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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonII-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	SWSL_resnext101_32x8d	.732
2	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.703
3	ViT_B_16_imagenet1k	.699
4	vision_transformer_vit_large_patch16_224	.696
5	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.692
6	vit_base_patch16_clip_224:openai_ft_in1k	.680
7	vit_relpos_base_patch16_clsgap_224:sw_in1k	.674
8	convnext_tiny_imagenet_full_seed-0	.665
9	resnext101_32x48d_wsl	.664
10	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.662
11	resnext101_32x16d_wsl	.662
12	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.659
13	AdvProp_efficientnet-b6	.656
14	resnext101_32x32d_wsl	.650
15	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.650
16	resnet_152_v2	.646
17	focalnet_tiny_lrf_in1k	.646
18	cvt_cvt-13-224-in1k_4_LucyV4	.643
19	pnasnet_large	.640
20	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.635
21	convnext_small_imagenet_100_seed-0	.632
22	resnet_50_v2	.630
23	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.626
24	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.625
25	resnext101_32x8d_wsl	.624
26	CLIP_ViT-B_32	.622
27	ViT_L_32_imagenet1k	.617
28	convnext_large:fb_in22k_ft_in1k	.613
29	vit_large_patch14_clip_224:laion2b_ft_in1k	.613
30	SWSL_resnet50	.613
31	resnet_101_v2	.610
32	vit_large_patch14_clip_224:openai_ft_in1k	.603
33	resnet-152_v2-tf	.602
34	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.597
35	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.590
36	convnext_femto_ols:d1_in1k	.588
37	cvt_cvt-13-384-in1k_4_LucyV4	.582
38	AdvProp_efficientnet-b7	.578
39	pnasnet_large-tf	.577
40	convnext_large_imagenet_full_seed-0	.569
41	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.564
42	AdvProp_efficientnet-b4	.559
43	AT_efficientnet-b4	.559
44	CLIP_resnet50	.558
45	CLIP_resnet50_float32	.558
46	convnext_base:clip_laiona_augreg_ft_in1k_384	.555
47	fixres_resnext101_32x48d_wsl	.549
48	convnext_tiny:in12k_ft_in1k	.547
49	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.546
50	effnetv2m_custom384	.540
51	efficientnet_b0	.538
52	cvt_cvt-21-384-in1k_4_LucyV4	.535
53	efficientnet-b7	.534
54	nasnet_large	.534
55	resnet152_imagenet_full	.527
56	resnet_152_v1	.524
57	resnet50-VITO-8deg-cc	.522
58	nasnet_large-tf	.520
59	cvt_cvt-21-224-in1k_4_LucyV4	.519
60	ViT_B_32_imagenet1k	.518
61	antialias-resnet152	.515
62	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.513
63	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.512
64	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
65	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.503
66	AT_efficientnet-b2	.501
67	AdvProp_efficientnet-b2	.501
68	deit_large_imagenet_full_seed-0	.496
69	effnetb1_272x240	.495
70	convnext_xlarge:fb_in22k_ft_in1k	.491
71	inception_v4	.482
72	vit_large_patch14_clip_336:laion2b_ft_in1k	.481
73	CLIP-RN50	.477
74	convnext_base_imagenet_full_seed-0	.475
75	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
76	resnet_101_v1	.470
77	antialiased-rnext101_32x8d	.469
78	convnext_small_imagenet_full_seed-0	.465
79	deit_base_imagenet_full_seed-0	.465
80	inception_v3_pytorch	.463
81	inception_v3	.458
82	resnet-152_v1-tf	.455
83	grcnn_109	.454
84	tv_efficientnet-b1	.454
85	resnet50-moclr8deg	.453
86	resnet-152_v2_pytorch	.453
