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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	dinov2_vitl14_lc	.791
6	vit_huge_clip336_laion	.788
7	SWSL_resnext101_32x8d	.787
8	convnext_large_in22k_supervised	.783
9	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.758
10	dinov2_vitg14_reg_lc	.755
11	dinov2_vitl14_lc_res336	.755
12	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.751
13	convnext_base:clip_laiona_augreg_ft_in1k_384	.750
14	convnext_xlarge:fb_in22k_ft_in1k	.746
15	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.737
16	dinov2_vitg14_lc	.735
17	vit_large_patch14_clip_336:laion2b_ft_in1k	.734
18	dinov2_vitl14_reg_lc	.729
19	convnext_xxlarge_clip	.727
20	vit_large_patch14_clip_224:laion2b_ft_in1k	.723
21	vit_large_clip336_openai	.720
22	convnext_large_clip_laion2b_finelayers	.717
23	vit_base_patch16_clip_224:openai_ft_in1k	.708
24	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.706
25	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.705
26	vision_transformer_vit_large_patch16_224	.705
27	convnext_large:fb_in22k_ft_in1k	.704
28	SWSL_resnet50	.702
29	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.697
30	vit_large_patch14_clip_224:openai_ft_in1k	.697
31	effnetb1_272x240	.693
32	effnetv2m_custom384	.680
33	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.680
34	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.678
35	cvt_cvt-13-384-in1k_4_LucyV4	.678
36	resnext101_32x8d_wsl	.666
37	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.654
38	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.653
39	eva02_large_in22k	.651
40	CLIP_resnet50	.646
41	CLIP_resnet50_float32	.646
42	nasnet_large	.646
43	cvt_cvt-21-384-in1k_4_LucyV4	.646
44	CLIP_ViT-B_32	.628
45	pnasnet_large_halfPrecision	.627
46	pnasnet_large	.627
47	resnet_101_v2	.623
48	convnext_base_fb_in22k_ft_in1k	.617
49	resnet_152_v2	.603
50	cv_18_dagger_408_pretrained	.600
51	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.590
52	resnet-152_v2-tf	.588
53	nasnet_large-tf	.583
54	pnasnet_large-tf	.579
55	ViT_B_16_imagenet1k	.570
56	deit3_base_patch16_224_fb_in1k	.566
57	resnet_50_v2	.555
58	ViT_L_32_imagenet1k	.538
59	vit_relpos_base_patch16_clsgap_224:sw_in1k	.516
60	xception-keras	.515
61	CLIP-RN50	.511
62	ViT_B_32_imagenet1k	.494
63	deit3_base_patch16_224_fb_in22k_ft_in1k	.491
64	inception_v4	.488
65	antialias-resnet152	.467
66	resnet50_st_wzc_seed3_2_323	.464
67	AdvProp_efficientnet-b6	.463
68	resnet152_imagenet_full	.457
69	resnet_152_v1	.456
70	resnet50_ewconplus_wzc	.454
71	xception	.450
72	resnet50-VITO-8deg-cc	.449
73	AdvProp_efficientnet-b4	.443
74	AT_efficientnet-b4	.443
75	resnet-50_v2-tf	.439
76	resnet50_imagenet_full	.438
77	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.438
78	resnet50_ewconplus_wzc_seed5_2__323	.434
79	convnext_base_imagenet_full_seed-0	.433
80	convnext_base_fb_in1k	.430
81	AlexNet_SIN_fov4	.430
82	convnext_small_imagenet_full_seed-0	.429
83	AdvProp_efficientnet-b7	.427
