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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-baseline	.281
167	alexnet	.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	ReAlnet09	.277
178	mobilenet_v2_1_0_224	.276
179	densenet-169-keras	.274
180	resnet50_l2_3_robust	.272
181	mobilenet_v1_1.0_192	.269
182	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.264
183	alexnet_random_linf8_perturb	.260
184	resnet50_moco_v2	.256
185	vgg_16	.255
186	mobilenet_v2_0_5_224	.253
187	alexnet_reduced_aliasing_early_checkpoint	.252
188	resnet50_byol	.251
189	alexnet_l2_3_robust	.250
190	efficientnet_b1_imagenet_full	.249
191	mobilenet_v2_1_3_224	.247
192	resnet-18	.245
193	resnet50-SIN	.243
194	resnet-50-robust	.242
195	imagenet_l2_3_0	.242
196	resnet50_robust_l2_eps3	.242
197	densenet-169	.241
198	mobilenet_v2_1_0_128	.239
199	BiT-S-R152x4	.238
200	mobilenet_v2_1_0_160	.235
201	mobilenet_v1_1.0_224	.230
202	mobilenet_v2_1.0_160-tf	.229
203	inception_v1-tf	.223
204	cv_18_dagger_408_pretrained	.223
205	shufflenet_v2_x1_0	.222
206	alexnet_linf_8_robust	.218
207	efficientnet_b0_imagenet_full	.217
208	BiT-S-R101x3	.213
209	resnet50-vitoimagevidnet8	.210
210	alexnet_random_l2_3_perturb	.210
211	mobilenet_v2_1.4_224-tf	.208
212	BiT-S-R50x1	.208
213	resnet-34	.208
214	mobilenet_v2_1.3_224-tf	.206
215	mobilenet_v2_0_75_160	.204
216	mobilenet_v1_1.0_160	.198
217	vgg-16-keras	.197
218	mobilenet_v2_0.5_192-tf	.196
219	resnet50_simclr	.196
220	mobilenet_v2_1.0_224-tf	.194
221	mobilenet_v1_1.0_128	.192
222	BiT-S-R101x1	.188
223	AlexNet_SIN_fov4	.186
224	squeezenet1_1	.182
225	mobilenet_v2_0.75_96	.181
226	mobilenet_v2_0_5_192	.180
227	alexnet_training_seed_05	.180
228	alexnet_training_seed_01	.180
229	alexnet_training_seed_01_fov4	.180
230	alexnet_training_seed_01_fov12	.180
231	alexnet_training_seed_01_fov16	.180
232	alexnet_robust_correct	.179
233	vgg-19-keras	.179
234	alexnet_fov12	.175
235	deit_small_imagenet_100_seed-0	.171
236	mobilenet_v2_0.75_128	.169
237	konkle_alexnetgn_ipcl_ref01_primary_model	.168
238	mobilenet_v2_0.75_160-tf	.164
239	convnext_tiny_sup	.161
240	alexnet_early_checkpoint	.161
241	mobilenet_v1_0.25_192	.160
242	0.5x_resnet-18_LC_w_sh_1_iter	.160
243	mobilenet_v2_1-4_224_pytorch	.159
244	mobilenet_v2_1_4_224	.159
245	mobilenet_v2_0.5_224-tf	.156
246	artResNet18_1	.156
247	mobilenet_v2_0.35_160	.150
248	alexnet_fov16	.149
249	mobilenet_v1_0.75_128	.148
250	mobilenet_v2_1.0_128-tf	.143
251	mobilenet_v1_0.75_192	.141
252	resnet50_imagenet_100_seed-0	.140
253	AdvProp_efficientnet-b0	.140
254	mobilenet_v1_0.5_128	.138
255	imagenet_l2_10_0	.132
256	nasnet_mobile-tf	.132
257	mobilenet_v2_1.0_96	.131
258	mobilenet_v2_0.35_96	.131
259	texture_shape_alexnet_trained_on_SIN	.129
260	squeezenet1_0	.126
261	mobilenet_v1_0.5_192	.126
