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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021stylized-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x32d_wsl	.871
2	resnext101_32x16d_wsl	.859
3	resnext101_32x48d_wsl	.851
4	fixres_resnext101_32x48d_wsl	.830
5	SWSL_resnext101_32x8d	.787
6	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.758
7	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.751
8	convnext_base:clip_laiona_augreg_ft_in1k_384	.750
9	convnext_xlarge:fb_in22k_ft_in1k	.746
10	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.737
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.734
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.723
13	vit_base_patch16_clip_224:openai_ft_in1k	.708
14	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.706
15	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.705
16	vision_transformer_vit_large_patch16_224	.705
17	convnext_large:fb_in22k_ft_in1k	.704
18	SWSL_resnet50	.702
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.697
20	vit_large_patch14_clip_224:openai_ft_in1k	.697
21	effnetb1_272x240	.693
22	effnetv2m_custom384	.680
23	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.680
24	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.678
25	cvt_cvt-13-384-in1k_4_LucyV4	.678
26	resnext101_32x8d_wsl	.666
27	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.654
28	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.653
29	CLIP_resnet50	.646
30	CLIP_resnet50_float32	.646
31	nasnet_large	.646
32	cvt_cvt-21-384-in1k_4_LucyV4	.646
33	CLIP_ViT-B_32	.628
34	pnasnet_large	.627
35	resnet_101_v2	.623
36	resnet_152_v2	.603
37	cv_18_dagger_408_pretrained	.600
38	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.590
39	resnet-152_v2-tf	.588
40	nasnet_large-tf	.583
41	pnasnet_large-tf	.579
42	ViT_B_16_imagenet1k	.570
43	resnet_50_v2	.555
44	ViT_L_32_imagenet1k	.538
45	vit_relpos_base_patch16_clsgap_224:sw_in1k	.516
46	xception-keras	.515
47	CLIP-RN50	.511
48	ViT_B_32_imagenet1k	.494
49	inception_v4	.488
50	antialias-resnet152	.467
51	AdvProp_efficientnet-b6	.463
52	resnet152_imagenet_full	.457
53	resnet_152_v1	.456
54	resnet50_ewconplus_wzc	.454
55	xception	.450
56	resnet50-VITO-8deg-cc	.449
57	AdvProp_efficientnet-b4	.443
58	AT_efficientnet-b4	.443
59	resnet-50_v2-tf	.439
60	resnet50_imagenet_full	.438
61	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.438
62	convnext_base_imagenet_full_seed-0	.433
63	convnext_small_imagenet_full_seed-0	.429
64	AdvProp_efficientnet-b7	.427
65	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.418
66	cvt_cvt-13-224-in1k_4_LucyV4	.417
67	convnext_large_imagenet_full_seed-0	.417
68	resnet101	.417
69	inception_v4-tf	.411
70	convnext_femto_ols:d1_in1k	.409
71	inception_v3	.405
72	resnet101_imagenet_full	.405
73	convnext_tiny_imagenet_full_seed-0	.395
74	efficientnet-b7	.392
75	inception_v3-tf	.391
76	resnet50-SIN_IN	.389
77	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.381
78	grcnn_109	.370
79	efficientnet_b0	.369
80	densenet-201-keras	.365
81	resnet50	.362
82	resnet_101_v1	.358
83	antialiased-rnext101_32x8d	.358
84	resnet50_random_l2_perturb	.351
85	focalnet_tiny_lrf_in1k	.346
86	densenet-201	.342
87	grcnn	.340
88	tv_efficientnet-b1	.324
89	densenet-121	.322
90	texture_shape_resnet50_trained_on_SIN	.321
91	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.320
92	densenet-121-keras	.319
