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

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