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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021colour-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	SWSL_resnext101_32x8d	.891
2	resnext101_32x8d_wsl	.885
3	convnext_femto_ols:d1_in1k	.866
4	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.815
5	pnasnet_large-tf	.812
6	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.811
7	cvt_cvt-13-224-in1k_4_LucyV4	.804
8	pnasnet_large_halfPrecision	.800
9	pnasnet_large	.800
10	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.799
11	vision_transformer_vit_large_patch16_224	.794
12	convnext_base_fb_in22k_ft_in1k	.792
13	effnetb1_272x240	.792
14	effnetv2m_custom384	.788
15	nasnet_large-tf	.788
16	resnext101_32x48d_wsl	.787
17	nasnet_large	.769
18	CLIP-RN50	.769
19	SWSL_resnet50	.766
20	vit_large_patch14_clip_224:laion2b_ft_in1k	.762
21	resnet50_st_wzc_seed5_2_323	.761
22	resnext101_32x32d_wsl	.756
23	resnet50-VITO-8deg-cc	.755
24	CLIP_ViT-B_32	.748
25	convnext_tiny:in12k_ft_in1k	.746
26	CLIP_resnet50_float32	.739
27	CLIP_resnet50	.739
28	resnet_50_v2	.738
29	resnet_101_v2	.731
30	cvt_cvt-21-384-in1k_4_LucyV4	.727
31	resnet-152_v2-tf	.721
32	resnet-152_v2_pytorch	.716
33	ViT_L_16_imagenet1k	.715
34	cvt_cvt-21-224-in1k_4_LucyV4	.703
35	resnet50_st_wzc_seed4_2_323	.702
36	resnext101_32x16d_wsl	.696
37	resnet152_imagenet_full	.692
38	resnet_152_v2	.691
39	inception_v3	.689
40	resnet50_ewconplus_wzc_2_323	.686
41	inception_v4	.685
42	efficientnet_b0	.684
43	resnet50_st_wzc_2_323	.683
44	fixres_resnext101_32x48d_wsl	.675
45	resnet101	.675
46	cvt_cvt-13-384-in1k_4_LucyV4	.670
47	deit3_base_patch16_224_fb_in22k_ft_in1k	.669
48	deit3_base_patch16_224_fb_in1k	.660
49	inception_v3-tf	.657
50	resnet101_imagenet_full	.653
51	resnet50_ewconplus_wzc_seed3_2_323	.649
52	focalnet_tiny_lrf_in1k	.647
53	ViT_B_16_imagenet1k	.640
54	tv_efficientnet-b1	.637
55	resnet50_ewconplus_wzc_seed5_2__323	.631
56	resnet50	.631
57	resnet_152_v1	.624
58	resnet50_st_wzc_noseed_2_323	.622
59	xception	.619
60	resnet50_random_l2_perturb	.617
61	resnet50_ewconplus_noseed_2_323	.601
62	inception_v4-tf	.591
63	resnet50_st_wzc_seed3_2_323	.588
64	resnet-50_v2-tf	.587
65	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.587
66	resnet50_imagenet_full	.585
67	antialiased-r50	.574
68	xception-keras	.561
69	vit_relpos_base_patch16_clsgap_224:sw_in1k	.560
70	resnet_101_v1	.551
71	AdvProp_efficientnet-b6	.546
72	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.543
73	vit_base_patch16_clip_224:openai_ft_in1k	.541
74	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.538
75	resnet50_ewconplus_wzc_seed7_2_323	.538
76	convnext_tiny_imagenet_full_seed-0	.534
77	antialiased-rnext101_32x8d	.528
78	resnet50_byol	.521
79	AdvProp_efficientnet-b7	.521
80	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.519
81	resnet50_ewconplus_wzc_seed4_2_323	.515
82	resnet50_st_wzc_2	.515
83	antialias-resnet152	.512
84	efficientnet-b7	.510
85	alexnet_fov4	.505
86	vgg_19	.505
87	resnet50_moco_v2	.504
88	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.499
