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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_halfPrecision	.501
37	pnasnet_large	.501
38	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.500
39	effnetb1_272x240	.495
40	cvt_cvt-13-384-in1k_4_LucyV4	.494
41	nasnet_large	.490
42	resnet_152_v2	.489
43	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.489
44	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.484
45	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.480
46	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.477
47	resnet-152_v2_pytorch	.476
48	resnet152_imagenet_full	.474
49	efficientnet-b7	.473
50	vit_relpos_base_patch16_clsgap_224:sw_in1k	.473
51	antialias-resnet152	.470
52	inception_v4	.469
53	AT_efficientnet-b2	.468
54	AdvProp_efficientnet-b2	.468
55	convnext_tiny:in12k_ft_in1k	.468
56	resnet_101_v1	.465
57	vit_large_patch14_clip_224:laion2b_ft_in1k	.464
58	CLIP-RN50	.464
59	resnet-152_v2-tf	.459
60	convnext_large_imagenet_full_seed-0	.453
61	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.453
62	vit_large_patch14_clip_336:laion2b_ft_in1k	.451
63	inception_v4-tf	.445
64	efficientnet_b0	.444
65	nasnet_large-tf	.443
66	CLIP_ViT-B_32	.442
67	deit_base_imagenet_full_seed-0	.441
68	deit_large_imagenet_full_seed-0	.439
69	vit_large_patch14_clip_224:openai_ft_in1k	.438
70	antialiased-rnext101_32x8d	.437
71	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.433
72	resnet_152_v1	.433
73	cvt_cvt-21-384-in1k_4_LucyV4	.432
74	vit_base_patch16_clip_224:openai_ft_in1k	.432
75	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.429
76	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.427
77	convnext_femto_ols:d1_in1k	.424
78	BiT-S-R50x3	.423
79	convnext_base_imagenet_full_seed-0	.418
80	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.416
81	deit_small_imagenet_full_seed-0	.415
82	CLIP_resnet50	.415
83	CLIP_resnet50_float32	.415
84	xception	.412
85	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.412
86	cv_18_dagger_408_pretrained	.400
87	grcnn_109	.397
88	inception_v3-tf	.395
89	resnet34_imagenet_full	.393
90	voneresnet-50-non_stochastic	.391
91	inception_v3	.388
92	Res2Net50_26w_4s	.383
93	grcnn	.380
94	tv_efficientnet-b1	.379
95	resnet101_imagenet_full	.377
96	densenet-121	.373
97	xception-keras	.369
98	resnet50_linf_4_robust	.368
99	cvt_cvt-21-224-in1k_4_LucyV4	.366
100	resnet-152_v1-tf	.364
101	resnet50_ewconplus_wzc	.363
102	densenet-201	.360
103	densenet_201_pytorch	.360
104	densenet-121-keras	.357
105	resnet50-SIN_IN	.356
106	densenet-201-keras	.353
107	efficientnet_b2_imagenet_full	.353
108	vonegrcnn_52e_full	.352
109	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.350
110	resnet50	.346
111	resnet-50_v2-tf	.343
112	resnet50-moclr8deg	.340
113	vonegrcnn_62e_nobn	.334
114	pnasnet_large_pytorch	.333
115	vonegrcnn_47e	.327
116	custom_model_cv_18_dagger_408	.324
117	blt_vs	.320
118	BiT-S-R152x2	.320
119	AdvProp_efficientnet-b8	.319
120	resnet50_random_linf8_perturb	.319
121	resnet50-VITO-8deg-cc	.319
122	resnet50_imagenet_full	.318
123	ReAlnet02	.317
124	resnet-50_v1-tf	.316
125	BiT-S-R101x3	.315
126	densenet-169	.314
127	resnet50-SIN_IN_IN	.313
128	resnet_SIN_IN_FT_IN	.313
129	nasnet_mobile	.312
130	vonegrcnn_62e_nobn	.304
131	vonegrcnn_52e_full	.302
132	inception_v2-tf	.302
133	mobilenet_v2_1-4_224_pytorch	.299
134	mobilenet_v2_1_4_224	.299
135	densenet-169-keras	.297
136	ReAlnet05	.297
137	alexnet_fov4	.296
138	texture_shape_resnet50_trained_on_SIN	.295
139	resnet_50_v1_spiking	.295
140	vonegrcnn_47e	.294
141	vgg_19	.294
142	resnet-101_v1-tf	.292
143	ReAlnet06	.292
144	resnet-50-pytorch	.291
145	resnet50-sup	.291
146	resnet50_tutorial	.291
147	resnet_50_v1	.291
148	antialiased-r50	.290
