Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021falsecolour-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-13-384-in1k_4_LucyV4	.818
2	SWSL_resnext101_32x8d	.804
3	nasnet_large	.780
4	resnext101_32x16d_wsl	.757
5	vision_transformer_vit_large_patch16_224	.756
6	resnext101_32x48d_wsl	.755
7	CLIP-RN50	.754
8	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.752
9	SWSL_resnet50	.752
10	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.739
11	inception_v4	.738
12	resnext101_32x32d_wsl	.735
13	cvt_cvt-21-384-in1k_4_LucyV4	.726
14	nasnet_large-tf	.708
15	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.706
16	pnasnet_large	.702
17	cvt_cvt-13-224-in1k_4_LucyV4	.699
18	CLIP_resnet50	.688
19	CLIP_resnet50_float32	.688
20	pnasnet_large-tf	.682
21	resnext101_32x8d_wsl	.668
22	effnetb1_272x240	.667
23	vit_relpos_base_patch16_clsgap_224:sw_in1k	.648
24	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.640
25	convnext_femto_ols:d1_in1k	.633
26	convnext_large_imagenet_full_seed-0	.622
27	resnet_50_v2	.622
28	effnetv2m_custom384	.617
29	focalnet_tiny_lrf_in1k	.610
30	fixres_resnext101_32x48d_wsl	.610
31	CLIP_ViT-B_32	.605
32	xception-keras	.599
33	ViT_B_16_imagenet1k	.588
34	vit_base_patch16_clip_224:openai_ft_in1k	.581
35	resnet50-VITO-8deg-cc	.580
36	resnet101	.580
37	convnext_xlarge:fb_in22k_ft_in1k	.579
38	convnext_large:fb_in22k_ft_in1k	.572
39	resnet_101_v2	.568
40	antialias-resnet152	.566
41	resnet-152_v2_pytorch	.564
42	convnext_tiny_imagenet_full_seed-0	.564
43	convnext_tiny:in12k_ft_in1k	.560
44	resnet152_imagenet_full	.559
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.559
46	inception_v3	.558
47	cvt_cvt-21-224-in1k_4_LucyV4	.554
48	inception_v4-tf	.547
49	resnet_152_v1	.546
50	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.542
51	vit_large_patch14_clip_224:laion2b_ft_in1k	.539
52	resnet_152_v2	.537
53	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.536
54	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.532
55	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.532
56	resnet50_imagenet_full	.531
57	resnet101_imagenet_full	.528
58	convnext_base_imagenet_full_seed-0	.520
59	inception_v3-tf	.519
60	antialiased-rnext101_32x8d	.511
61	efficientnet_b0	.507
62	inception_v3_pytorch	.505
63	resnet-152_v2-tf	.504
64	resnet50_random_l2_perturb	.497
65	resnet50_moco_v2	.495
66	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.493
67	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.493
68	cv_18_dagger_408_pretrained	.492
69	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.487
70	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
71	convnext_small_imagenet_full_seed-0	.483
72	resnet50	.475
73	vgg_19	.471
74	xception	.470
75	grcnn_109	.462
76	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.461
77	deit_large_imagenet_full_seed-0	.453
78	resnet-50_v2-tf	.449
79	deit_base_imagenet_full_seed-0	.447
80	resnet_101_v1	.440
81	AdvProp_efficientnet-b7	.434
82	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.429
83	ViT_L_32_imagenet1k	.419
84	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.416
85	resnet-152_v1-tf	.409
86	resnet50_byol	.407
87	nasnet_mobile-tf	.401
88	pnasnet_large_pytorch	.392
89	resnet34_imagenet_full	.390