89	Res2Net50_26w_4s	.491
90	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.491
91	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.489
92	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.488
93	densenet-201-keras	.479
94	resnet34_imagenet_full	.478
95	resnet-50-pytorch	.474
96	resnet50-sup	.474
97	resnet50_tutorial	.474
98	resnet_50_v1	.474
99	grcnn_109	.474
100	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.472
101	convnext_large:fb_in22k_ft_in1k	.470
102	AT_efficientnet-b2	.468
103	AdvProp_efficientnet-b2	.468
104	resnet50_ewconplus_wzc	.466
105	resnet50-SIN_IN_IN	.464
106	resnet_SIN_IN_FT_IN	.464
107	ViT_L_32_imagenet1k	.463
108	convnext_small_imagenet_full_seed-0	.462
109	resnet50-moclr8deg	.461
110	densenet-169	.456
111	deit_small_imagenet_full_seed-0	.454
112	AdvProp_efficientnet-b4	.450
113	AT_efficientnet-b4	.450
114	resnet_50_v1_spiking	.448
115	densenet-169-keras	.448
116	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.448
117	densenet-121-keras	.448
118	cornet_s	.445
119	resnet18_imagenet_full	.443
120	densenet_201_pytorch	.442
121	convnext_xlarge:fb_in22k_ft_in1k	.441
122	grcnn	.439
123	densenet-201	.438
124	resnet-101_v1-tf	.432
125	mobilenet_v2_1_3_224	.430
126	efficientnet_b2_imagenet_full	.429
127	vit_large_patch14_clip_224:openai_ft_in1k	.428
128	resnet50_random_linf8_perturb	.427
129	convnext_base:clip_laiona_augreg_ft_in1k_384	.422
130	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.419
131	convnext_base_fb_in1k	.412
132	deit_large_imagenet_full_seed-0	.411
133	convnext_small_imagenet_100_seed-0	.406
134	deit_base_imagenet_full_seed-0	.406
135	densenet-121	.404
136	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.403
137	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.403
138	cv_18_dagger_408_pretrained	.400
139	resnet-50_v1-tf	.395
140	convnext_large_imagenet_full_seed-0	.395
141	BiT-S-R50x3	.391
142	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.390
143	vit_large_patch14_clip_336:laion2b_ft_in1k	.387
144	convnext_base_fb_in1k_gram_aligned_dinov3	.385
145	ReAlnet03	.377
146	resnet-152_v1-tf	.373
147	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.370
148	ReAlnet01	.370
149	convnext_base_imagenet_full_seed-0	.365
150	mobilenet_v2_1.4_224-tf	.363
151	voneresnet-50-non_stochastic	.361
152	BiT-S-R152x2	.356
153	custom_model_cv_18_dagger_408	.346
154	resnet50-SIN_IN	.344
155	inception_v2-tf	.344
156	mobilenet_v2_1.3_224-tf	.343
157	ReAlnet04	.342
158	vgg_16	.341
159	ReAlnet08	.328
160	ReAlnet09	.325
161	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.324
162	ReAlnet05	.322
163	efficientnet_b0_imagenet_full	.320
164	ViT_B_32_imagenet1k	.320
165	ReAlnet07	.316
166	CORnet-S	.314
167	ReAlnet06	.314
168	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.311
169	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.311
170	nasnet_mobile-tf	.309
171	inception_v1	.300
172	ReAlnet02	.299
173	vonegrcnn_62e_nobn	.298
174	vonegrcnn_62e_nobn	.293
175	efficientnet_b1_imagenet_full	.290
176	ReAlnet10	.290
