Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonIII-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x16d_wsl	.726
2	ViT_L_32_imagenet1k	.644
3	resnext101_32x48d_wsl	.641
4	SWSL_resnet50	.638
5	resnext101_32x32d_wsl	.629
6	vision_transformer_vit_large_patch16_224	.610
7	AdvProp_efficientnet-b4	.597
8	AT_efficientnet-b4	.597
9	ViT_B_16_imagenet1k	.582
10	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.579
11	fixres_resnext101_32x48d_wsl	.578
12	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.574
13	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.569
14	resnext101_32x8d_wsl	.567
15	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.564
16	cvt_cvt-13-224-in1k_4_LucyV4	.561
17	effnetv2m_custom384	.558
18	convnext_xlarge:fb_in22k_ft_in1k	.556
19	AdvProp_efficientnet-b6	.556
20	convnext_small_imagenet_full_seed-0	.554
21	SWSL_resnext101_32x8d	.551
22	focalnet_tiny_lrf_in1k	.550
23	resnet_101_v2	.548
24	convnext_small_imagenet_100_seed-0	.546
25	resnet_50_v2	.541
26	pnasnet_large-tf	.535
27	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.535
28	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.535
29	convnext_tiny_imagenet_full_seed-0	.521
30	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.518
31	resnet101	.517
32	convnext_base:clip_laiona_augreg_ft_in1k_384	.511
33	AdvProp_efficientnet-b7	.505
34	convnext_large:fb_in22k_ft_in1k	.502
35	ViT_B_32_imagenet1k	.502
36	pnasnet_large	.501
37	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.500
38	effnetb1_272x240	.495
39	cvt_cvt-13-384-in1k_4_LucyV4	.494
40	nasnet_large	.490
41	resnet_152_v2	.489
42	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.489
43	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.484
44	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.480
45	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.477
46	resnet-152_v2_pytorch	.476
47	resnet152_imagenet_full	.474
48	efficientnet-b7	.473
49	vit_relpos_base_patch16_clsgap_224:sw_in1k	.473
50	antialias-resnet152	.470
51	inception_v4	.469
52	AT_efficientnet-b2	.468
53	AdvProp_efficientnet-b2	.468
54	convnext_tiny:in12k_ft_in1k	.468
55	resnet_101_v1	.465
56	vit_large_patch14_clip_224:laion2b_ft_in1k	.464
57	CLIP-RN50	.464
58	resnet-152_v2-tf	.459
59	convnext_large_imagenet_full_seed-0	.453
60	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.453
61	vit_large_patch14_clip_336:laion2b_ft_in1k	.451
62	inception_v4-tf	.445
63	efficientnet_b0	.444
64	nasnet_large-tf	.443
65	CLIP_ViT-B_32	.442
66	deit_base_imagenet_full_seed-0	.441
67	deit_large_imagenet_full_seed-0	.439
68	vit_large_patch14_clip_224:openai_ft_in1k	.438
69	antialiased-rnext101_32x8d	.437
70	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.433
71	resnet_152_v1	.433
72	cvt_cvt-21-384-in1k_4_LucyV4	.432
73	vit_base_patch16_clip_224:openai_ft_in1k	.432
74	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.429
75	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.427
76	convnext_femto_ols:d1_in1k	.424
77	BiT-S-R50x3	.423
78	convnext_base_imagenet_full_seed-0	.418
79	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.416
80	deit_small_imagenet_full_seed-0	.415
81	CLIP_resnet50	.415
82	CLIP_resnet50_float32	.415
83	xception	.412
