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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	Res2Net50_26w_4s	.350
124	AdvProp_efficientnet-b8	.349
125	densenet-121-keras	.346
126	resnet50	.344
127	inception_v3	.341
128	deit_small_imagenet_full_seed-0	.338
129	BiT-S-R152x2	.336
130	tv_efficientnet-b1	.336
131	nasnet_mobile	.335
132	ReAlnet04	.334
133	grcnn_109	.333
134	blt_vs	.331
135	ReAlnet07	.329
136	ReAlnet01	.328
137	xception	.327
138	ReAlnet06	.326
139	grcnn	.325
140	inception_v1	.324
141	ReAlnet08	.324
142	mobilenet_v2_1.0_192-tf	.318
143	antialiased-r50	.318
144	ReAlnet05	.317
145	inception_v2-tf	.309
146	densenet-121	.304
147	mobilenet_v2_1_0_192	.301
148	deit_base_imagenet_full_seed-0	.299
149	vonealexnet_gaussian_noise_std4_fixed	.297
150	ReAlnet10	.297
151	CORnet-S	.297
152	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.296
153	resnet18_imagenet_full	.295
154	vgg_19	.295
155	cornet_s	.293
156	resnet50_robust_l2_eps1	.291
157	alexnet	.290
158	regnet_y_400mf	.288
159	mobilenet_v1_0.75_224	.287
160	resnet50_linf_8_robust	.287
161	texture_shape_resnet50_trained_on_SIN	.286
162	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.281
163	voneresnet-50	.281
164	alexnet_ks_torevert	.281
165	alexnet-baseline	.281
166	alexnet	.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	ReAlnet09	.277
176	mobilenet_v2_1_0_224	.276
177	densenet-169-keras	.274
178	resnet50_l2_3_robust	.272
179	mobilenet_v1_1.0_192	.269
180	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.264
181	alexnet_random_linf8_perturb	.260
182	resnet50_moco_v2	.256
183	vgg_16	.255
184	mobilenet_v2_0_5_224	.253
185	alexnet_reduced_aliasing_early_checkpoint	.252
186	resnet50_byol	.251
187	alexnet_l2_3_robust	.250
188	efficientnet_b1_imagenet_full	.249
189	mobilenet_v2_1_3_224	.247
190	resnet-18	.245
191	resnet50-SIN	.243
192	resnet-50-robust	.242
193	imagenet_l2_3_0	.242
194	resnet50_robust_l2_eps3	.242
195	densenet-169	.241
196	mobilenet_v2_1_0_128	.239
197	BiT-S-R152x4	.238
198	mobilenet_v2_1_0_160	.235
199	mobilenet_v1_1.0_224	.230
200	mobilenet_v2_1.0_160-tf	.229
201	inception_v1-tf	.223
202	cv_18_dagger_408_pretrained	.223
203	shufflenet_v2_x1_0	.222
204	alexnet_linf_8_robust	.218
205	efficientnet_b0_imagenet_full	.217
206	BiT-S-R101x3	.213
207	resnet50-vitoimagevidnet8	.210
208	alexnet_random_l2_3_perturb	.210
209	mobilenet_v2_1.4_224-tf	.208
210	BiT-S-R50x1	.208
211	resnet-34	.208
212	mobilenet_v2_1.3_224-tf	.206
213	mobilenet_v2_0_75_160	.204
214	mobilenet_v1_1.0_160	.198
215	vgg-16-keras	.197
216	mobilenet_v2_0.5_192-tf	.196
217	resnet50_simclr	.196
218	mobilenet_v2_1.0_224-tf	.194
219	mobilenet_v1_1.0_128	.192
220	BiT-S-R101x1	.188
221	squeezenet1_1	.182
222	mobilenet_v2_0.75_96	.181
223	mobilenet_v2_0_5_192	.180
224	alexnet_training_seed_05	.180
225	alexnet_training_seed_01	.180
226	alexnet_robust_correct	.179
227	vgg-19-keras	.179
228	deit_small_imagenet_100_seed-0	.171
229	mobilenet_v2_0.75_128	.169
230	konkle_alexnetgn_ipcl_ref01_primary_model	.168