90	deit_small_imagenet_full_seed-0	.371
91	ViT_B_32_imagenet1k	.368
92	resnet_SIN_IN_FT_IN	.367
93	resnet50-SIN_IN_IN	.367
94	cornet_s	.364
95	densenet-201-keras	.362
96	resnet-101_v1-tf	.359
97	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.356
98	AT_efficientnet-b4	.353
99	AdvProp_efficientnet-b4	.353
100	Res2Net50_26w_4s	.353
101	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
102	vit_large_patch14_clip_336:laion2b_ft_in1k	.348
103	densenet-121-keras	.346
104	AdvProp_efficientnet-b2	.338
105	AT_efficientnet-b2	.338
106	vgg_16	.337
107	vit_large_patch14_clip_224:openai_ft_in1k	.337
108	antialiased-r50	.336
109	AdvProp_efficientnet-b6	.336
110	mobilenet_v2_1.4_224-tf	.336
111	efficientnet-b7	.336
112	densenet-169-keras	.335
113	densenet-201	.333
114	resnet18_imagenet_full	.333
115	resnet50-moclr8deg	.332
116	resnet50_random_linf8_perturb	.326
117	densenet-121	.324
118	inception_v1	.312
119	resnet_50_v1	.309
120	resnet50_tutorial	.309
121	resnet50-sup	.309
122	resnet-50-pytorch	.309
123	nasnet_mobile	.306
124	BiT-S-R50x3	.300
125	BiT-S-R101x3	.299
126	densenet-169	.299
127	mobilenet_v2_1_3_224	.297
128	inception_v1-tf	.296
129	mobilenet_v2_1.3_224-tf	.295
130	CORnet-S	.292
131	resnet50-SIN_IN	.291
132	BiT-S-R101x1	.289
133	resnet-34	.282
134	grcnn	.280
135	convnext_tiny_sup	.269
136	custom_model_cv_18_dagger_408	.269
137	resnet50_simclr	.268
138	resnet-50_v1-tf	.267
139	resnet50-vitoimagevidnet8	.263
140	inception_v2-tf	.262
141	ReAlnet09	.257
142	ReAlnet01	.257
143	ReAlnet_incorrect	.257
144	BiT-S-R152x2	.256
145	convnext_base:clip_laiona_augreg_ft_in1k_384	.256
146	convnext_small_imagenet_100_seed-0	.255
147	ReAlnet06	.253
148	vonegrcnn_62e_nobn	.252
149	mobilenet_v2_1_0_224	.251
150	ReAlnet03	.251
151	efficientnet_b2_imagenet_full	.249
152	ReAlnet04	.246
153	efficientnet_b0_imagenet_full	.240
154	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.238
155	BiT-S-R152x4	.237
156	mobilenet_v2_1_0_192	.233
157	voneresnet-50-non_stochastic	.231
158	AdvProp_efficientnet-b8	.230
159	vonegrcnn_47e	.228
160	vonegrcnn_52e_full	.226
161	mobilenet_v2_1.0_224-tf	.225
162	BiT-S-R50x1	.225
163	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.223
164	vonegrcnn_62e_nobn	.221
165	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.220
166	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.219
167	ReAlnet02	.219
168	vonegrcnn_47e	.218
169	mobilenet_v2_0_75_224	.216
170	vonegrcnn_52e_full	.215
171	resnet-18	.212
172	ReAlnet07	.212
173	mobilenet_v2_0.75_224-tf	.212
174	ReAlnet10	.209
175	ReAlnet08	.195
176	mobilenet_v2_1-4_224_pytorch	.193
177	mobilenet_v2_1_4_224	.193
178	blt_vs	.188
179	mobilenet_v2_1.0_192-tf	.187
180	ReAlnet05	.183
181	efficientnet_b1_imagenet_full	.181
182	regnet_y_400mf	.179
183	mobilenet_v2_1_0_160	.175
184	mobilenet_v2_0_5_224	.174
185	mobilenet_v1_0.75_224	.164
186	mobilenet_v2_0_75_192	.163
187	mobilenet_v2_0_5_192	.157
188	mobilenet_v2_1.0_160-tf	.157
189	vgg-16-keras	.155
190	voneresnet-50	.152
191	mobilenet_v2_0.5_224-tf	.145
192	mobilenet_v1_1.0_224	.143
193	mobilenet_v2_0_75_160	.139
194	mobilenet_v1_1.0_192	.137
195	resnet50_linf_4_robust	.136
196	mobilenet_v2_0.5_192-tf	.123
197	texture_shape_resnet50_trained_on_SIN	.119
198	konkle_alexnetgn_ipcl_ref01_primary_model	.116
