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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_halfPrecision	.702
17	pnasnet_large	.702
18	cvt_cvt-13-224-in1k_4_LucyV4	.699
19	CLIP_resnet50	.688
20	CLIP_resnet50_float32	.688
21	pnasnet_large-tf	.682
22	resnext101_32x8d_wsl	.668
23	effnetb1_272x240	.667
24	vit_relpos_base_patch16_clsgap_224:sw_in1k	.648
25	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.640
26	convnext_femto_ols:d1_in1k	.633
27	convnext_large_imagenet_full_seed-0	.622
28	resnet_50_v2	.622
29	effnetv2m_custom384	.617
30	focalnet_tiny_lrf_in1k	.610
31	fixres_resnext101_32x48d_wsl	.610
32	CLIP_ViT-B_32	.605
33	xception-keras	.599
34	ViT_B_16_imagenet1k	.588
35	vit_base_patch16_clip_224:openai_ft_in1k	.581
36	resnet50-VITO-8deg-cc	.580
37	resnet101	.580
38	convnext_xlarge:fb_in22k_ft_in1k	.579
39	convnext_large:fb_in22k_ft_in1k	.572
40	resnet_101_v2	.568
41	antialias-resnet152	.566
42	resnet-152_v2_pytorch	.564
43	convnext_tiny_imagenet_full_seed-0	.564
44	convnext_tiny:in12k_ft_in1k	.560
45	resnet152_imagenet_full	.559
46	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.559
47	inception_v3	.558
48	cvt_cvt-21-224-in1k_4_LucyV4	.554
49	inception_v4-tf	.547
50	resnet_152_v1	.546
51	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.542
52	vit_large_patch14_clip_224:laion2b_ft_in1k	.539
53	resnet_152_v2	.537
54	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.536
55	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.532
56	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.532
57	resnet50_imagenet_full	.531
58	resnet101_imagenet_full	.528
59	convnext_base_imagenet_full_seed-0	.520
60	inception_v3-tf	.519
61	tv_efficientnet-b1	.517
62	antialiased-rnext101_32x8d	.511
63	efficientnet_b0	.507
64	inception_v3_pytorch	.505
65	resnet-152_v2-tf	.504
66	resnet50_random_l2_perturb	.497
67	resnet50_moco_v2	.495
68	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.493
69	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.493
70	cv_18_dagger_408_pretrained	.492
71	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.487
72	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.486
73	convnext_small_imagenet_full_seed-0	.483
74	resnet50	.475
75	vgg_19	.471
76	xception	.470
77	grcnn_109	.462
78	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.461
79	deit_large_imagenet_full_seed-0	.453
80	resnet-50_v2-tf	.449
81	deit_base_imagenet_full_seed-0	.447
82	resnet_101_v1	.440
83	AdvProp_efficientnet-b7	.434
84	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.429
85	resnet50_ewconplus_wzc	.428
86	ViT_L_32_imagenet1k	.419
87	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.416
88	resnet-152_v1-tf	.409
89	resnet50_byol	.407
90	nasnet_mobile-tf	.401
91	pnasnet_large_pytorch	.392
92	resnet34_imagenet_full	.390
93	alexnet_fov4	.388
94	deit_small_imagenet_full_seed-0	.371
95	ViT_B_32_imagenet1k	.368
96	resnet50-SIN_IN_IN	.367
97	resnet_SIN_IN_FT_IN	.367
98	cornet_s	.364
99	densenet-201-keras	.362
100	resnet-101_v1-tf	.359
101	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.356
102	AdvProp_efficientnet-b4	.353
103	AT_efficientnet-b4	.353
104	Res2Net50_26w_4s	.353
