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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	pnasnet_large_halfPrecision	.636
21	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.635
22	convnext_small_imagenet_100_seed-0	.632
23	resnet_50_v2	.630
24	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.626
25	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.625
26	resnext101_32x8d_wsl	.624
27	CLIP_ViT-B_32	.622
28	ViT_L_32_imagenet1k	.617
29	convnext_large:fb_in22k_ft_in1k	.613
30	vit_large_patch14_clip_224:laion2b_ft_in1k	.613
31	SWSL_resnet50	.613
32	resnet_101_v2	.610
33	vit_large_patch14_clip_224:openai_ft_in1k	.603
34	resnet-152_v2-tf	.602
35	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.597
36	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.590
37	convnext_femto_ols:d1_in1k	.588
38	cvt_cvt-13-384-in1k_4_LucyV4	.582
39	AdvProp_efficientnet-b7	.578
40	pnasnet_large-tf	.577
41	convnext_large_imagenet_full_seed-0	.569
42	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.564
43	AdvProp_efficientnet-b4	.559
44	AT_efficientnet-b4	.559
45	CLIP_resnet50	.558
46	CLIP_resnet50_float32	.558
47	convnext_base:clip_laiona_augreg_ft_in1k_384	.555
48	fixres_resnext101_32x48d_wsl	.549
49	convnext_tiny:in12k_ft_in1k	.547
50	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.546
51	effnetv2m_custom384	.540
52	efficientnet_b0	.538
53	cvt_cvt-21-384-in1k_4_LucyV4	.535
54	efficientnet-b7	.534
55	nasnet_large	.534
56	resnet152_imagenet_full	.527
57	resnet_152_v1	.524
58	resnet50-VITO-8deg-cc	.522
59	nasnet_large-tf	.520
60	cvt_cvt-21-224-in1k_4_LucyV4	.519
61	ViT_B_32_imagenet1k	.518
62	antialias-resnet152	.515
63	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.513
64	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.512
65	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.509
66	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.503
67	AT_efficientnet-b2	.501
68	AdvProp_efficientnet-b2	.501
69	deit_large_imagenet_full_seed-0	.496
70	effnetb1_272x240	.495
71	convnext_xlarge:fb_in22k_ft_in1k	.491
72	inception_v4	.482
73	vit_large_patch14_clip_336:laion2b_ft_in1k	.481
74	CLIP-RN50	.477
75	convnext_base_imagenet_full_seed-0	.475
76	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.473
77	resnet_101_v1	.470
78	antialiased-rnext101_32x8d	.469
79	convnext_small_imagenet_full_seed-0	.465
80	deit_base_imagenet_full_seed-0	.465
81	inception_v3_pytorch	.463
82	inception_v3	.458
83	resnet-152_v1-tf	.455
84	grcnn_109	.454
85	tv_efficientnet-b1	.454
86	resnet50-moclr8deg	.453
87	resnet-152_v2_pytorch	.453
88	resnet101	.451
89	resnet50_imagenet_full	.448
90	pnasnet_large_pytorch	.445
91	cv_18_dagger_408_pretrained	.443
92	resnet101_imagenet_full	.433
93	densenet-201-keras	.423
94	inception_v4-tf	.423
95	densenet-201	.421
96	densenet_201_pytorch	.421
97	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
98	resnet-50_v2-tf	.416
99	grcnn	.414
100	alexnet_fov4	.411
101	deit_small_imagenet_full_seed-0	.410
102	resnet50_random_linf8_perturb	.405
103	custom_model_cv_18_dagger_408	.401
104	xception-keras	.399
105	BiT-S-R50x3	.399
106	Res2Net50_26w_4s	.394
107	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.379
108	resnet34_imagenet_full	.379
109	inception_v3-tf	.375
110	BiT-S-R152x2	.371
111	resnet50_random_l2_perturb	.371
112	ReAlnet03	.366
113	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.365
114	resnet50_moco_v2	.364
115	resnet50	.363
116	cornet_s	.361
117	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.361
118	vonegrcnn_62e_nobn	.359
119	efficientnet_b2_imagenet_full	.359
120	resnet50_linf_4_robust	.358
121	vonegrcnn_47e	.354
122	vonegrcnn_52e_full	.351
123	AdvProp_efficientnet-b8	.350
124	ReAlnet01	.345
125	ReAlnet04	.342
126	resnet50_byol	.337