87	resnet101	.451
88	resnet50_imagenet_full	.448
89	pnasnet_large_pytorch	.445
90	cv_18_dagger_408_pretrained	.443
91	resnet101_imagenet_full	.433
92	densenet-201-keras	.423
93	inception_v4-tf	.423
94	densenet-201	.421
95	densenet_201_pytorch	.421
96	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
97	resnet-50_v2-tf	.416
98	grcnn	.414
99	deit_small_imagenet_full_seed-0	.410
100	resnet50_random_linf8_perturb	.405
101	custom_model_cv_18_dagger_408	.401
102	xception-keras	.399
103	BiT-S-R50x3	.399
104	Res2Net50_26w_4s	.394
105	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.379
106	resnet34_imagenet_full	.379
107	inception_v3-tf	.375
108	BiT-S-R152x2	.371
109	resnet50_random_l2_perturb	.371
110	ReAlnet03	.366
111	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.365
112	resnet50_moco_v2	.364
113	resnet50	.363
114	cornet_s	.361
115	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.361
116	vonegrcnn_62e_nobn	.359
117	efficientnet_b2_imagenet_full	.359
118	resnet50_linf_4_robust	.358
119	vonegrcnn_47e	.354
120	vonegrcnn_52e_full	.351
121	AdvProp_efficientnet-b8	.350
122	ReAlnet01	.345
123	ReAlnet04	.342
124	resnet50_byol	.337
125	BiT-S-R101x3	.331
126	vonegrcnn_47e	.330
127	resnet50_tutorial	.329
128	resnet_50_v1	.329
129	resnet-50-pytorch	.329
130	resnet50-sup	.329
131	resnet18_imagenet_full	.329
132	resnet50_ewconplus_wzc	.327
133	vonegrcnn_52e_full	.325
134	densenet-121-keras	.320
135	resnet-50_v1-tf	.317
136	vgg_19	.317
137	antialiased-r50	.315
138	alexnet_random_linf8_perturb	.314
139	resnet50-SIN_IN_IN	.313
140	resnet_SIN_IN_FT_IN	.313
141	ReAlnet06	.313
142	densenet-169-keras	.311
143	vonegrcnn_62e_nobn	.311
144	alexnet_reduced_aliasing_early_checkpoint	.308
145	CORnet-S	.304
146	densenet-121	.302
147	ReAlnet05	.299
148	ReAlnet07	.298
149	xception	.298
150	resnet_50_v1_spiking	.297
151	voneresnet-50-robust	.296
152	mobilenet_v2_1.4_224-tf	.296
153	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.294
154	inception_v2-tf	.294
155	voneresnet-50	.289
156	inception_v1-tf	.289
157	mobilenet_v2_1-4_224_pytorch	.287
158	mobilenet_v2_1_4_224	.287
159	voneresnet-50-non_stochastic	.286
160	resnet-101_v1-tf	.285
161	ReAlnet10	.279
162	ReAlnet08	.278
163	ReAlnet02	.274
164	densenet-169	.270
165	vgg_16	.270
166	mobilenet_v2_1_0_224	.267
167	resnet50_l2_3_robust	.266
168	mobilenet_v2_1_0_160	.266
169	ReAlnet09	.265
170	mobilenet_v2_1_3_224	.260
171	alexnet	.260
172	resnet50-SIN_IN	.258
173	BiT-S-R152x4	.257
174	BiT-S-R50x1	.256
175	alexnet_l2_3_robust	.255
176	resnet50-vitoimagevidnet8	.254
177	alexnet-baseline	.252
178	alexnet	.252
179	alexnet	.252
180	alexnet	.252
181	alexnet	.252
182	alexnet	.252
183	alexnet	.252
184	alexnet	.252
185	alexnet	.252
186	alexnet	.252
187	alexnet_ks_torevert	.252
188	nasnet_mobile	.250
189	blt_vs	.247
190	alexnet_linf_8_robust	.246
191	alexnet_robust_correct	.236
192	vonealexnet_gaussian_noise_std4_fixed	.235
193	inception_v1	.235
194	mobilenet_v2_0_5_224	.226
195	resnet-50-robust	.226
196	imagenet_l2_3_0	.226
197	resnet50_robust_l2_eps3	.226
198	mobilenet_v2_1.0_160-tf	.225
199	deit_small_imagenet_100_seed-0	.224
200	efficientnet_b0_imagenet_full	.224
201	konkle_alexnetgn_ipcl_ref01_primary_model	.223
202	convnext_tiny_sup	.223
203	resnet-18	.216
204	resnet-34	.216
205	alexnet_random_l2_3_perturb	.215
206	resnet50_robust_l2_eps1	.206
207	mobilenet_v2_1_0_192	.206