105	resnet_50_v1_spiking	.351
106	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.351
107	vit_large_patch14_clip_336:laion2b_ft_in1k	.348
108	densenet-121-keras	.346
109	AT_efficientnet-b2	.338
110	AdvProp_efficientnet-b2	.338
111	vgg_16	.337
112	vit_large_patch14_clip_224:openai_ft_in1k	.337
113	antialiased-r50	.336
114	AdvProp_efficientnet-b6	.336
115	mobilenet_v2_1.4_224-tf	.336
116	efficientnet-b7	.336
117	densenet-169-keras	.335
118	densenet-201	.333
119	resnet18_imagenet_full	.333
120	resnet50-moclr8deg	.332
121	resnet50_random_linf8_perturb	.326
122	densenet-121	.324
123	inception_v1	.312
124	resnet50_tutorial	.309
125	resnet_50_v1	.309
126	resnet-50-pytorch	.309
127	resnet50-sup	.309
128	nasnet_mobile	.306
129	BiT-S-R50x3	.300
130	BiT-S-R101x3	.299
131	densenet-169	.299
132	mobilenet_v2_1_3_224	.297
133	inception_v1-tf	.296
134	mobilenet_v2_1.3_224-tf	.295
135	eBarlow_lmda_02_1000ep	.292
136	CORnet-S	.292
137	resnet50-SIN_IN	.291
138	BiT-S-R101x1	.289
139	resnet-34	.282
140	grcnn	.280
141	convnext_tiny_sup	.269
142	custom_model_cv_18_dagger_408	.269
143	resnet50_simclr	.268
144	resnet-50_v1-tf	.267
145	resnet50-vitoimagevidnet8	.263
146	inception_v2-tf	.262
147	ReAlnet09	.257
148	ReAlnet01	.257
149	BiT-S-R152x2	.256
150	convnext_base:clip_laiona_augreg_ft_in1k_384	.256
151	convnext_small_imagenet_100_seed-0	.255
152	ReAlnet06	.253
153	vonegrcnn_62e_nobn	.252
154	mobilenet_v2_1_0_224	.251
155	ReAlnet03	.251
156	efficientnet_b2_imagenet_full	.249
157	ReAlnet04	.246
158	efficientnet_b0_imagenet_full	.240
159	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.238
160	BiT-S-R152x4	.237
161	mobilenet_v2_1_0_192	.233
162	voneresnet-50-non_stochastic	.231
163	AdvProp_efficientnet-b8	.230
164	vonegrcnn_47e	.228
165	vonegrcnn_52e_full	.226
166	mobilenet_v2_1.0_224-tf	.225
167	BiT-S-R50x1	.225
168	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.223
169	vonegrcnn_62e_nobn	.221
170	AlexNet_SIN_fov4	.221
171	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.220
172	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.219
173	ReAlnet02	.219
174	resnet50_eMMCR_Vanilla	.218
175	vonegrcnn_47e	.218
176	mobilenet_v2_0_75_224	.216
177	vonegrcnn_52e_full	.215
178	resnet-18	.212
179	ReAlnet07	.212
180	mobilenet_v2_0.75_224-tf	.212
181	ReAlnet10	.209
182	ReAlnet08	.195
183	mobilenet_v2_1-4_224_pytorch	.193
184	mobilenet_v2_1_4_224	.193
185	blt_vs	.188
186	mobilenet_v2_1.0_192-tf	.187
187	ReAlnet05	.183
188	efficientnet_b1_imagenet_full	.181
189	regnet_y_400mf	.179
190	mobilenet_v2_1_0_160	.175
191	mobilenet_v2_0_5_224	.174
192	mobilenet_v1_0.75_224	.164
193	mobilenet_v2_0_75_192	.163
194	mobilenet_v2_0_5_192	.157
195	mobilenet_v2_1.0_160-tf	.157
196	vgg-16-keras	.155
197	voneresnet-50	.152
198	mobilenet_v2_0.5_224-tf	.145
199	resnet_50_v1_spiking_l4a	.144
200	mobilenet_v1_1.0_224	.143
201	mobilenet_v2_0_75_160	.139
202	mobilenet_v1_1.0_192	.137
203	resnet50_linf_4_robust	.136
204	mobilenet_v2_0.5_192-tf	.123
205	texture_shape_resnet50_trained_on_SIN	.119
206	konkle_alexnetgn_ipcl_ref01_primary_model	.116
207	mobilenet_v1_0.75_192	.114
208	resnet50_imagenet_100_seed-0	.113
209	shufflenet_v2_x1_0	.107
210	vgg-19-keras	.107