177	mobilenet_v2_1-4_224_pytorch	.288
178	mobilenet_v2_1_4_224	.288
179	inception_v1-tf	.288
180	nasnet_mobile	.286
181	vonegrcnn_47e	.284
182	BiT-S-R50x1	.269
183	resnet50_linf_4_robust	.268
184	AdvProp_efficientnet-b8	.263
185	BiT-S-R101x3	.263
186	BiT-S-R152x4	.261
187	resnet-18	.260
188	vonegrcnn_47e	.260
189	AlexNet_SIN_fov4	.260
190	convnext_tiny_sup	.254
191	mobilenet_v2_0_5_224	.253
192	resnet-34	.252
193	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.248
194	regnet_y_400mf	.248
195	mobilenet_v2_1_0_192	.246
196	vonegrcnn_52e_full	.239
197	vonegrcnn_52e_full	.236
198	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.232
199	mobilenet_v2_0_75_224	.231
200	mobilenet_v2_1_0_160	.228
201	voneresnet-50	.218
202	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.216
203	mobilenet_v1_1.0_224	.215
204	blt_vs	.214
205	mobilenet_v2_1_0_224	.214
206	vgg-16-keras	.211
207	mobilenet_v1_0.75_224	.211
208	vgg-19-keras	.188
209	BiT-S-R101x1	.182
210	texture_shape_resnet50_trained_on_SIN	.182
211	voneresnet-50-robust	.180
212	resnet50_eMMCR_Vanilla	.179
213	mobilenet_v2_1.0_160-tf	.177
214	mobilenet_v1_1.0_160	.173
215	resnet50-vitoimagevidnet8	.170
216	mobilenet_v2_1.0_224-tf	.168
217	mobilenet_v2_0_5_192	.165
218	alexnet_random_linf8_perturb	.162
219	mobilenet_v2_1.0_192-tf	.161
220	mobilenet_v2_0.75_224-tf	.159
221	alexnet_random_l2_3_perturb	.152
222	resnet50_robust_l2_eps1	.152
223	mobilenet_v2_0.5_224-tf	.150
224	mobilenet_v2_0_75_192	.150
225	resnet50_l2_3_robust	.146
226	resnet_50_v1_spiking_l4a	.143
227	resnet50_simclr	.143
228	alexnet_reduced_aliasing_early_checkpoint	.137
229	mobilenet_v2_0_75_160	.134
230	alexnet	.134
231	alexnet_ks_torevert	.134
232	konkle_alexnetgn_ipcl_ref01_primary_model	.134
233	mobilenet_v2_0.5_192-tf	.131
234	shufflenet_v2_x1_0	.130
235	mobilenet_v1_0.5_224	.123
236	mobilenet_v2_1_0_128	.122
237	resnet50_imagenet_100_seed-0	.120
238	alexnet_early_checkpoint	.119
239	AdvProp_efficientnet-b0	.115
240	mobilenet_v1_0.75_192	.113
241	resnet50-SIN	.111
242	mobilenet_v2_0.75_160-tf	.108
243	vonealexnet_gaussian_noise_std4_fixed	.104
244	resnet-50-robust	.104
245	imagenet_l2_3_0	.104
246	resnet50_robust_l2_eps3	.104
247	resnet50_linf_8_robust	.104
248	mobilenet_v1_1.0_192	.096
249	mobilenet_v1_0.5_192	.090
250	0.5x_resnet-18_LC_w_sh_1_iter	.084
251	mobilenet_v2_0.75_192-tf	.072
252	mobilenet_v2_0.75_96	.072
253	texture_shape_alexnet_trained_on_SIN	.071
254	mobilenet_v1_0.75_160	.068
255	mobilenet_v1_1.0_128	.068
256	squeezenet1_0	.067
257	AlexNet_SIN	.065
258	mobilenet_v2_0.75_128	.062
259	mobilenet_v2_1.0_128-tf	.061
260	alexnet_linf_8_robust	.060
261	alexnet_l2_3_robust	.060
262	mobilenet_v2_0.35_160	.058
263	alexnet_training_seed_07	.050
264	alexnet_training_seed_02	.049
265	alexnet_training_seed_04	.049
266	alexnet_training_seed_08	.049
267	alexnet_training_seed_10	.049
268	mobilenet_v2_0.35_192	.048
269	squeezenet1_1	.047
270	mobilenet_v1_0.25_192	.045
271	mobilenet_v1_0.25_224	.044
272	alexnet_robust_correct	.043
273	resnet50-vicreg	.043
