagenet_full	.295
155	vgg_19	.295
156	cornet_s	.293
157	resnet50_robust_l2_eps1	.291
158	alexnet	.290
159	regnet_y_400mf	.288
160	mobilenet_v1_0.75_224	.287
161	resnet50_linf_8_robust	.287
162	texture_shape_resnet50_trained_on_SIN	.286
163	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.281
164	voneresnet-50	.281
165	alexnet_ks_torevert	.281
166	alexnet_workflow_trigger	.281
167	alexnet-baseline	.281
168	alexnet	.281
169	alexnet	.281
170	alexnet	.281
171	alexnet	.281
172	alexnet	.281
173	alexnet	.281
174	alexnet	.281
175	alexnet	.281
176	alexnet	.281
177	alexnet	.281
178	ReAlnet09	.277
179	mobilenet_v2_1_0_224	.276
180	densenet-169-keras	.274
181	resnet50_l2_3_robust	.272
182	mobilenet_v1_1.0_192	.269
183	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.264
184	alexnet_random_linf8_perturb	.260
185	resnet50_moco_v2	.256
186	vgg_16	.255
187	mobilenet_v2_0_5_224	.253
188	alexnet_reduced_aliasing_early_checkpoint	.252
189	resnet50_byol	.251
190	alexnet_l2_3_robust	.250
191	efficientnet_b1_imagenet_full	.249
192	mobilenet_v2_1_3_224	.247
193	resnet-18	.245
194	resnet50-SIN	.243
195	resnet-50-robust	.242
196	imagenet_l2_3_0	.242
197	resnet50_robust_l2_eps3	.242
198	densenet-169	.241
199	mobilenet_v2_1_0_128	.239
200	BiT-S-R152x4	.238
201	mobilenet_v2_1_0_160	.235
202	mobilenet_v1_1.0_224	.230
203	mobilenet_v2_1.0_160-tf	.229
204	inception_v1-tf	.223
205	cv_18_dagger_408_pretrained	.223
206	shufflenet_v2_x1_0	.222
207	alexnet_linf_8_robust	.218
208	efficientnet_b0_imagenet_full	.217
209	BiT-S-R101x3	.213
210	resnet50-vitoimagevidnet8	.210
211	alexnet_random_l2_3_perturb	.210
212	mobilenet_v2_1.4_224-tf	.208
213	BiT-S-R50x1	.208
214	resnet-34	.208
215	mobilenet_v2_1.3_224-tf	.206
216	mobilenet_v2_0_75_160	.204
217	mobilenet_v1_1.0_160	.198
218	vgg-16-keras	.197
219	mobilenet_v2_0.5_192-tf	.196
220	resnet50_simclr	.196
221	mobilenet_v2_1.0_224-tf	.194
222	mobilenet_v1_1.0_128	.192
223	BiT-S-R101x1	.188
224	AlexNet_SIN_fov4	.186
225	squeezenet1_1	.182
226	mobilenet_v2_0.75_96	.181
227	mobilenet_v2_0_5_192	.180
228	alexnet_training_seed_05	.180
229	alexnet_training_seed_01	.180
230	alexnet_training_seed_01_fov4	.180
231	alexnet_training_seed_01_fov12	.180
232	alexnet_training_seed_01_fov16	.180
233	alexnet_robust_correct	.179
234	vgg-19-keras	.179
235	alexnet_fov12	.175
236	deit_small_imagenet_100_seed-0	.171
237	mobilenet_v2_0.75_128	.169
238	konkle_alexnetgn_ipcl_ref01_primary_model	.168
239	mobilenet_v2_0.75_160-tf	.164
240	convnext_tiny_sup	.161
241	alexnet_early_checkpoint	.161
242	mobilenet_v1_0.25_192	.160
243	0.5x_resnet-18_LC_w_sh_1_iter	.160
244	mobilenet_v2_1-4_224_pytorch	.159
245	mobilenet_v2_1_4_224	.159
246	mobilenet_v2_0.5_224-tf	.156
247	artResNet18_1	.156
248	mobilenet_v2_0.35_160	.150
249	alexnet_fov16	.149
250	mobilenet_v1_0.75_128	.148
251	mobilenet_v2_1.0_128-tf	.143
252	mobilenet_v1_0.75_192	.141
253	resnet50_imagenet_100_seed-0	.140
254	AdvProp_efficientnet-b0	.140
