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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	tv_efficientnet-b1	.517
61	antialiased-rnext101_32x8d	.511
62	efficientnet_b0	.507
63	inception_v3_pytorch	.505
64	resnet-152_v2-tf	.504
65	resnet50_random_l2_perturb	.497
66	resnet50_moco_v2	.495
67	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.493
68	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.493
69	cv_18_dagger_408_pretrained	.492
70	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.487
71	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
72	convnext_small_imagenet_full_seed-0	.483
73	resnet50	.475
74	vgg_19	.471
75	xception	.470
76	grcnn_109	.462
77	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.461
78	deit_large_imagenet_full_seed-0	.453
79	resnet-50_v2-tf	.449
80	deit_base_imagenet_full_seed-0	.447
81	resnet_101_v1	.440
82	AdvProp_efficientnet-b7	.434
83	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.429
84	ViT_L_32_imagenet1k	.419
85	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.416
86	resnet-152_v1-tf	.409
87	resnet50_byol	.407
88	nasnet_mobile-tf	.401
89	pnasnet_large_pytorch	.392
90	resnet34_imagenet_full	.390
91	deit_small_imagenet_full_seed-0	.371
92	ViT_B_32_imagenet1k	.368
93	resnet50-SIN_IN_IN	.367
94	resnet_SIN_IN_FT_IN	.367
95	cornet_s	.364
96	densenet-201-keras	.362
97	resnet-101_v1-tf	.359
98	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.356
99	AdvProp_efficientnet-b4	.353
100	AT_efficientnet-b4	.353
101	Res2Net50_26w_4s	.353
102	resnet_50_v1_spiking	.351
103	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
104	vit_large_patch14_clip_336:laion2b_ft_in1k	.348
105	densenet-121-keras	.346
106	AT_efficientnet-b2	.338
107	AdvProp_efficientnet-b2	.338
108	vgg_16	.337
109	vit_large_patch14_clip_224:openai_ft_in1k	.337
110	antialiased-r50	.336
111	AdvProp_efficientnet-b6	.336
112	mobilenet_v2_1.4_224-tf	.336
113	efficientnet-b7	.336
114	densenet-169-keras	.335
115	densenet-201	.333
116	resnet18_imagenet_full	.333
117	resnet50-moclr8deg	.332
118	resnet50_random_linf8_perturb	.326
119	densenet-121	.324
120	inception_v1	.312
121	resnet50_tutorial	.309
122	resnet_50_v1	.309
123	resnet-50-pytorch	.309
124	resnet50-sup	.309
125	nasnet_mobile	.306
126	BiT-S-R50x3	.300
127	BiT-S-R101x3	.299
128	densenet-169	.299
129	mobilenet_v2_1_3_224	.297
130	inception_v1-tf	.296
131	mobilenet_v2_1.3_224-tf	.295
132	CORnet-S	.292
133	resnet50-SIN_IN	.291
134	BiT-S-R101x1	.289
135	resnet-34	.282
136	grcnn	.280
137	convnext_tiny_sup	.269
138	custom_model_cv_18_dagger_408	.269
139	resnet50_simclr	.268
140	resnet-50_v1-tf	.267
141	resnet50-vitoimagevidnet8	.263
142	inception_v2-tf	.262
143	ReAlnet09	.257
144	ReAlnet01	.257
145	BiT-S-R152x2	.256
146	convnext_base:clip_laiona_augreg_ft_in1k_384	.256
147	convnext_small_imagenet_100_seed-0	.255
148	ReAlnet06	.253
149	vonegrcnn_62e_nobn	.252
150	mobilenet_v2_1_0_224	.251
151	ReAlnet03	.251
152	efficientnet_b2_imagenet_full	.249
153	ReAlnet04	.246
154	efficientnet_b0_imagenet_full	.240
155	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.238
156	BiT-S-R152x4	.237
157	mobilenet_v2_1_0_192	.233
158	voneresnet-50-non_stochastic	.231
159	AdvProp_efficientnet-b8	.230
160	vonegrcnn_47e	.228
161	vonegrcnn_52e_full	.226
162	mobilenet_v2_1.0_224-tf	.225
163	BiT-S-R50x1	.225
164	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.223
165	vonegrcnn_62e_nobn	.221
166	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.220
167	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.219
168	ReAlnet02	.219
169	resnet50_eMMCR_Vanilla	.218
170	vonegrcnn_47e	.218
171	mobilenet_v2_0_75_224	.216
