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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021uniformnoise-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.781
2	resnext101_32x32d_wsl	.754
3	resnext101_32x48d_wsl	.743
4	vit_base_patch16_clip_224:openai_ft_in1k	.734
5	resnext101_32x16d_wsl	.731
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.728
7	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.723
8	fixres_resnext101_32x48d_wsl	.720
9	vit_large_patch14_clip_224:laion2b_ft_in1k	.719
10	convnext_large:fb_in22k_ft_in1k	.686
11	vision_transformer_vit_large_patch16_224	.685
12	SWSL_resnext101_32x8d	.675
13	resnet-152_v2_pytorch	.674
14	resnet_152_v2	.670
15	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.661
16	resnet_101_v2	.647
17	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.647
18	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.645
19	vit_large_patch14_clip_224:openai_ft_in1k	.639
20	resnext101_32x8d_wsl	.637
21	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.635
22	CLIP_resnet50	.627
23	CLIP_resnet50_float32	.627
24	convnext_femto_ols:d1_in1k	.617
25	cvt_cvt-13-384-in1k_4_LucyV4	.614
26	vit_large_patch14_clip_336:laion2b_ft_in1k	.604
27	convnext_xlarge:fb_in22k_ft_in1k	.601
28	focalnet_tiny_lrf_in1k	.601
29	SWSL_resnet50	.599
30	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.591
31	convnext_tiny:in12k_ft_in1k	.590
32	cvt_cvt-13-224-in1k_4_LucyV4	.587
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.583
34	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.577
35	convnext_base:clip_laiona_augreg_ft_in1k_384	.576
36	vit_relpos_base_patch16_clsgap_224:sw_in1k	.576
37	CLIP_ViT-B_32	.572
38	nasnet_large	.555
39	CLIP-RN50	.537
40	ViT_L_32_imagenet1k	.524
41	convnext_tiny_imagenet_full_seed-0	.522
42	convnext_base_imagenet_full_seed-0	.520
43	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.520
44	cvt_cvt-21-384-in1k_4_LucyV4	.515
45	convnext_small_imagenet_full_seed-0	.509
46	cvt_cvt-21-224-in1k_4_LucyV4	.493
47	pnasnet_large	.493
48	ViT_B_16_imagenet1k	.476
49	effnetv2m_custom384	.475
50	pnasnet_large-tf	.473
51	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.462
52	resnet_50_v2	.458
53	ViT_B_32_imagenet1k	.455
54	convnext_large_imagenet_full_seed-0	.443
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.441
56	nasnet_large-tf	.418
57	inception_v4	.404
58	AdvProp_efficientnet-b6	.401
59	resnet-152_v2-tf	.386
60	resnet50-VITO-8deg-cc	.381
61	inception_v3	.377
62	cv_18_dagger_408_pretrained	.354
63	inception_v3-tf	.348
64	xception-keras	.344
65	resnet101	.340
66	tv_efficientnet-b1	.338
67	inception_v4-tf	.330
68	deit_large_imagenet_full_seed-0	.328
69	xception	.322
70	AdvProp_efficientnet-b4	.309
71	AT_efficientnet-b4	.309
72	antialias-resnet152	.309
73	densenet-201	.289
74	densenet_201_pytorch	.289
75	effnetb1_272x240	.285
76	resnet-50_v2-tf	.284
77	resnet50_ewconplus_wzc	.282
78	antialiased-rnext101_32x8d	.279
79	densenet-201-keras	.272
80	resnet50-moclr8deg	.266
81	BiT-S-R50x3	.265
82	AdvProp_efficientnet-b7	.260
83	deit_base_imagenet_full_seed-0	.257
84	deit_small_imagenet_full_seed-0	.257
85	efficientnet-b7	.254
86	resnet_101_v1	.253
87	AT_efficientnet-b2	.250
88	AdvProp_efficientnet-b2	.250
89	efficientnet_b0	.249
90	resnet50_random_l2_perturb	.245
91	resnet50	.244
92	Res2Net50_26w_4s	.243
93	resnet50_byol	.231
94	resnet_152_v1	.231
95	antialiased-r50	.224
96	resnet_50_v1_spiking	.209
97	resnet50_moco_v2	.209