84	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.412
85	cv_18_dagger_408_pretrained	.400
86	grcnn_109	.397
87	inception_v3-tf	.395
88	resnet34_imagenet_full	.393
89	voneresnet-50-non_stochastic	.391
90	inception_v3	.388
91	Res2Net50_26w_4s	.383
92	grcnn	.380
93	tv_efficientnet-b1	.379
94	resnet101_imagenet_full	.377
95	densenet-121	.373
96	xception-keras	.369
97	resnet50_linf_4_robust	.368
98	cvt_cvt-21-224-in1k_4_LucyV4	.366
99	resnet-152_v1-tf	.364
100	densenet-201	.360
101	densenet_201_pytorch	.360
102	densenet-121-keras	.357
103	resnet50-SIN_IN	.356
104	densenet-201-keras	.353
105	efficientnet_b2_imagenet_full	.353
106	vonegrcnn_52e_full	.352
107	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.350
108	resnet50	.346
109	resnet-50_v2-tf	.343
110	resnet50-moclr8deg	.340
111	vonegrcnn_62e_nobn	.334
112	pnasnet_large_pytorch	.333
113	vonegrcnn_47e	.327
114	custom_model_cv_18_dagger_408	.324
115	blt_vs	.320
116	BiT-S-R152x2	.320
117	AdvProp_efficientnet-b8	.319
118	resnet50_random_linf8_perturb	.319
119	resnet50-VITO-8deg-cc	.319
120	resnet50_imagenet_full	.318
121	ReAlnet02	.317
122	resnet-50_v1-tf	.316
123	BiT-S-R101x3	.315
124	densenet-169	.314
125	resnet50-SIN_IN_IN	.313
126	resnet_SIN_IN_FT_IN	.313
127	nasnet_mobile	.312
128	vonegrcnn_62e_nobn	.304
129	vonegrcnn_52e_full	.302
130	inception_v2-tf	.302
131	mobilenet_v2_1-4_224_pytorch	.299
132	mobilenet_v2_1_4_224	.299
133	densenet-169-keras	.297
134	ReAlnet05	.297
135	texture_shape_resnet50_trained_on_SIN	.295
136	vonegrcnn_47e	.294
137	vgg_19	.294
138	resnet-101_v1-tf	.292
139	ReAlnet06	.292
140	resnet-50-pytorch	.291
141	resnet50-sup	.291
142	resnet_50_v1	.291
143	resnet50_tutorial	.291
144	antialiased-r50	.290
145	ReAlnet10	.290
146	BiT-S-R152x4	.287
147	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.286
148	resnet50_random_l2_perturb	.286
149	ReAlnet04	.285
150	voneresnet-50	.284
151	voneresnet-50-robust	.284
152	mobilenet_v2_1_0_160	.283
153	resnet50_moco_v2	.282
154	ReAlnet07	.281
155	inception_v1	.278
156	CORnet-S	.276
157	ReAlnet08	.274
158	ReAlnet_incorrect	.273
159	ReAlnet01	.273
160	resnet50_l2_3_robust	.273
161	mobilenet_v2_1.4_224-tf	.272
162	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
163	ReAlnet03	.267
164	cornet_s	.263
165	mobilenet_v2_1_3_224	.262
166	mobilenet_v2_1_0_224	.258
167	resnet50_linf_8_robust	.257
168	resnet50-SIN	.256
169	resnet50_byol	.255
170	mobilenet_v2_1.0_192-tf	.252
171	mobilenet_v2_1.0_224-tf	.251
172	inception_v1-tf	.250
173	resnet18_imagenet_full	.247
174	mobilenet_v2_1_0_192	.245
175	resnet-34	.245
176	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.244
177	vgg_16	.242
178	BiT-S-R50x1	.241
179	mobilenet_v2_1.3_224-tf	.240
180	efficientnet_b1_imagenet_full	.238
181	resnet50_robust_l2_eps1	.233
182	mobilenet_v2_1.0_160-tf	.231
183	alexnet_random_linf8_perturb	.228
184	ReAlnet09	.226
185	efficientnet_b0_imagenet_full	.221
186	mobilenet_v1_0.75_224	.220
187	resnet-50-robust	.219
188	imagenet_l2_3_0	.219
189	resnet50_robust_l2_eps3	.219
190	resnet-18	.215
191	mobilenet_v1_1.0_224	.214
192	mobilenet_v2_0_5_224	.213
193	resnet50-vitoimagevidnet8	.212
194	alexnet	.204
195	regnet_y_400mf	.203
196	vonealexnet_gaussian_noise_std4_fixed	.200
197	mobilenet_v2_0.75_128	.196
198	alexnet_random_l2_3_perturb	.190