208	vgg-16-keras	.204
209	mobilenet_v2_1.3_224-tf	.198
210	mobilenet_v1_1.0_224	.196
211	resnet50_linf_8_robust	.195
212	BiT-S-R101x1	.191
213	regnet_y_400mf	.190
214	efficientnet_b1_imagenet_full	.189
215	mobilenet_v2_1.0_192-tf	.189
216	resnet50_imagenet_10_seed-0	.182
217	deit_small_imagenet_10_seed-0	.182
218	mobilenet_v1_0.75_224	.180
219	mobilenet_v2_1.0_224-tf	.180
220	resnet50-barlow	.179
221	resnet50-vicreg	.178
222	texture_shape_resnet50_trained_on_SIN	.178
223	deit_small_imagenet_1_seed-0	.174
224	resnet50_eMMCR_Vanilla	.173
225	nasnet_mobile-tf	.169
226	mobilenet_v2_0_5_192	.166
227	convnext_small_imagenet_10_seed-0	.161
228	mobilenet_v2_0.5_192-tf	.161
229	resnet50_simclr	.158
230	shufflenet_v2_x1_0	.158
231	mobilenet_v2_0_75_160	.155
232	mobilenet_v1_1.0_192	.154
233	alexnet_early_checkpoint	.151
234	imagenet_l2_10_0	.149
235	mobilenet_v1_0.75_128	.145
236	mobilenet_v2_0.5_224-tf	.143
237	alexnet_training_seed_05	.143
238	alexnet_training_seed_01	.143
239	0.5x_resnet-18_LC_w_sh_1_iter	.142
240	mobilenet_v2_0.35_160	.137
241	vgg-19-keras	.136
242	resnet50-SIN	.134
243	mobilenet_v2_0.5_96	.133
244	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.128
245	texture_shape_alexnet_trained_on_SIN	.124
246	mobilenet_v2_0.75_192-tf	.124
247	AdvProp_efficientnet-b0	.118
248	resnet50_imagenet_100_seed-0	.118
249	mobilenet_v1_0.75_192	.116
250	AlexNet_SIN	.115
251	mobilenet_v2_0.75_224-tf	.114
252	mobilenet_v2_0.5_160	.114
253	mobilenet_v1_0.25_160	.112
254	mobilenet_v2_0_75_192	.110
255	mobilenet_v2_0.35_128	.109
256	mobilenet_v2_0_75_224	.107
257	mobilenet_v2_0.75_128	.105
258	mobilenet_v2_1_0_128	.105
259	mobilenet_v2_0.35_96	.104
260	mobilenet_v1_0.75_160	.100
261	alexnet_training_seed_06	.098
262	mobilenet_v1_1.0_160	.098
263	mobilenet_v1_0.5_192	.096
264	mobilenet_v2_1.0_96	.089
265	mobilenet_v2_1.0_128-tf	.088
266	mobilenet_v2_0.75_160-tf	.086
267	mobilenet_v1_0.5_224	.086
268	mobilenet_v1_0.25_192	.086
269	mobilenet_v2_0.35_192	.078
270	squeezenet1_1	.075
271	mobilenet_v1_1.0_128	.071
272	my-model	.065
273	mobilenet_v2_0.75_96	.065
274	resnet_50_v1_spiking_l4a	.061
275	mobilenet_v2_0.35_224	.061
276	squeezenet1_0	.058
277	resnet50-simclr-vissl	.058
278	resnet50-vicregl0p9	.057
279	mobilevit_small	.052
280	alexnet_training_seed_02	.049
281	alexnet_training_seed_04	.049
282	alexnet_training_seed_08	.049
283	alexnet_training_seed_10	.049
284	resnet50-vicregl0p75	.048
285	mobilenet_v1_0.25_128	.046
286	resnet50-meshes-lt-100-original-scratch	.043
287	mobilenet_v2_0.5_128	.042
288	resnet-50_untrained	.041
289	mobilenet_v1_0.5_160	.040
290	mobilenet_v1_0.25_224	.039
291	convnext_small_imagenet_1_seed-0	.032
292	alexnet_training_seed_03	.030
293	resnet50-meshes-lt-100-original-pretrained	.030
294	resnet_50_v1_spiking_l3	.026
295	resnet50_FractalDB	.024
296	alexnet_training_seed_07	.024
297	resnet_50_v1_spiking_l4	.020
298	alexnet_training_seed_09	.013
299	resnet50_imagenet_1_seed-0	.010
300	mobilenet_v1_0.5_128	.007
301	artResNet18_1	.006
302	barlow-twins-resnet50	.002
303	resnet-18-LC_w_sh_100_iter_conv_init_m
304	ViT-B/32
305	BiT-M-R50x3
306	BiT-M-R50x1
307	BiT-M-R152x2
308	resnet-18-LC_w_sh_1_iter_conv_init_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_1_iter_m
312	resnet-18-LC_w_sh_10_iter_conv_init_m
313	FrankRobWobv0
314	resnet18-local_aggregation
315	0.5x_resnet-18_LC
316	CORnetZ_CIFAR10