255	mobilenet_v1_0.5_128	.138
256	imagenet_l2_10_0	.132
257	nasnet_mobile-tf	.132
258	mobilenet_v2_1.0_96	.131
259	mobilenet_v2_0.35_96	.131
260	texture_shape_alexnet_trained_on_SIN	.129
261	squeezenet1_0	.126
262	mobilenet_v1_0.5_192	.126
263	mobilenet_v1_0.25_160	.125
264	AlexNet_SIN	.122
265	mobilenet_v2_0.5_160	.120
266	mobilenet_v2_0_75_192	.120
267	mobilenet_v1_0.25_224	.113
268	mobilenet_v2_0.5_128	.113
269	mobilenet_v2_0.75_192-tf	.111
270	mobilenet_v1_0.75_160	.107
271	resnet_50_v1_spiking_l4a	.098
272	mobilenet_v1_0.5_224	.097
273	mobilenet_v2_0_75_224	.097
274	resnet50-meshes-lt-100-original-scratch	.094
275	deit_small_imagenet_1_seed-0	.094
276	deit_small_imagenet_10_seed-0	.094
277	resnet50_imagenet_10_seed-0	.093
278	alexnet_training_seed_06	.089
279	alexnet_training_seed_02	.085
280	alexnet_training_seed_04	.085
281	alexnet_training_seed_08	.085
282	alexnet_training_seed_10	.085
283	convnext_small_imagenet_10_seed-0	.084
284	alexnet_training_seed_07	.084
285	mobilenet_v2_0.5_96	.077
286	resnet50_eMMCR_Vanilla	.077
287	mobilenet_v2_0.35_128	.070
288	AlexNet_SIN_fov12	.065
289	AlexNet_SIN_fov16	.064
290	barlow-twins-resnet50	.063
291	resnet50-vicregl0p9	.059
292	mobilenet_v2_0.75_224-tf	.058
293	resnet50-meshes-lt-100-original-pretrained	.058
294	resnet50_FractalDB	.057
295	mobilenet_v2_0.35_224	.056
296	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.055
297	resnet50-vicregl0p75	.051
298	resnet50-vicreg	.044
299	alexnet_training_seed_09	.042
300	resnet50-barlow	.041
301	mobilenet_v1_0.5_160	.038
302	mobilenet_v2_0.35_192	.036
303	my-model	.029
304	convnext_small_imagenet_1_seed-0	.015
305	resnet_50_v1_spiking_l4	.014
306	mobilenet_v1_0.25_128	.013
307	resnet50-simclr-vissl	.009
308	mobilevit_small	.008
309	resnet50_imagenet_1_seed-0	.008
310	resnet_50_v1_spiking_l3	.004
311	alexnet_training_seed_03	.002
312	resnet-50_untrained	.002
313	resnet-18-LC_w_sh_100_iter_conv_init_m
314	ViT-B/32
315	BiT-M-R50x3
316	BiT-M-R50x1
317	BiT-M-R152x2
318	resnet-18-LC_w_sh_1_iter_conv_init_m
319	resnet-18-LC_w_sh_1_iter_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	FrankRobWobv0
324	resnet18-local_aggregation
325	0.5x_resnet-18_LC
326	CORnetZ_CIFAR10
327	resnet18-deepcluster
328	r3m_resnet18
329	CORnet-Z
330	resnet18-instance_recognition
331	my-model
332	vggface
333	resnet18-colorization
334	0.5x_resnet-18_LC_w_sh_10_iter
335	r3m_resnet50
336	r3m_resnet34
337	r3m_resnet50_nocrop
338	resnet-18-LC_m
339	resnet18-supervised
340	resnet-18-LC_1st_conv_m
341	resnet-18-LC_conv_init_m
342	resnet18-contrastive_multiview
343	0.5x_resnet-18
344	RN50
345	0.5x_resnet-18_LC_w_sh_100_iter
346	RN50
347	resnet18-relative_position
348	resnet18-autoencoder
349	resnet18-simclr
350	ViT_L_32
351	ViT_B_32
352	resnet18-depth_prediction
353	ViT_B_16
354	r3m_resnet34_nocrop
355	resnet18-contrastive_predictive
356	omnivore_swinT
357	resnet-18-LC_untrained
358	resnet-18_untrained
359	resnet-50x2_untrained
