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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	resnext101_32x32d_wsl	.756
22	resnet50-VITO-8deg-cc	.755
23	CLIP_ViT-B_32	.748
24	convnext_tiny:in12k_ft_in1k	.746
25	CLIP_resnet50_float32	.739
26	CLIP_resnet50	.739
27	resnet_50_v2	.738
28	resnet_101_v2	.731
29	cvt_cvt-21-384-in1k_4_LucyV4	.727
30	resnet-152_v2-tf	.721
31	resnet-152_v2_pytorch	.716
32	ViT_L_16_imagenet1k	.715
33	cvt_cvt-21-224-in1k_4_LucyV4	.703
34	resnext101_32x16d_wsl	.696
35	resnet152_imagenet_full	.692
36	resnet_152_v2	.691
37	inception_v3	.689
38	inception_v4	.685
39	efficientnet_b0	.684
40	fixres_resnext101_32x48d_wsl	.675
41	resnet101	.675
42	cvt_cvt-13-384-in1k_4_LucyV4	.670
43	deit3_base_patch16_224_fb_in22k_ft_in1k	.669
44	deit3_base_patch16_224_fb_in1k	.660
45	inception_v3-tf	.657
46	resnet101_imagenet_full	.653
47	focalnet_tiny_lrf_in1k	.647
48	ViT_B_16_imagenet1k	.640
49	tv_efficientnet-b1	.637
50	resnet50_ewconplus_wzc_seed5_2__323	.631
51	resnet50	.631
52	resnet_152_v1	.624
53	xception	.619
54	resnet50_random_l2_perturb	.617
55	inception_v4-tf	.591
56	resnet50_st_wzc_seed3_2_323	.588
57	resnet-50_v2-tf	.587
58	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.587
59	resnet50_imagenet_full	.585
60	antialiased-r50	.574
61	xception-keras	.561
62	vit_relpos_base_patch16_clsgap_224:sw_in1k	.560
63	resnet_101_v1	.551
64	AdvProp_efficientnet-b6	.546
65	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.543
66	vit_base_patch16_clip_224:openai_ft_in1k	.541
67	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.538
68	convnext_tiny_imagenet_full_seed-0	.534
69	antialiased-rnext101_32x8d	.528
70	resnet50_byol	.521
71	AdvProp_efficientnet-b7	.521
72	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.519
73	resnet50_st_wzc_2	.515
74	antialias-resnet152	.512
75	efficientnet-b7	.510
76	alexnet_fov4	.505
77	vgg_19	.505
78	resnet50_moco_v2	.504
79	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.499
80	Res2Net50_26w_4s	.491
81	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.491
82	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.489
83	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.488
84	densenet-201-keras	.479
85	resnet34_imagenet_full	.478
86	resnet-50-pytorch	.474
87	resnet50-sup	.474
88	resnet50_tutorial	.474
89	resnet_50_v1	.474
90	grcnn_109	.474
91	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.472
92	convnext_large:fb_in22k_ft_in1k	.470
93	AT_efficientnet-b2	.468
94	AdvProp_efficientnet-b2	.468
95	resnet50_ewconplus_wzc	.466
96	resnet50-SIN_IN_IN	.464
97	resnet_SIN_IN_FT_IN	.464
98	ViT_L_32_imagenet1k	.463
99	convnext_small_imagenet_full_seed-0	.462
100	resnet50-moclr8deg	.461
101	densenet-169	.456
102	deit_small_imagenet_full_seed-0	.454
103	AdvProp_efficientnet-b4	.450
104	AT_efficientnet-b4	.450
105	resnet_50_v1_spiking	.448
106	densenet-169-keras	.448
107	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.448
108	densenet-121-keras	.448
109	cornet_s	.445
110	resnet18_imagenet_full	.443
111	densenet_201_pytorch	.442
112	convnext_xlarge:fb_in22k_ft_in1k	.441
113	grcnn	.439
114	densenet-201	.438
115	resnet-101_v1-tf	.432
116	mobilenet_v2_1_3_224	.430
117	efficientnet_b2_imagenet_full	.429
118	vit_large_patch14_clip_224:openai_ft_in1k	.428
119	resnet50_random_linf8_perturb	.427
120	convnext_base:clip_laiona_augreg_ft_in1k_384	.422
121	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.419
122	convnext_base_fb_in1k	.412
123	deit_large_imagenet_full_seed-0	.411