98	convnext_small_imagenet_100_seed-0	.202
99	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.196
100	BiT-S-R101x3	.194
101	resnet-50-pytorch	.194
102	resnet50-sup	.194
103	resnet50_tutorial	.194
104	resnet_50_v1	.194
105	BiT-S-R152x2	.188
106	convnext_tiny_sup	.188
107	vonegrcnn_62e_nobn	.183
108	grcnn	.182
109	AdvProp_efficientnet-b8	.180
110	nasnet_mobile	.179
111	vonegrcnn_47e	.174
112	densenet-121-keras	.173
113	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.172
114	BiT-S-R152x4	.172
115	densenet-121	.166
116	resnet50-SIN_IN_IN	.162
117	resnet_SIN_IN_FT_IN	.162
118	vonegrcnn_62e_nobn	.157
119	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.157
120	grcnn_109	.156
121	densenet-169	.153
122	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.149
123	vonegrcnn_52e_full	.144
124	densenet-169-keras	.143
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.137
126	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.136
127	BiT-S-R101x1	.134
128	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.133
129	voneresnet-50-non_stochastic	.132
130	resnet101_imagenet_full	.131
131	alexnet_training_seed_05	.130
132	alexnet_training_seed_01	.130
133	alexnet_training_seed_01_fov4	.130
134	alexnet_training_seed_01_fov12	.130
135	alexnet_training_seed_01_fov16	.130
136	resnet152_imagenet_full	.129
137	vonegrcnn_47e	.127
138	resnet50-simclr-vissl	.127
139	vonegrcnn_52e_full	.125
140	resnet50_random_linf8_perturb	.122
141	BiT-S-R50x1	.121
142	voneresnet-50-robust	.121
143	mobilenet_v2_1_0_224	.121
144	resnet50-vitoimagevidnet8	.119
145	blt_vs	.119
146	resnet50-SIN_IN	.116
147	inception_v2-tf	.112
148	imagenet_l2_10_0	.105
149	resnet50_imagenet_full	.105
150	resnet50_simclr	.103
151	alexnet_fov16	.102
152	alexnet_fov12	.101
153	alexnet_robust_correct	.099
154	alexnet_l2_3_robust	.098
155	custom_model_cv_18_dagger_408	.097
156	vgg-19-keras	.091
157	mobilenet_v1_0.25_128	.090
158	resnet50_imagenet_10_seed-0	.089
159	deit_small_imagenet_10_seed-0	.089
160	deit_small_imagenet_1_seed-0	.089
161	resnet34_imagenet_full	.088
162	alexnet_training_seed_02	.088
163	alexnet_training_seed_04	.088
164	alexnet_training_seed_08	.088
165	alexnet_training_seed_10	.088
166	resnet50-meshes-lt-100-original-pretrained	.088
167	vgg_19	.088
168	inception_v1	.080
169	voneresnet-50	.079
170	konkle_alexnetgn_ipcl_ref01_primary_model	.079
171	nasnet_mobile-tf	.078
172	mobilenet_v2_0.5_160	.078
173	mobilenet_v1_0.5_128	.078
174	alexnet_training_seed_07	.077
175	mobilenet_v2_1_3_224	.076
176	mobilenet_v2_0.5_128	.075
177	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
178	mobilenet_v2_1_0_192	.075
179	deit_small_imagenet_100_seed-0	.073
180	AdvProp_efficientnet-b0	.073
181	ReAlnet04	.071
182	mobilenet_v2_0.35_160	.069
183	mobilenet_v2_0.35_192	.068
184	ReAlnet08	.068
185	inception_v1-tf	.067
186	mobilenet_v1_0.5_160	.067
187	ReAlnet03	.067
188	ReAlnet09	.064
189	mobilenet_v2_0.35_128	.062
190	alexnet_fov4	.061
191	resnet50_eMMCR_Vanilla	.061
192	mobilenet_v1_0.75_128	.061
193	squeezenet1_0	.059
194	mobilenet_v1_0.5_224	.059
195	mobilenet_v2_0.75_96	.058
196	convnext_small_imagenet_1_seed-0	.058
197	AlexNet_SIN_fov4	.058
198	resnet50-vicregl0p9	.057
199	resnet50-barlow	.057
200	mobilenet_v2_0.5_192-tf	.057
201	mobilenet_v2_1-4_224_pytorch	.057
202	mobilenet_v2_1_4_224	.057
203	ReAlnet05	.056
204	mobilenet_v2_0.5_224-tf	.056
205	resnet50_imagenet_1_seed-0	.054
206	mobilenet_v1_0.25_192	.054
207	resnet50_linf_4_robust	.050