262	mobilenet_v1_0.25_160	.125
263	AlexNet_SIN	.122
264	mobilenet_v2_0.5_160	.120
265	mobilenet_v2_0_75_192	.120
266	mobilenet_v1_0.25_224	.113
267	mobilenet_v2_0.5_128	.113
268	mobilenet_v2_0.75_192-tf	.111
269	mobilenet_v1_0.75_160	.107
270	resnet_50_v1_spiking_l4a	.098
271	mobilenet_v1_0.5_224	.097
272	mobilenet_v2_0_75_224	.097
273	resnet50-meshes-lt-100-original-scratch	.094
274	deit_small_imagenet_1_seed-0	.094
275	deit_small_imagenet_10_seed-0	.094
276	resnet50_imagenet_10_seed-0	.093
277	alexnet_training_seed_06	.089
278	alexnet_training_seed_02	.085
279	alexnet_training_seed_04	.085
280	alexnet_training_seed_08	.085
281	alexnet_training_seed_10	.085
282	convnext_small_imagenet_10_seed-0	.084
283	alexnet_training_seed_07	.084
284	mobilenet_v2_0.5_96	.077
285	resnet50_eMMCR_Vanilla	.077
286	mobilenet_v2_0.35_128	.070
287	AlexNet_SIN_fov12	.065
288	AlexNet_SIN_fov16	.064
289	barlow-twins-resnet50	.063
290	resnet50-vicregl0p9	.059
291	mobilenet_v2_0.75_224-tf	.058
292	resnet50-meshes-lt-100-original-pretrained	.058
293	resnet50_FractalDB	.057
294	mobilenet_v2_0.35_224	.056
295	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.055
296	resnet50-vicregl0p75	.051
297	resnet50-vicreg	.044
298	alexnet_training_seed_09	.042
299	resnet50-barlow	.041
300	mobilenet_v1_0.5_160	.038
301	mobilenet_v2_0.35_192	.036
302	my-model	.029
303	convnext_small_imagenet_1_seed-0	.015
304	resnet_50_v1_spiking_l4	.014
305	mobilenet_v1_0.25_128	.013
306	resnet50-simclr-vissl	.009
307	mobilevit_small	.008
308	resnet50_imagenet_1_seed-0	.008
309	resnet_50_v1_spiking_l3	.004
310	alexnet_training_seed_03	.002
311	resnet-50_untrained	.002
312	resnet-18-LC_w_sh_100_iter_conv_init_m
313	ViT-B/32
314	BiT-M-R50x3
315	BiT-M-R50x1
316	BiT-M-R152x2
317	resnet-18-LC_w_sh_1_iter_conv_init_m
318	resnet-18-LC_w_sh_1_iter_m
319	resnet-18-LC_w_sh_10_iter_m
320	resnet-18-LC_w_sh_100_iter_m
321	resnet-18-LC_w_sh_10_iter_conv_init_m
322	FrankRobWobv0
323	resnet18-local_aggregation
324	0.5x_resnet-18_LC
325	CORnetZ_CIFAR10
326	resnet18-deepcluster
327	r3m_resnet18
328	CORnet-Z
329	resnet18-instance_recognition
330	my-model
331	vggface
332	resnet18-colorization
333	0.5x_resnet-18_LC_w_sh_10_iter
334	r3m_resnet50
335	r3m_resnet34
336	r3m_resnet50_nocrop
337	resnet-18-LC_m
338	resnet18-supervised
339	resnet-18-LC_1st_conv_m
340	resnet-18-LC_conv_init_m
341	resnet18-contrastive_multiview
342	0.5x_resnet-18
343	RN50
344	0.5x_resnet-18_LC_w_sh_100_iter
345	RN50
346	resnet18-relative_position
347	resnet18-autoencoder
348	resnet18-simclr
349	ViT_L_32
350	ViT_B_32
351	resnet18-depth_prediction
352	ViT_B_16
353	r3m_resnet34_nocrop
354	resnet18-contrastive_predictive
355	omnivore_swinT
356	resnet-18-LC_untrained
357	resnet-18_untrained
358	resnet-50x2_untrained
359	dcgan
