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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_halfPrecision	.592
36	pnasnet_large-tf	.584
37	efficientnet_b0	.583
38	fixres_resnext101_32x48d_wsl	.582
39	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.577
40	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.575
41	vit_large_patch14_clip_336:laion2b_ft_in1k	.574
42	convnext_xlarge:fb_in22k_ft_in1k	.574
43	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.567
44	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.556
45	AdvProp_efficientnet-b6	.553
46	cvt_cvt-13-384-in1k_4_LucyV4	.551
47	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.549
48	resnet_101_v1	.546
49	AT_efficientnet-b2	.541
50	AdvProp_efficientnet-b2	.541
51	cvt_cvt-13-224-in1k_4_LucyV4	.537
52	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.529
53	vonegrcnn_62e_nobn	.527
54	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.527
55	antialias-resnet152	.524
56	ViT_B_32_imagenet1k	.522
57	resnet-152_v2-tf	.520
58	inception_v4	.519
59	CLIP_resnet50	.513
60	CLIP_resnet50_float32	.513
61	custom_model_cv_18_dagger_408	.512
62	nasnet_large	.505
63	nasnet_large-tf	.499
64	convnext_tiny_imagenet_full_seed-0	.498
65	vonegrcnn_47e	.497
66	convnext_large:fb_in22k_ft_in1k	.492
67	resnet_50_v2	.491
68	vonegrcnn_52e_full	.487
69	convnext_femto_ols:d1_in1k	.482
70	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.481
71	vonegrcnn_62e_nobn	.476
72	resnet50_ewconplus_wzc	.476
73	convnext_base:clip_laiona_augreg_ft_in1k_384	.472
74	convnext_tiny:in12k_ft_in1k	.471
75	resnet_152_v1	.470
76	cvt_cvt-21-384-in1k_4_LucyV4	.464
77	resnet50_linf_4_robust	.459
78	resnet-152_v2_pytorch	.459
79	resnet-152_v1-tf	.459
80	vonegrcnn_47e	.456
81	efficientnet-b7	.453
82	densenet-201-keras	.448
83	densenet_201_pytorch	.446
84	inception_v3_pytorch	.445
85	inception_v4-tf	.443
86	resnet50_imagenet_full	.443
87	densenet-201	.443
88	resnet34_imagenet_full	.442
89	antialiased-rnext101_32x8d	.436
90	vonegrcnn_52e_full	.435
91	resnet50-SIN_IN_IN	.432
92	resnet_SIN_IN_FT_IN	.432
93	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.431
94	resnet50-VITO-8deg-cc	.430
95	cvt_cvt-21-224-in1k_4_LucyV4	.429
96	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.424
97	voneresnet-50-non_stochastic	.417
98	convnext_small_imagenet_100_seed-0	.417
99	deit_large_imagenet_full_seed-0	.416
100	resnet_50_v1_spiking	.413
101	resnet101_imagenet_full	.412
102	CLIP-RN50	.409
103	BiT-S-R50x3	.408
104	convnext_base_imagenet_full_seed-0	.406
105	voneresnet-50-robust	.403
106	xception-keras	.401
107	resnet-50-pytorch	.400
108	resnet50-sup	.400
109	resnet50_tutorial	.400
110	resnet_50_v1	.400
111	resnet50-moclr8deg	.394
112	resnet50_random_linf8_perturb	.393
113	ReAlnet03	.384
114	efficientnet_b2_imagenet_full	.383
115	resnet-50_v2-tf	.378
116	inception_v3-tf	.377
117	resnet50_random_l2_perturb	.371
118	convnext_small_imagenet_full_seed-0	.367
119	resnet-101_v1-tf	.363
120	resnet-50_v1-tf	.359
121	ReAlnet02	.355
122	resnet50-SIN_IN	.355
123	convnext_large_imagenet_full_seed-0	.354
124	alexnet_fov4	.352
125	Res2Net50_26w_4s	.350
126	AdvProp_efficientnet-b8	.349