208	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.049
209	mobilenet_v1_0.25_160	.049
210	ReAlnet01	.049
211	ReAlnet06	.049
212	mobilenet_v2_0.35_224	.048
213	mobilenet_v2_0.75_192-tf	.048
214	ReAlnet07	.048
215	mobilenet_v2_1_0_160	.048
216	mobilenet_v2_0.75_224-tf	.047
217	alexnet_random_l2_3_perturb	.047
218	resnet-34	.047
219	mobilenet_v1_0.5_192	.046
220	mobilenet_v2_1.0_224-tf	.046
221	mobilenet_v1_0.75_160	.046
222	resnet50_l2_3_robust	.046
223	resnet_50_v1_spiking_l4	.046
224	resnet-152_v1-tf	.045
225	vgg-16-keras	.045
226	resnet50_imagenet_100_seed-0	.045
227	resnet50_FractalDB	.044
228	mobilenet_v1_0.75_192	.044
229	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.043
230	alexnet_linf_8_robust	.043
231	alexnet_random_linf8_perturb	.041
232	resnet-101_v1-tf	.041
233	alexnet_early_checkpoint	.040
234	vgg_16	.039
235	resnet-18	.039
236	alexnet-baseline	.038
237	alexnet	.038
238	alexnet	.038
239	alexnet	.038
240	alexnet	.038
241	alexnet	.038
242	alexnet	.038
243	alexnet	.038
244	alexnet	.038
245	alexnet	.038
246	alexnet	.038
247	alexnet_ks_torevert	.038
248	resnet-50-robust	.038
249	imagenet_l2_3_0	.038
250	resnet50_robust_l2_eps3	.038
251	resnet18_imagenet_full	.038
252	mobilenet_v2_0.75_160-tf	.038
253	efficientnet_b1_imagenet_full	.036
254	mobilenet_v2_0.75_128	.036
255	mobilenet_v2_1.0_128-tf	.036
256	texture_shape_resnet50_trained_on_SIN	.036
257	mobilenet_v1_1.0_160	.036
258	efficientnet_b0_imagenet_full	.035
259	shufflenet_v2_x1_0	.034
260	resnet_50_v1_spiking_l4a	.034
261	alexnet	.034
262	resnet50_robust_l2_eps1	.033
263	mobilenet_v1_0.25_224	.033
264	alexnet_reduced_aliasing_early_checkpoint	.031
265	squeezenet1_1	.031
266	mobilenet_v1_1.0_192	.030
267	mobilenet_v2_1.0_96	.028
268	resnet50-meshes-lt-100-original-scratch	.028
269	alexnet_training_seed_09	.027
270	regnet_y_400mf	.027
271	resnet50_linf_8_robust	.026
272	alexnet_training_seed_03	.024
273	mobilenet_v2_0.5_96	.024
274	resnet50-SIN	.023
275	mobilenet_v2_1_0_128	.022
276	mobilenet_v1_1.0_224	.022
277	convnext_small_imagenet_10_seed-0	.022
278	resnet50-vicregl0p75	.022
279	resnet-50_untrained	.021
280	0.5x_resnet-18_LC_w_sh_1_iter	.021
281	mobilenet_v2_1.0_160-tf	.020
282	mobilenet_v1_0.75_224	.017
283	mobilenet_v1_1.0_128	.016
284	mobilenet_v2_0_75_224	.016
285	AlexNet_SIN_fov12	.014
286	mobilenet_v2_0.35_96	.013
287	AlexNet_SIN_fov16	.012
288	ReAlnet02	.011
289	cornet_s	.011
290	ReAlnet10	.011
291	mobilenet_v2_1.3_224-tf	.010
292	vonealexnet_gaussian_noise_std4_fixed	.010
293	resnet_50_v1_spiking_l3	.009
294	mobilenet_v2_1.4_224-tf	.008
295	barlow-twins-resnet50	.006
296	mobilenet_v2_0_5_192	.006
297	efficientnet_b2_imagenet_full	.006
298	CORnet-S	.005
299	mobilenet_v2_0_75_160	.005
300	alexnet_training_seed_06	.005
301	texture_shape_alexnet_trained_on_SIN	.005
302	artResNet18_1	.004
303	mobilenet_v2_1.0_192-tf	.004
304	resnet50-vicreg	.003
305	mobilenet_v2_0_75_192	.003
306	AlexNet_SIN	.002
307	mobilenet_v2_0_5_224	.001
308	resnet-50_v1-tf	.000
309	resnet-18-LC_w_sh_100_iter_conv_init_m
310	ViT-B/32
311	BiT-M-R50x3
312	BiT-M-R50x1
313	resnet-18-LC_w_sh_1_iter_conv_init_m
314	resnet-18-LC_w_sh_1_iter_m
315	BiT-M-R152x2
316	resnet-18-LC_w_sh_10_iter_m
317	ReAlnet09_ALL
318	resnet-18-LC_w_sh_100_iter_m
319	resnet-18-LC_w_sh_10_iter_conv_init_m
320	FrankRobWobv0
321	0.5x_resnet-18_LC
322	CORnetZ_CIFAR10
323	resnet18-local_aggregation
324	r3m_resnet18
325	CORnet-Z
326	resnet18-deepcluster
327	vggface
328	my-model
329	resnet18-instance_recognition
