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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	resnet50_ewconplus_wzc	.428
85	ViT_L_32_imagenet1k	.419
86	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.416
87	resnet-152_v1-tf	.409
88	resnet50_byol	.407
89	nasnet_mobile-tf	.401
90	pnasnet_large_pytorch	.392
91	resnet34_imagenet_full	.390
92	alexnet_fov4	.388
93	deit_small_imagenet_full_seed-0	.371
94	ViT_B_32_imagenet1k	.368
95	resnet50-SIN_IN_IN	.367
96	resnet_SIN_IN_FT_IN	.367
97	cornet_s	.364
98	densenet-201-keras	.362
99	resnet-101_v1-tf	.359
100	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.356
101	AdvProp_efficientnet-b4	.353
102	AT_efficientnet-b4	.353
103	Res2Net50_26w_4s	.353
104	resnet_50_v1_spiking	.351
105	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
106	vit_large_patch14_clip_336:laion2b_ft_in1k	.348
107	densenet-121-keras	.346
108	AT_efficientnet-b2	.338
109	AdvProp_efficientnet-b2	.338
110	vgg_16	.337
111	vit_large_patch14_clip_224:openai_ft_in1k	.337
112	antialiased-r50	.336
113	AdvProp_efficientnet-b6	.336
114	mobilenet_v2_1.4_224-tf	.336
115	efficientnet-b7	.336
116	densenet-169-keras	.335
117	densenet-201	.333
118	resnet18_imagenet_full	.333
119	resnet50-moclr8deg	.332
120	resnet50_random_linf8_perturb	.326
121	densenet-121	.324
122	inception_v1	.312
123	resnet50_tutorial	.309
124	resnet_50_v1	.309
125	resnet-50-pytorch	.309
126	resnet50-sup	.309
127	nasnet_mobile	.306
128	BiT-S-R50x3	.300
129	BiT-S-R101x3	.299
130	densenet-169	.299
131	mobilenet_v2_1_3_224	.297
132	inception_v1-tf	.296
133	mobilenet_v2_1.3_224-tf	.295
134	CORnet-S	.292
135	resnet50-SIN_IN	.291
136	BiT-S-R101x1	.289
137	resnet-34	.282
138	grcnn	.280
139	convnext_tiny_sup	.269
140	custom_model_cv_18_dagger_408	.269
141	resnet50_simclr	.268
142	resnet-50_v1-tf	.267
143	resnet50-vitoimagevidnet8	.263
144	inception_v2-tf	.262
145	ReAlnet09	.257
146	ReAlnet01	.257
147	BiT-S-R152x2	.256
148	convnext_base:clip_laiona_augreg_ft_in1k_384	.256
149	convnext_small_imagenet_100_seed-0	.255
150	ReAlnet06	.253
151	vonegrcnn_62e_nobn	.252
152	mobilenet_v2_1_0_224	.251
153	ReAlnet03	.251
154	efficientnet_b2_imagenet_full	.249
155	ReAlnet04	.246
156	efficientnet_b0_imagenet_full	.240
157	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.238
158	BiT-S-R152x4	.237
159	mobilenet_v2_1_0_192	.233
160	voneresnet-50-non_stochastic	.231
161	AdvProp_efficientnet-b8	.230
162	vonegrcnn_47e	.228
163	vonegrcnn_52e_full	.226
164	mobilenet_v2_1.0_224-tf	.225
165	BiT-S-R50x1	.225
166	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.223
167	vonegrcnn_62e_nobn	.221
168	AlexNet_SIN_fov4	.221
169	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.220