274	imagenet_l2_10_0	.041
275	mobilenet_v1_0.75_128	.039
276	mobilenet_v2_0.35_128	.038
277	alexnet_fov12	.038
278	mobilenet_v2_0.5_128	.037
279	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.037
280	mobilenet_v1_0.5_160	.036
281	mobilenet_v2_0.35_96	.036
282	mobilenet_v2_0.35_224	.035
283	deit_small_imagenet_100_seed-0	.035
284	resnet50-vicregl0p9	.031
285	mobilenet_v1_0.25_128	.030
286	mobilenet_v2_1.0_96	.030
287	convnext_small_imagenet_10_seed-0	.030
288	deit_small_imagenet_10_seed-0	.027
289	resnet50_imagenet_10_seed-0	.027
290	deit_small_imagenet_1_seed-0	.027
291	alexnet_fov16	.026
292	alexnet_training_seed_03	.025
293	mobilenet_v2_0.5_160	.025
294	mobilenet_v2_0.5_96	.025
295	resnet50-meshes-lt-100-original-pretrained	.022
296	alexnet_training_seed_06	.022
297	AlexNet_SIN_fov12	.020
298	resnet50-barlow	.020
299	mobilenet_v1_0.5_128	.020
300	alexnet_training_seed_05	.020
301	alexnet_training_seed_01	.020
302	alexnet_training_seed_01_fov4	.020
303	alexnet_training_seed_01_fov12	.020
304	alexnet_training_seed_01_fov16	.020
305	resnet_50_v1_spiking_l4	.019
306	artResNet18_1	.015
307	my-model	.014
308	resnet50-vicregl0p75	.014
309	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.012
310	resnet_50_v1_spiking_l3	.012
311	AlexNet_SIN_fov16	.011
312	barlow-twins-resnet50	.009
313	resnet-50_untrained	.009
314	resnet50-meshes-lt-100-original-scratch	.009
315	convnext_small_imagenet_1_seed-0	.009
316	alexnet_training_seed_09	.007
317	mobilenet_v1_0.25_160	.006
318	resnet50_FractalDB	.004
319	mobilevit_small	.004
320	resnet50_imagenet_1_seed-0	.003
321	resnet50-simclr-vissl	.002
322	resnet-18-LC_w_sh_100_iter_conv_init_m
323	ViT-B/32
324	BiT-M-R50x3
325	BiT-M-R152x4
326	BiT-M-R50x1
327	BiT-M-R152x2
328	resnet-18-LC_w_sh_1_iter_conv_init_m
329	resnet-18-LC_w_sh_1_iter_m
330	resnet-18-LC_w_sh_10_iter_m
331	resnet-18-LC_w_sh_100_iter_m
332	resnet-18-LC_w_sh_10_iter_conv_init_m
333	FrankRobWobv0
334	resnet18-local_aggregation
335	0.5x_resnet-18_LC
336	CORnetZ_CIFAR10
337	resnet18-colorization
338	resnet18-deepcluster
339	r3m_resnet18
340	resnet18-instance_recognition
341	CORnet-Z
342	my-model
343	vggface
344	0.5x_resnet-18_LC_w_sh_10_iter
345	r3m_resnet50
346	resnet18-relative_position
347	r3m_resnet34
348	resnet-18-LC_m
349	r3m_resnet50_nocrop
350	resnet18-supervised
351	resnet-18-LC_1st_conv_m
352	resnet-18-LC_conv_init_m
353	resnet18-contrastive_multiview
354	0.5x_resnet-18
355	RN50
356	0.5x_resnet-18_LC_w_sh_100_iter
357	RN50
358	resnet18-autoencoder
359	resnet18-simclr
360	ViT_L_32
361	ViT_B_32
362	resnet18-depth_prediction
363	ViT_B_16
364	r3m_resnet34_nocrop
365	resnet18-contrastive_predictive
366	omnivore_swinT
367	resnet-18-LC_untrained
368	resnet-18_untrained
369	resnet-50x2_untrained
370	dcgan
371	v1-pyr-nodown
372	unet_entire
373	CORnetZ_CIFAR10_bs32_20_04
374	pixels
375	dorinet_cornet_z
376	resnet50
377	openclip
378	resnet50-cifar
379	resnet50_pretrained_with_retinal_waves
380	resnet50_ImageNet
381	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
382	yudixie_resnet50_random_0_240908