231	mobilenet_v2_0.75_160-tf	.164
232	convnext_tiny_sup	.161
233	alexnet_early_checkpoint	.161
234	mobilenet_v1_0.25_192	.160
235	0.5x_resnet-18_LC_w_sh_1_iter	.160
236	mobilenet_v2_1-4_224_pytorch	.159
237	mobilenet_v2_1_4_224	.159
238	mobilenet_v2_0.5_224-tf	.156
239	artResNet18_1	.156
240	mobilenet_v2_0.35_160	.150
241	mobilenet_v1_0.75_128	.148
242	mobilenet_v2_1.0_128-tf	.143
243	mobilenet_v1_0.75_192	.141
244	resnet50_imagenet_100_seed-0	.140
245	AdvProp_efficientnet-b0	.140
246	mobilenet_v1_0.5_128	.138
247	imagenet_l2_10_0	.132
248	nasnet_mobile-tf	.132
249	mobilenet_v2_1.0_96	.131
250	mobilenet_v2_0.35_96	.131
251	texture_shape_alexnet_trained_on_SIN	.129
252	squeezenet1_0	.126
253	mobilenet_v1_0.5_192	.126
254	mobilenet_v1_0.25_160	.125
255	AlexNet_SIN	.122
256	mobilenet_v2_0.5_160	.120
257	mobilenet_v2_0_75_192	.120
258	mobilenet_v1_0.25_224	.113
259	mobilenet_v2_0.5_128	.113
260	mobilenet_v2_0.75_192-tf	.111
261	mobilenet_v1_0.75_160	.107
262	resnet_50_v1_spiking_l4a	.098
263	mobilenet_v1_0.5_224	.097
264	mobilenet_v2_0_75_224	.097
265	resnet50-meshes-lt-100-original-scratch	.094
266	deit_small_imagenet_1_seed-0	.094
267	deit_small_imagenet_10_seed-0	.094
268	resnet50_imagenet_10_seed-0	.093
269	alexnet_training_seed_06	.089
270	alexnet_training_seed_02	.085
271	alexnet_training_seed_04	.085
272	alexnet_training_seed_08	.085
273	alexnet_training_seed_10	.085
274	convnext_small_imagenet_10_seed-0	.084
275	alexnet_training_seed_07	.084
276	mobilenet_v2_0.5_96	.077
277	resnet50_eMMCR_Vanilla	.077
278	mobilenet_v2_0.35_128	.070
279	barlow-twins-resnet50	.063
280	resnet50-vicregl0p9	.059
281	mobilenet_v2_0.75_224-tf	.058
282	resnet50-meshes-lt-100-original-pretrained	.058
283	resnet50_FractalDB	.057
284	mobilenet_v2_0.35_224	.056
285	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.055
286	resnet50-vicregl0p75	.051
287	resnet50-vicreg	.044
288	alexnet_training_seed_09	.042
289	resnet50-barlow	.041
290	mobilenet_v1_0.5_160	.038
291	mobilenet_v2_0.35_192	.036
292	my-model	.029
293	convnext_small_imagenet_1_seed-0	.015
294	resnet_50_v1_spiking_l4	.014
295	mobilenet_v1_0.25_128	.013
296	resnet50-simclr-vissl	.009
297	mobilevit_small	.008
298	resnet50_imagenet_1_seed-0	.008
299	resnet_50_v1_spiking_l3	.004
300	alexnet_training_seed_03	.002
301	resnet-50_untrained	.002
302	resnet-18-LC_w_sh_100_iter_conv_init_m
303	ViT-B/32
304	BiT-M-R50x3
305	BiT-M-R50x1
306	BiT-M-R152x2
307	resnet-18-LC_w_sh_1_iter_conv_init_m
308	resnet-18-LC_w_sh_1_iter_m
309	resnet-18-LC_w_sh_10_iter_m
310	resnet-18-LC_w_sh_100_iter_m
311	resnet-18-LC_w_sh_10_iter_conv_init_m
312	FrankRobWobv0
313	resnet18-local_aggregation
314	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-deepcluster
317	r3m_resnet18
318	CORnet-Z
319	resnet18-instance_recognition
320	my-model
321	vggface
322	resnet18-colorization
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	r3m_resnet34
326	r3m_resnet50_nocrop
327	resnet-18-LC_m
328	resnet18-supervised