199	mobilenet_v2_0_75_160	.189
200	alexnet_reduced_aliasing_early_checkpoint	.174
201	mobilenet_v2_0.5_192-tf	.173
202	mobilenet_v2_0.75_160-tf	.171
203	shufflenet_v2_x1_0	.168
204	nasnet_mobile-tf	.168
205	alexnet	.167
206	alexnet	.167
207	alexnet	.167
208	alexnet	.167
209	alexnet	.167
210	alexnet	.167
211	alexnet	.167
212	alexnet	.167
213	alexnet	.167
214	alexnet-baseline	.167
215	resnet50_simclr	.162
216	mobilenet_v1_0.75_192	.161
217	mobilenet_v2_1_0_128	.157
218	deit_small_imagenet_100_seed-0	.154
219	BiT-S-R101x1	.154
220	vgg-19-keras	.151
221	AdvProp_efficientnet-b0	.151
222	alexnet_robust_correct	.146
223	mobilenet_v1_1.0_192	.145
224	mobilenet_v1_0.75_160	.142
225	convnext_tiny_sup	.138
226	mobilenet_v2_0_5_192	.137
227	mobilenet_v2_0.35_192	.136
228	alexnet_l2_3_robust	.132
229	vgg-16-keras	.130
230	resnet50-barlow	.129
231	alexnet_training_seed_05	.129
232	alexnet_training_seed_01	.129
233	alexnet_linf_8_robust	.127
234	resnet50-meshes-lt-100-original-scratch	.126
235	mobilenet_v2_0_75_192	.125
236	alexnet_early_checkpoint	.124
237	mobilenet_v2_0.35_96	.124
238	mobilenet_v1_1.0_128	.124
239	mobilenet_v1_1.0_160	.123
240	0.5x_resnet-18_LC_w_sh_1_iter	.121
241	resnet50-simclr-vissl	.121
242	mobilenet_v1_0.5_224	.117
243	mobilenet_v2_0_75_224	.113
244	resnet50_imagenet_10_seed-0	.112
245	deit_small_imagenet_10_seed-0	.112
246	deit_small_imagenet_1_seed-0	.112
247	mobilenet_v1_0.5_128	.112
248	mobilenet_v1_0.5_192	.108
249	squeezenet1_1	.105
250	konkle_alexnetgn_ipcl_ref01_primary_model	.105
251	mobilenet_v1_0.75_128	.104
252	resnet50-vicreg	.103
253	mobilenet_v2_1.0_128-tf	.101
254	resnet50_imagenet_100_seed-0	.099
255	mobilenet_v2_0.5_96	.097
256	mobilenet_v2_0.5_160	.096
257	mobilenet_v2_0.35_224	.095
258	mobilenet_v2_1.0_96	.094
259	AlexNet_SIN	.093
260	mobilenet_v1_0.25_224	.093
261	mobilenet_v2_0.75_96	.093
262	mobilenet_v1_0.5_160	.091
263	convnext_small_imagenet_10_seed-0	.091
264	mobilenet_v2_0.75_192-tf	.090
265	mobilenet_v2_0.75_224-tf	.090
266	imagenet_l2_10_0	.089
267	mobilenet_v2_0.35_128	.085
268	mobilenet_v1_0.25_160	.081
269	mobilenet_v2_0.5_224-tf	.080
270	mobilenet_v2_0.35_160	.079
271	texture_shape_alexnet_trained_on_SIN	.078
272	alexnet_training_seed_06	.076
273	squeezenet1_0	.071
274	mobilenet_v1_0.25_192	.069
275	resnet50-vicregl0p75	.063
276	mobilenet_v2_0.5_128	.056
277	my-model	.040
278	resnet50-meshes-lt-100-original-pretrained	.039
279	alexnet_training_seed_10	.037
280	artResNet18_1	.037
281	alexnet_training_seed_08	.037
282	alexnet_training_seed_02	.037
283	alexnet_training_seed_04	.037
284	resnet50-vicregl0p9	.036
285	alexnet_training_seed_09	.034
286	mobilenet_v1_0.25_128	.033
287	alexnet_training_seed_07	.033
288	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.026
289	resnet50_FractalDB	.023
290	resnet-50_untrained	.018
291	resnet50_imagenet_1_seed-0	.010
292	convnext_small_imagenet_1_seed-0	.007
293	barlow-twins-resnet50	.003
294	alexnet_training_seed_03	.001
295	mobilevit_small	.000
296	resnet34_ecoset_full	X
297	VOneCORnet-S	X
298	resnet50_ecoset_full	X
299	vggface	X
300	resnet101_ecoset_full	X
301	yudixie_resnet50_distance_translation_0_240908	X
302	resnet-18-LC_w_sh_100_iter_conv_init_m	X
303	my-model	X
304	yudixie_resnet18_distance_translation_0_240719	X