84	convnext_base_fb_in1k_gram_aligned_dinov3	.422
85	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.418
86	cvt_cvt-13-224-in1k_4_LucyV4	.417
87	convnext_large_imagenet_full_seed-0	.417
88	resnet101	.417
89	inception_v4-tf	.411
90	resnet50_st_wzc_seed6_2_323	.411
91	convnext_femto_ols:d1_in1k	.409
92	inception_v3	.405
93	resnet101_imagenet_full	.405
94	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.400
95	convnext_tiny_imagenet_full_seed-0	.395
96	efficientnet-b7	.392
97	inception_v3-tf	.391
98	resnet50-SIN_IN	.389
99	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.381
100	grcnn_109	.370
101	efficientnet_b0	.369
102	densenet-201-keras	.365
103	resnet50	.362
104	resnet_101_v1	.358
105	antialiased-rnext101_32x8d	.358
106	resnet50_random_l2_perturb	.351
107	focalnet_tiny_lrf_in1k	.346
108	densenet-201	.342
109	resnet50_ewconplus_wzc_seed6_2_323	.341
110	grcnn	.340
111	resnet50_st_wzc_2	.328
112	tv_efficientnet-b1	.324
113	densenet-121	.322
114	texture_shape_resnet50_trained_on_SIN	.321
115	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.320
116	densenet-121-keras	.319
117	custom_model_cv_18_dagger_408	.319
118	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.317
119	ReAlnet01	.315
120	ReAlnet03	.315
121	AT_efficientnet-b2	.315
122	AdvProp_efficientnet-b2	.315
123	deit_large_imagenet_full_seed-0	.314
124	resnet50_byol	.314
125	Res2Net50_26w_4s	.313
126	cvt_cvt-21-224-in1k_4_LucyV4	.311
127	ReAlnet09	.307
128	ReAlnet02	.305
129	AdvProp_efficientnet-b8	.303
130	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.302
131	voneresnet-50-non_stochastic	.301
132	ReAlnet04	.300
133	resnet34_imagenet_full	.299
134	cornet_s	.299
135	inception_v1	.298
136	ReAlnet06	.297
137	convnext_tiny:in12k_ft_in1k	.295
138	resnet50-moclr8deg	.295
139	BiT-S-R152x4	.293
140	resnet18_imagenet_full	.292
141	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.290
142	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.289
143	mobilenet_v2_1.4_224-tf	.288
144	resnet50_hvs_wzc	.285
145	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.281
146	antialiased-r50	.281
147	mobilenet_v2_1_0_224	.278
148	resnet50-SIN_IN_IN	.274
149	resnet_SIN_IN_FT_IN	.274
150	densenet-169	.273
151	resnet50_moco_v2	.272
152	ReAlnet08	.271
153	inception_v2-tf	.270
154	ReAlnet07	.267
155	inception_v1-tf	.266
156	vgg_19	.263
157	ReAlnet10	.263
158	densenet-169-keras	.262
159	resnet50_random_linf8_perturb	.262
160	vonegrcnn_47e	.258
161	nasnet_mobile	.255
162	resnet50_tutorial	.254
163	resnet_50_v1	.254
164	resnet-50-pytorch	.254
165	resnet50-sup	.254
166	resnet50-vitoimagevidnet8	.252
167	ReAlnet05	.251
168	BiT-S-R101x3	.251
169	alexnet_fov4	.243
170	CORnet-S	.236
171	resnet_50_v1_spiking	.233
172	resnet-50_v1-tf	.233
173	vonegrcnn_62e_nobn	.232
174	BiT-S-R50x3	.230
175	resnet-34	.229
176	vonegrcnn_52e_full	.229
177	resnet50_simclr	.224
178	BiT-S-R152x2	.223
179	voneresnet-50-robust	.219
180	BiT-S-R50x1	.218
181	mobilenet_v2_1_3_224	.216
182	vonegrcnn_62e_nobn	.213