172	vonegrcnn_52e_full	.215
173	resnet-18	.212
174	ReAlnet07	.212
175	mobilenet_v2_0.75_224-tf	.212
176	ReAlnet10	.209
177	ReAlnet08	.195
178	mobilenet_v2_1-4_224_pytorch	.193
179	mobilenet_v2_1_4_224	.193
180	blt_vs	.188
181	mobilenet_v2_1.0_192-tf	.187
182	ReAlnet05	.183
183	efficientnet_b1_imagenet_full	.181
184	regnet_y_400mf	.179
185	mobilenet_v2_1_0_160	.175
186	mobilenet_v2_0_5_224	.174
187	mobilenet_v1_0.75_224	.164
188	mobilenet_v2_0_75_192	.163
189	mobilenet_v2_0_5_192	.157
190	mobilenet_v2_1.0_160-tf	.157
191	vgg-16-keras	.155
192	voneresnet-50	.152
193	mobilenet_v2_0.5_224-tf	.145
194	resnet_50_v1_spiking_l4a	.144
195	mobilenet_v1_1.0_224	.143
196	mobilenet_v2_0_75_160	.139
197	mobilenet_v1_1.0_192	.137
198	resnet50_linf_4_robust	.136
199	mobilenet_v2_0.5_192-tf	.123
200	texture_shape_resnet50_trained_on_SIN	.119
201	konkle_alexnetgn_ipcl_ref01_primary_model	.116
202	mobilenet_v1_0.75_192	.114
203	resnet50_imagenet_100_seed-0	.113
204	shufflenet_v2_x1_0	.107
205	vgg-19-keras	.107
206	squeezenet1_1	.106
207	mobilenet_v1_0.5_192	.104
208	mobilenet_v1_1.0_160	.103
209	mobilenet_v2_0.75_160-tf	.101
210	mobilenet_v2_0.75_128	.101
211	mobilenet_v2_0.75_192-tf	.100
212	mobilenet_v2_0.35_192	.092
213	resnet50_robust_l2_eps1	.088
214	squeezenet1_0	.087
215	alexnet_random_linf8_perturb	.086
216	AdvProp_efficientnet-b0	.085
217	alexnet	.082
218	mobilenet_v1_0.5_224	.073
219	alexnet_early_checkpoint	.073
220	mobilenet_v1_0.5_160	.070
221	mobilenet_v2_0.35_224	.069
222	mobilenet_v2_0.5_128	.068
223	mobilenet_v2_1.0_96	.067
224	mobilenet_v2_0.75_96	.066
225	resnet50_l2_3_robust	.065
226	resnet50_linf_8_robust	.064
227	voneresnet-50-robust	.064
228	resnet_50_v1_spiking_l4	.064
229	mobilenet_v2_0.35_160	.063
230	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.056
231	mobilenet_v2_0.5_160	.055
232	alexnet-baseline	.055
233	alexnet	.055
234	alexnet	.055
235	alexnet	.055
236	alexnet	.055
237	alexnet	.055
238	alexnet	.055
239	alexnet	.055
240	alexnet	.055
241	alexnet	.055
242	alexnet_ks_torevert	.055
243	mobilenet_v1_0.75_160	.054
244	mobilenet_v1_0.25_128	.051
245	alexnet_random_l2_3_perturb	.049
246	resnet50-SIN	.048
247	mobilenet_v2_1_0_128	.045
248	vonealexnet_gaussian_noise_std4_fixed	.045
249	mobilenet_v2_1.0_128-tf	.043
250	alexnet_training_seed_05	.043
251	alexnet_training_seed_01	.043
252	convnext_small_imagenet_1_seed-0	.039
253	mobilenet_v1_0.25_192	.038
254	resnet50-vicregl0p9	.037
255	alexnet_reduced_aliasing_early_checkpoint	.036
256	mobilenet_v1_0.25_160	.035
257	mobilenet_v1_1.0_128	.035
258	resnet-50-robust	.034
259	imagenet_l2_3_0	.034
260	resnet50_robust_l2_eps3	.034
261	alexnet_training_seed_06	.034
262	deit_small_imagenet_100_seed-0	.033
263	mobilenet_v1_0.75_128	.031
264	resnet50_imagenet_10_seed-0	.029
265	alexnet_training_seed_09	.027
266	texture_shape_alexnet_trained_on_SIN	.027
267	AlexNet_SIN	.025
268	my-model	.019
269	resnet50-vicregl0p75	.019
270	resnet50_FractalDB	.019
271	imagenet_l2_10_0	.017
272	resnet50-vicreg	.016
273	deit_small_imagenet_10_seed-0	.016
274	deit_small_imagenet_1_seed-0	.016
275	mobilenet_v2_0.5_96	.016
276	resnet50_imagenet_1_seed-0	.015
277	mobilenet_v2_0.35_96	.015
278	mobilenet_v1_0.25_224	.014
279	resnet_50_v1_spiking_l3	.013
280	alexnet_training_seed_07	.013
281	convnext_small_imagenet_10_seed-0	.012
282	mobilenet_v1_0.5_128	.011
283	mobilenet_v2_0.35_128	.011
284	alexnet_training_seed_02	.010
285	alexnet_training_seed_04	.010
286	alexnet_training_seed_08	.010
287	alexnet_training_seed_10	.010
288	resnet50-meshes-lt-100-original-scratch	.010
289	mobilevit_small	.010
290	alexnet_linf_8_robust	.010
291	alexnet_training_seed_03	.009
