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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	resnet50_ewconplus_wzc	.363
101	densenet-201	.360
102	densenet_201_pytorch	.360
103	densenet-121-keras	.357
104	resnet50-SIN_IN	.356
105	densenet-201-keras	.353
106	efficientnet_b2_imagenet_full	.353
107	vonegrcnn_52e_full	.352
108	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.350
109	resnet50	.346
110	resnet-50_v2-tf	.343
111	resnet50-moclr8deg	.340
112	vonegrcnn_62e_nobn	.334
113	pnasnet_large_pytorch	.333
114	vonegrcnn_47e	.327
115	custom_model_cv_18_dagger_408	.324
116	blt_vs	.320
117	BiT-S-R152x2	.320
118	AdvProp_efficientnet-b8	.319
119	resnet50_random_linf8_perturb	.319
120	resnet50-VITO-8deg-cc	.319
121	resnet50_imagenet_full	.318
122	ReAlnet02	.317
123	resnet-50_v1-tf	.316
124	BiT-S-R101x3	.315
125	densenet-169	.314
126	resnet50-SIN_IN_IN	.313
127	resnet_SIN_IN_FT_IN	.313
128	nasnet_mobile	.312
129	vonegrcnn_62e_nobn	.304
130	vonegrcnn_52e_full	.302
131	inception_v2-tf	.302
132	mobilenet_v2_1-4_224_pytorch	.299
133	mobilenet_v2_1_4_224	.299
134	densenet-169-keras	.297
135	ReAlnet05	.297
136	alexnet_fov4	.296
137	texture_shape_resnet50_trained_on_SIN	.295
138	resnet_50_v1_spiking	.295
139	vonegrcnn_47e	.294
140	vgg_19	.294
141	resnet-101_v1-tf	.292
142	ReAlnet06	.292
143	resnet-50-pytorch	.291
144	resnet50-sup	.291
145	resnet50_tutorial	.291
146	resnet_50_v1	.291
147	antialiased-r50	.290
148	ReAlnet10	.290
149	BiT-S-R152x4	.287
150	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.286
151	resnet50_random_l2_perturb	.286
152	ReAlnet04	.285
153	voneresnet-50	.284
154	voneresnet-50-robust	.284
155	mobilenet_v2_1_0_160	.283
156	resnet50_moco_v2	.282
157	ReAlnet07	.281
158	inception_v1	.278
159	CORnet-S	.276
160	ReAlnet08	.274
161	ReAlnet01	.273
162	resnet50_l2_3_robust	.273
163	mobilenet_v2_1.4_224-tf	.272
164	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
165	ReAlnet03	.267
166	cornet_s	.263
167	mobilenet_v2_1_3_224	.262
168	mobilenet_v2_1_0_224	.258
169	resnet50_linf_8_robust	.257
170	resnet50-SIN	.256
171	resnet50_byol	.255
172	AlexNet_SIN_fov4	.252
173	mobilenet_v2_1.0_192-tf	.252
174	mobilenet_v2_1.0_224-tf	.251
175	inception_v1-tf	.250
176	resnet18_imagenet_full	.247
177	mobilenet_v2_1_0_192	.245
178	resnet-34	.245
179	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.244
180	vgg_16	.242
181	BiT-S-R50x1	.241
182	mobilenet_v2_1.3_224-tf	.240
183	efficientnet_b1_imagenet_full	.238
184	resnet50_robust_l2_eps1	.233
185	mobilenet_v2_1.0_160-tf	.231
186	alexnet_random_linf8_perturb	.228
187	ReAlnet09	.226
188	efficientnet_b0_imagenet_full	.221
189	mobilenet_v1_0.75_224	.220
190	resnet-50-robust	.219
191	imagenet_l2_3_0	.219
192	resnet50_robust_l2_eps3	.219
193	resnet-18	.215
194	mobilenet_v1_1.0_224	.214
195	mobilenet_v2_0_5_224	.213
196	resnet50-vitoimagevidnet8	.212
197	alexnet	.204
198	regnet_y_400mf	.203
199	vonealexnet_gaussian_noise_std4_fixed	.200
200	mobilenet_v2_0.75_128	.196
201	alexnet_random_l2_3_perturb	.190
202	mobilenet_v2_0_75_160	.189
203	alexnet_reduced_aliasing_early_checkpoint	.174
204	mobilenet_v2_0.5_192-tf	.173
205	mobilenet_v2_0.75_160-tf	.171
206	shufflenet_v2_x1_0	.168
207	nasnet_mobile-tf	.168
208	alexnet_ks_torevert	.167
209	alexnet-baseline	.167
210	alexnet	.167
211	alexnet	.167
212	alexnet	.167