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	RN50
334	0.5x_resnet-18_LC_w_sh_100_iter
335	RN50
336	resnet18-relative_position
337	resnet18-autoencoder
338	resnet18-simclr
339	ViT_L_32
340	ViT_B_32
341	resnet18-depth_prediction
342	ViT_B_16
343	r3m_resnet34_nocrop
344	resnet18-contrastive_predictive
345	omnivore_swinT
346	resnet-18-LC_untrained
347	resnet-18_untrained
348	resnet-50x2_untrained
349	dcgan
350	v1-pyr-nodown
351	unet_entire
352	CORnetZ_CIFAR10_bs32_20_04
353	pixels
354	dorinet_cornet_z
355	resnet50
356	openclip
357	resnet50-cifar
358	resnet50_pretrained_with_retinal_waves
359	resnet50_ImageNet
360	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
361	yudixie_resnet50_random_0_240908
362	yudixie_resnet50_imagenet1kpret_0_240908
363	yudixie_resnet50_imagenet1kpret_0_240312
364	yudixie_resnet50_cat_obj_class_all_latents_0_240908
365	yudixie_resnet50_object_class_0_240908
366	yudixie_resnet50_category_class_0_240908
367	resnet18_imagenet21kP
368	yudixie_resnet50_distance_reg_0_240908
369	ConvLSTM
370	PredRNN
371	SimVP
372	TAU
373	MIM
374	hmax
375	pnasnet_large_pytorch
376	ReAlnet01_cornet
377	yolos_tiny
378	yudixie_resnet18_translation_reg_0_240719
379	compact_resnet50_V4
380	briaai_rmbg_1_4
381	yudixie_resnet18_distance_reg_0_240719
382	yudixie_resnet18_rotation_reg_0_240719
383	ReAlnet03_cornet
384	ReAlnet02_cornet
385	resnet18_ecoset_full
386	compact_resnet50_IT
387	resnet34_ecoset_full
388	VOneCORnet-S
389	resnet50_ecoset_full
390	resnet101_ecoset_full
391	compact_vgg19_V4
392	yudixie_resnet50_distance_rotation_0_240908
393	yudixie_resnet50_rotation_reg_0_240908
394	yudixie_resnet50_distance_translation_0_240908
395	resnet152_ecoset_full
396	yudixie_resnet18_distance_translation_0_240719
397	yudixie_resnet18_translation_rotation_0_240719
398	yudixie_resnet18_distance_rotation_0_240719
399	yudixie_resnet18_category_class_0_240719
400	ReAlnet05_cornet
401	yudixie_resnet18_random_0_240719
402	compact_resnet50_robust_V4
403	yudixie_resnet18_cat_obj_class_all_latents_0_240719
404	resnet-18_test_m
405	yudixie_resnet18_distance_translation_rotation_0_240719
406	yudixie_resnet18_object_class_0_240719
407	SeLaVi-AVE
408	yudixie_resnet18_imagenet1kpret_0_240719
409	r3d_18
410	compact_vgg19_IT
411	ReAlnet06_cornet
412	yudixie_resnet50_translation_reg_0_240908
413	ReAlnet01_ALL
414	mc3_18
415	yudixie_resnet50_translation_rotation_0_240908
416	ReAlnet07_cornet
417	ReAlnet02_ALL
418	s3d
419	mvit_v1_b
420	ReAlnet04_ALL
421	ReAlnet08_cornet
422	compact_cornets_V4
423	ReAlnet03_ALL
424	compact_cornets_IT
425	omnivore_swinB
426	SeLaVi-Kinetics400
427	ReAlnet09_cornet
428	ReAlnet05_ALL
429	SeLaVi-VGG-Sound
430	SeLaVi-Kinetics-Sound
431	ReAlnet06_ALL
432	compact_resnet50_robust_IT
433	omnivore_swinS
434	ReAlnet07_ALL
435	mvit_v2_s
436	r2plus1d_18
437	ReAlnet10_cornet
438	yudixie_resnet50_distance_translation_rotation_0_240908
439	ReAlnet08_ALL
440	ReAlnet09_ALL
441	ReAlnet04_cornet
442	resnet50_primary_visual_cortex
443	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