124	convnext_small_imagenet_100_seed-0	.406
125	deit_base_imagenet_full_seed-0	.406
126	densenet-121	.404
127	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.403
128	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.403
129	cv_18_dagger_408_pretrained	.400
130	resnet-50_v1-tf	.395
131	convnext_large_imagenet_full_seed-0	.395
132	dinov2_vitl14_lc	.394
133	BiT-S-R50x3	.391
134	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.390
135	vit_large_patch14_clip_336:laion2b_ft_in1k	.387
136	convnext_base_fb_in1k_gram_aligned_dinov3	.385
137	resnet50_hvs_wzc	.381
138	ReAlnet03	.377
139	resnet-152_v1-tf	.373
140	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.370
141	ReAlnet01	.370
142	convnext_base_imagenet_full_seed-0	.365
143	vit_huge_clip336_laion	.364
144	mobilenet_v2_1.4_224-tf	.363
145	voneresnet-50-non_stochastic	.361
146	BiT-S-R152x2	.356
147	dinov2_vitg14_lc	.353
148	custom_model_cv_18_dagger_408	.346
149	resnet50-SIN_IN	.344
150	inception_v2-tf	.344
151	mobilenet_v2_1.3_224-tf	.343
152	ReAlnet04	.342
153	vgg_16	.341
154	ReAlnet08	.328
155	ReAlnet09	.325
156	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.324
157	ReAlnet05	.322
158	efficientnet_b0_imagenet_full	.320
159	ViT_B_32_imagenet1k	.320
160	vit_large_clip336_openai	.319
161	convnext_xxlarge_clip	.319
162	ReAlnet07	.316
163	CORnet-S	.314
164	ReAlnet06	.314
165	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.311
166	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.311
167	nasnet_mobile-tf	.309
168	convnext_large_clip_laion2b_finelayers	.307
169	inception_v1	.300
170	dinov2_vitl14_reg_lc	.300
171	ReAlnet02	.299
172	vonegrcnn_62e_nobn	.298
173	vonegrcnn_62e_nobn	.293
174	efficientnet_b1_imagenet_full	.290
175	ReAlnet10	.290
176	mobilenet_v2_1-4_224_pytorch	.288
177	mobilenet_v2_1_4_224	.288
178	inception_v1-tf	.288
179	nasnet_mobile	.286
180	convnext_large_in22k_supervised	.284
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	dinov2_vitl14_lc_res336	.202
209	dinov2_vitg14_reg_lc	.193
210	vgg-19-keras	.188
211	BiT-S-R101x1	.182
212	texture_shape_resnet50_trained_on_SIN	.182
213	voneresnet-50-robust	.180
214	resnet50_eMMCR_Vanilla	.179
215	mobilenet_v2_1.0_160-tf	.177
216	mobilenet_v1_1.0_160	.173
217	resnet50-vitoimagevidnet8	.170
218	mobilenet_v2_1.0_224-tf	.168
219	mobilenet_v2_0_5_192	.165
220	alexnet_random_linf8_perturb	.162
221	mobilenet_v2_1.0_192-tf	.161
222	mobilenet_v2_0.75_224-tf	.159
223	alexnet_random_l2_3_perturb	.152
224	resnet50_robust_l2_eps1	.152
225	mobilenet_v2_0.5_224-tf	.150
226	mobilenet_v2_0_75_192	.150
227	resnet50_l2_3_robust	.146
228	resnet_50_v1_spiking_l4a	.143
229	resnet50_simclr	.143
230	alexnet_reduced_aliasing_early_checkpoint	.137
231	mobilenet_v2_0_75_160	.134
232	alexnet	.134
233	alexnet_ks_torevert	.134
234	konkle_alexnetgn_ipcl_ref01_primary_model	.134
235	mobilenet_v2_0.5_192-tf	.131
236	shufflenet_v2_x1_0	.130
237	mobilenet_v1_0.5_224	.123
238	mobilenet_v2_1_0_128	.122
239	resnet50_imagenet_100_seed-0	.120
240	alexnet_early_checkpoint	.119
241	eva02_large_in22k	.118
242	AdvProp_efficientnet-b0	.115
243	mobilenet_v1_0.75_192	.113
244	resnet50-SIN	.111
245	mobilenet_v2_0.75_160-tf	.108
246	vonealexnet_gaussian_noise_std4_fixed	.104
247	resnet-50-robust	.104
248	imagenet_l2_3_0	.104
249	resnet50_robust_l2_eps3	.104
250	resnet50_linf_8_robust	.104
251	mobilenet_v1_1.0_192	.096
252	mobilenet_v1_0.5_192	.090
253	0.5x_resnet-18_LC_w_sh_1_iter	.084
254	mobilenet_v2_0.75_192-tf	.072