199	mobilenet_v1_0.75_192	.114
200	resnet50_imagenet_100_seed-0	.113
201	shufflenet_v2_x1_0	.107
202	vgg-19-keras	.107
203	squeezenet1_1	.106
204	mobilenet_v1_0.5_192	.104
205	mobilenet_v1_1.0_160	.103
206	mobilenet_v2_0.75_160-tf	.101
207	mobilenet_v2_0.75_128	.101
208	mobilenet_v2_0.75_192-tf	.100
209	mobilenet_v2_0.35_192	.092
210	resnet50_robust_l2_eps1	.088
211	squeezenet1_0	.087
212	alexnet_random_linf8_perturb	.086
213	AdvProp_efficientnet-b0	.085
214	alexnet	.082
215	mobilenet_v1_0.5_224	.073
216	alexnet_early_checkpoint	.073
217	mobilenet_v1_0.5_160	.070
218	mobilenet_v2_0.35_224	.069
219	mobilenet_v2_0.5_128	.068
220	mobilenet_v2_1.0_96	.067
221	mobilenet_v2_0.75_96	.066
222	resnet50_l2_3_robust	.065
223	resnet50_linf_8_robust	.064
224	voneresnet-50-robust	.064
225	mobilenet_v2_0.35_160	.063
226	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.056
227	mobilenet_v2_0.5_160	.055
228	alexnet	.055
229	alexnet	.055
230	alexnet-baseline	.055
231	alexnet	.055
232	alexnet	.055
233	alexnet	.055
234	alexnet	.055
235	alexnet	.055
236	alexnet	.055
237	alexnet	.055
238	alexnet_ks_torevert	.055
239	mobilenet_v1_0.75_160	.054
240	mobilenet_v1_0.25_128	.051
241	alexnet_random_l2_3_perturb	.049
242	resnet50-SIN	.048
243	mobilenet_v2_1_0_128	.045
244	vonealexnet_gaussian_noise_std4_fixed	.045
245	mobilenet_v2_1.0_128-tf	.043
246	alexnet_training_seed_05	.043
247	alexnet_training_seed_01	.043
248	convnext_small_imagenet_1_seed-0	.039
249	mobilenet_v1_0.25_192	.038
250	resnet50-vicregl0p9	.037
251	alexnet_reduced_aliasing_early_checkpoint	.036
252	mobilenet_v1_0.25_160	.035
253	mobilenet_v1_1.0_128	.035
254	resnet-50-robust	.034
255	imagenet_l2_3_0	.034
256	resnet50_robust_l2_eps3	.034
257	alexnet_training_seed_06	.034
258	deit_small_imagenet_100_seed-0	.033
259	mobilenet_v1_0.75_128	.031
260	resnet50_imagenet_10_seed-0	.029
261	alexnet_training_seed_09	.027
262	texture_shape_alexnet_trained_on_SIN	.027
263	my-model	.019
264	resnet50-vicregl0p75	.019
265	resnet50_FractalDB	.019
266	imagenet_l2_10_0	.017
267	resnet50-vicreg	.016
268	deit_small_imagenet_10_seed-0	.016
269	deit_small_imagenet_1_seed-0	.016
270	mobilenet_v2_0.5_96	.016
271	resnet50_imagenet_1_seed-0	.015
272	mobilenet_v2_0.35_96	.015
273	mobilenet_v1_0.25_224	.014
274	alexnet_training_seed_07	.013
275	convnext_small_imagenet_10_seed-0	.012
276	mobilenet_v1_0.5_128	.011
277	mobilenet_v2_0.35_128	.011
278	alexnet_training_seed_02	.010
279	alexnet_training_seed_04	.010
280	alexnet_training_seed_10	.010
281	alexnet_training_seed_08	.010
282	resnet50-meshes-lt-100-original-scratch	.010
283	mobilevit_small	.010
284	alexnet_linf_8_robust	.010
285	alexnet_training_seed_03	.009
286	0.5x_resnet-18_LC_w_sh_1_iter	.009
287	resnet50-barlow	.009
288	resnet50-meshes-lt-100-original-pretrained	.008
289	barlow-twins-resnet50	.004
290	artResNet18_1	.004
291	alexnet_robust_correct	.003
292	resnet-50_untrained	.003
293	alexnet_l2_3_robust	.003
294	resnet50-simclr-vissl	.001
295	resnet18_imagenet21kP	X
296	0.5x_resnet-18_LC	X
297	CORnetZ_CIFAR10	X
298	resnet18-local_aggregation	X
299	resnet18-deepcluster	X
300	r3m_resnet18	X
301	CORnet-Z	X
302	resnet18-instance_recognition	X
303	my-model	X
304	vggface	X
305	0.5x_resnet-18_LC_w_sh_10_iter	X