127	BiT-S-R101x3	.331
128	vonegrcnn_47e	.330
129	resnet50_tutorial	.329
130	resnet_50_v1	.329
131	resnet-50-pytorch	.329
132	resnet50-sup	.329
133	resnet18_imagenet_full	.329
134	resnet50_ewconplus_wzc	.327
135	vonegrcnn_52e_full	.325
136	densenet-121-keras	.320
137	resnet-50_v1-tf	.317
138	vgg_19	.317
139	antialiased-r50	.315
140	alexnet_random_linf8_perturb	.314
141	resnet50-SIN_IN_IN	.313
142	resnet_SIN_IN_FT_IN	.313
143	ReAlnet06	.313
144	densenet-169-keras	.311
145	vonegrcnn_62e_nobn	.311
146	alexnet_reduced_aliasing_early_checkpoint	.308
147	CORnet-S	.304
148	densenet-121	.302
149	ReAlnet05	.299
150	ReAlnet07	.298
151	xception	.298
152	resnet_50_v1_spiking	.297
153	voneresnet-50-robust	.296
154	mobilenet_v2_1.4_224-tf	.296
155	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.294
156	inception_v2-tf	.294
157	voneresnet-50	.289
158	inception_v1-tf	.289
159	mobilenet_v2_1-4_224_pytorch	.287
160	mobilenet_v2_1_4_224	.287
161	voneresnet-50-non_stochastic	.286
162	resnet-101_v1-tf	.285
163	ReAlnet10	.279
164	ReAlnet08	.278
165	ReAlnet02	.274
166	densenet-169	.270
167	vgg_16	.270
168	mobilenet_v2_1_0_224	.267
169	resnet50_l2_3_robust	.266
170	mobilenet_v2_1_0_160	.266
171	ReAlnet09	.265
172	mobilenet_v2_1_3_224	.260
173	resnet50-SIN_IN	.258
174	BiT-S-R152x4	.257
175	BiT-S-R50x1	.256
176	alexnet_l2_3_robust	.255
177	resnet50-vitoimagevidnet8	.254
178	alexnet	.252
179	alexnet_ks_torevert	.252
180	nasnet_mobile	.250
181	blt_vs	.247
182	alexnet_linf_8_robust	.246
183	alexnet_robust_correct	.236
184	vonealexnet_gaussian_noise_std4_fixed	.235
185	inception_v1	.235
186	alexnet_fov12	.232
187	mobilenet_v2_0_5_224	.226
188	resnet-50-robust	.226
189	imagenet_l2_3_0	.226
190	resnet50_robust_l2_eps3	.226
191	mobilenet_v2_1.0_160-tf	.225
192	deit_small_imagenet_100_seed-0	.224
193	efficientnet_b0_imagenet_full	.224
194	konkle_alexnetgn_ipcl_ref01_primary_model	.223
195	convnext_tiny_sup	.223
196	resnet-18	.216
197	resnet-34	.216
198	alexnet_random_l2_3_perturb	.215
199	resnet50_robust_l2_eps1	.206
200	mobilenet_v2_1_0_192	.206
201	vgg-16-keras	.204
202	mobilenet_v2_1.3_224-tf	.198
203	mobilenet_v1_1.0_224	.196
204	resnet50_linf_8_robust	.195
205	BiT-S-R101x1	.191
206	regnet_y_400mf	.190
207	efficientnet_b1_imagenet_full	.189
208	mobilenet_v2_1.0_192-tf	.189
209	resnet50_imagenet_10_seed-0	.182
210	deit_small_imagenet_10_seed-0	.182
211	mobilenet_v1_0.75_224	.180
212	mobilenet_v2_1.0_224-tf	.180
213	resnet50-barlow	.179
214	resnet50-vicreg	.178
215	texture_shape_resnet50_trained_on_SIN	.178
216	deit_small_imagenet_1_seed-0	.174
217	alexnet_fov16	.174
218	resnet50_eMMCR_Vanilla	.173
219	nasnet_mobile-tf	.169
220	mobilenet_v2_0_5_192	.166
221	convnext_small_imagenet_10_seed-0	.161
222	mobilenet_v2_0.5_192-tf	.161
223	resnet50_simclr	.158
224	shufflenet_v2_x1_0	.158
225	mobilenet_v2_0_75_160	.155
226	mobilenet_v1_1.0_192	.154
227	alexnet_early_checkpoint	.151
228	imagenet_l2_10_0	.149
229	mobilenet_v1_0.75_128	.145
230	mobilenet_v2_0.5_224-tf	.143
231	alexnet_training_seed_05	.143
232	alexnet_training_seed_01	.143
233	alexnet_training_seed_01_fov4	.143
234	alexnet_training_seed_01_fov16	.143
235	alexnet_training_seed_01_fov12	.143
236	0.5x_resnet-18_LC_w_sh_1_iter	.142
237	mobilenet_v2_0.35_160	.137
238	vgg-19-keras	.136
239	resnet50-SIN	.134
240	mobilenet_v2_0.5_96	.133
241	AlexNet_SIN_fov4	.130
242	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.128
243	texture_shape_alexnet_trained_on_SIN	.124
244	mobilenet_v2_0.75_192-tf	.124
245	AdvProp_efficientnet-b0	.118
246	resnet50_imagenet_100_seed-0	.118
247	mobilenet_v1_0.75_192	.116
248	AlexNet_SIN	.115
249	mobilenet_v2_0.75_224-tf	.114
250	mobilenet_v2_0.5_160	.114
251	mobilenet_v1_0.25_160	.112
252	mobilenet_v2_0_75_192	.110
253	mobilenet_v2_0.35_128	.109