383	yudixie_resnet50_imagenet1kpret_0_240908
384	yudixie_resnet50_cat_obj_class_all_latents_0_240908
385	yudixie_resnet50_object_class_0_240908
386	yudixie_resnet50_category_class_0_240908
387	resnet18_imagenet21kP
388	ConvLSTM
389	PredRNN
390	yudixie_resnet50_distance_reg_0_240908
391	SimVP
392	TAU
393	MIM
394	hmax
395	ReAlnet01_cornet
396	pnasnet_large_pytorch
397	inception_v3_pytorch
398	briaai_rmbg_1_4
399	yudixie_resnet50_imagenet1kpret_0_240312
400	yudixie_resnet18_translation_reg_0_240719
401	yudixie_resnet18_rotation_reg_0_240719
402	compact_resnet50_V4
403	yudixie_resnet18_imagenet1kpret_0_240719
404	ReAlnet02_cornet
405	eBarlow_lmda_02_1000ep
406	resnet18_ecoset_full
407	compact_resnet50_IT
408	ReAlnet01_cornet_fov4
409	vit_base_patch14_reg4_dinov2-lvd142m
410	vit_base_patch14_dinov2-lvd142m
411	resnet50_ecoset_full
412	resnet101_ecoset_full
413	yudixie_resnet50_distance_rotation_0_240908
414	yudixie_resnet50_rotation_reg_0_240908
415	resnet152_ecoset_full
416	yudixie_resnet18_distance_translation_0_240719
417	ReAlnet04_cornet
418	yudixie_resnet50_distance_translation_0_240908
419	ReAlnet01_cornet_fov12
420	yudixie_resnet18_translation_rotation_0_240719
421	compact_resnet50_robust_V4
422	compact_vgg19_V4
423	aughost_fair_resnet18
424	ReAlnet05_cornet
425	yudixie_resnet18_random_0_240719
426	vit_base_patch16_dinov3-lvd1689m
427	yudixie_resnet18_cat_obj_class_all_latents_0_240719
428	resnet-18_test_m
429	SeLaVi-AVE
430	yudixie_resnet18_distance_reg_0_240719
431	r3d_18
432	ReAlnet06_cornet
433	yudixie_resnet50_translation_reg_0_240908
434	compact_vgg19_IT
435	ReAlnet01_ALL
436	mc3_18
437	ReAlnet07_cornet
438	yudixie_resnet50_translation_rotation_0_240908
439	s3d
440	ReAlnet01_cornet_fov16
441	mvit_v1_b
442	ReAlnet08_cornet
443	compact_cornets_V4
444	ReAlnet02_ALL
445	ReAlnet03_ALL
446	VOneCORnet-S
447	ReAlnet04_ALL
448	omnivore_swinB
449	SeLaVi-Kinetics400
450	ReAlnet09_cornet
451	SeLaVi-VGG-Sound
452	compact_cornets_IT
453	ReAlnet05_ALL
454	SeLaVi-Kinetics-Sound
455	omnivore_swinS
456	ReAlnet06_ALL
457	mvit_v2_s
458	compact_resnet50_robust_IT
459	r2plus1d_18
460	ReAlnet07_ALL
461	resnet34_ecoset_full
462	yudixie_resnet18_object_class_0_240719
463	yudixie_resnet18_category_class_0_240719
464	ReAlnet08_ALL
465	yudixie_resnet50_distance_translation_rotation_0_240908
466	yudixie_resnet18_distance_translation_rotation_0_240719
467	yudixie_resnet18_distance_rotation_0_240719
468	ReAlnet03_cornet
469	ReAlnet09_ALL
470	ReAlnet10_cornet
471	resnet50_primary_visual_cortex
472	ReAlnet10_ALL
473	yolos_tiny
474	resnet18_fair
475	pr1_stl10_disentangled_alpha04
476	predictive_coding_pc
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_imagenet
480	yudixie_wbh_object_class_scaling_0415_run_2
481	yudixie_wbh_object_class_scaling_0415_run_1
482	deepeisnn_ei_cifar10_vgg19
483	deepeisnn_ei_cifar10_resnet18
484	deepeisnn_ei_imagenet_resnet18

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

0.42.

Note that scores are relative to this ceiling.

Data: Geirhos2021colour

Metric: error_consistency