211	squeezenet1_1	.106
212	mobilenet_v1_0.5_192	.104
213	mobilenet_v1_1.0_160	.103
214	mobilenet_v2_0.75_160-tf	.101
215	mobilenet_v2_0.75_128	.101
216	mobilenet_v2_0.75_192-tf	.100
217	mobilenet_v2_0.35_192	.092
218	resnet50_robust_l2_eps1	.088
219	squeezenet1_0	.087
220	alexnet_random_linf8_perturb	.086
221	AdvProp_efficientnet-b0	.085
222	mobilenet_v1_0.5_224	.073
223	alexnet_early_checkpoint	.073
224	mobilenet_v1_0.5_160	.070
225	mobilenet_v2_0.35_224	.069
226	mobilenet_v2_0.5_128	.068
227	mobilenet_v2_1.0_96	.067
228	mobilenet_v2_0.75_96	.066
229	resnet50_l2_3_robust	.065
230	resnet50_linf_8_robust	.064
231	voneresnet-50-robust	.064
232	resnet_50_v1_spiking_l4	.064
233	mobilenet_v2_0.35_160	.063
234	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.056
235	mobilenet_v2_0.5_160	.055
236	alexnet	.055
237	alexnet_ks_torevert	.055
238	mobilenet_v1_0.75_160	.054
239	mobilenet_v1_0.25_128	.051
240	alexnet_random_l2_3_perturb	.049
241	resnet50-SIN	.048
242	mobilenet_v2_1_0_128	.045
243	vonealexnet_gaussian_noise_std4_fixed	.045
244	mobilenet_v2_1.0_128-tf	.043
245	alexnet_training_seed_05	.043
246	alexnet_training_seed_01	.043
247	alexnet_training_seed_01_fov4	.043
248	alexnet_training_seed_01_fov12	.043
249	alexnet_training_seed_01_fov16	.043
250	convnext_small_imagenet_1_seed-0	.039
251	mobilenet_v1_0.25_192	.038
252	resnet50-vicregl0p9	.037
253	alexnet_reduced_aliasing_early_checkpoint	.036
254	mobilenet_v1_0.25_160	.035
255	mobilenet_v1_1.0_128	.035
256	resnet-50-robust	.034
257	imagenet_l2_3_0	.034
258	resnet50_robust_l2_eps3	.034
259	alexnet_training_seed_06	.034
260	deit_small_imagenet_100_seed-0	.033
261	mobilenet_v1_0.75_128	.031
262	resnet50_imagenet_10_seed-0	.029
263	alexnet_training_seed_09	.027
264	texture_shape_alexnet_trained_on_SIN	.027
265	AlexNet_SIN	.025
266	alexnet_fov16	.021
267	my-model	.019
268	resnet50-vicregl0p75	.019
269	resnet50_FractalDB	.019
270	AlexNet_SIN_fov16	.018
271	imagenet_l2_10_0	.017
272	alexnet_fov12	.017
273	resnet50-vicreg	.016
274	deit_small_imagenet_10_seed-0	.016
275	deit_small_imagenet_1_seed-0	.016
276	mobilenet_v2_0.5_96	.016
277	resnet50_imagenet_1_seed-0	.015
278	mobilenet_v2_0.35_96	.015
279	mobilenet_v1_0.25_224	.014
280	resnet_50_v1_spiking_l3	.013
281	alexnet_training_seed_07	.013
282	convnext_small_imagenet_10_seed-0	.012
283	mobilenet_v1_0.5_128	.011
284	AlexNet_SIN_fov12	.011
285	mobilenet_v2_0.35_128	.011
286	alexnet_training_seed_02	.010
287	alexnet_training_seed_04	.010
288	alexnet_training_seed_08	.010
289	alexnet_training_seed_10	.010
290	resnet50-meshes-lt-100-original-scratch	.010
291	mobilevit_small	.010
292	alexnet_linf_8_robust	.010
293	alexnet_training_seed_03	.009
294	0.5x_resnet-18_LC_w_sh_1_iter	.009
295	resnet50-barlow	.009
296	resnet50-meshes-lt-100-original-pretrained	.008
297	barlow-twins-resnet50	.004
298	artResNet18_1	.004
299	alexnet_robust_correct	.003
300	resnet-50_untrained	.003
301	alexnet_l2_3_robust	.003
302	resnet50-simclr-vissl	.001
303	resnet-18-LC_w_sh_100_iter_conv_init_m
304	ViT-B/32
305	BiT-M-R50x3
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_10_iter_m