254	mobilenet_v2_0_75_224	.107
255	mobilenet_v2_0.75_128	.105
256	mobilenet_v2_1_0_128	.105
257	mobilenet_v2_0.35_96	.104
258	mobilenet_v1_0.75_160	.100
259	alexnet_training_seed_06	.098
260	mobilenet_v1_1.0_160	.098
261	mobilenet_v1_0.5_192	.096
262	mobilenet_v2_1.0_96	.089
263	mobilenet_v2_1.0_128-tf	.088
264	mobilenet_v2_0.75_160-tf	.086
265	mobilenet_v1_0.5_224	.086
266	mobilenet_v1_0.25_192	.086
267	mobilenet_v2_0.35_192	.078
268	squeezenet1_1	.075
269	mobilenet_v1_1.0_128	.071
270	my-model	.065
271	mobilenet_v2_0.75_96	.065
272	resnet_50_v1_spiking_l4a	.061
273	mobilenet_v2_0.35_224	.061
274	squeezenet1_0	.058
275	resnet50-simclr-vissl	.058
276	resnet50-vicregl0p9	.057
277	AlexNet_SIN_fov12	.053
278	mobilevit_small	.052
279	alexnet_training_seed_02	.049
280	alexnet_training_seed_04	.049
281	alexnet_training_seed_08	.049
282	alexnet_training_seed_10	.049
283	resnet50-vicregl0p75	.048
284	mobilenet_v1_0.25_128	.046
285	resnet50-meshes-lt-100-original-scratch	.043
286	mobilenet_v2_0.5_128	.042
287	resnet-50_untrained	.041
288	mobilenet_v1_0.5_160	.040
289	mobilenet_v1_0.25_224	.039
290	convnext_small_imagenet_1_seed-0	.032
291	alexnet_training_seed_03	.030
292	resnet50-meshes-lt-100-original-pretrained	.030
293	resnet_50_v1_spiking_l3	.026
294	resnet50_FractalDB	.024
295	alexnet_training_seed_07	.024
296	resnet_50_v1_spiking_l4	.020
297	alexnet_training_seed_09	.013
298	resnet50_imagenet_1_seed-0	.010
299	mobilenet_v1_0.5_128	.007
300	artResNet18_1	.006
301	AlexNet_SIN_fov16	.003
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	vit_base_patch16_dinov3-lvd1689m
379	yolos_tiny
380	yudixie_resnet18_translation_reg_0_240719
381	compact_resnet50_V4
382	yudixie_resnet18_distance_reg_0_240719
383	ReAlnet03_cornet
384	yudixie_resnet18_rotation_reg_0_240719
385	yudixie_resnet18_imagenet1kpret_0_240719
386	ReAlnet02_cornet
387	eBarlow_lmda_02_1000ep
388	resnet18_ecoset_full
389	vit_base_patch14_dinov2-lvd142m
390	compact_resnet50_IT
391	VOneCORnet-S
392	resnet50_ecoset_full
393	ReAlnet01_cornet_fov4
394	vit_base_patch14_reg4_dinov2-lvd142m
395	yudixie_resnet50_distance_rotation_0_240908
396	compact_resnet50_robust_V4
397	aughost_fair_resnet18
398	yudixie_resnet50_rotation_reg_0_240908
399	resnet152_ecoset_full
400	yudixie_resnet50_distance_translation_0_240908
401	yudixie_resnet18_distance_translation_0_240719
402	ReAlnet01_cornet_fov12
403	yudixie_resnet18_translation_rotation_0_240719
404	yudixie_resnet18_distance_rotation_0_240719
405	compact_vgg19_V4
406	yudixie_resnet18_category_class_0_240719
407	ReAlnet05_cornet
408	resnet-18_test_m
409	yudixie_resnet18_cat_obj_class_all_latents_0_240719
410	yudixie_resnet18_distance_translation_rotation_0_240719
411	yudixie_resnet18_object_class_0_240719
412	SeLaVi-AVE
413	ReAlnet06_cornet
414	compact_vgg19_IT
415	r3d_18
416	ReAlnet01_cornet_fov16
417	yudixie_resnet50_translation_reg_0_240908
418	yudixie_resnet50_translation_rotation_0_240908
419	ReAlnet01_ALL
420	ReAlnet07_cornet
421	mc3_18
422	s3d
423	mvit_v1_b
424	ReAlnet08_cornet
425	ReAlnet02_ALL
426	ReAlnet03_ALL
427	compact_cornets_V4
428	ReAlnet04_ALL
429	omnivore_swinB
430	SeLaVi-Kinetics400
431	SeLaVi-VGG-Sound
432	compact_cornets_IT
433	ReAlnet05_ALL
434	SeLaVi-Kinetics-Sound
435	ReAlnet06_ALL
436	omnivore_swinS
437	r2plus1d_18
438	ReAlnet10_cornet
439	mvit_v2_s
440	ReAlnet07_ALL
441	compact_resnet50_robust_IT
442	resnet101_ecoset_full
443	resnet34_ecoset_full
444	yudixie_resnet50_distance_translation_rotation_0_240908
445	ReAlnet08_ALL
446	yudixie_resnet18_random_0_240719
447	ReAlnet04_cornet
448	ReAlnet09_ALL
449	ReAlnet09_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

0.45.

Note that scores are relative to this ceiling.

Data: Geirhos2021eidolonII

Metric: error_consistency