93	custom_model_cv_18_dagger_408	.319
94	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.317
95	ReAlnet01	.315
96	ReAlnet03	.315
97	AT_efficientnet-b2	.315
98	AdvProp_efficientnet-b2	.315
99	deit_large_imagenet_full_seed-0	.314
100	resnet50_byol	.314
101	Res2Net50_26w_4s	.313
102	cvt_cvt-21-224-in1k_4_LucyV4	.311
103	ReAlnet09	.307
104	ReAlnet02	.305
105	AdvProp_efficientnet-b8	.303
106	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.302
107	voneresnet-50-non_stochastic	.301
108	ReAlnet04	.300
109	resnet34_imagenet_full	.299
110	cornet_s	.299
111	inception_v1	.298
112	ReAlnet06	.297
113	convnext_tiny:in12k_ft_in1k	.295
114	resnet50-moclr8deg	.295
115	BiT-S-R152x4	.293
116	resnet18_imagenet_full	.292
117	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.290
118	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.289
119	mobilenet_v2_1.4_224-tf	.288
120	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.281
121	antialiased-r50	.281
122	mobilenet_v2_1_0_224	.278
123	resnet50-SIN_IN_IN	.274
124	resnet_SIN_IN_FT_IN	.274
125	densenet-169	.273
126	resnet50_moco_v2	.272
127	ReAlnet08	.271
128	inception_v2-tf	.270
129	ReAlnet07	.267
130	inception_v1-tf	.266
131	vgg_19	.263
132	ReAlnet10	.263
133	densenet-169-keras	.262
134	resnet50_random_linf8_perturb	.262
135	vonegrcnn_47e	.258
136	nasnet_mobile	.255
137	resnet50_tutorial	.254
138	resnet_50_v1	.254
139	resnet-50-pytorch	.254
140	resnet50-sup	.254
141	resnet50-vitoimagevidnet8	.252
142	ReAlnet05	.251
143	BiT-S-R101x3	.251
144	CORnet-S	.236
145	resnet_50_v1_spiking	.233
146	resnet-50_v1-tf	.233
147	vonegrcnn_62e_nobn	.232
148	BiT-S-R50x3	.230
149	resnet-34	.229
150	vonegrcnn_52e_full	.229
151	resnet50_simclr	.224
152	BiT-S-R152x2	.223
153	voneresnet-50-robust	.219
154	BiT-S-R50x1	.218
155	mobilenet_v2_1_3_224	.216
156	vonegrcnn_62e_nobn	.213
157	mobilenet_v2_1_0_192	.208
158	vonegrcnn_47e	.202
159	efficientnet_b2_imagenet_full	.202
160	mobilenet_v2_1.0_224-tf	.201
161	resnet-18	.199
162	alexnet_training_seed_02	.198
163	alexnet_training_seed_04	.198
164	alexnet_training_seed_08	.198
165	alexnet_training_seed_10	.198
166	resnet-101_v1-tf	.197
167	resnet-152_v1-tf	.195
168	resnet50_linf_4_robust	.195
169	BiT-S-R101x1	.194
170	artResNet18_1	.192
171	vgg_16	.192
172	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.191
173	resnet50-SIN	.187
174	deit_base_imagenet_full_seed-0	.185
175	nasnet_mobile-tf	.185
176	vonegrcnn_52e_full	.184
177	vonealexnet_gaussian_noise_std4_fixed	.182
178	mobilenet_v2_1-4_224_pytorch	.181
179	mobilenet_v2_1_4_224	.181
180	mobilenet_v2_1.3_224-tf	.176
181	alexnet_training_seed_07	.166
182	resnet50-simclr-vissl	.166
183	resnet50_robust_l2_eps1	.163
184	mobilenet_v2_1_0_160	.160
185	resnet50_eMMCR_Vanilla	.159
186	efficientnet_b1_imagenet_full	.159
187	resnet50_imagenet_100_seed-0	.157
188	mobilenet_v1_0.75_224	.155
189	voneresnet-50	.154
190	resnet50-vicregl0p9	.146
191	mobilenet_v2_0_5_224	.145
192	AlexNet_SIN	.141
193	alexnet_ks_torevert	.139
194	alexnet-baseline	.139
195	alexnet	.139
196	alexnet	.139
197	alexnet	.139
198	alexnet	.139
199	alexnet	.139
200	alexnet	.139
201	alexnet	.139
202	alexnet	.139
203	alexnet	.139
204	vgg-19-keras	.134
205	texture_shape_alexnet_trained_on_SIN	.133
206	alexnet	.132
207	alexnet_linf_8_robust	.131
208	deit_small_imagenet_full_seed-0	.131
209	blt_vs	.129
210	resnet-50_untrained	.127
211	resnet50-vicregl0p75	.127
