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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	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	resnet_50_v1_spiking	.295
137	vonegrcnn_47e	.294
138	vgg_19	.294
139	resnet-101_v1-tf	.292
140	ReAlnet06	.292
141	resnet-50-pytorch	.291
142	resnet50-sup	.291
143	resnet50_tutorial	.291
144	resnet_50_v1	.291
145	antialiased-r50	.290
146	ReAlnet10	.290
147	BiT-S-R152x4	.287
148	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.286
149	resnet50_random_l2_perturb	.286
150	ReAlnet04	.285
151	voneresnet-50	.284
152	voneresnet-50-robust	.284
153	mobilenet_v2_1_0_160	.283
154	resnet50_moco_v2	.282
155	ReAlnet07	.281
156	inception_v1	.278
157	CORnet-S	.276
158	ReAlnet08	.274
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_ks_torevert	.167
206	alexnet-baseline	.167
207	alexnet	.167
208	alexnet	.167
209	alexnet	.167
210	alexnet	.167
211	alexnet	.167
212	alexnet	.167
213	alexnet	.167
214	alexnet	.167
215	alexnet	.167
216	resnet50_simclr	.162
217	mobilenet_v1_0.75_192	.161
218	mobilenet_v2_1_0_128	.157
219	deit_small_imagenet_100_seed-0	.154
220	BiT-S-R101x1	.154
221	vgg-19-keras	.151
222	AdvProp_efficientnet-b0	.151
223	alexnet_robust_correct	.146
224	mobilenet_v1_1.0_192	.145
225	mobilenet_v1_0.75_160	.142
226	convnext_tiny_sup	.138
227	mobilenet_v2_0_5_192	.137
228	mobilenet_v2_0.35_192	.136
229	alexnet_l2_3_robust	.132
230	vgg-16-keras	.130
231	resnet50-barlow	.129
232	alexnet_training_seed_05	.129
233	alexnet_training_seed_01	.129
234	alexnet_linf_8_robust	.127
235	resnet50-meshes-lt-100-original-scratch	.126
236	mobilenet_v2_0_75_192	.125
237	alexnet_early_checkpoint	.124
238	mobilenet_v2_0.35_96	.124
239	mobilenet_v1_1.0_128	.124
240	mobilenet_v1_1.0_160	.123
241	resnet50_eMMCR_Vanilla	.123
242	0.5x_resnet-18_LC_w_sh_1_iter	.121
243	resnet50-simclr-vissl	.121
244	mobilenet_v1_0.5_224	.117
245	mobilenet_v2_0_75_224	.113
246	resnet50_imagenet_10_seed-0	.112
247	deit_small_imagenet_10_seed-0	.112
248	deit_small_imagenet_1_seed-0	.112
249	mobilenet_v1_0.5_128	.112
250	mobilenet_v1_0.5_192	.108
251	squeezenet1_1	.105
252	konkle_alexnetgn_ipcl_ref01_primary_model	.105
253	mobilenet_v1_0.75_128	.104
254	resnet50-vicreg	.103
255	mobilenet_v2_1.0_128-tf	.101
256	resnet50_imagenet_100_seed-0	.099
257	mobilenet_v2_0.5_96	.097
258	mobilenet_v2_0.5_160	.096
259	mobilenet_v2_0.35_224	.095
260	mobilenet_v2_1.0_96	.094
261	AlexNet_SIN	.093
262	mobilenet_v1_0.25_224	.093
263	mobilenet_v2_0.75_96	.093
264	mobilenet_v1_0.5_160	.091
265	convnext_small_imagenet_10_seed-0	.091
266	mobilenet_v2_0.75_192-tf	.090
267	mobilenet_v2_0.75_224-tf	.090
268	imagenet_l2_10_0	.089
269	resnet_50_v1_spiking_l4a	.089
270	mobilenet_v2_0.35_128	.085
271	mobilenet_v1_0.25_160	.081
272	mobilenet_v2_0.5_224-tf	.080
273	mobilenet_v2_0.35_160	.079
274	texture_shape_alexnet_trained_on_SIN	.078
275	alexnet_training_seed_06	.076
276	squeezenet1_0	.071
277	mobilenet_v1_0.25_192	.069
278	resnet50-vicregl0p75	.063
279	resnet_50_v1_spiking_l4	.062
280	resnet_50_v1_spiking_l3	.057
281	mobilenet_v2_0.5_128	.056
282	my-model	.040
283	resnet50-meshes-lt-100-original-pretrained	.039
284	alexnet_training_seed_02	.037
285	alexnet_training_seed_04	.037
286	alexnet_training_seed_08	.037
287	alexnet_training_seed_10	.037
288	artResNet18_1	.037
289	resnet50-vicregl0p9	.036
290	alexnet_training_seed_09	.034
291	mobilenet_v1_0.25_128	.033
