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