330	0.5x_resnet-18_LC_w_sh_10_iter
331	r3m_resnet50
332	r3m_resnet34
333	resnet-18-LC_m
334	r3m_resnet50_nocrop
335	resnet18-supervised
336	resnet-18-LC_1st_conv_m
337	resnet-18-LC_conv_init_m
338	resnet18-contrastive_multiview
339	0.5x_resnet-18
340	RN50
341	0.5x_resnet-18_LC_w_sh_100_iter
342	RN50
343	resnet18-colorization
344	resnet18-relative_position
345	resnet18-autoencoder
346	resnet18-simclr
347	ViT_L_32
348	ViT_B_32
349	resnet18-depth_prediction
350	ViT_B_16
351	r3m_resnet34_nocrop
352	resnet18-contrastive_predictive
353	resnet-18-LC_untrained
354	resnet-18_untrained
355	resnet-50x2_untrained
356	dcgan
357	unet_entire
358	v1-pyr-nodown
359	CORnetZ_CIFAR10_bs32_20_04
360	pixels
361	dorinet_cornet_z
362	omnivore_swinT
363	my-model
364	resnet50
365	openclip
366	resnet50-cifar
367	resnet50_pretrained_with_retinal_waves
368	resnet50_ImageNet
369	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
370	ReAlnet08_ALL
371	yudixie_resnet50_random_0_240908
372	yudixie_resnet50_imagenet1kpret_0_240908
373	yudixie_resnet50_imagenet1kpret_0_240312
374	yudixie_resnet50_cat_obj_class_all_latents_0_240908
375	yudixie_resnet50_object_class_0_240908
376	yudixie_resnet50_category_class_0_240908
377	resnet18_imagenet21kP
378	yudixie_resnet50_distance_reg_0_240908
379	ConvLSTM
380	PredRNN
381	SimVP
382	TAU
383	MIM
384	hmax
385	briaai_rmbg_1_4
386	pnasnet_large_pytorch
387	inception_v3_pytorch
388	ReAlnet01_cornet
389	yolos_tiny
390	yudixie_resnet18_translation_reg_0_240719
391	ReAlnet02_cornet
392	yudixie_resnet18_distance_reg_0_240719
393	yudixie_resnet18_rotation_reg_0_240719
394	yudixie_resnet18_imagenet1kpret_0_240719
395	compact_resnet50_V4
396	compact_resnet50_IT
397	resnet18_ecoset_full
398	ReAlnet03_cornet
399	ReAlnet01_cornet_fov4
400	resnet34_ecoset_full
401	VOneCORnet-S
402	resnet50_ecoset_full
403	resnet101_ecoset_full
404	yudixie_resnet50_distance_translation_0_240908
405	compact_vgg19_V4
406	yudixie_resnet18_distance_translation_0_240719
407	ReAlnet01_cornet_fov12
408	yudixie_resnet18_translation_rotation_0_240719
409	ReAlnet04_cornet
410	omnivore_swinB
411	yudixie_resnet18_distance_rotation_0_240719
412	yudixie_resnet18_category_class_0_240719
413	yudixie_resnet50_rotation_reg_0_240908
414	ReAlnet05_cornet
415	yudixie_resnet18_random_0_240719
416	resnet-18_test_m
417	yudixie_resnet50_distance_rotation_0_240908
418	yudixie_resnet18_cat_obj_class_all_latents_0_240719
419	SeLaVi-AVE
420	yudixie_resnet18_distance_translation_rotation_0_240719
421	yudixie_resnet18_object_class_0_240719
422	compact_resnet50_robust_V4
423	compact_vgg19_IT
424	ReAlnet06_cornet
425	yudixie_resnet50_translation_reg_0_240908
426	r3d_18
427	ReAlnet07_cornet
428	mc3_18
429	ReAlnet02_ALL
430	s3d
431	ReAlnet08_cornet
432	mvit_v1_b
433	ReAlnet04_ALL
434	compact_cornets_V4
435	ReAlnet01_cornet_fov16
436	ReAlnet03_ALL
437	compact_cornets_IT
438	ReAlnet09_cornet
439	SeLaVi-Kinetics400
440	ReAlnet05_ALL
441	SeLaVi-VGG-Sound
442	ReAlnet06_ALL
443	omnivore_swinS
444	r2plus1d_18
445	compact_resnet50_robust_IT
446	ReAlnet07_ALL
447	mvit_v2_s
448	ReAlnet10_cornet
449	SeLaVi-Kinetics-Sound
450	resnet152_ecoset_full
451	yudixie_resnet50_distance_translation_rotation_0_240908
452	yudixie_resnet50_translation_rotation_0_240908
453	resnet50_primary_visual_cortex
454	ReAlnet10_ALL
455	ReAlnet01_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

0.43.

Note that scores are relative to this ceiling.

Data: Geirhos2021uniformnoise

Metric: error_consistency