292	alexnet_training_seed_07	.033
293	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.026
294	resnet50_FractalDB	.023
295	resnet-50_untrained	.018
296	resnet50_imagenet_1_seed-0	.010
297	convnext_small_imagenet_1_seed-0	.007
298	barlow-twins-resnet50	.003
299	alexnet_training_seed_03	.001
300	mobilevit_small	.000
301	resnet-18-LC_w_sh_100_iter_conv_init_m
302	ViT-B/32
303	BiT-M-R50x3
304	BiT-M-R50x1
305	BiT-M-R152x2
306	resnet-18-LC_w_sh_1_iter_conv_init_m
307	resnet-18-LC_w_sh_10_iter_m
308	resnet-18_test_m
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_1_iter_m
311	resnet-18-LC_w_sh_10_iter_conv_init_m
312	FrankRobWobv0
313	0.5x_resnet-18_LC
314	CORnetZ_CIFAR10
315	resnet18-local_aggregation
316	resnet18-deepcluster
317	r3m_resnet18
318	CORnet-Z
319	resnet18-instance_recognition
320	my-model
321	vggface
322	0.5x_resnet-18_LC_w_sh_10_iter
323	resnet18-colorization
324	r3m_resnet50
325	r3m_resnet34
326	resnet-18-LC_m
327	r3m_resnet50_nocrop
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	yudixie_resnet50_random_0_240908
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	CORnetZ_CIFAR10_bs32_20_04
354	pixels
355	dorinet_cornet_z
356	resnet50
357	openclip
358	resnet50-cifar
359	resnet50_pretrained_with_retinal_waves
360	resnet50_ImageNet
361	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
362	yudixie_resnet50_imagenet1kpret_0_240908
363	yudixie_resnet50_imagenet1kpret_0_240312
364	yudixie_resnet50_object_class_0_240908
365	yudixie_resnet50_cat_obj_class_all_latents_0_240908
366	yudixie_resnet50_category_class_0_240908
367	inception_v3_pytorch
368	resnet18_imagenet21kP
369	yudixie_resnet50_distance_reg_0_240908
370	ConvLSTM
371	PredRNN
372	TAU
373	MIM
374	hmax
375	briaai_rmbg_1_4
376	ReAlnet01_cornet
377	ReAlnet02_cornet
378	yudixie_resnet18_distance_reg_0_240719
379	yudixie_resnet18_rotation_reg_0_240719
380	ReAlnet03_cornet
381	resnet34_ecoset_full
382	VOneCORnet-S
383	resnet50_ecoset_full
384	yudixie_resnet50_distance_translation_0_240908
385	yudixie_resnet18_distance_translation_0_240719
386	yudixie_resnet18_translation_rotation_0_240719
387	yudixie_resnet18_cat_obj_class_all_latents_0_240719
388	ReAlnet05_cornet
389	yudixie_resnet18_translation_reg_0_240719
390	yudixie_resnet50_rotation_reg_0_240908
391	yudixie_resnet18_distance_translation_rotation_0_240719
392	yudixie_resnet18_object_class_0_240719
393	SeLaVi-AVE
394	yudixie_resnet18_imagenet1kpret_0_240719
395	r3d_18
396	yudixie_resnet50_translation_reg_0_240908
397	ReAlnet06_cornet
398	yudixie_resnet50_translation_rotation_0_240908
399	mc3_18
400	ReAlnet01_ALL
401	ReAlnet07_cornet
402	s3d
403	mvit_v1_b
404	ReAlnet08_cornet
405	ReAlnet02_ALL
406	omnivore_swinB
407	ReAlnet04_ALL
408	SeLaVi-Kinetics400
409	ReAlnet09_cornet
410	SeLaVi-VGG-Sound
411	ReAlnet05_ALL
412	SeLaVi-Kinetics-Sound
413	omnivore_swinS
414	resnet152_ecoset_full
415	ReAlnet06_ALL
416	r2plus1d_18
417	mvit_v2_s
418	ReAlnet10_cornet
419	ReAlnet07_ALL
420	resnet18_ecoset_full
421	resnet101_ecoset_full
422	yudixie_resnet18_category_class_0_240719
423	yudixie_resnet50_distance_translation_rotation_0_240908
424	ReAlnet08_ALL
425	yudixie_resnet18_random_0_240719
426	yudixie_resnet18_distance_rotation_0_240719
427	yudixie_resnet50_distance_rotation_0_240908
428	ReAlnet04_cornet
429	ReAlnet09_ALL
430	resnet50_primary_visual_cortex
431	yolos_tiny
432	ReAlnet10_ALL
433	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