360	v1-pyr-nodown
361	unet_entire
362	CORnetZ_CIFAR10_bs32_20_04
363	pixels
364	dorinet_cornet_z
365	resnet50
366	openclip
367	resnet50-cifar
368	resnet50_pretrained_with_retinal_waves
369	resnet50_ImageNet
370	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
371	yudixie_resnet50_random_0_240908
372	yudixie_resnet50_imagenet1kpret_0_240908
373	yudixie_resnet50_imagenet1kpret_0_240312
374	yudixie_resnet50_cat_obj_class_all_latents_0_240908
375	yudixie_resnet50_object_class_0_240908
376	yudixie_resnet50_category_class_0_240908
377	resnet18_imagenet21kP
378	yudixie_resnet50_distance_reg_0_240908
379	ConvLSTM
380	PredRNN
381	SimVP
382	TAU
383	MIM
384	hmax
385	pnasnet_large_pytorch
386	ReAlnet01_cornet
387	yolos_tiny
388	yudixie_resnet18_translation_reg_0_240719
389	compact_resnet50_V4
390	briaai_rmbg_1_4
391	yudixie_resnet18_distance_reg_0_240719
392	yudixie_resnet18_rotation_reg_0_240719
393	ReAlnet03_cornet
394	ReAlnet02_cornet
395	resnet18_ecoset_full
396	compact_resnet50_IT
397	ReAlnet01_cornet_fov4
398	resnet34_ecoset_full
399	VOneCORnet-S
400	resnet50_ecoset_full
401	resnet101_ecoset_full
402	compact_vgg19_V4
403	yudixie_resnet50_distance_rotation_0_240908
404	yudixie_resnet50_rotation_reg_0_240908
405	yudixie_resnet50_distance_translation_0_240908
406	resnet152_ecoset_full
407	yudixie_resnet18_distance_translation_0_240719
408	yudixie_resnet18_translation_rotation_0_240719
409	yudixie_resnet18_distance_rotation_0_240719
410	yudixie_resnet18_category_class_0_240719
411	ReAlnet05_cornet
412	yudixie_resnet18_random_0_240719
413	compact_resnet50_robust_V4
414	yudixie_resnet18_cat_obj_class_all_latents_0_240719
415	resnet-18_test_m
416	yudixie_resnet18_distance_translation_rotation_0_240719
417	ReAlnet01_cornet_fov12
418	yudixie_resnet18_object_class_0_240719
419	SeLaVi-AVE
420	yudixie_resnet18_imagenet1kpret_0_240719
421	r3d_18
422	compact_vgg19_IT
423	ReAlnet06_cornet
424	ReAlnet01_cornet_fov16
425	yudixie_resnet50_translation_reg_0_240908
426	ReAlnet01_ALL
427	mc3_18
428	yudixie_resnet50_translation_rotation_0_240908
429	ReAlnet07_cornet
430	ReAlnet02_ALL
431	s3d
432	mvit_v1_b
433	ReAlnet04_ALL
434	ReAlnet08_cornet
435	compact_cornets_V4
436	ReAlnet03_ALL
437	compact_cornets_IT
438	omnivore_swinB
439	SeLaVi-Kinetics400
440	ReAlnet09_cornet
441	ReAlnet05_ALL
442	SeLaVi-VGG-Sound
443	SeLaVi-Kinetics-Sound
444	ReAlnet06_ALL
445	compact_resnet50_robust_IT
446	omnivore_swinS
447	ReAlnet07_ALL
448	mvit_v2_s
449	r2plus1d_18
450	ReAlnet10_cornet
451	yudixie_resnet50_distance_translation_rotation_0_240908
452	ReAlnet08_ALL
453	ReAlnet09_ALL
454	ReAlnet04_cornet
455	resnet50_primary_visual_cortex
456	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

0.39.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonI

Metric: error_consistency