183	mobilenet_v2_1_0_192	.208
184	vonegrcnn_47e	.202
185	efficientnet_b2_imagenet_full	.202
186	mobilenet_v2_1.0_224-tf	.201
187	resnet-18	.199
188	alexnet_training_seed_02	.198
189	alexnet_training_seed_04	.198
190	alexnet_training_seed_08	.198
191	alexnet_training_seed_10	.198
192	resnet-101_v1-tf	.197
193	resnet-152_v1-tf	.195
194	resnet50_linf_4_robust	.195
195	BiT-S-R101x1	.194
196	artResNet18_1	.192
197	vgg_16	.192
198	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.191
199	resnet50-SIN	.187
200	deit_base_imagenet_full_seed-0	.185
201	nasnet_mobile-tf	.185
202	vonegrcnn_52e_full	.184
203	vonealexnet_gaussian_noise_std4_fixed	.182
204	mobilenet_v2_1-4_224_pytorch	.181
205	mobilenet_v2_1_4_224	.181
206	mobilenet_v2_1.3_224-tf	.176
207	alexnet_training_seed_07	.166
208	resnet50-simclr-vissl	.166
209	resnet50_robust_l2_eps1	.163
210	mobilenet_v2_1_0_160	.160
211	resnet50_eMMCR_Vanilla	.159
212	efficientnet_b1_imagenet_full	.159
213	resnet50_imagenet_100_seed-0	.157
214	mobilenet_v1_0.75_224	.155
215	voneresnet-50	.154
216	resnet50-vicregl0p9	.146
217	mobilenet_v2_0_5_224	.145
218	AlexNet_SIN	.141
219	alexnet_ks_torevert	.139
220	alexnet	.139
221	vgg-19-keras	.134
222	texture_shape_alexnet_trained_on_SIN	.133
223	alexnet_linf_8_robust	.131
224	deit_small_imagenet_full_seed-0	.131
225	blt_vs	.129
226	resnet-50_untrained	.127
227	resnet50-vicregl0p75	.127
228	mobilenet_v2_0.75_128	.123
229	resnet50_l2_3_robust	.121
230	regnet_y_400mf	.121
231	efficientnet_b0_imagenet_full	.120
232	mobilenet_v1_1.0_192	.119
233	resnet50-barlow	.119
234	mobilenet_v2_0_75_192	.116
235	AdvProp_efficientnet-b0	.114
236	alexnet_random_linf8_perturb	.113
237	mobilenet_v2_0.75_224-tf	.111
238	mobilenet_v1_0.75_192	.110
239	mobilenet_v2_0.5_192-tf	.110
240	alexnet_robust_correct	.107
241	alexnet_random_l2_3_perturb	.107
242	mobilenet_v2_1_0_128	.106
243	mobilenet_v1_0.5_192	.106
244	alexnet_l2_3_robust	.103
245	mobilenet_v1_1.0_224	.103
246	vgg-16-keras	.102
247	resnet50_imagenet_10_seed-0	.100
248	mobilenet_v2_1.0_192-tf	.098
249	mobilenet_v2_0_75_160	.097
250	konkle_alexnetgn_ipcl_ref01_primary_model	.097
251	alexnet_reduced_aliasing_early_checkpoint	.097
252	imagenet_l2_10_0	.097
253	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.096
254	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.096
255	resnet50_linf_8_robust	.094
256	mobilenet_v1_1.0_160	.094
257	alexnet_training_seed_05	.093
258	alexnet_training_seed_01	.093
259	alexnet_training_seed_01_fov4	.093
260	alexnet_training_seed_01_fov12	.093
261	alexnet_training_seed_01_fov16	.093
262	mobilenet_v2_1.0_160-tf	.092
263	mobilenet_v2_0.5_224-tf	.090
264	mobilenet_v2_0_75_224	.089
265	convnext_small_imagenet_1_seed-0	.088
266	mobilenet_v2_0.35_96	.088
267	mobilenet_v1_0.25_128	.087
268	mobilenet_v2_0_5_192	.087
269	resnet50_imagenet_1_seed-0	.086
270	resnet-50-robust	.084
271	imagenet_l2_3_0	.084
272	resnet50_robust_l2_eps3	.084
273	convnext_tiny_sup	.080
274	mobilenet_v2_0.35_224	.079