149	ReAlnet10	.290
150	BiT-S-R152x4	.287
151	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.286
152	resnet50_random_l2_perturb	.286
153	ReAlnet04	.285
154	voneresnet-50	.284
155	voneresnet-50-robust	.284
156	mobilenet_v2_1_0_160	.283
157	resnet50_moco_v2	.282
158	ReAlnet07	.281
159	inception_v1	.278
160	CORnet-S	.276
161	ReAlnet08	.274
162	ReAlnet01	.273
163	resnet50_l2_3_robust	.273
164	mobilenet_v2_1.4_224-tf	.272
165	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
166	ReAlnet03	.267
167	cornet_s	.263
168	mobilenet_v2_1_3_224	.262
169	mobilenet_v2_1_0_224	.258
170	resnet50_linf_8_robust	.257
171	resnet50-SIN	.256
172	resnet50_byol	.255
173	AlexNet_SIN_fov4	.252
174	mobilenet_v2_1.0_192-tf	.252
175	mobilenet_v2_1.0_224-tf	.251
176	inception_v1-tf	.250
177	resnet18_imagenet_full	.247
178	mobilenet_v2_1_0_192	.245
179	resnet-34	.245
180	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.244
181	vgg_16	.242
182	BiT-S-R50x1	.241
183	mobilenet_v2_1.3_224-tf	.240
184	efficientnet_b1_imagenet_full	.238
185	resnet50_robust_l2_eps1	.233
186	mobilenet_v2_1.0_160-tf	.231
187	eBarlow_lmda_02_1000ep	.230
188	alexnet_random_linf8_perturb	.228
189	ReAlnet09	.226
190	efficientnet_b0_imagenet_full	.221
191	mobilenet_v1_0.75_224	.220
192	resnet-50-robust	.219
193	imagenet_l2_3_0	.219
194	resnet50_robust_l2_eps3	.219
195	resnet-18	.215
196	mobilenet_v1_1.0_224	.214
197	mobilenet_v2_0_5_224	.213
198	resnet50-vitoimagevidnet8	.212
199	regnet_y_400mf	.203
200	vonealexnet_gaussian_noise_std4_fixed	.200
201	mobilenet_v2_0.75_128	.196
202	alexnet_random_l2_3_perturb	.190
203	mobilenet_v2_0_75_160	.189
204	alexnet_reduced_aliasing_early_checkpoint	.174
205	mobilenet_v2_0.5_192-tf	.173
206	mobilenet_v2_0.75_160-tf	.171
207	shufflenet_v2_x1_0	.168
208	nasnet_mobile-tf	.168
209	alexnet_ks_torevert	.167
210	alexnet	.167
211	resnet50_simclr	.162
212	mobilenet_v1_0.75_192	.161
213	mobilenet_v2_1_0_128	.157
214	deit_small_imagenet_100_seed-0	.154
215	BiT-S-R101x1	.154
216	vgg-19-keras	.151
217	AdvProp_efficientnet-b0	.151
218	alexnet_robust_correct	.146
219	mobilenet_v1_1.0_192	.145
220	mobilenet_v1_0.75_160	.142
221	convnext_tiny_sup	.138
222	mobilenet_v2_0_5_192	.137
223	mobilenet_v2_0.35_192	.136
224	alexnet_fov16	.135
225	alexnet_l2_3_robust	.132
226	vgg-16-keras	.130
227	resnet50-barlow	.129
228	alexnet_training_seed_05	.129
229	alexnet_training_seed_01	.129
230	alexnet_training_seed_01_fov4	.129
231	alexnet_training_seed_01_fov12	.129
232	alexnet_training_seed_01_fov16	.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	resnet50_eMMCR_Vanilla	.123
241	0.5x_resnet-18_LC_w_sh_1_iter	.121
242	resnet50-simclr-vissl	.121
243	mobilenet_v1_0.5_224	.117
244	mobilenet_v2_0_75_224	.113
245	resnet50_imagenet_10_seed-0	.112
246	deit_small_imagenet_10_seed-0	.112
247	deit_small_imagenet_1_seed-0	.112
248	alexnet_fov12	.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	AlexNet_SIN_fov12	.042
283	my-model	.040
284	resnet50-meshes-lt-100-original-pretrained	.039
285	alexnet_training_seed_02	.037
286	alexnet_training_seed_04	.037
287	alexnet_training_seed_08	.037
288	alexnet_training_seed_10	.037
289	artResNet18_1	.037
290	resnet50-vicregl0p9	.036
291	alexnet_training_seed_09	.034
292	mobilenet_v1_0.25_128	.033
293	alexnet_training_seed_07	.033
294	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.026
295	resnet50_FractalDB	.023
296	resnet-50_untrained	.018
297	AlexNet_SIN_fov16	.010
298	resnet50_imagenet_1_seed-0	.010
299	convnext_small_imagenet_1_seed-0	.007
300	barlow-twins-resnet50	.003
301	alexnet_training_seed_03	.001
302	mobilevit_small	.000
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_test_m
311	resnet-18-LC_w_sh_100_iter_m