305	ReAlnet04_cornet	X
306	yudixie_resnet18_translation_rotation_0_240719	X
307	yudixie_resnet50_distance_rotation_0_240908	X
308	yudixie_resnet18_distance_rotation_0_240719	X
309	yudixie_resnet18_category_class_0_240719	X
310	yudixie_resnet18_random_0_240719	X
311	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
312	ReAlnet05_cornet	X
313	yudixie_resnet50_rotation_reg_0_240908	X
314	yudixie_resnet18_distance_translation_rotation_0_240719	X
315	yudixie_resnet18_object_class_0_240719	X
316	yudixie_resnet18_imagenet1kpret_0_240719	X
317	yudixie_resnet18_translation_reg_0_240719	X
318	resnet18-instance_recognition	X
319	yudixie_resnet50_translation_reg_0_240908	X
320	ReAlnet06_cornet	X
321	yudixie_resnet50_translation_rotation_0_240908	X
322	ReAlnet07_cornet	X
323	CORnet-Z	X
324	r3m_resnet18	X
325	resnet18-deepcluster	X
326	ReAlnet08_cornet	X
327	resnet18-local_aggregation	X
328	CORnetZ_CIFAR10	X
329	0.5x_resnet-18_LC	X
330	omnivore_swinB	X
331	ReAlnet09_cornet	X
332	FrankRobWobv0	X
333	resnet-18-LC_w_sh_10_iter_conv_init_m	X
334	resnet-18-LC_w_sh_1_iter_m	X
335	omnivore_swinS	X
336	resnet-18-LC_w_sh_100_iter_m	X
337	resnet-18_test_m	X
338	ReAlnet10_cornet	X
339	resnet-18-LC_w_sh_10_iter_m	X
340	resnet-18-LC_w_sh_1_iter_conv_init_m	X
341	BiT-M-R152x2	X
342	yudixie_resnet50_distance_translation_rotation_0_240908	X
343	BiT-M-R50x1	X
344	BiT-M-R50x3	X
345	ViT-B/32	X
346	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
347	resnet50_ImageNet	X
348	resnet50_pretrained_with_retinal_waves	X
349	resnet50-cifar	X
350	alexnet_ks_torevert	X
351	yudixie_resnet50_imagenet1kpret_0_240908	X
352	yudixie_resnet50_imagenet1kpret_0_240312	X
353	openclip	X
354	resnet50	X
355	yudixie_resnet50_object_class_0_240908	X
356	dorinet_cornet_z	X
357	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
358	pixels	X
359	CORnetZ_CIFAR10_bs32_20_04	X
360	unet_entire	X
361	v1-pyr-nodown	X
362	dcgan	X
363	resnet-50x2_untrained	X
364	resnet-18_untrained	X
365	resnet-18-LC_untrained	X
366	omnivore_swinT	X
367	resnet18-contrastive_predictive	X
368	r3m_resnet34_nocrop	X
369	ViT_B_16	X
370	resnet18-depth_prediction	X
371	yudixie_resnet50_random_0_240908	X
372	yudixie_resnet50_category_class_0_240908	X
373	ViT_B_32	X
374	ViT_L_32	X
375	inception_v3_pytorch	X
376	resnet18-simclr	X
377	resnet18-autoencoder	X
378	resnet18_imagenet21kP	X
379	resnet18-relative_position	X
380	yudixie_resnet50_distance_reg_0_240908	X
381	ConvLSTM	X
382	PredRNN	X
383	TAU	X
384	MIM	X
385	hmax	X
386	RN50	X
387	0.5x_resnet-18_LC_w_sh_100_iter	X
388	RN50	X
389	0.5x_resnet-18	X
390	resnet18-contrastive_multiview	X
391	briaai_rmbg_1_4	X
392	resnet-18-LC_conv_init_m	X
393	resnet152_ecoset_full	X
394	ReAlnet01_cornet	X
395	resnet-18-LC_1st_conv_m	X
396	resnet18-supervised	X
397	r3m_resnet50_nocrop	X
398	resnet-18-LC_m	X
399	r3m_resnet34	X
400	ReAlnet02_cornet	X
401	yolos_tiny	X
402	yudixie_resnet18_distance_reg_0_240719	X
403	yudixie_resnet18_rotation_reg_0_240719	X
404	ReAlnet03_cornet	X
405	r3m_resnet50	X
406	resnet18-colorization	X
407	0.5x_resnet-18_LC_w_sh_10_iter	X
408	resnet18_ecoset_full	X

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

0.46.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonIII

Metric: error_consistency