310	resnet-18-LC_w_sh_1_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	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-local_aggregation
317	resnet18-deepcluster
318	r3m_resnet18
319	CORnet-Z
320	resnet18-instance_recognition
321	my-model
322	vggface
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	resnet18-colorization
326	r3m_resnet34
327	resnet-18-LC_m
328	r3m_resnet50_nocrop
329	resnet18-supervised
330	resnet-18-LC_1st_conv_m
331	resnet-18-LC_conv_init_m
332	resnet18-contrastive_multiview
333	0.5x_resnet-18
334	RN50
335	0.5x_resnet-18_LC_w_sh_100_iter
336	RN50
337	resnet18-relative_position
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	ReAlnet09_ALL
363	yudixie_resnet50_imagenet1kpret_0_240312
364	yudixie_resnet50_random_0_240908
365	yudixie_resnet50_imagenet1kpret_0_240908
366	yudixie_resnet50_cat_obj_class_all_latents_0_240908
367	yudixie_resnet50_object_class_0_240908
368	yudixie_resnet50_category_class_0_240908
369	resnet18_imagenet21kP
370	ConvLSTM
371	yudixie_resnet50_distance_reg_0_240908
372	PredRNN
373	SimVP
374	TAU
375	MIM
376	densenet_201_pytorch
377	vit_base_patch16_dinov3-lvd1689m
378	ReAlnet01_cornet
379	compact_resnet50_V4
380	yudixie_resnet18_translation_reg_0_240719
381	yudixie_resnet18_distance_reg_0_240719
382	yolos_tiny
383	yudixie_resnet18_rotation_reg_0_240719
384	ReAlnet02_cornet
385	ReAlnet03_cornet
386	vit_base_patch14_dinov2-lvd142m
387	resnet18_ecoset_full
388	resnet34_ecoset_full
389	VOneCORnet-S
390	resnet50_ecoset_full
391	compact_resnet50_IT
392	vit_base_patch14_reg4_dinov2-lvd142m
393	ReAlnet01_cornet_fov4
394	yudixie_resnet50_rotation_reg_0_240908
395	resnet101_ecoset_full
396	compact_resnet50_robust_V4
397	yudixie_resnet50_distance_translation_0_240908
398	ReAlnet01_cornet_fov12
399	yudixie_resnet18_distance_translation_0_240719
400	yudixie_resnet18_translation_rotation_0_240719
401	yudixie_resnet18_distance_rotation_0_240719
402	compact_vgg19_V4
403	yudixie_resnet18_category_class_0_240719
404	resnet-18_test_m
405	yudixie_resnet18_random_0_240719
406	yudixie_resnet18_cat_obj_class_all_latents_0_240719
407	ReAlnet05_cornet
408	yudixie_resnet50_distance_rotation_0_240908
409	SeLaVi-AVE
410	yudixie_resnet18_distance_translation_rotation_0_240719
411	yudixie_resnet18_object_class_0_240719
412	hmax
413	yudixie_resnet50_translation_reg_0_240908
414	compact_vgg19_IT
415	r3d_18
416	yudixie_resnet50_translation_rotation_0_240908
417	ReAlnet01_ALL
418	mc3_18
419	ReAlnet07_cornet
420	s3d
421	ReAlnet01_cornet_fov16
422	mvit_v1_b
423	ReAlnet08_cornet
424	ReAlnet02_ALL
425	aughost_fair_resnet18
426	ReAlnet03_ALL
427	ReAlnet04_ALL
428	compact_cornets_V4
429	omnivore_swinB
430	SeLaVi-Kinetics400
431	ReAlnet09_cornet
432	SeLaVi-VGG-Sound
433	ReAlnet05_ALL
434	compact_cornets_IT
435	SeLaVi-Kinetics-Sound
436	r2plus1d_18
437	ReAlnet06_ALL
438	omnivore_swinS
439	mvit_v2_s
440	compact_resnet50_robust_IT
441	ReAlnet10_cornet
442	resnet152_ecoset_full
443	ReAlnet07_ALL
444	ReAlnet08_ALL
445	yudixie_resnet18_imagenet1kpret_0_240719
446	ReAlnet04_cornet
447	ReAlnet06_cornet
448	yudixie_resnet50_distance_translation_rotation_0_240908
449	resnet50_primary_visual_cortex
450	briaai_rmbg_1_4
451	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