292	0.5x_resnet-18_LC_w_sh_1_iter	.009
293	resnet50-barlow	.009
294	resnet50-meshes-lt-100-original-pretrained	.008
295	barlow-twins-resnet50	.004
296	artResNet18_1	.004
297	alexnet_robust_correct	.003
298	resnet-50_untrained	.003
299	alexnet_l2_3_robust	.003
300	resnet50-simclr-vissl	.001
301	resnet-18-LC_w_sh_100_iter_conv_init_m
302	ViT-B/32
303	BiT-M-R50x3
304	BiT-M-R50x1
305	BiT-M-R152x2
306	resnet-18-LC_w_sh_1_iter_conv_init_m
307	resnet-18-LC_w_sh_10_iter_m
308	resnet-18-LC_w_sh_1_iter_m
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_10_iter_conv_init_m
311	FrankRobWobv0
312	0.5x_resnet-18_LC
313	CORnetZ_CIFAR10
314	resnet18-local_aggregation
315	resnet18-deepcluster
316	r3m_resnet18
317	CORnet-Z
318	resnet18-instance_recognition
319	my-model
320	vggface
321	0.5x_resnet-18_LC_w_sh_10_iter
322	r3m_resnet50
323	resnet18-colorization
324	r3m_resnet34
325	resnet-18-LC_m
326	r3m_resnet50_nocrop
327	resnet18-supervised
328	resnet-18-LC_1st_conv_m
329	resnet-18-LC_conv_init_m
330	resnet18-contrastive_multiview
331	0.5x_resnet-18
332	RN50
333	0.5x_resnet-18_LC_w_sh_100_iter
334	RN50
335	resnet18-relative_position
336	resnet18-autoencoder
337	resnet18-simclr
338	ViT_L_32
339	ViT_B_32
340	resnet18-depth_prediction
341	ViT_B_16
342	r3m_resnet34_nocrop
343	resnet18-contrastive_predictive
344	omnivore_swinT
345	resnet-18-LC_untrained
346	resnet-18_untrained
347	resnet-50x2_untrained
348	dcgan
349	v1-pyr-nodown
350	unet_entire
351	CORnetZ_CIFAR10_bs32_20_04
352	pixels
353	dorinet_cornet_z
354	resnet50
355	openclip
356	resnet50-cifar
357	resnet50_pretrained_with_retinal_waves
358	resnet50_ImageNet
359	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
360	ReAlnet09_ALL
361	yudixie_resnet50_imagenet1kpret_0_240312
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	yudixie_resnet50_distance_reg_0_240908
370	PredRNN
371	SimVP
372	TAU
373	MIM
374	densenet_201_pytorch
375	ReAlnet01_cornet
376	yudixie_resnet18_translation_reg_0_240719
377	yudixie_resnet18_distance_reg_0_240719
378	yolos_tiny
379	yudixie_resnet18_rotation_reg_0_240719
380	ReAlnet02_cornet
381	ReAlnet03_cornet
382	resnet18_ecoset_full
383	resnet34_ecoset_full
384	VOneCORnet-S
385	resnet50_ecoset_full
386	yudixie_resnet50_rotation_reg_0_240908
387	resnet101_ecoset_full
388	yudixie_resnet50_distance_translation_0_240908
389	yudixie_resnet18_distance_translation_0_240719
390	yudixie_resnet18_translation_rotation_0_240719
391	yudixie_resnet18_distance_rotation_0_240719
392	yudixie_resnet18_category_class_0_240719
393	resnet-18_test_m
394	yudixie_resnet18_random_0_240719
395	yudixie_resnet18_cat_obj_class_all_latents_0_240719
396	ReAlnet05_cornet
397	yudixie_resnet50_distance_rotation_0_240908
398	SeLaVi-AVE
399	yudixie_resnet18_distance_translation_rotation_0_240719
400	yudixie_resnet18_object_class_0_240719
401	hmax
402	yudixie_resnet50_translation_reg_0_240908
403	r3d_18
404	yudixie_resnet50_translation_rotation_0_240908
405	ReAlnet01_ALL
406	mc3_18
407	ReAlnet07_cornet
408	s3d
409	mvit_v1_b
410	ReAlnet08_cornet
411	ReAlnet02_ALL
412	ReAlnet03_ALL
413	ReAlnet04_ALL
414	omnivore_swinB
415	SeLaVi-Kinetics400
416	ReAlnet09_cornet
417	SeLaVi-VGG-Sound
418	ReAlnet05_ALL
419	SeLaVi-Kinetics-Sound
420	r2plus1d_18
421	ReAlnet06_ALL
422	omnivore_swinS
423	mvit_v2_s
424	ReAlnet10_cornet
425	resnet152_ecoset_full
426	ReAlnet07_ALL
427	ReAlnet08_ALL
428	yudixie_resnet18_imagenet1kpret_0_240719
429	ReAlnet04_cornet
430	ReAlnet06_cornet
431	yudixie_resnet50_distance_translation_rotation_0_240908
432	resnet50_primary_visual_cortex
433	briaai_rmbg_1_4
434	ReAlnet10_ALL

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