312	resnet-18-LC_w_sh_1_iter_m
313	resnet-18-LC_w_sh_10_iter_conv_init_m
314	FrankRobWobv0
315	0.5x_resnet-18_LC
316	CORnetZ_CIFAR10
317	resnet18-local_aggregation
318	resnet18-deepcluster
319	r3m_resnet18
320	CORnet-Z
321	resnet18-instance_recognition
322	my-model
323	vggface
324	0.5x_resnet-18_LC_w_sh_10_iter
325	resnet18-colorization
326	r3m_resnet50
327	r3m_resnet34
328	resnet-18-LC_m
329	r3m_resnet50_nocrop
330	resnet18-supervised
331	resnet-18-LC_1st_conv_m
332	resnet-18-LC_conv_init_m
333	resnet18-contrastive_multiview
334	0.5x_resnet-18
335	RN50
336	0.5x_resnet-18_LC_w_sh_100_iter
337	RN50
338	resnet18-relative_position
339	resnet18-autoencoder
340	resnet18-simclr
341	ViT_L_32
342	ViT_B_32
343	yudixie_resnet50_random_0_240908
344	resnet18-depth_prediction
345	ViT_B_16
346	r3m_resnet34_nocrop
347	resnet18-contrastive_predictive
348	omnivore_swinT
349	resnet-18-LC_untrained
350	resnet-18_untrained
351	resnet-50x2_untrained
352	dcgan
353	v1-pyr-nodown
354	unet_entire
355	CORnetZ_CIFAR10_bs32_20_04
356	pixels
357	dorinet_cornet_z
358	resnet50
359	openclip
360	resnet50-cifar
361	resnet50_pretrained_with_retinal_waves
362	resnet50_ImageNet
363	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
364	yudixie_resnet50_imagenet1kpret_0_240908
365	yudixie_resnet50_imagenet1kpret_0_240312
366	yudixie_resnet50_object_class_0_240908
367	yudixie_resnet50_cat_obj_class_all_latents_0_240908
368	yudixie_resnet50_category_class_0_240908
369	inception_v3_pytorch
370	resnet18_imagenet21kP
371	yudixie_resnet50_distance_reg_0_240908
372	ConvLSTM
373	PredRNN
374	TAU
375	MIM
376	hmax
377	briaai_rmbg_1_4
378	ReAlnet01_cornet
379	vit_base_patch16_dinov3-lvd1689m
380	ReAlnet02_cornet
381	yudixie_resnet18_distance_reg_0_240719
382	compact_resnet50_V4
383	yudixie_resnet18_rotation_reg_0_240719
384	ReAlnet03_cornet
385	vit_base_patch14_dinov2-lvd142m
386	resnet34_ecoset_full
387	VOneCORnet-S
388	resnet50_ecoset_full
389	ReAlnet01_cornet_fov4
390	vit_base_patch14_reg4_dinov2-lvd142m
391	yudixie_resnet50_distance_translation_0_240908
392	compact_resnet50_robust_V4
393	yudixie_resnet18_distance_translation_0_240719
394	aughost_fair_resnet18
395	yudixie_resnet18_translation_rotation_0_240719
396	compact_resnet50_IT
397	yudixie_resnet18_cat_obj_class_all_latents_0_240719
398	ReAlnet05_cornet
399	yudixie_resnet18_translation_reg_0_240719
400	ReAlnet01_cornet_fov12
401	yudixie_resnet50_rotation_reg_0_240908
402	yudixie_resnet18_distance_translation_rotation_0_240719
403	yudixie_resnet18_object_class_0_240719
404	SeLaVi-AVE
405	yudixie_resnet18_imagenet1kpret_0_240719
406	r3d_18
407	compact_vgg19_V4
408	yudixie_resnet50_translation_reg_0_240908
409	ReAlnet06_cornet
410	ReAlnet01_cornet_fov16
411	compact_vgg19_IT
412	yudixie_resnet50_translation_rotation_0_240908
413	mc3_18
414	ReAlnet01_ALL
415	ReAlnet07_cornet
416	s3d
417	mvit_v1_b
418	ReAlnet08_cornet
419	ReAlnet02_ALL
420	compact_cornets_V4
421	omnivore_swinB
422	ReAlnet04_ALL
423	SeLaVi-Kinetics400
424	ReAlnet09_cornet
425	SeLaVi-VGG-Sound
426	compact_cornets_IT
427	ReAlnet05_ALL
428	SeLaVi-Kinetics-Sound
429	omnivore_swinS
430	resnet152_ecoset_full
431	ReAlnet06_ALL
432	r2plus1d_18
433	mvit_v2_s
434	compact_resnet50_robust_IT
435	ReAlnet10_cornet
436	ReAlnet07_ALL
437	resnet18_ecoset_full
438	resnet101_ecoset_full
439	yudixie_resnet18_category_class_0_240719
440	yudixie_resnet50_distance_translation_rotation_0_240908
441	ReAlnet08_ALL
442	yudixie_resnet18_random_0_240719
443	yudixie_resnet18_distance_rotation_0_240719
444	yudixie_resnet50_distance_rotation_0_240908
445	ReAlnet04_cornet
446	ReAlnet09_ALL
447	resnet50_primary_visual_cortex
448	yolos_tiny
449	ReAlnet10_ALL
450	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