317	resnet18-deepcluster
318	r3m_resnet18
319	CORnet-Z
320	resnet18-instance_recognition
321	my-model
322	vggface
323	0.5x_resnet-18_LC_w_sh_10_iter
324	resnet18-colorization
325	r3m_resnet50
326	r3m_resnet34
327	resnet-18-LC_m
328	r3m_resnet50_nocrop
329	resnet18-supervised
330	resnet-18-LC_1st_conv_m
331	resnet-18-LC_conv_init_m
332	resnet18-contrastive_multiview
333	0.5x_resnet-18
334	RN50
335	0.5x_resnet-18_LC_w_sh_100_iter
336	RN50
337	resnet18-relative_position
338	resnet18-autoencoder
339	resnet18-simclr
340	ViT_L_32
341	ViT_B_32
342	resnet18-depth_prediction
343	ViT_B_16
344	r3m_resnet34_nocrop
345	resnet18-contrastive_predictive
346	omnivore_swinT
347	resnet-18-LC_untrained
348	resnet-18_untrained
349	resnet-50x2_untrained
350	dcgan
351	v1-pyr-nodown
352	unet_entire
353	yudixie_resnet50_imagenet1kpret_0_240908
354	CORnetZ_CIFAR10_bs32_20_04
355	pixels
356	dorinet_cornet_z
357	resnet50
358	openclip
359	resnet50-cifar
360	resnet50_pretrained_with_retinal_waves
361	resnet50_ImageNet
362	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
363	yudixie_resnet50_random_0_240908
364	yudixie_resnet50_cat_obj_class_all_latents_0_240908
365	yudixie_resnet50_imagenet1kpret_0_240312
366	yudixie_resnet50_object_class_0_240908
367	yudixie_resnet50_category_class_0_240908
368	resnet18_imagenet21kP
369	yudixie_resnet50_distance_reg_0_240908
370	ConvLSTM
371	PredRNN
372	SimVP
373	TAU
374	MIM
375	hmax
376	ReAlnet01_cornet
377	briaai_rmbg_1_4
378	yolos_tiny
379	yudixie_resnet18_translation_reg_0_240719
380	compact_resnet50_V4
381	yudixie_resnet18_distance_reg_0_240719
382	ReAlnet03_cornet
383	yudixie_resnet18_rotation_reg_0_240719
384	yudixie_resnet18_imagenet1kpret_0_240719
385	ReAlnet02_cornet
386	resnet18_ecoset_full
387	compact_resnet50_IT
388	VOneCORnet-S
389	resnet50_ecoset_full
390	yudixie_resnet50_distance_rotation_0_240908
391	compact_resnet50_robust_V4
392	yudixie_resnet50_rotation_reg_0_240908
393	resnet152_ecoset_full
394	yudixie_resnet50_distance_translation_0_240908
395	yudixie_resnet18_distance_translation_0_240719
396	yudixie_resnet18_translation_rotation_0_240719
397	yudixie_resnet18_distance_rotation_0_240719
398	compact_vgg19_V4
399	yudixie_resnet18_category_class_0_240719
400	ReAlnet05_cornet
401	resnet-18_test_m
402	yudixie_resnet18_cat_obj_class_all_latents_0_240719
403	yudixie_resnet18_distance_translation_rotation_0_240719
404	yudixie_resnet18_object_class_0_240719
405	SeLaVi-AVE
406	ReAlnet06_cornet
407	compact_vgg19_IT
408	r3d_18
409	yudixie_resnet50_translation_reg_0_240908
410	yudixie_resnet50_translation_rotation_0_240908
411	ReAlnet01_ALL
412	ReAlnet07_cornet
413	mc3_18
414	s3d
415	mvit_v1_b
416	ReAlnet08_cornet
417	ReAlnet02_ALL
418	ReAlnet03_ALL
419	compact_cornets_V4
420	ReAlnet04_ALL
421	omnivore_swinB
422	SeLaVi-Kinetics400
423	SeLaVi-VGG-Sound
424	compact_cornets_IT
425	ReAlnet05_ALL
426	SeLaVi-Kinetics-Sound
427	ReAlnet06_ALL
428	omnivore_swinS
429	r2plus1d_18
430	ReAlnet10_cornet
431	mvit_v2_s
432	ReAlnet07_ALL
433	compact_resnet50_robust_IT
434	resnet101_ecoset_full
435	resnet34_ecoset_full
436	yudixie_resnet50_distance_translation_rotation_0_240908
437	ReAlnet08_ALL
438	yudixie_resnet18_random_0_240719
439	ReAlnet04_cornet
440	ReAlnet09_ALL
441	ReAlnet09_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

0.45.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonII

Metric: error_consistency