127	densenet-121-keras	.346
128	resnet50	.344
129	inception_v3	.341
130	deit_small_imagenet_full_seed-0	.338
131	BiT-S-R152x2	.336
132	tv_efficientnet-b1	.336
133	nasnet_mobile	.335
134	ReAlnet04	.334
135	grcnn_109	.333
136	blt_vs	.331
137	ReAlnet07	.329
138	ReAlnet01	.328
139	xception	.327
140	ReAlnet06	.326
141	grcnn	.325
142	inception_v1	.324
143	ReAlnet08	.324
144	mobilenet_v2_1.0_192-tf	.318
145	antialiased-r50	.318
146	ReAlnet05	.317
147	inception_v2-tf	.309
148	densenet-121	.304
149	mobilenet_v2_1_0_192	.301
150	deit_base_imagenet_full_seed-0	.299
151	vonealexnet_gaussian_noise_std4_fixed	.297
152	ReAlnet10	.297
153	CORnet-S	.297
154	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.296
155	resnet18_imagenet_full	.295
156	vgg_19	.295
157	cornet_s	.293
158	resnet50_robust_l2_eps1	.291
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	.281
167	ReAlnet09	.277
168	mobilenet_v2_1_0_224	.276
169	densenet-169-keras	.274
170	resnet50_l2_3_robust	.272
171	mobilenet_v1_1.0_192	.269
172	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.264
173	alexnet_random_linf8_perturb	.260
174	resnet50_moco_v2	.256
175	vgg_16	.255
176	mobilenet_v2_0_5_224	.253
177	alexnet_reduced_aliasing_early_checkpoint	.252
178	resnet50_byol	.251
179	alexnet_l2_3_robust	.250
180	efficientnet_b1_imagenet_full	.249
181	mobilenet_v2_1_3_224	.247
182	resnet-18	.245
183	resnet50-SIN	.243
184	resnet-50-robust	.242
185	imagenet_l2_3_0	.242
186	resnet50_robust_l2_eps3	.242
187	densenet-169	.241
188	mobilenet_v2_1_0_128	.239
189	BiT-S-R152x4	.238
190	mobilenet_v2_1_0_160	.235
191	mobilenet_v1_1.0_224	.230
192	mobilenet_v2_1.0_160-tf	.229
193	inception_v1-tf	.223
194	cv_18_dagger_408_pretrained	.223
195	shufflenet_v2_x1_0	.222
196	alexnet_linf_8_robust	.218
197	efficientnet_b0_imagenet_full	.217
198	BiT-S-R101x3	.213
199	resnet50-vitoimagevidnet8	.210
200	alexnet_random_l2_3_perturb	.210
201	mobilenet_v2_1.4_224-tf	.208
202	BiT-S-R50x1	.208
203	resnet-34	.208
204	mobilenet_v2_1.3_224-tf	.206
205	mobilenet_v2_0_75_160	.204
206	mobilenet_v1_1.0_160	.198
207	vgg-16-keras	.197
208	mobilenet_v2_0.5_192-tf	.196
209	resnet50_simclr	.196
210	mobilenet_v2_1.0_224-tf	.194
211	mobilenet_v1_1.0_128	.192
212	BiT-S-R101x1	.188
213	AlexNet_SIN_fov4	.186
214	squeezenet1_1	.182
215	mobilenet_v2_0.75_96	.181
216	mobilenet_v2_0_5_192	.180
217	alexnet_training_seed_05	.180
218	alexnet_training_seed_01	.180
219	alexnet_training_seed_01_fov4	.180
220	alexnet_training_seed_01_fov12	.180
221	alexnet_training_seed_01_fov16	.180
222	alexnet_robust_correct	.179
223	vgg-19-keras	.179
224	alexnet_fov12	.175
225	deit_small_imagenet_100_seed-0	.171
226	mobilenet_v2_0.75_128	.169
227	konkle_alexnetgn_ipcl_ref01_primary_model	.168
228	mobilenet_v2_0.75_160-tf	.164
229	convnext_tiny_sup	.161
230	alexnet_early_checkpoint	.161
231	mobilenet_v1_0.25_192	.160
232	0.5x_resnet-18_LC_w_sh_1_iter	.160
233	mobilenet_v2_1-4_224_pytorch	.159
234	mobilenet_v2_1_4_224	.159
235	mobilenet_v2_0.5_224-tf	.156
236	artResNet18_1	.156
237	mobilenet_v2_0.35_160	.150
238	alexnet_fov16	.149
239	mobilenet_v1_0.75_128	.148
240	mobilenet_v2_1.0_128-tf	.143
241	mobilenet_v1_0.75_192	.141
242	resnet50_imagenet_100_seed-0	.140