212	mobilenet_v2_0.75_128	.123
213	resnet50_l2_3_robust	.121
214	regnet_y_400mf	.121
215	efficientnet_b0_imagenet_full	.120
216	mobilenet_v1_1.0_192	.119
217	resnet50-barlow	.119
218	mobilenet_v2_0_75_192	.116
219	AdvProp_efficientnet-b0	.114
220	alexnet_random_linf8_perturb	.113
221	mobilenet_v2_0.75_224-tf	.111
222	mobilenet_v1_0.75_192	.110
223	mobilenet_v2_0.5_192-tf	.110
224	alexnet_robust_correct	.107
225	alexnet_random_l2_3_perturb	.107
226	mobilenet_v2_1_0_128	.106
227	mobilenet_v1_0.5_192	.106
228	alexnet_l2_3_robust	.103
229	mobilenet_v1_1.0_224	.103
230	vgg-16-keras	.102
231	resnet50_imagenet_10_seed-0	.100
232	mobilenet_v2_1.0_192-tf	.098
233	mobilenet_v2_0_75_160	.097
234	konkle_alexnetgn_ipcl_ref01_primary_model	.097
235	alexnet_reduced_aliasing_early_checkpoint	.097
236	imagenet_l2_10_0	.097
237	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.096
238	resnet50_linf_8_robust	.094
239	mobilenet_v1_1.0_160	.094
240	alexnet_training_seed_05	.093
241	alexnet_training_seed_01	.093
242	mobilenet_v2_1.0_160-tf	.092
243	mobilenet_v2_0.5_224-tf	.090
244	mobilenet_v2_0_75_224	.089
245	convnext_small_imagenet_1_seed-0	.088
246	mobilenet_v2_0.35_96	.088
247	mobilenet_v1_0.25_128	.087
248	mobilenet_v2_0_5_192	.087
249	resnet50_imagenet_1_seed-0	.086
250	resnet-50-robust	.084
251	imagenet_l2_3_0	.084
252	resnet50_robust_l2_eps3	.084
253	convnext_tiny_sup	.080
254	mobilenet_v2_0.35_224	.079
255	mobilenet_v2_0.35_160	.078
256	mobilenet_v2_1.0_96	.078
257	resnet_50_v1_spiking_l3	.075
258	mobilenet_v2_0.75_96	.074
259	alexnet_early_checkpoint	.070
260	squeezenet1_1	.066
261	mobilenet_v2_0.5_128	.065
262	convnext_small_imagenet_100_seed-0	.064
263	mobilenet_v2_0.75_192-tf	.064
264	mobilevit_small	.064
265	mobilenet_v2_0.75_160-tf	.062
266	mobilenet_v2_0.5_96	.061
267	mobilenet_v1_0.25_224	.060
268	resnet50_FractalDB	.057
269	deit_small_imagenet_100_seed-0	.057
270	mobilenet_v2_0.5_160	.049
271	deit_small_imagenet_1_seed-0	.047
272	shufflenet_v2_x1_0	.046
273	alexnet_training_seed_09	.045
274	deit_small_imagenet_10_seed-0	.045
275	resnet50-meshes-lt-100-original-pretrained	.044
276	alexnet_training_seed_03	.044
277	resnet50-meshes-lt-100-original-scratch	.044
278	mobilenet_v2_0.35_192	.040
279	mobilenet_v1_0.25_160	.040
280	convnext_small_imagenet_10_seed-0	.040
281	mobilenet_v1_1.0_128	.038
282	mobilenet_v2_1.0_128-tf	.037
283	mobilenet_v1_0.5_160	.028
284	squeezenet1_0	.026
285	mobilenet_v2_0.35_128	.026
286	0.5x_resnet-18_LC_w_sh_1_iter	.025
287	resnet_50_v1_spiking_l4	.016
288	resnet50-vicreg	.016
289	mobilenet_v1_0.25_192	.015
290	mobilenet_v1_0.5_128	.015
291	mobilenet_v1_0.5_224	.015
292	mobilenet_v1_0.75_160	.014
293	barlow-twins-resnet50	.012
294	mobilenet_v1_0.75_128	.012
295	alexnet_training_seed_06	.008
296	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.006
297	resnet_50_v1_spiking_l4a	.005
298	resnet-18-LC_w_sh_100_iter_conv_init_m
299	ViT-B/32
300	BiT-M-R50x3
301	BiT-M-R50x1
302	BiT-M-R152x2
303	resnet-18-LC_w_sh_1_iter_conv_init_m
304	resnet-18-LC_w_sh_1_iter_m
305	resnet-18-LC_w_sh_10_iter_m
306	resnet-18-LC_w_sh_100_iter_m
307	resnet-18-LC_w_sh_10_iter_conv_init_m
308	FrankRobWobv0
309	0.5x_resnet-18_LC
310	CORnetZ_CIFAR10
311	resnet18-local_aggregation
312	resnet18-deepcluster
313	r3m_resnet18
314	CORnet-Z
315	vggface
316	my-model
317	resnet18-instance_recognition
318	0.5x_resnet-18_LC_w_sh_10_iter
319	r3m_resnet50
