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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	texture_shape_resnet50_trained_on_SIN	.295
137	resnet_50_v1_spiking	.295
138	vonegrcnn_47e	.294
139	vgg_19	.294
140	resnet-101_v1-tf	.292
141	ReAlnet06	.292
142	resnet-50-pytorch	.291
143	resnet50-sup	.291
144	resnet50_tutorial	.291
145	resnet_50_v1	.291
146	antialiased-r50	.290
147	ReAlnet10	.290
148	BiT-S-R152x4	.287
149	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.286
150	resnet50_random_l2_perturb	.286
151	ReAlnet04	.285
152	voneresnet-50	.284
153	voneresnet-50-robust	.284
154	mobilenet_v2_1_0_160	.283
155	resnet50_moco_v2	.282
156	ReAlnet07	.281
157	inception_v1	.278
158	CORnet-S	.276
159	ReAlnet08	.274
160	ReAlnet01	.273
161	resnet50_l2_3_robust	.273
162	mobilenet_v2_1.4_224-tf	.272
163	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.271
164	ReAlnet03	.267
165	cornet_s	.263
166	mobilenet_v2_1_3_224	.262
167	mobilenet_v2_1_0_224	.258
168	resnet50_linf_8_robust	.257
169	resnet50-SIN	.256
170	resnet50_byol	.255
171	mobilenet_v2_1.0_192-tf	.252
172	mobilenet_v2_1.0_224-tf	.251
173	inception_v1-tf	.250
174	resnet18_imagenet_full	.247
175	mobilenet_v2_1_0_192	.245
176	resnet-34	.245
177	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.244
178	vgg_16	.242
179	BiT-S-R50x1	.241
180	mobilenet_v2_1.3_224-tf	.240
181	efficientnet_b1_imagenet_full	.238
182	resnet50_robust_l2_eps1	.233
183	mobilenet_v2_1.0_160-tf	.231
184	alexnet_random_linf8_perturb	.228
185	ReAlnet09	.226
186	efficientnet_b0_imagenet_full	.221
187	mobilenet_v1_0.75_224	.220
188	resnet-50-robust	.219
189	imagenet_l2_3_0	.219
190	resnet50_robust_l2_eps3	.219
191	resnet-18	.215
192	mobilenet_v1_1.0_224	.214
193	mobilenet_v2_0_5_224	.213
194	resnet50-vitoimagevidnet8	.212
195	alexnet	.204
196	regnet_y_400mf	.203
197	vonealexnet_gaussian_noise_std4_fixed	.200
198	mobilenet_v2_0.75_128	.196
199	alexnet_random_l2_3_perturb	.190
200	mobilenet_v2_0_75_160	.189
201	alexnet_reduced_aliasing_early_checkpoint	.174
202	mobilenet_v2_0.5_192-tf	.173
203	mobilenet_v2_0.75_160-tf	.171
204	shufflenet_v2_x1_0	.168
205	nasnet_mobile-tf	.168
206	alexnet_ks_torevert	.167
207	alexnet-baseline	.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	alexnet	.167
217	resnet50_simclr	.162
218	mobilenet_v1_0.75_192	.161
219	mobilenet_v2_1_0_128	.157
220	deit_small_imagenet_100_seed-0	.154
221	BiT-S-R101x1	.154
222	vgg-19-keras	.151
223	AdvProp_efficientnet-b0	.151
224	alexnet_robust_correct	.146
225	mobilenet_v1_1.0_192	.145
226	mobilenet_v1_0.75_160	.142
227	convnext_tiny_sup	.138
228	mobilenet_v2_0_5_192	.137
229	mobilenet_v2_0.35_192	.136
230	alexnet_l2_3_robust	.132
231	vgg-16-keras	.130
232	resnet50-barlow	.129
233	alexnet_training_seed_05	.129
234	alexnet_training_seed_01	.129
235	alexnet_linf_8_robust	.127
236	resnet50-meshes-lt-100-original-scratch	.126
237	mobilenet_v2_0_75_192	.125
238	alexnet_early_checkpoint	.124
239	mobilenet_v2_0.35_96	.124
240	mobilenet_v1_1.0_128	.124
241	mobilenet_v1_1.0_160	.123
242	resnet50_eMMCR_Vanilla	.123
243	0.5x_resnet-18_LC_w_sh_1_iter	.121
244	resnet50-simclr-vissl	.121
245	mobilenet_v1_0.5_224	.117
246	mobilenet_v2_0_75_224	.113
247	resnet50_imagenet_10_seed-0	.112
248	deit_small_imagenet_10_seed-0	.112
249	deit_small_imagenet_1_seed-0	.112
250	mobilenet_v1_0.5_128	.112
251	mobilenet_v1_0.5_192	.108
252	squeezenet1_1	.105
253	konkle_alexnetgn_ipcl_ref01_primary_model	.105
254	mobilenet_v1_0.75_128	.104
255	resnet50-vicreg	.103
256	mobilenet_v2_1.0_128-tf	.101
257	resnet50_imagenet_100_seed-0	.099
258	mobilenet_v2_0.5_96	.097
259	mobilenet_v2_0.5_160	.096
260	mobilenet_v2_0.35_224	.095
261	mobilenet_v2_1.0_96	.094
262	AlexNet_SIN	.093
263	mobilenet_v1_0.25_224	.093
264	mobilenet_v2_0.75_96	.093