170	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.219
171	ReAlnet02	.219
172	resnet50_eMMCR_Vanilla	.218
173	vonegrcnn_47e	.218
174	mobilenet_v2_0_75_224	.216
175	vonegrcnn_52e_full	.215
176	resnet-18	.212
177	ReAlnet07	.212
178	mobilenet_v2_0.75_224-tf	.212
179	ReAlnet10	.209
180	ReAlnet08	.195
181	mobilenet_v2_1-4_224_pytorch	.193
182	mobilenet_v2_1_4_224	.193
183	blt_vs	.188
184	mobilenet_v2_1.0_192-tf	.187
185	ReAlnet05	.183
186	efficientnet_b1_imagenet_full	.181
187	regnet_y_400mf	.179
188	mobilenet_v2_1_0_160	.175
189	mobilenet_v2_0_5_224	.174
190	mobilenet_v1_0.75_224	.164
191	mobilenet_v2_0_75_192	.163
192	mobilenet_v2_0_5_192	.157
193	mobilenet_v2_1.0_160-tf	.157
194	vgg-16-keras	.155
195	voneresnet-50	.152
196	mobilenet_v2_0.5_224-tf	.145
197	resnet_50_v1_spiking_l4a	.144
198	mobilenet_v1_1.0_224	.143
199	mobilenet_v2_0_75_160	.139
200	mobilenet_v1_1.0_192	.137
201	resnet50_linf_4_robust	.136
202	mobilenet_v2_0.5_192-tf	.123
203	texture_shape_resnet50_trained_on_SIN	.119
204	konkle_alexnetgn_ipcl_ref01_primary_model	.116
205	mobilenet_v1_0.75_192	.114
206	resnet50_imagenet_100_seed-0	.113
207	shufflenet_v2_x1_0	.107
208	vgg-19-keras	.107
209	squeezenet1_1	.106
210	mobilenet_v1_0.5_192	.104
211	mobilenet_v1_1.0_160	.103
212	mobilenet_v2_0.75_160-tf	.101
213	mobilenet_v2_0.75_128	.101
214	mobilenet_v2_0.75_192-tf	.100
215	mobilenet_v2_0.35_192	.092
216	resnet50_robust_l2_eps1	.088
217	squeezenet1_0	.087
218	alexnet_random_linf8_perturb	.086
219	AdvProp_efficientnet-b0	.085
220	alexnet	.082
221	mobilenet_v1_0.5_224	.073
222	alexnet_early_checkpoint	.073
223	mobilenet_v1_0.5_160	.070
224	mobilenet_v2_0.35_224	.069
225	mobilenet_v2_0.5_128	.068
226	mobilenet_v2_1.0_96	.067
227	mobilenet_v2_0.75_96	.066
228	resnet50_l2_3_robust	.065
229	resnet50_linf_8_robust	.064
230	voneresnet-50-robust	.064
231	resnet_50_v1_spiking_l4	.064
232	mobilenet_v2_0.35_160	.063
233	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.056
234	mobilenet_v2_0.5_160	.055
235	alexnet-baseline	.055
236	alexnet	.055
237	alexnet	.055
238	alexnet	.055
239	alexnet	.055
240	alexnet	.055
241	alexnet	.055
242	alexnet	.055
243	alexnet	.055
244	alexnet	.055
245	alexnet	.055
246	alexnet_ks_torevert	.055
247	mobilenet_v1_0.75_160	.054
248	mobilenet_v1_0.25_128	.051
249	alexnet_random_l2_3_perturb	.049
250	resnet50-SIN	.048
251	mobilenet_v2_1_0_128	.045
252	vonealexnet_gaussian_noise_std4_fixed	.045
253	mobilenet_v2_1.0_128-tf	.043
254	alexnet_training_seed_05	.043
255	alexnet_training_seed_01	.043
256	alexnet_training_seed_01_fov4	.043
257	alexnet_training_seed_01_fov12	.043
258	alexnet_training_seed_01_fov16	.043
259	convnext_small_imagenet_1_seed-0	.039
260	mobilenet_v1_0.25_192	.038
261	resnet50-vicregl0p9	.037