275	alexnet_fov12	.078
276	mobilenet_v2_0.35_160	.078
277	mobilenet_v2_1.0_96	.078
278	resnet_50_v1_spiking_l3	.075
279	AlexNet_SIN_fov12	.075
280	mobilenet_v2_0.75_96	.074
281	alexnet_early_checkpoint	.070
282	alexnet_fov16	.067
283	squeezenet1_1	.066
284	mobilenet_v2_0.5_128	.065
285	convnext_small_imagenet_100_seed-0	.064
286	mobilenet_v2_0.75_192-tf	.064
287	mobilevit_small	.064
288	mobilenet_v2_0.75_160-tf	.062
289	mobilenet_v2_0.5_96	.061
290	mobilenet_v1_0.25_224	.060
291	AlexNet_SIN_fov16	.060
292	resnet50_FractalDB	.057
293	deit_small_imagenet_100_seed-0	.057
294	mobilenet_v2_0.5_160	.049
295	deit_small_imagenet_1_seed-0	.047
296	shufflenet_v2_x1_0	.046
297	alexnet_training_seed_09	.045
298	deit_small_imagenet_10_seed-0	.045
299	alexnet_training_seed_03	.044
300	mobilenet_v2_0.35_192	.040
301	mobilenet_v1_0.25_160	.040
302	convnext_small_imagenet_10_seed-0	.040
303	mobilenet_v1_1.0_128	.038
304	mobilenet_v2_1.0_128-tf	.037
305	mobilenet_v1_0.5_160	.028
306	squeezenet1_0	.026
307	mobilenet_v2_0.35_128	.026
308	0.5x_resnet-18_LC_w_sh_1_iter	.025
309	resnet_50_v1_spiking_l4	.016
310	resnet50-vicreg	.016
311	mobilenet_v1_0.25_192	.015
312	mobilenet_v1_0.5_128	.015
313	mobilenet_v1_0.5_224	.015
314	mobilenet_v1_0.75_160	.014
315	barlow-twins-resnet50	.012
316	mobilenet_v1_0.75_128	.012
317	alexnet_training_seed_06	.008
318	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.006
319	resnet_50_v1_spiking_l4a	.005
320	resnet-18-LC_w_sh_100_iter_conv_init_m
321	ViT-B/32
322	BiT-M-R50x3
323	BiT-M-R50x1
324	BiT-M-R152x2
325	resnet-18-LC_w_sh_1_iter_conv_init_m
326	resnet-18-LC_w_sh_1_iter_m
327	resnet-18-LC_w_sh_10_iter_m
328	resnet-18-LC_w_sh_100_iter_m
329	resnet-18-LC_w_sh_10_iter_conv_init_m
330	FrankRobWobv0
331	0.5x_resnet-18_LC
332	CORnetZ_CIFAR10
333	resnet18-local_aggregation
334	resnet18-deepcluster
335	r3m_resnet18
336	CORnet-Z
337	vggface
338	my-model
339	resnet18-instance_recognition
340	0.5x_resnet-18_LC_w_sh_10_iter
341	r3m_resnet50
342	r3m_resnet34
343	resnet-18-LC_m
344	r3m_resnet50_nocrop
345	resnet18-supervised
346	resnet-18-LC_1st_conv_m
347	resnet-18-LC_conv_init_m
348	resnet18-contrastive_multiview
349	0.5x_resnet-18
350	RN50
351	0.5x_resnet-18_LC_w_sh_100_iter
352	RN50
353	resnet18-colorization
354	resnet18-relative_position
355	resnet18-autoencoder
356	resnet18-simclr
357	ViT_L_32
358	omnivore_swinT
359	ViT_B_32
360	resnet18-depth_prediction
361	ViT_B_16
362	r3m_resnet34_nocrop
363	hmax
364	resnet18-contrastive_predictive
365	resnet-18-LC_untrained
366	resnet-18_untrained
367	resnet-50x2_untrained
368	dcgan
369	unet_entire
370	v1-pyr-nodown
371	CORnetZ_CIFAR10_bs32_20_04
372	pixels
373	dorinet_cornet_z
374	my-model
375	openclip
376	resnet50-cifar
377	resnet50_pretrained_with_retinal_waves
378	resnet50_ImageNet
379	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
380	yudixie_resnet50_imagenet1kpret_0_240908
381	yudixie_resnet50_random_0_240908
382	yudixie_resnet50_imagenet1kpret_0_240312
383	yudixie_resnet50_object_class_0_240908