243	AdvProp_efficientnet-b0	.140
244	mobilenet_v1_0.5_128	.138
245	imagenet_l2_10_0	.132
246	nasnet_mobile-tf	.132
247	mobilenet_v2_1.0_96	.131
248	mobilenet_v2_0.35_96	.131
249	texture_shape_alexnet_trained_on_SIN	.129
250	squeezenet1_0	.126
251	mobilenet_v1_0.5_192	.126
252	mobilenet_v1_0.25_160	.125
253	AlexNet_SIN	.122
254	mobilenet_v2_0.5_160	.120
255	mobilenet_v2_0_75_192	.120
256	mobilenet_v1_0.25_224	.113
257	mobilenet_v2_0.5_128	.113
258	mobilenet_v2_0.75_192-tf	.111
259	mobilenet_v1_0.75_160	.107
260	resnet_50_v1_spiking_l4a	.098
261	mobilenet_v1_0.5_224	.097
262	mobilenet_v2_0_75_224	.097
263	resnet50-meshes-lt-100-original-scratch	.094
264	deit_small_imagenet_1_seed-0	.094
265	deit_small_imagenet_10_seed-0	.094
266	resnet50_imagenet_10_seed-0	.093
267	alexnet_training_seed_06	.089
268	alexnet_training_seed_02	.085
269	alexnet_training_seed_04	.085
270	alexnet_training_seed_08	.085
271	alexnet_training_seed_10	.085
272	convnext_small_imagenet_10_seed-0	.084
273	alexnet_training_seed_07	.084
274	mobilenet_v2_0.5_96	.077
275	resnet50_eMMCR_Vanilla	.077
276	mobilenet_v2_0.35_128	.070
277	AlexNet_SIN_fov12	.065
278	AlexNet_SIN_fov16	.064
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	eBarlow_lmda_02_1000ep
385	ReAlnet02_cornet
386	resnet18_ecoset_full
387	vit_base_patch14_dinov2-lvd142m
388	compact_resnet50_IT
389	ReAlnet01_cornet_fov4
390	resnet34_ecoset_full
391	VOneCORnet-S
392	resnet50_ecoset_full
393	vit_base_patch14_reg4_dinov2-lvd142m
394	resnet101_ecoset_full
395	compact_vgg19_V4
396	yudixie_resnet50_distance_rotation_0_240908
397	yudixie_resnet50_rotation_reg_0_240908
398	yudixie_resnet50_distance_translation_0_240908
399	resnet152_ecoset_full
400	yudixie_resnet18_distance_translation_0_240719
401	aughost_fair_resnet18
402	yudixie_resnet18_translation_rotation_0_240719
403	yudixie_resnet18_distance_rotation_0_240719
404	yudixie_resnet18_category_class_0_240719
405	ReAlnet05_cornet
406	yudixie_resnet18_random_0_240719
407	compact_resnet50_robust_V4
408	yudixie_resnet18_cat_obj_class_all_latents_0_240719
409	resnet-18_test_m
410	yudixie_resnet18_distance_translation_rotation_0_240719
411	ReAlnet01_cornet_fov12
412	yudixie_resnet18_object_class_0_240719
413	SeLaVi-AVE
414	yudixie_resnet18_imagenet1kpret_0_240719
415	r3d_18
416	compact_vgg19_IT
417	ReAlnet06_cornet
418	ReAlnet01_cornet_fov16
419	yudixie_resnet50_translation_reg_0_240908
420	ReAlnet01_ALL
421	mc3_18
422	yudixie_resnet50_translation_rotation_0_240908
423	ReAlnet07_cornet
424	ReAlnet02_ALL
425	s3d
426	mvit_v1_b
427	ReAlnet04_ALL
428	ReAlnet08_cornet
429	compact_cornets_V4
430	ReAlnet03_ALL
431	compact_cornets_IT
432	omnivore_swinB
433	SeLaVi-Kinetics400
434	ReAlnet09_cornet
435	ReAlnet05_ALL
436	vit_base_patch16_dinov3-lvd1689m
437	SeLaVi-VGG-Sound
438	SeLaVi-Kinetics-Sound
439	ReAlnet06_ALL
440	compact_resnet50_robust_IT
441	omnivore_swinS
442	ReAlnet07_ALL
443	mvit_v2_s
444	r2plus1d_18
445	ReAlnet10_cornet
446	yudixie_resnet50_distance_translation_rotation_0_240908
447	ReAlnet08_ALL
448	ReAlnet09_ALL
449	ReAlnet04_cornet
450	resnet50_primary_visual_cortex
451	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