262	alexnet_reduced_aliasing_early_checkpoint	.036
263	mobilenet_v1_0.25_160	.035
264	mobilenet_v1_1.0_128	.035
265	resnet-50-robust	.034
266	imagenet_l2_3_0	.034
267	resnet50_robust_l2_eps3	.034
268	alexnet_training_seed_06	.034
269	deit_small_imagenet_100_seed-0	.033
270	mobilenet_v1_0.75_128	.031
271	resnet50_imagenet_10_seed-0	.029
272	alexnet_training_seed_09	.027
273	texture_shape_alexnet_trained_on_SIN	.027
274	AlexNet_SIN	.025
275	alexnet_fov16	.021
276	my-model	.019
277	resnet50-vicregl0p75	.019
278	resnet50_FractalDB	.019
279	AlexNet_SIN_fov16	.018
280	imagenet_l2_10_0	.017
281	alexnet_fov12	.017
282	resnet50-vicreg	.016
283	deit_small_imagenet_10_seed-0	.016
284	deit_small_imagenet_1_seed-0	.016
285	mobilenet_v2_0.5_96	.016
286	resnet50_imagenet_1_seed-0	.015
287	mobilenet_v2_0.35_96	.015
288	mobilenet_v1_0.25_224	.014
289	resnet_50_v1_spiking_l3	.013
290	alexnet_training_seed_07	.013
291	convnext_small_imagenet_10_seed-0	.012
292	mobilenet_v1_0.5_128	.011
293	AlexNet_SIN_fov12	.011
294	mobilenet_v2_0.35_128	.011
295	alexnet_training_seed_02	.010
296	alexnet_training_seed_04	.010
297	alexnet_training_seed_08	.010
298	alexnet_training_seed_10	.010
299	resnet50-meshes-lt-100-original-scratch	.010
300	mobilevit_small	.010
301	alexnet_linf_8_robust	.010
302	alexnet_training_seed_03	.009
303	0.5x_resnet-18_LC_w_sh_1_iter	.009
304	resnet50-barlow	.009
305	resnet50-meshes-lt-100-original-pretrained	.008
306	barlow-twins-resnet50	.004
307	artResNet18_1	.004
308	alexnet_robust_correct	.003
309	resnet-50_untrained	.003
310	alexnet_l2_3_robust	.003
311	resnet50-simclr-vissl	.001
312	resnet-18-LC_w_sh_100_iter_conv_init_m
313	ViT-B/32
314	BiT-M-R50x3
315	BiT-M-R50x1
316	BiT-M-R152x2
317	resnet-18-LC_w_sh_1_iter_conv_init_m
318	resnet-18-LC_w_sh_10_iter_m
319	resnet-18-LC_w_sh_1_iter_m
320	resnet-18-LC_w_sh_100_iter_m
321	resnet-18-LC_w_sh_10_iter_conv_init_m
322	FrankRobWobv0
323	0.5x_resnet-18_LC
324	CORnetZ_CIFAR10
325	resnet18-local_aggregation
326	resnet18-deepcluster
327	r3m_resnet18
328	CORnet-Z
329	resnet18-instance_recognition
330	my-model
331	vggface
332	0.5x_resnet-18_LC_w_sh_10_iter
333	r3m_resnet50
334	resnet18-colorization
335	r3m_resnet34
336	resnet-18-LC_m
337	r3m_resnet50_nocrop
338	resnet18-supervised
339	resnet-18-LC_1st_conv_m
340	resnet-18-LC_conv_init_m
341	resnet18-contrastive_multiview
342	0.5x_resnet-18
343	RN50
344	0.5x_resnet-18_LC_w_sh_100_iter
345	RN50
346	resnet18-relative_position
347	resnet18-autoencoder
348	resnet18-simclr
349	ViT_L_32
350	ViT_B_32
351	resnet18-depth_prediction
352	ViT_B_16
353	r3m_resnet34_nocrop
354	resnet18-contrastive_predictive
355	omnivore_swinT
356	resnet-18-LC_untrained
357	resnet-18_untrained
358	resnet-50x2_untrained