265	mobilenet_v1_0.5_160	.091
266	convnext_small_imagenet_10_seed-0	.091
267	mobilenet_v2_0.75_192-tf	.090
268	mobilenet_v2_0.75_224-tf	.090
269	imagenet_l2_10_0	.089
270	resnet_50_v1_spiking_l4a	.089
271	mobilenet_v2_0.35_128	.085
272	mobilenet_v1_0.25_160	.081
273	mobilenet_v2_0.5_224-tf	.080
274	mobilenet_v2_0.35_160	.079
275	texture_shape_alexnet_trained_on_SIN	.078
276	alexnet_training_seed_06	.076
277	squeezenet1_0	.071
278	mobilenet_v1_0.25_192	.069
279	resnet50-vicregl0p75	.063
280	resnet_50_v1_spiking_l4	.062
281	resnet_50_v1_spiking_l3	.057
282	mobilenet_v2_0.5_128	.056
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	resnet50_imagenet_1_seed-0	.010
298	convnext_small_imagenet_1_seed-0	.007
299	barlow-twins-resnet50	.003
300	alexnet_training_seed_03	.001
301	mobilevit_small	.000
302	resnet-18-LC_w_sh_100_iter_conv_init_m
303	ViT-B/32
304	BiT-M-R50x3
305	BiT-M-R50x1
306	BiT-M-R152x2
307	resnet-18-LC_w_sh_1_iter_conv_init_m
308	resnet-18-LC_w_sh_10_iter_m
309	resnet-18_test_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	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-local_aggregation
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	yudixie_resnet50_random_0_240908
343	resnet18-depth_prediction
344	ViT_B_16
345	r3m_resnet34_nocrop
346	resnet18-contrastive_predictive
347	omnivore_swinT
348	resnet-18-LC_untrained
349	resnet-18_untrained
350	resnet-50x2_untrained
351	dcgan
352	v1-pyr-nodown
353	unet_entire
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_imagenet1kpret_0_240908
364	yudixie_resnet50_imagenet1kpret_0_240312
365	yudixie_resnet50_object_class_0_240908
366	yudixie_resnet50_cat_obj_class_all_latents_0_240908
367	yudixie_resnet50_category_class_0_240908
368	inception_v3_pytorch
369	resnet18_imagenet21kP
370	yudixie_resnet50_distance_reg_0_240908
371	ConvLSTM
372	PredRNN
373	TAU
374	MIM
375	hmax
376	briaai_rmbg_1_4
377	ReAlnet01_cornet
378	ReAlnet02_cornet
379	yudixie_resnet18_distance_reg_0_240719
380	compact_resnet50_V4
381	yudixie_resnet18_rotation_reg_0_240719
382	ReAlnet03_cornet
383	resnet34_ecoset_full
384	VOneCORnet-S
385	resnet50_ecoset_full
386	yudixie_resnet50_distance_translation_0_240908
387	compact_resnet50_robust_V4
388	yudixie_resnet18_distance_translation_0_240719
389	yudixie_resnet18_translation_rotation_0_240719
390	compact_resnet50_IT
391	yudixie_resnet18_cat_obj_class_all_latents_0_240719
392	ReAlnet05_cornet
393	yudixie_resnet18_translation_reg_0_240719
394	yudixie_resnet50_rotation_reg_0_240908
395	yudixie_resnet18_distance_translation_rotation_0_240719
396	yudixie_resnet18_object_class_0_240719
397	SeLaVi-AVE
398	yudixie_resnet18_imagenet1kpret_0_240719
399	r3d_18
400	compact_vgg19_V4
401	yudixie_resnet50_translation_reg_0_240908
402	ReAlnet06_cornet
403	compact_vgg19_IT
404	yudixie_resnet50_translation_rotation_0_240908
405	mc3_18
406	ReAlnet01_ALL
407	ReAlnet07_cornet
408	s3d
409	mvit_v1_b
410	ReAlnet08_cornet
411	ReAlnet02_ALL
412	compact_cornets_V4
413	omnivore_swinB
414	ReAlnet04_ALL
415	SeLaVi-Kinetics400
416	ReAlnet09_cornet
417	SeLaVi-VGG-Sound
418	compact_cornets_IT
419	ReAlnet05_ALL
420	SeLaVi-Kinetics-Sound
421	omnivore_swinS
422	resnet152_ecoset_full
423	ReAlnet06_ALL
424	r2plus1d_18
425	mvit_v2_s
426	compact_resnet50_robust_IT
427	ReAlnet10_cornet
428	ReAlnet07_ALL
429	resnet18_ecoset_full
430	resnet101_ecoset_full
431	yudixie_resnet18_category_class_0_240719
432	yudixie_resnet50_distance_translation_rotation_0_240908
433	ReAlnet08_ALL
434	yudixie_resnet18_random_0_240719
435	yudixie_resnet18_distance_rotation_0_240719
436	yudixie_resnet50_distance_rotation_0_240908
437	ReAlnet04_cornet
438	ReAlnet09_ALL
439	resnet50_primary_visual_cortex
440	yolos_tiny
441	ReAlnet10_ALL
442	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