360	dcgan
361	v1-pyr-nodown
362	unet_entire
363	CORnetZ_CIFAR10_bs32_20_04
364	pixels
365	dorinet_cornet_z
366	resnet50
367	openclip
368	resnet50-cifar
369	resnet50_pretrained_with_retinal_waves
370	resnet50_ImageNet
371	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
372	yudixie_resnet50_random_0_240908
373	yudixie_resnet50_imagenet1kpret_0_240908
374	yudixie_resnet50_imagenet1kpret_0_240312
375	yudixie_resnet50_cat_obj_class_all_latents_0_240908
376	yudixie_resnet50_object_class_0_240908
377	yudixie_resnet50_category_class_0_240908
378	resnet18_imagenet21kP
379	yudixie_resnet50_distance_reg_0_240908
380	ConvLSTM
381	PredRNN
382	SimVP
383	TAU
384	MIM
385	hmax
386	pnasnet_large_pytorch
387	ReAlnet01_cornet
388	yolos_tiny
389	vit_base_patch14_reg4_dinov2:lvd142m
390	yudixie_resnet18_translation_reg_0_240719
391	compact_resnet50_V4
392	briaai_rmbg_1_4
393	yudixie_resnet18_distance_reg_0_240719
394	yudixie_resnet18_rotation_reg_0_240719
395	ReAlnet03_cornet
396	ReAlnet02_cornet
397	resnet18_ecoset_full
398	compact_resnet50_IT
399	ReAlnet01_cornet_fov4
400	resnet34_ecoset_full
401	VOneCORnet-S
402	resnet50_ecoset_full
403	resnet101_ecoset_full
404	compact_vgg19_V4
405	yudixie_resnet50_distance_rotation_0_240908
406	yudixie_resnet50_rotation_reg_0_240908
407	yudixie_resnet50_distance_translation_0_240908
408	resnet152_ecoset_full
409	yudixie_resnet18_distance_translation_0_240719
410	yudixie_resnet18_translation_rotation_0_240719
411	yudixie_resnet18_distance_rotation_0_240719
412	yudixie_resnet18_category_class_0_240719
413	vit_base_patch14_dinov2:lvd142m
414	ReAlnet05_cornet
415	yudixie_resnet18_random_0_240719
416	compact_resnet50_robust_V4
417	yudixie_resnet18_cat_obj_class_all_latents_0_240719
418	resnet-18_test_m
419	yudixie_resnet18_distance_translation_rotation_0_240719
420	ReAlnet01_cornet_fov12
421	yudixie_resnet18_object_class_0_240719
422	SeLaVi-AVE
423	yudixie_resnet18_imagenet1kpret_0_240719
424	r3d_18
425	compact_vgg19_IT
426	ReAlnet06_cornet
427	ReAlnet01_cornet_fov16
428	yudixie_resnet50_translation_reg_0_240908
429	ReAlnet01_ALL
430	mc3_18
431	yudixie_resnet50_translation_rotation_0_240908
432	ReAlnet07_cornet
433	ReAlnet02_ALL
434	s3d
435	mvit_v1_b
436	ReAlnet04_ALL
437	ReAlnet08_cornet
438	compact_cornets_V4
439	ReAlnet03_ALL
440	compact_cornets_IT
441	omnivore_swinB
442	SeLaVi-Kinetics400
443	ReAlnet09_cornet
444	ReAlnet05_ALL
445	SeLaVi-VGG-Sound
446	SeLaVi-Kinetics-Sound
447	ReAlnet06_ALL
448	compact_resnet50_robust_IT
449	omnivore_swinS
450	ReAlnet07_ALL
451	mvit_v2_s
452	r2plus1d_18
453	ReAlnet10_cornet
454	yudixie_resnet50_distance_translation_rotation_0_240908
455	ReAlnet08_ALL
456	ReAlnet09_ALL
457	ReAlnet04_cornet
458	resnet50_primary_visual_cortex
459	ReAlnet10_ALL
460	vit_base_patch16_dinov3:lvd1689m

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

0.39.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonI

Metric: error_consistency