306	r3m_resnet50	X
307	resnet18-colorization	X
308	r3m_resnet34	X
309	resnet-18-LC_m	X
310	r3m_resnet50_nocrop	X
311	resnet18-supervised	X
312	resnet-18-LC_1st_conv_m	X
313	resnet-18-LC_conv_init_m	X
314	resnet18-contrastive_multiview	X
315	0.5x_resnet-18	X
316	RN50	X
317	0.5x_resnet-18_LC_w_sh_100_iter	X
318	RN50	X
319	resnet18-relative_position	X
320	resnet18-autoencoder	X
321	resnet18-simclr	X
322	FrankRobWobv0	X
323	ViT_L_32	X
324	ViT_B_32	X
325	resnet18-depth_prediction	X
326	ViT_B_16	X
327	r3m_resnet34_nocrop	X
328	hmax	X
329	resnet18-contrastive_predictive	X
330	omnivore_swinT	X
331	resnet-18-LC_untrained	X
332	resnet-18_untrained	X
333	resnet-50x2_untrained	X
334	dcgan	X
335	v1-pyr-nodown	X
336	unet_entire	X
337	CORnetZ_CIFAR10_bs32_20_04	X
338	pixels	X
339	dorinet_cornet_z	X
340	resnet50	X
341	openclip	X
342	resnet-18-LC_w_sh_10_iter_conv_init_m	X
343	resnet-18-LC_w_sh_100_iter_m	X
344	resnet-18-LC_w_sh_1_iter_m	X
345	resnet50-cifar	X
346	resnet50_pretrained_with_retinal_waves	X
347	resnet50_ImageNet	X
348	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
349	yudixie_resnet50_imagenet1kpret_0_240312	X
350	yudixie_resnet50_random_0_240908	X
351	yudixie_resnet50_imagenet1kpret_0_240908	X
352	resnet-18-LC_w_sh_10_iter_m	X
353	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
354	yudixie_resnet50_object_class_0_240908	X
355	yudixie_resnet50_category_class_0_240908	X
356	AlexNet_SIN	X
357	ConvLSTM	X
358	yudixie_resnet50_distance_reg_0_240908	X
359	PredRNN	X
360	SimVP	X
361	TAU	X
362	MIM	X
363	densenet_201_pytorch	X
364	ReAlnet01_cornet	X
365	resnet-18-LC_w_sh_1_iter_conv_init_m	X
366	briaai_rmbg_1_4	X
367	yudixie_resnet18_translation_reg_0_240719	X
368	BiT-M-R152x2	X
369	yudixie_resnet18_distance_reg_0_240719	X
370	yolos_tiny	X
371	yudixie_resnet18_rotation_reg_0_240719	X
372	yudixie_resnet18_imagenet1kpret_0_240719	X
373	ReAlnet02_cornet	X
374	ReAlnet03_cornet	X
375	resnet18_ecoset_full	X
376	resnet34_ecoset_full	X
377	VOneCORnet-S	X
378	resnet50_ecoset_full	X
379	BiT-M-R50x1	X
380	yudixie_resnet50_rotation_reg_0_240908	X
381	resnet101_ecoset_full	X
382	BiT-M-R50x3	X
383	yudixie_resnet50_distance_translation_0_240908	X
384	resnet152_ecoset_full	X
385	yudixie_resnet18_distance_translation_0_240719	X
386	yudixie_resnet18_translation_rotation_0_240719	X
387	ReAlnet04_cornet	X
388	yudixie_resnet18_distance_rotation_0_240719	X
389	yudixie_resnet18_category_class_0_240719	X
390	resnet-18_test_m	X
391	yudixie_resnet18_random_0_240719	X
392	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
393	ReAlnet05_cornet	X
394	yudixie_resnet50_distance_rotation_0_240908	X
395	yudixie_resnet18_distance_translation_rotation_0_240719	X
396	yudixie_resnet18_object_class_0_240719	X
397	yudixie_resnet50_translation_reg_0_240908	X
398	ReAlnet06_cornet	X
399	yudixie_resnet50_translation_rotation_0_240908	X
400	ReAlnet07_cornet	X
401	ViT-B/32	X
402	ReAlnet08_cornet	X
403	omnivore_swinB	X
404	ReAlnet09_cornet	X
405	omnivore_swinS	X
406	ReAlnet10_cornet	X
407	yudixie_resnet50_distance_translation_rotation_0_240908	X
408	resnet-18-LC_w_sh_100_iter_conv_init_m	X

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

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021falsecolour

Metric: error_consistency