255	mobilenet_v2_0.75_96	.072
256	texture_shape_alexnet_trained_on_SIN	.071
257	mobilenet_v1_0.75_160	.068
258	mobilenet_v1_1.0_128	.068
259	squeezenet1_0	.067
260	AlexNet_SIN	.065
261	mobilenet_v2_0.75_128	.062
262	mobilenet_v2_1.0_128-tf	.061
263	alexnet_linf_8_robust	.060
264	alexnet_l2_3_robust	.060
265	mobilenet_v2_0.35_160	.058
266	alexnet_training_seed_07	.050
267	alexnet_training_seed_02	.049
268	alexnet_training_seed_04	.049
269	alexnet_training_seed_08	.049
270	alexnet_training_seed_10	.049
271	mobilenet_v2_0.35_192	.048
272	squeezenet1_1	.047
273	mobilenet_v1_0.25_192	.045
274	mobilenet_v1_0.25_224	.044
275	alexnet_robust_correct	.043
276	resnet50-vicreg	.043
277	imagenet_l2_10_0	.041
278	mobilenet_v1_0.75_128	.039
279	mobilenet_v2_0.35_128	.038
280	alexnet_fov12	.038
281	mobilenet_v2_0.5_128	.037
282	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.037
283	mobilenet_v1_0.5_160	.036
284	mobilenet_v2_0.35_96	.036
285	mobilenet_v2_0.35_224	.035
286	deit_small_imagenet_100_seed-0	.035
287	resnet50-vicregl0p9	.031
288	mobilenet_v1_0.25_128	.030
289	mobilenet_v2_1.0_96	.030
290	convnext_small_imagenet_10_seed-0	.030
291	deit_small_imagenet_10_seed-0	.027
292	resnet50_imagenet_10_seed-0	.027
293	deit_small_imagenet_1_seed-0	.027
294	alexnet_fov16	.026
295	alexnet_training_seed_03	.025
296	mobilenet_v2_0.5_160	.025
297	mobilenet_v2_0.5_96	.025
298	alexnet_training_seed_06	.022
299	AlexNet_SIN_fov12	.020
300	resnet50-barlow	.020
301	mobilenet_v1_0.5_128	.020
302	alexnet_training_seed_05	.020
303	alexnet_training_seed_01	.020
304	alexnet_training_seed_01_fov4	.020
305	alexnet_training_seed_01_fov12	.020
306	alexnet_training_seed_01_fov16	.020
307	resnet_50_v1_spiking_l4	.019
308	artResNet18_1	.015
309	my-model	.014
310	resnet50-vicregl0p75	.014
311	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.012
312	resnet_50_v1_spiking_l3	.012
313	AlexNet_SIN_fov16	.011
314	barlow-twins-resnet50	.009
315	resnet-50_untrained	.009
316	convnext_small_imagenet_1_seed-0	.009
317	alexnet_training_seed_09	.007
318	mobilenet_v1_0.25_160	.006
319	resnet50_FractalDB	.004
320	mobilevit_small	.004
321	resnet50_imagenet_1_seed-0	.003
322	resnet50-simclr-vissl	.002
323	resnet-18-LC_w_sh_100_iter_conv_init_m
324	ViT-B/32
325	BiT-M-R50x3
326	BiT-M-R152x4
327	BiT-M-R50x1
328	BiT-M-R152x2
329	resnet-18-LC_w_sh_1_iter_conv_init_m
330	resnet-18-LC_w_sh_1_iter_m
331	resnet-18-LC_w_sh_10_iter_m
332	resnet-18-LC_w_sh_100_iter_m
333	resnet-18-LC_w_sh_10_iter_conv_init_m
334	FrankRobWobv0
335	resnet18-local_aggregation
336	0.5x_resnet-18_LC
337	CORnetZ_CIFAR10
338	resnet18-colorization
339	resnet18-deepcluster
340	r3m_resnet18
341	resnet18-instance_recognition
342	CORnet-Z
343	my-model
344	vggface
345	0.5x_resnet-18_LC_w_sh_10_iter
346	r3m_resnet50
347	resnet18-relative_position
348	r3m_resnet34
349	resnet-18-LC_m
350	r3m_resnet50_nocrop
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-autoencoder
360	resnet18-simclr
361	ViT_L_32
362	ViT_B_32
363	resnet18-depth_prediction
364	ViT_B_16
365	r3m_resnet34_nocrop
366	resnet18-contrastive_predictive
367	omnivore_swinT
368	resnet-18-LC_untrained
369	resnet-18_untrained
370	resnet-50x2_untrained
371	dcgan
372	v1-pyr-nodown
373	unet_entire
374	CORnetZ_CIFAR10_bs32_20_04
375	pixels
376	dorinet_cornet_z
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
485	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

0.42.

Note that scores are relative to this ceiling.

Data: Geirhos2021colour

Metric: error_consistency