213	alexnet	.167
214	alexnet	.167
215	alexnet	.167
216	alexnet	.167
217	alexnet	.167
218	alexnet	.167
219	alexnet	.167
220	resnet50_simclr	.162
221	mobilenet_v1_0.75_192	.161
222	mobilenet_v2_1_0_128	.157
223	deit_small_imagenet_100_seed-0	.154
224	BiT-S-R101x1	.154
225	vgg-19-keras	.151
226	AdvProp_efficientnet-b0	.151
227	alexnet_robust_correct	.146
228	mobilenet_v1_1.0_192	.145
229	mobilenet_v1_0.75_160	.142
230	convnext_tiny_sup	.138
231	mobilenet_v2_0_5_192	.137
232	mobilenet_v2_0.35_192	.136
233	alexnet_fov16	.135
234	alexnet_l2_3_robust	.132
235	vgg-16-keras	.130
236	resnet50-barlow	.129
237	alexnet_training_seed_05	.129
238	alexnet_training_seed_01	.129
239	alexnet_training_seed_01_fov4	.129
240	alexnet_training_seed_01_fov12	.129
241	alexnet_training_seed_01_fov16	.129
242	alexnet_linf_8_robust	.127
243	resnet50-meshes-lt-100-original-scratch	.126
244	mobilenet_v2_0_75_192	.125
245	alexnet_early_checkpoint	.124
246	mobilenet_v2_0.35_96	.124
247	mobilenet_v1_1.0_128	.124
248	mobilenet_v1_1.0_160	.123
249	resnet50_eMMCR_Vanilla	.123
250	0.5x_resnet-18_LC_w_sh_1_iter	.121
251	resnet50-simclr-vissl	.121
252	mobilenet_v1_0.5_224	.117
253	mobilenet_v2_0_75_224	.113
254	resnet50_imagenet_10_seed-0	.112
255	deit_small_imagenet_10_seed-0	.112
256	deit_small_imagenet_1_seed-0	.112
257	alexnet_fov12	.112
258	mobilenet_v1_0.5_128	.112
259	mobilenet_v1_0.5_192	.108
260	squeezenet1_1	.105
261	konkle_alexnetgn_ipcl_ref01_primary_model	.105
262	mobilenet_v1_0.75_128	.104
263	resnet50-vicreg	.103
264	mobilenet_v2_1.0_128-tf	.101
265	resnet50_imagenet_100_seed-0	.099
266	mobilenet_v2_0.5_96	.097
267	mobilenet_v2_0.5_160	.096
268	mobilenet_v2_0.35_224	.095
269	mobilenet_v2_1.0_96	.094
270	AlexNet_SIN	.093
271	mobilenet_v1_0.25_224	.093
272	mobilenet_v2_0.75_96	.093
273	mobilenet_v1_0.5_160	.091
274	convnext_small_imagenet_10_seed-0	.091
275	mobilenet_v2_0.75_192-tf	.090
276	mobilenet_v2_0.75_224-tf	.090
277	imagenet_l2_10_0	.089
278	resnet_50_v1_spiking_l4a	.089
279	mobilenet_v2_0.35_128	.085
280	mobilenet_v1_0.25_160	.081
281	mobilenet_v2_0.5_224-tf	.080
282	mobilenet_v2_0.35_160	.079
283	texture_shape_alexnet_trained_on_SIN	.078
284	alexnet_training_seed_06	.076
285	squeezenet1_0	.071
286	mobilenet_v1_0.25_192	.069
287	resnet50-vicregl0p75	.063
288	resnet_50_v1_spiking_l4	.062
289	resnet_50_v1_spiking_l3	.057
290	mobilenet_v2_0.5_128	.056
291	AlexNet_SIN_fov12	.042
292	my-model	.040
293	resnet50-meshes-lt-100-original-pretrained	.039
294	alexnet_training_seed_02	.037
295	alexnet_training_seed_04	.037
296	alexnet_training_seed_08	.037
297	alexnet_training_seed_10	.037
298	artResNet18_1	.037
299	resnet50-vicregl0p9	.036
300	alexnet_training_seed_09	.034
301	mobilenet_v1_0.25_128	.033
302	alexnet_training_seed_07	.033
303	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.026
304	resnet50_FractalDB	.023
305	resnet-50_untrained	.018
306	AlexNet_SIN_fov16	.010
307	resnet50_imagenet_1_seed-0	.010
308	convnext_small_imagenet_1_seed-0	.007
309	barlow-twins-resnet50	.003
310	alexnet_training_seed_03	.001
311	mobilevit_small	.000
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_10_iter_m
319	resnet-18_test_m
320	resnet-18-LC_w_sh_100_iter_m
321	resnet-18-LC_w_sh_1_iter_m
322	resnet-18-LC_w_sh_10_iter_conv_init_m
323	FrankRobWobv0
324	0.5x_resnet-18_LC
325	CORnetZ_CIFAR10