359	dcgan
360	v1-pyr-nodown
361	unet_entire
362	CORnetZ_CIFAR10_bs32_20_04
363	pixels
364	dorinet_cornet_z
365	resnet50
366	openclip
367	resnet50-cifar
368	resnet50_pretrained_with_retinal_waves
369	resnet50_ImageNet
370	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
371	ReAlnet09_ALL
372	yudixie_resnet50_imagenet1kpret_0_240312
373	yudixie_resnet50_random_0_240908
374	yudixie_resnet50_imagenet1kpret_0_240908
375	yudixie_resnet50_cat_obj_class_all_latents_0_240908
376	yudixie_resnet50_object_class_0_240908
377	yudixie_resnet50_category_class_0_240908
378	resnet18_imagenet21kP
379	ConvLSTM
380	yudixie_resnet50_distance_reg_0_240908
381	PredRNN
382	SimVP
383	TAU
384	MIM
385	densenet_201_pytorch
386	ReAlnet01_cornet
387	compact_resnet50_V4
388	yudixie_resnet18_translation_reg_0_240719
389	yudixie_resnet18_distance_reg_0_240719
390	yolos_tiny
391	yudixie_resnet18_rotation_reg_0_240719
392	ReAlnet02_cornet
393	ReAlnet03_cornet
394	resnet18_ecoset_full
395	resnet34_ecoset_full
396	VOneCORnet-S
397	resnet50_ecoset_full
398	compact_resnet50_IT
399	ReAlnet01_cornet_fov4
400	yudixie_resnet50_rotation_reg_0_240908
401	resnet101_ecoset_full
402	compact_resnet50_robust_V4
403	yudixie_resnet50_distance_translation_0_240908
404	ReAlnet01_cornet_fov12
405	yudixie_resnet18_distance_translation_0_240719
406	yudixie_resnet18_translation_rotation_0_240719
407	yudixie_resnet18_distance_rotation_0_240719
408	compact_vgg19_V4
409	yudixie_resnet18_category_class_0_240719
410	resnet-18_test_m
411	yudixie_resnet18_random_0_240719
412	yudixie_resnet18_cat_obj_class_all_latents_0_240719
413	ReAlnet05_cornet
414	yudixie_resnet50_distance_rotation_0_240908
415	SeLaVi-AVE
416	yudixie_resnet18_distance_translation_rotation_0_240719
417	yudixie_resnet18_object_class_0_240719
418	hmax
419	yudixie_resnet50_translation_reg_0_240908
420	compact_vgg19_IT
421	r3d_18
422	yudixie_resnet50_translation_rotation_0_240908
423	ReAlnet01_ALL
424	mc3_18
425	ReAlnet07_cornet
426	s3d
427	ReAlnet01_cornet_fov16
428	mvit_v1_b
429	ReAlnet08_cornet
430	ReAlnet02_ALL
431	ReAlnet03_ALL
432	ReAlnet04_ALL
433	compact_cornets_V4
434	omnivore_swinB
435	SeLaVi-Kinetics400
436	ReAlnet09_cornet
437	SeLaVi-VGG-Sound
438	ReAlnet05_ALL
439	compact_cornets_IT
440	SeLaVi-Kinetics-Sound
441	r2plus1d_18
442	ReAlnet06_ALL
443	omnivore_swinS
444	mvit_v2_s
445	compact_resnet50_robust_IT
446	ReAlnet10_cornet
447	resnet152_ecoset_full
448	ReAlnet07_ALL
449	ReAlnet08_ALL
450	yudixie_resnet18_imagenet1kpret_0_240719
451	ReAlnet04_cornet
452	ReAlnet06_cornet
453	yudixie_resnet50_distance_translation_rotation_0_240908
454	resnet50_primary_visual_cortex
455	briaai_rmbg_1_4
456	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