320	r3m_resnet34
321	resnet-18-LC_m
322	r3m_resnet50_nocrop
323	resnet18-supervised
324	resnet-18-LC_1st_conv_m
325	resnet-18-LC_conv_init_m
326	resnet18-contrastive_multiview
327	0.5x_resnet-18
328	RN50
329	0.5x_resnet-18_LC_w_sh_100_iter
330	RN50
331	resnet18-colorization
332	resnet18-relative_position
333	resnet18-autoencoder
334	resnet18-simclr
335	ViT_L_32
336	omnivore_swinT
337	ViT_B_32
338	resnet18-depth_prediction
339	ViT_B_16
340	r3m_resnet34_nocrop
341	hmax
342	resnet18-contrastive_predictive
343	resnet-18-LC_untrained
344	resnet-18_untrained
345	resnet-50x2_untrained
346	dcgan
347	unet_entire
348	v1-pyr-nodown
349	CORnetZ_CIFAR10_bs32_20_04
350	pixels
351	dorinet_cornet_z
352	my-model
353	resnet50
354	openclip
355	resnet50-cifar
356	resnet50_pretrained_with_retinal_waves
357	resnet50_ImageNet
358	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
359	yudixie_resnet50_imagenet1kpret_0_240908
360	yudixie_resnet50_random_0_240908
361	yudixie_resnet50_imagenet1kpret_0_240312
362	yudixie_resnet50_object_class_0_240908
363	yudixie_resnet50_category_class_0_240908
364	yudixie_resnet50_distance_reg_0_240908
365	ConvLSTM
366	PredRNN
367	SimVP
368	TAU
369	MIM
370	resnet-152_v2_pytorch
371	briaai_rmbg_1_4
372	densenet_201_pytorch
373	pnasnet_large_pytorch
374	inception_v3_pytorch
375	compact_resnet50_V4
376	yudixie_resnet18_translation_reg_0_240719
377	yudixie_resnet18_distance_reg_0_240719
378	yolos_tiny
379	yudixie_resnet18_rotation_reg_0_240719
380	ReAlnet03_cornet
381	yudixie_resnet18_imagenet1kpret_0_240719
382	resnet18_ecoset_full
383	ReAlnet02_cornet
384	resnet34_ecoset_full
385	compact_resnet50_IT
386	VOneCORnet-S
387	resnet50_ecoset_full
388	compact_resnet50_robust_V4
389	resnet101_ecoset_full
390	ReAlnet04_cornet
391	resnet152_ecoset_full
392	yudixie_resnet50_distance_translation_0_240908
393	yudixie_resnet18_translation_rotation_0_240719
394	yudixie_resnet18_distance_rotation_0_240719
395	ReAlnet05_cornet
396	yudixie_resnet50_distance_rotation_0_240908
397	yudixie_resnet18_cat_obj_class_all_latents_0_240719
398	compact_vgg19_V4
399	resnet-18_test_m
400	SeLaVi-AVE
401	yudixie_resnet18_distance_translation_rotation_0_240719
402	yudixie_resnet18_object_class_0_240719
403	ReAlnet06_cornet
404	r3d_18
405	yudixie_resnet50_translation_reg_0_240908
406	compact_vgg19_IT
407	yudixie_resnet50_rotation_reg_0_240908
408	yudixie_resnet50_translation_rotation_0_240908
409	ReAlnet01_ALL
410	mc3_18
411	ReAlnet07_cornet
412	s3d
413	ReAlnet08_cornet
414	mvit_v1_b
415	ReAlnet02_ALL
416	compact_cornets_IT
417	omnivore_swinB
418	resnet18_imagenet21kP
419	ReAlnet03_ALL
420	compact_cornets_V4
421	ReAlnet09_cornet
422	ReAlnet04_ALL
423	SeLaVi-Kinetics400
424	SeLaVi-VGG-Sound
425	ReAlnet05_ALL
426	r2plus1d_18
427	omnivore_swinS
428	ReAlnet06_ALL
429	ReAlnet10_cornet
430	SeLaVi-Kinetics-Sound
431	mvit_v2_s
432	compact_resnet50_robust_IT
433	ReAlnet07_ALL
434	yudixie_resnet18_category_class_0_240719
435	ReAlnet08_ALL
436	yudixie_resnet18_distance_translation_0_240719
437	yudixie_resnet18_random_0_240719
438	ReAlnet01_cornet
439	resnet50_primary_visual_cortex
440	ReAlnet09_ALL
441	yudixie_resnet50_distance_translation_rotation_0_240908
442	yudixie_resnet50_cat_obj_class_all_latents_0_240908
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

0.50.

Note that scores are relative to this ceiling.

Data: Geirhos2021stylized

Metric: error_consistency