326	resnet18-local_aggregation
327	resnet18-deepcluster
328	r3m_resnet18
329	CORnet-Z
330	resnet18-instance_recognition
331	my-model
332	vggface
333	0.5x_resnet-18_LC_w_sh_10_iter
334	resnet18-colorization
335	r3m_resnet50
336	r3m_resnet34
337	resnet-18-LC_m
338	r3m_resnet50_nocrop
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	yudixie_resnet50_random_0_240908
353	resnet18-depth_prediction
354	ViT_B_16
355	r3m_resnet34_nocrop
356	resnet18-contrastive_predictive
357	omnivore_swinT
358	resnet-18-LC_untrained
359	resnet-18_untrained
360	resnet-50x2_untrained
361	dcgan
362	v1-pyr-nodown
363	unet_entire
364	CORnetZ_CIFAR10_bs32_20_04
365	pixels
366	dorinet_cornet_z
367	resnet50
368	openclip
369	resnet50-cifar
370	resnet50_pretrained_with_retinal_waves
371	resnet50_ImageNet
372	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
373	yudixie_resnet50_imagenet1kpret_0_240908
374	yudixie_resnet50_imagenet1kpret_0_240312
375	yudixie_resnet50_object_class_0_240908
376	yudixie_resnet50_cat_obj_class_all_latents_0_240908
377	yudixie_resnet50_category_class_0_240908
378	inception_v3_pytorch
379	resnet18_imagenet21kP
380	yudixie_resnet50_distance_reg_0_240908
381	ConvLSTM
382	PredRNN
383	TAU
384	MIM
385	hmax
386	briaai_rmbg_1_4
387	ReAlnet01_cornet
388	ReAlnet02_cornet
389	yudixie_resnet18_distance_reg_0_240719
390	compact_resnet50_V4
391	yudixie_resnet18_rotation_reg_0_240719
392	ReAlnet03_cornet
393	resnet34_ecoset_full
394	VOneCORnet-S
395	resnet50_ecoset_full
396	ReAlnet01_cornet_fov4
397	yudixie_resnet50_distance_translation_0_240908
398	compact_resnet50_robust_V4
399	yudixie_resnet18_distance_translation_0_240719
400	yudixie_resnet18_translation_rotation_0_240719
401	compact_resnet50_IT
402	yudixie_resnet18_cat_obj_class_all_latents_0_240719
403	ReAlnet05_cornet
404	yudixie_resnet18_translation_reg_0_240719
405	ReAlnet01_cornet_fov12
406	yudixie_resnet50_rotation_reg_0_240908
407	yudixie_resnet18_distance_translation_rotation_0_240719
408	yudixie_resnet18_object_class_0_240719
409	SeLaVi-AVE
410	yudixie_resnet18_imagenet1kpret_0_240719
411	r3d_18
412	compact_vgg19_V4
413	yudixie_resnet50_translation_reg_0_240908
414	ReAlnet06_cornet
415	ReAlnet01_cornet_fov16
416	compact_vgg19_IT
417	yudixie_resnet50_translation_rotation_0_240908
418	mc3_18
419	ReAlnet01_ALL
420	ReAlnet07_cornet
421	s3d
422	mvit_v1_b
423	ReAlnet08_cornet
424	ReAlnet02_ALL
425	compact_cornets_V4
426	omnivore_swinB
427	ReAlnet04_ALL
428	SeLaVi-Kinetics400
429	ReAlnet09_cornet
430	SeLaVi-VGG-Sound
431	compact_cornets_IT
432	ReAlnet05_ALL
433	SeLaVi-Kinetics-Sound
434	omnivore_swinS
435	resnet152_ecoset_full
436	ReAlnet06_ALL
437	r2plus1d_18
438	mvit_v2_s
439	compact_resnet50_robust_IT
440	ReAlnet10_cornet
441	ReAlnet07_ALL
442	resnet18_ecoset_full
443	resnet101_ecoset_full
444	yudixie_resnet18_category_class_0_240719
445	yudixie_resnet50_distance_translation_rotation_0_240908
446	ReAlnet08_ALL
447	yudixie_resnet18_random_0_240719
448	yudixie_resnet18_distance_rotation_0_240719
449	yudixie_resnet50_distance_rotation_0_240908
450	ReAlnet04_cornet
451	ReAlnet09_ALL
452	resnet50_primary_visual_cortex
453	yolos_tiny
454	ReAlnet10_ALL
455	ReAlnet03_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

0.46.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonIII

Metric: error_consistency