384	yudixie_resnet50_category_class_0_240908
385	yudixie_resnet50_distance_reg_0_240908
386	ConvLSTM
387	PredRNN
388	SimVP
389	TAU
390	MIM
391	resnet-152_v2_pytorch
392	briaai_rmbg_1_4
393	densenet_201_pytorch
394	pnasnet_large_pytorch
395	inception_v3_pytorch
396	compact_resnet50_V4
397	yudixie_resnet18_translation_reg_0_240719
398	yudixie_resnet18_distance_reg_0_240719
399	yolos_tiny
400	yudixie_resnet18_rotation_reg_0_240719
401	ReAlnet03_cornet
402	yudixie_resnet18_imagenet1kpret_0_240719
403	eBarlow_lmda_02_1000ep
404	vit_base_patch14_reg4_dinov2-lvd142m
405	vit_base_patch16_dinov3-lvd1689m
406	resnet18_ecoset_full
407	ReAlnet02_cornet
408	resnet34_ecoset_full
409	compact_resnet50_IT
410	VOneCORnet-S
411	resnet50_ecoset_full
412	ReAlnet01_cornet_fov4
413	compact_resnet50_robust_V4
414	resnet101_ecoset_full
415	ReAlnet04_cornet
416	resnet152_ecoset_full
417	vit_base_patch14_dinov2-lvd142m
418	yudixie_resnet50_distance_translation_0_240908
419	ReAlnet01_cornet_fov12
420	yudixie_resnet18_translation_rotation_0_240719
421	aughost_fair_resnet18
422	yudixie_resnet18_distance_rotation_0_240719
423	ReAlnet05_cornet
424	yudixie_resnet50_distance_rotation_0_240908
425	yudixie_resnet18_cat_obj_class_all_latents_0_240719
426	compact_vgg19_V4
427	resnet-18_test_m
428	SeLaVi-AVE
429	yudixie_resnet18_distance_translation_rotation_0_240719
430	yudixie_resnet18_object_class_0_240719
431	ReAlnet06_cornet
432	r3d_18
433	yudixie_resnet50_translation_reg_0_240908
434	compact_vgg19_IT
435	yudixie_resnet50_rotation_reg_0_240908
436	yudixie_resnet50_translation_rotation_0_240908
437	ReAlnet01_ALL
438	mc3_18
439	ReAlnet07_cornet
440	s3d
441	ReAlnet08_cornet
442	mvit_v1_b
443	ReAlnet01_cornet_fov16
444	ReAlnet02_ALL
445	compact_cornets_IT
446	omnivore_swinB
447	resnet18_imagenet21kP
448	ReAlnet03_ALL
449	compact_cornets_V4
450	ReAlnet09_cornet
451	ReAlnet04_ALL
452	SeLaVi-Kinetics400
453	SeLaVi-VGG-Sound
454	ReAlnet05_ALL
455	r2plus1d_18
456	omnivore_swinS
457	ReAlnet06_ALL
458	ReAlnet10_cornet
459	SeLaVi-Kinetics-Sound
460	mvit_v2_s
461	compact_resnet50_robust_IT
462	ReAlnet07_ALL
463	yudixie_resnet18_category_class_0_240719
464	ReAlnet08_ALL
465	yudixie_resnet18_distance_translation_0_240719
466	yudixie_resnet18_random_0_240719
467	ReAlnet01_cornet
468	resnet50_primary_visual_cortex
469	ReAlnet09_ALL
470	yudixie_resnet50_distance_translation_rotation_0_240908
471	yudixie_resnet50_cat_obj_class_all_latents_0_240908
472	ReAlnet10_ALL
473	resnet18_fair
474	pr1_stl10_disentangled_alpha04
475	predictive_coding_pc
476	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3
477	yudixie_wbh_object_class_scaling_0415_run_0
478	yudixie_wbh_object_class_scaling_0415_run_3
479	yudixie_wbh_object_class_scaling_0415_run_2
480	yudixie_wbh_object_class_scaling_0415_run_1
481	deepeisnn_ei_cifar10_vgg19
482	deepeisnn_ei_cifar10_resnet18
483	deepeisnn_ei_imagenet_resnet18
484	dinov2_vitl14_reg_lc_featread

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